Peer Review of EPA’s Response Surface 

Equation Report 

Final Report
EPA-420-R-18-006 

May 2018

NOTICE 

This technical report does not necessarily represent final EPA decisions or 

positions. It is intended to present technical analysis of issues using data 

that are currently available. The purpose in the release of such reports is to 

facilitate the exchange of technical information and to inform the public of 

technical developments.

Assessment and Standards Division 

Office of Transportation and Air Quality 

U.S. Environmental Protection Agency

Prepared for EPA by

RTI International

EPA Contract No. EP-C-16-021

Peer Review of EPA’s Response Surface 

Equation Report 

Final Report
RTI International Peer Review of EPA’s RSE Report iii

Table of Contents

1 Introduction 1

2 Selection of Peer Reviewers 2

3 Peer-Review Process 2

4 Review Comments Grouped by Charge Letter Topic 3

Appendix A. Peer Reviewers’ Resumes 11

Appendix B. Charge Letter 84

Appendix C. Sanya Carley Comments 88

Appendix D. Sujit Das Comments 98

Appendix E. Doug Montgomery Comments 104

Appendix F. Response Surface Report 109

List of Tables

2-1. Selected Peer Reviewers...................................................................................................2

List of Figures

4-1. Histogram of Residuals.....................................................................................................3

4-2. Plot of Actual Versus Predicted Response........................................................................4

4-3. Summary of Fit and Analysis of Variance for the RSM Model........................................5

4-4. RSM Model Parameters Estimates ...................................................................................5

4-5. PRESS Statistic.................................................................................................................6
RTI International Peer Review of EPA’s RSE Report 1

1 Introduction

The U.S. Environmental Protection Agency’s (EPA’s) Office of Transportation and Air Quality has 

developed a statistical approach to access results from the Advanced Light-Duty Powertrain and Hybrid 

Analysis (ALPHA) model. To demonstrate the credibility of the methodology and gain acceptance in the 

light-duty automotive community, EPA contracted with RTI International to support an independent 

peer review. 

The ALPHA model is a full vehicle simulation model that is used to assess the effectiveness of different 

technology packages in vehicles. Effectiveness values from ALPHA act as robust inputs to the 

Optimization Model for Reducing Emissions of Greenhouse Gases from Automobiles (OMEGA) and to

the overall rulemaking process. 

Because operating the ALPHA model in real time to conduct full vehicle simulations is cost and time

prohibitive, EPA developed a method of deriving the necessary effectiveness values using an industry 

standard statistical methodology known as a Response Surface Model (RSM). An RSM is used to 

computationally synthesize a large set of simulation outputs to derive response surface equations 

(RSEs). The derived RSEs can then be used in place of running the ALPHA model in real time for 

determining the effectiveness of vehicle technologies.

The peer review was conducted in a manner that is consistent with the guidance in EPA’s Peer Review 

Handbook (4th edition).

This report is organized as follows:

▪ Section 2 details the selection of peer reviewers.

▪ Section 3 describes the peer-review process.

▪ Section 4 groups review comments by charge letter topic.

▪ Appendix A provides peer reviewers’ resumes.

▪ Appendix B provides a copy of the charge letter sent to reviewers.

▪ Appendices C, D, and E provide exact copies of the reviews submitted by the peer-review panel.
RTI International Peer Review of EPA’s RSE Report 2

2 Selection of Peer Reviewers

RTI compiled a list of 10 reviewer candidates who had the necessary expertise to make a contribution to 

this review. RTI contacted each candidate to inquire about their interest, availability, and any potential 

conflicts of interest with the topic. 

Table 2-1 lists the final panel of reviewers. Based on availability and the need to comprehensively cover 

the topic, RTI selected three peer reviewers. EPA approved all three chosen reviewers. Appendix A 

contains resumes for each reviewer.

Table 2-1. Selected Peer Reviewers

Reviewer Affiliation Expertise? Conflict of 

Interest?

Sanya Carley Indiana University–Bloomington

School of Public and Environmental Affairs Yes No

Sujit Das Oak Ridge National Laboratory Yes No

Doug Montgomery Arizona State University Yes No

3 Peer-Review Process

Upon completing the peer-reviewer selection process, RTI distributed a charge letter (Appendix B) and 

review documentation to each reviewer. The charge letter contained instructions for each peer reviewer 

with respect to the review schedule and the general topics to be addressed in their review. 

Documentation provided by EPA was sufficient for the reviewers to reproduce the RSEs from the EPA 

report and test the robustness of the results.

Reviewers were given 3 weeks to write their review report. RTI coordinated a kick-off conference call 

and a mid-review conference call to ensure that reviewers had every resource they required to conduct 

a full and comprehensive review of the report. During the review period, reviewers had regular access to 

both RTI and EPA to ask questions about the RSE report or the peer-review process. All correspondence 

between a reviewer and EPA was shared with all the review panel members to ensure that everyone 

had the same information for their review.

At the end of the 3-week period, each reviewer submitted a written report to RTI, and these reports are 

reproduced in Appendices C, D, and E. RTI adhered to the provisions of EPA’s Peer Review Handbook

guidelines to ensure that the peer-review process followed EPA policy.
RTI International Peer Review of EPA’s RSE Report 3

4 Review Comments Grouped by Charge Letter Topic

The following section compiles the feedback from peer reviewers by charge letter topic. With 

the exception of grouping by topic, the comments have not been altered or paraphrased in any 

way. 

TOPIC 1: EPA’s overall approach to applying response surface modelling to accessing ALPHA model 

results and whether the resulting response surface equations provide accurate and robust inputs for 

the OMEGA model.

Sanya Carley

1. There are a variety of performance metrics that one could use to assess response surface equation 

accuracy and adequacy. For this review, I evaluated the size of the residuals, the percent error, and 

the distribution of the residuals. 

2. These statistics confirm that the predicted values have excellent accuracy. The average residual is 

0.0013 and the average percent error is -0.0004 percent. All combinations of vehicle type and 

powertrain perform similarly. The combination that has the highest residual is the High 

Power/Weight 2014 Atkinson.

3. I also plotted the residuals to see if they fit a normal distribution, as suggested by Bezerra et al. 

(2008). Figure 4-1 presents a histogram of all residuals across the 8,257 model runs. The distribution 

appears normal. I also looked at the histograms for all vehicle types, powertrain technologies, and 

vehicle type-powertrain combinations separately (not shown here). These plots provide no cause for 

concern.

Figure 4-1. Histogram of Residuals
RTI International Peer Review of EPA’s RSE Report 4

Sujit Das

1. A comparison of CO2 results between RSE and ALPHA has confirmed the validity of the data transfer 

between these two models thereby proving the accuracy of the technical application of response 

surface modeling. A total of 21 results (only 2020_TURB24 was available for LPW_LRL vehicle) out of 

total 24 vehicle types were examined for the RSM validation. Residuals were found to be between a 

narrow range of -1.0 and 1.0 gCO2/mile in all cases. The line slope of the plot of results of ALPHA and 

RSE was also found to be 45o and thus has ensured the validity of data transfer between them. In 

addition, as the physics behind the Mass, Aero, and Roll are quite linear in reality, and so CO2 

emission impacts of any values between the range of these parameters were also found to be 

reasonable using the RSE results.

Doug Montgomery

I selected a subset of the 24 models for further investigation. I loaded the experimental designs for these 

models into JMP PRO V 13 and performed my own RSM analysis, fitting the standard second-order 

model. The results for one of these RSM model from spreadsheet HPW 1026 2017a tab 2014 GDI are 

discussed below. This is typical of the results I obtained for all models that I investigated.

Figure 4-2. Plot of Actual Versus Predicted Response

The points in this plot lie almost exactly along a straight line, indicating excellent agreement 

between the simulation model output and the predicted value from the second-order RSM 

model.
RTI International Peer Review of EPA’s RSE Report 5

Figure 4-3. Summary of Fit and Analysis of Variance for the RSM Model

The R

2

statistic for the model exceeds 0.99, indicating that most of the variability in the sample 

data (in excess of 99%) is explained by the RSM model. Also, the R

2

-adjusted statistic is also in 

excess or 0.99. R

2

-adjusted is a reflection of potential overfitting; that is including terms not 

really important in the model just to inflate the ordinary R

2

. When these two statistics are in 

close agreement as they are here there is likely to be no substantial issue with overfitting. The 

analysis of variance indicates that the model contains at least one statistically significant term.

Figure 4-4. RSM Model Parameters Estimates

The second –order model contains 15 parameters; an intercept, four main effects, six 2-factor 

interactions, and four quadratic terms. The parameter estimates display indicates that all but 

two of these terms are statistically significant at the 0.05 level. However, in RSM we usually 

think that it’s the order of the model that is most important so we often do not remove nonsignificant terms from the model unless there are many of them. That is not the case here.

1. The PRESS Statistic

In model validation it is important that the model both fit the sample and that it provide good 

predictions of new data. The PRESS (Prediction Error Sum of Squares) statistic, reported below, is a 

standard one-sample-at-a-time cross-validation used to assess potential prediction performance.
RTI International Peer Review of EPA’s RSE Report 6

Figure 4-5. PRESS Statistic

Notice that the PRESS statistic is very similar to the residual sum of squares from the analysis of 

variance. An R

2

-like prediction error statistic can be computed from PRESS simply by replacing 

the residual sum of squares in the equation for R2 by PRESS. This gives:

2

Prediction

22.449 1 1 0.9999

273428

PRESS R

TotalSS

    

We would expect the RSM model to explain in excess of 99% of the variability in data produced 

by the simulation model. This is excellent validation of potential prediction performance.

2. Summary of Conclusions

I conclude that the RSM approach has produced statistical metamodels that are an excellent 

alternative to the ALPHA simulation model. So long as they are used to interpolate over the ranges 

of the four factor used in their construction I expect that they will be excellent alternatives to the 

ALPHA simulation procedure.

TOPIC 2: Reasonableness of any assumptions, implicit or explicit, contained in EPA’s execution of the 

methodology.

Sanya Carley

1. After a thorough review of the report and supporting documentation, my general impression is that 

response surface statistical methods are an appropriate and efficient approach to generate data 

needed to populate the OMEGA model. The RSM is an analysis tool that is increasingly accepted in 

engineering and other disciplines, and subjected to rigorous peer review. An analysis of the model 

performance in this specific case also leads me to believe that the RSM approach is highly accurate, 

and capable of generating results that match the significantly more time-intensive ALPHA 

simulations.

Doug Montgomery

1. I investigated the adequacy of the RSM models by first analyzing the residuals from these models in 

the spreadsheets that were provided. I constructed normal probability plots of the residuals and 

plots of the residuals versus the predicted response. These plots investigate the normality of the 

response variable and the equality of variance assumption, both of which are standard RSM 

assumptions. The normality assumption is of only moderate importance since the underlying 

statistical methodology is robust to all but severe departures from normality. A few of the normal 

probability plots exhibited very small potential departures from normality but nothing severe 
RTI International Peer Review of EPA’s RSE Report 7

enough to call model validity into question.

2. The equal variance assumption is more important, and moderate to large departures from this 

assumption may require remedial measure such as the use of variance-stabilizing transformation.

Similarly [to the normal probability plots], some of the plots of residuals versus the predicted 

response exhibited a non-random pattern, but none of the patterns were serious enough to 

question the equal variance assumptions. 

3. It is also worth noting that the model residuals are extremely small as all models provide extremely 

good fits to the data obtained from the simulation model.

TOPIC 3: Clarity, completeness and accuracy of the technical application of response surface 

modelling.

Sujit Das

1. Response surface methodology (RSM) explores the relationships between several explanatory 

variables and one or more response variables. A sequence of designed experiments (DOE) was used, 

i.e., the main idea of RSM to obtain an optimal response. A DOE used in this case was based on an 

automated process that is configured to produce a complete set of ALPHA results for all 

combinations of engines, transmissions, roadloads, and vehicle types to be used in the OMEGA 

analysis. It is a relatively easy statistical model to estimate and apply, even when little is known 

about the process. It maximizes the production of a special substance by optimization of operational 

factors. A factorial experiment or a fractional factorial design generally used to estimate RSE process 

has generated as series of equations from a complete set of ALPHA data for each vehicle type and 

powertrain model. A second-degree RSE polynomial model was developed for each 24 vehicle cases 

based on a combination of 6 vehicle types and 4 powertrain types in the present analysis.

2. Overall, the quality of RSE methodology appears to be reasonable for the four independent variables 

considered. The validity of this methodology need to reexamined if it is expanded to a higher 

number of independent variables in the future.

TOPIC 4: Any recommendations for specific improvements to the functioning or the quality of the 

methodology.

Sanya Carley

1. Design of Experiments: A future extension of model validation could be an assessment of the RSM 

output with actual testing data. One should assume that the results would be similar to the 

estimates of comparison between ALPHA and RSM, however, since the EPA’s previous work found 

that ALPHA estimates were within the margin of 3% error as compared to actual vehicle 

performance testing.
RTI International Peer Review of EPA’s RSE Report 8

2. Transparency: As stated in the report, one of the benefits of the RSM is “increased transparency 

regarding synthesis of ALPHA simulation into OMEGA modeling”. It is not entirely clear to me how 

the use of RSM will increase transparency. But I strongly encourage and support full transparency of 

modeling inputs, outputs, processes, and supporting information.

EPA response: EPA’s mention of transparency refers to comments received from stakeholders 

discussing the challenge to understand portions of the Lumped Parameter Model that was previously 

used to determine the effectiveness of vehicle technologies. In response, a full matrix of ALPHA 

model runs along with the industry standard RSE methodology completely replaces the Lumped 

Parameter Model providing a straightforward method for stakeholders to evaluate.

Sujit Das

1. Section 6. Baseline Vehicle Adaptation needs further details in terms of the necessary process steps 

for adjusting the effectiveness of a baseline vehicle to match the ALPHA model. The adjustment 

approach for the baseline vehicle adaptation is an interesting one as it allows ~ 50 alternative 

options to consider in a baseline vehicle.

EPA response: Since the writing of the report, the ALPHA model parameters were expanded 

eliminating the need for the Baseline Vehicle Adaptation described in section 6. This section of the 

report has also been deleted as this process is no longer applicable.

2. Since the RSE final output is CO2 emissions provided to the OMEGA model with the technology 

alternatives necessary to produce the most cost-effective path for compliance, a short discussion of 

it will be useful for unfamiliar users.

EPA response: One of the preprocessing steps for the OMEGA model is to produce approximately 50 

technology improvement options for each vehicle in the current baseline fleet. The OMEGA model 

iterates through the technology options for all vehicles in a manufacturers fleet until compliance is 

achieved.

3. A description of three different transmission types considered and denoted by numerals (i.e., 2, 4, & 

5) would be useful. An appropriate justification needs to be included why other two types, i.e., 1 and 

3 were not considered for the RSM DOE analysis.

EPA response: Transmissions 2, 4, and 5 represent three actual transmissions that EPA benchmarked 

for efficiency. For successful use in the RSM, the increase in efficiency needs to be as linear as 

possible. Randomly assigning the numbers 1, 2, and 3 for example would have resulted in a very 

nonlinear response and not suitable for the RSM. The efficiencies for the three transmissions plotted 

against the numbers 2, 4, and 5 were quite linear in this case and was chosen for simplicity. A future 

case with a different mix of transmissions may require more resolution either by using decimal points 

or larger numbers to find the proper number to represent a particular transmission for a linear 
RTI International Peer Review of EPA’s RSE Report 9

response.

4. It is unclear why the assumed vehicle mass reduction value is not actually reflected in the ALPHA 

spreadsheets provided, e.g., for 2020 TURB24 vehicle, 3109.15 lbs and 2961.3 lbs Test Weight have 

been assumed for a mass reduction of 5% and 10%, respectively, for a baseline vehicle Test Weight 

of 3257 lbs? Similar level of difference was found in all 21 different vehicle type/powertrain 

considered for RSM.

EPA response: The mass reduction is calculated from the curb weight of the vehicle, not the test 

weight. The test weight adds 300 lbs. to the curb weight. For this example, 3257 lbs. test weight –

300 lbs. = 2957 lbs. curb weight. 10% of 2957 lbs. = 295.7 lbs. Subtracting 10% of the curb weight 

from the test weight (3257 lbs. - 295.7 lbs.) results in a final test weight of 2961.3 lbs. 

5. The draft report mentions about six vehicle types in OMEGA analysis and four powertrain categories 

in the ALPHA. It is unclear about the consistency in the number of vehicle types and powertrain 

categories between these two tools and thereby to what extent does the current RSM cover the 

overall analysis scope of the OMEGA technology options?

EPA response: At the time of this report, there were 6 vehicle types and 4 powertrain types resulting 

in 24 RSEs. The 6 spreadsheets provided represented the complete set of ALPHA runs for each 

vehicle type. The 4 individual tabs in each spreadsheet filtered the specific set of runs for each of the 

4 powertrain types resulting in 24 combinations and a 1 to 1 correlation between the ALPHA DOE 

and the RSM.

6. In spite of the fact that there are four independent variables, i.e., mass reduction, aerodynamic drag 

reduction, rolling resistance reduction, and transmission type have been used for the development 

of RSE equations, but +50 ALPHA data variables have been included in the several vehicle 

spreadsheets provided. It’d be good to provide the description of each of the ALPHA variables for an 

understanding of impacts of the four major dependent variables considered.

EPA response: The ALPHA variables define each powertrain type and are held constant for each DOE 

generated. The descriptions of the individual ALPHA data variables are beyond the scope of this 

review.

7. As the RSE “Effectiveness” implementation is expanded beyond the currently limited six vehicles, 

four powertrains, and three transmission type options provided, the user-friendliness in terms of 
RTI International Peer Review of EPA’s RSE Report 10

inputs should be kept in mind. Using the current framework provided as an example, it is difficult for 

a novice user to perform a quick analysis. Specifically, a discussion on the “Baseline Vehicle 

Adaptation” procedure needs to be included in the documentation, when all original LPM 

technology options are also available for RSM for the baseline vehicle adaptation. Some 

Comments/Warning should be included if the results are invalid for transmission cases 1 & 3 as is 

the case now. The inputs for Vehicle Type, Model, and Transmission in Column A should be 

interlinked with the corresponding numeric value in Column B on this worksheet.

EPA response: As stated above, the Baseline Vehicle Adaptation is no longer used in the RSM 

process. The RSM tool in the form presented is designed as part of an automated process and not for 

manual input at this release.

8. It’d be useful for the EPA draft report completeness to provide some background information on the 

models and tools used in EPA’s light-duty Greenhouse Gas (GHG) rulemakings for unfamiliar 

audience. 

EPA response: The report mentions the tools for historical context without extensive detail as this is 

beyond the scope of this review. Details for the previous tools used can be found here:

https://www.epa.gov/regulations-emissions-vehicles-and-engines/midterm-evaluation-light-dutyvehicle-greenhouse-gas

9. Not sure whether any model validation was done in terms of using the model to predict the 

response for one or more combinations of design factors that were not used to build the RSM 

models? What agreements between the two results were found for such a validation?

EPA response: As stated earlier, the road load factors included in the RSM are linear and predictable. 

Many additional ALPHA runs were performed to verify that the ALPHA model and the RSM remain 

stable for these intermediate values.
RTI International Peer Review of EPA’s RSE Report 11

Appendix A. Peer Reviewers’ Resumes
RTI International Peer Review of EPA’s RSE Report 12

SANYA CARL EY

School of Public and Environmental Affairs

Indiana University, Room 353, 1315 East Tenth Street, Bloomington, IN 47405

(812) 856-0920; scarley@indiana.edu

PROFESSIONAL APPOINTMENTS

Associate Professor, School of Public and Environmental Affairs, Indiana University 2014 - present

Chair, Policy Analysis and Public Finance Faculty Group 2016 - present

Assistant Professor, School of Public and Environmental Affairs, Indiana University 2010 - 2014

PROFESSIONAL AFFILIATIONS

Research Fellow, Center for Organization Research and Design 2016 - present

Research Member, The Richard G. Lugar Center for Renewable Energy 2013 - present

Member, Scholars Strategy Network 2017 - present

Brain Trust Member, IronOak 2016 - present

EDUCATI ON

University of North Carolina at Chapel Hill. Ph.D. Public Policy, 2010.

Dissertation Committee: Richard Andrews (Chair), Doug Crawford-Brown, Gary Henry, Richard 

Newell, Tim Johnson

University of Wisconsin-Madison. M.S. Urban and Regional Planning, Masters Certificate, Energy Analysis 

and Policy, 2006.

Swarthmore College. B.A. Economics, B.A. Sustainable Development, 2003.

AREAS OF RESEARCH

Energy Policy, Electricity Markets, Transportation Industry, Energy-based Economic Development, Policy

Instruments, Electric Vehicles, Distributed Generation

CONSULTING AND WORK EXPERIENCE 

Consultant, Institute for International Business, Indiana University, Bloomington, IN. (2013)

Consultant, Environmental Protection Agency, Conflict Prevention and Resolution Center, Washington D.C. 

(2010)

Consultant, Research Triangle Institute International, Center for Technology Applications, Research Triangle 

Park, NC. (2009 - 2010)

Consultant, ARCeconomics, SC. (2007 - 2010)

Consultant, The Nicholas Institute for Environmental Policy Solutions, Durham, NC. (2008)

Graduate Fellow, Center for Sustainable Energy, Environment, and Economic Development, Chapel Hill, 

NC. (2006 - 2010)

Energy Program Specialist, Wisconsin Public Utility Institute, Madison, WI. (2005 - 2006)

Consultant, World Bank Group, Development Economic Research of the Public Sector, Washington D.C. 

(2003 - 2006)
RTI International Peer Review of EPA’s RSE Report 13

BOOKS

Carley, S., Lawrence, S. 2014. Energy-based Economic Development: How clean energy can drive 

development and stimulate economic growth. Springer: New York.

Reviews of the Book:

1. Sharma, K. R., Wilson, E. 2016. Book Review of “Energy-based Economic Development: How clean 

energy can drive development and stimulate economic growth.” Journal of Policy Analysis and 

Management 35(3): 728-731.

2. Ghadimi, H. 2017. Book review: Energy-based Economic Development: How clean energy can drive 

development and stimulate economic growth. Economic Development Quarterly 31(1): 92-96.

PEER OR EDIT OR REVIEWED PUBLICATI ONS

* Denotes student co-author at time of writing

Carley, S., Evans, T. P., Konisky, D. M. 2018. Adaptation, culture, and the energy transition in American 

coal country. Energy Research & Social Science.

Nicholson-Crotty, S., Carley, S. 2017. Policy Learning in the Context of State Energy Policy. Forthcoming,

State Politics and Policy Quarterly.

Carley, S., Baldwin, E.*, MacLean, L. M., Brass, J. N. 2017. Global Expansion of Renewable Energy 

Generation: An Analysis of Policy Instruments. Environmental and Resource Economics 68(2): 397-440. 

- Winner of the 2014 Best Paper Award for Research in Comparative Policy Analysis, honored by the 

Association of Public Policy Analysis and Management and the International Comparative Policy 

Analysis Forum.

Carley, S., Nicholson-Crotty, S., Miller, C.* 2017. Adoption, Reinvention, and Amendment of Renewable 

Portfolio Standards in the American States. Journal of Public Policy 37(4): 1-28. 

Baldwin, E.*, Carley, S., Brass, J. N., MacLean, L. M. 2017. Global renewable energy policy: A comparative 

analysis of countries by economic development status. Journal of Comparative Policy Analysis 19(3): 277-

298.

Davies, L. L., Carley, S. 2017. Emerging shadows in national solar policy? Nevada’s Net Metering 

Transition in Context. The Electricity Journal.

Krause, R., Lane, B., Carley, S., Sperl J.*, Graham, J. 2016. Assessing the Demand for Electric Vehicles 

under Future Cost and Technological Scenarios. International Journal of Sustainable Transportation 10(8): 

742-751.

Clark-Sutton, K.*, Siddiki, S., Carley, S., Wanner, C.*, Rupp, J., Graham, J.D. 2016. Plug-in electric vehicle 

readiness: Rating cities in the United States. The Electricity Journal 29(1): 30-40.

Carley, S. 2016. Energy programs of the American Recovery and Reinvestment Act of 2009. Review of 

Policy Research 33(2): 201-223. 

Carley, S. 2016. The American Recovery and Reinvestment Act of 2009: What have we learned? Review of 

Policy Research 33(2): 119-123.

Zirogiannis, N., Alcorn, J.*, Rupp, J., Carley, S., Graham, J. 2016. State regulation of unconventional gas 

development in the U.S.: An empirical evaluation. Energy Research and Social Science 11:142-154.
RTI International Peer Review of EPA’s RSE Report 14

Nicholson-Crotty, S., Carley, S. 2016. Effectiveness, Implementation Capacity, and Policy Diffusion: Or, 

"Can We Make that Work for Us?" State Politics and Policy Quarterly 16(1), 78-97.

Paydar, N.*, Schenk, O., Alcorn, J.*, Bowers, A., Carley, S., Rupp, J., Graham, J.D. 2015. The Effect of 

Community Reinvestment Funds on Local Acceptance of Unconventional Gas Development. Economics of 

Energy & Environmental Policy 15(1): 1-26.

Esposito, D.*, Rupp, J., Carley, S. 2015. Interaction of risks associated with natural gas and renewable based 

electricity. The Electricity Journal 28(8): 69-84.

Siddiki, S., Dumortier, J., Curley, C., Carley, S., Krause, R. 2015. Regulating for Innovation and Technology 

Adoption: The Case of Plug-In Vehicles. Review of Policy Research 32(6): 649-674.

Warren, D.*, Wendling, Z.*, Bower-Bir, J.*, Fields, H.*, Richards, K., Carley, S., Rubin, B. 2015. 

Estimating State and Sub-State Economic Effects of a Carbon Dioxide Tax Policy: An Application of a New 

Multi-Region Energy-Economy Econometric Model. Regional Science, Policy and Practice 7(3): 119-139.

MacLean, L., Brass, J., Carley, S., El-Arini, A.*, Breen, S.* 2015. Democracy and the distribution of NGOs 

promoting renewable energy in Africa. Journal of Development Studies 51(6): 725-742.

Dumortier, J., Siddiki, S., Carley, S., Cisney, J.*, Krause, R., Lane, B., Rupp, J., Graham, J. 2015. Effects of 

providing total cost of ownership information on consumers' intent to purchase a hybrid or plug-in electric 

vehicle. Transportation Research Part A: Policy and Practice 72: 71-86.

Carley, S., Nicholson-Crotty, S., Fisher, E.* 2015. Capacity, Guidance, and the Implementation of the 

American Recovery and Reinvestment Act. Public Administration Review 75(1): 113-125.

Carley, S., Hyman, M.* 2014. The American Recovery and Reinvestment Act: Lessons from Energy 

Program Implementation Efforts. State and Local Government Review 46(2): 140-147.

Baldwin, E.*, Brass, J., Carley, S., MacLean, L. 2014. Issues of scale in distributed generation electrification 

for rural development. WIRES: Energy and Environment.

Warren, D.*, Carley, S., Krause, R., Rupp, J., Graham, J. 2014. Predictors of attitudes toward carbon capture 

and storage using data on world views and CCS-specific attitudes. Science and Public Policy.

Krause, R., Carley, S., Warren, D.*, Rupp, J., Graham, J. 2014. Not Under My Backyard: Geographic 

proximity and public acceptance of CCS facilities. Risk Analysis 34(3): 529-540.

Wendling, Z. A.*, Attari, S. Z., Carley, S., Krause, R. M., Warren, D.*, Rupp, J., Graham, J. D. 2013. On the 

importance of strengthening moderate beliefs in climate science to foster support for immediate action. 

Sustainability 5(12): 5153-5170.

Krause, R., Carley, S., Lane, B., Graham, J. 2013. Perception and Reality: Public Knowledge of Plug-in 

Electric Vehicles. Energy Policy 63: 443-440. 

Lane, B., Messer, N.*, Hartman, D.*, Carley, S., Krause, R., Graham, J. 2013. Government promotion of the 

electric car: Risk management or industrial policy? European Journal of Risk Regulation 2: 227-245.
RTI International Peer Review of EPA’s RSE Report 15

Carley, S., Krause, R., Lane, B. Graham, J. 2013. Intent to purchase a plug-in electric vehicle: A survey of 

early impressions in large US cites. Transportation Research Part D: Transport and Environment 18: 39-45.

Brass, J., Carley, S., MacLean, L., Baldwin, E.* 2012. Power for development: An analysis of on-the-ground 

experiences of distributed generation in the developing world. Annual Review of Environment and Resources 

37: 107-136.

Carley, S., Browne, T.* 2012. Innovative US Energy Policy: A review of states’ policy experiences. WIREs: 

Energy and Environment 00: 1-19.

Carley, S., Miller, C.* 2012. Regulatory stringency and policy adoption: Reassessment of renewable 

portfolio standards. Policy Studies Journal 40(4): 730-756.

Carley, S., Krause, R., Warren, D.*, Rupp, J., Graham, J. 2012. Early public impressions of terrestrial CCS 

in a coal-intensive state. Environmental Science & Technology 46: 7086-7093.

Gaul, C.*, Carley, S. 2012. Solar set asides and renewable energy certificates: Early lessons from North 

Carolina’s experience with its Renewable Portfolio Standard. Energy Policy 48: 460-469.

Carley, S., Andrews, R. L. 2012. Creating a sustainable U.S. electricity sector: The question of scale. Policy 

Sciences 45(2): 97-121.

Carley, S. 2012. Energy demand-side management: New perspectives for a new era. Journal of Policy 

Analysis and Management 31(1): 6-32.

Carley, S., Brown, A., Lawrence, S. 2012. Economic development and energy: From fad to a sustainable 

discipline? Economic Development Quarterly 26(2): 111-123.

Carley, S. 2012. National clean energy standards: Experience from the states. Review of Policy Research 

29(2): 301-307. Originally printed in SPEA Insights, July 2011.

Carley, S. 2011. Decarbonization of the U.S. electricity sector: Are state energy policy portfolios the 

solution? Energy Economics 33(5): 1004-1023.

Carley, S. 2011. Normative dimensions of sustainable energy policy. Ethics, Policy & Environment 14(2): 

211-229.

Carley, S. 2011. The era of state energy policy innovation: A review of policy instruments. Review of Policy 

Research 28(3): 265-294.

Carley, S., Lawrence, S., Brown, A., Nourafshan, A.*, Benami, E.* 2011. Energy-Based Economic 

Development. Renewable and Sustainable Energy Reviews 15(1): 282-295.

Carley, S. 2010. Historical analysis of U.S. electricity markets: Reassessing carbon lock-in. Energy Policy

39(2): 720-732. 

Carley, S. 2009. Distributed generation: An empirical analysis of primary motivators. Energy Policy 37(5): 

1648-1659.

Carley, S. 2009. State renewable energy electricity policies: An empirical evaluation of effectiveness. Energy 

Policy 37(8): 3071-3081. 
RTI International Peer Review of EPA’s RSE Report 16

LAW JOURNAL PUBLICATIONS

Carley, S., Messer, N.* Graham, J. 2012. Innovation in the Auto Industry: The Role of the U.S. 

Environmental Protection Agency. Duke Environmental Law and Policy Forum 21: 367-399.

Carleyolsen, S. 2006. Tangled in the Wires: An Assessment of the Existing U.S. Renewable Energy Legal 

Framework. Natural Resources Journal 46 (3): 759-792.

BOOK REVIEWS

Carley, S., Graff, M.* 2017. Review of “Climate and Clean Energy Policy: State Institutions and Economic 

Implications.” American Review of Public Administration. Forthcoming.

PEER-REVIEWED POLICY AND BUSINESS REPORTS

Carley, S., Duncan, D., Graham, J. D., Siddiki, S., Zirogiannis, N., 2017. “A Macroeconomic Study of 

Federal and State Auto Regulations with Recommendations for Analysts, Regulators, and Legislators.” 

Carley, S., Davies, L. 2016. “Nevada’s Net Energy Metering Experience: The Making of a Policy Eclipse?” 

Brookings Institution Report.

Carley, S., Duncan, D., Esposito, D.*, Graham, J. D., Siddiki, S., Zirogiannis, N., 2016. “Rethinking Auto 

Fuel Economy: Technical and Policy Suggestions for the 2016-17 Midterm Reviews.” 

Carley, S., Jasinowski, J., Glassley, G.*, Strahan, P.,* Attari, S., Shackelford, S. October 2014. “Success 

Paths to Sustainable Manufacturing.” 

School of Public and Environmental Affairs, 2011. “Plug-in Electric Vehicles: A Practical Plan for 

Progress.” The report of an expert panel [Contributing author].

POLICY REPORTS AND WHITE PAPERS

Indiana University Public Policy Institute, February 2012. “An environmentally sound energy policy: One 

key to Indiana’s economic future.” Policy brief prepared for Indiana policymakers by the Indiana Policy 

Choices Energy and Environment Commission [Commission member and contributing author].

Carley, S., Hyman, M.* The “Grand Experiment:” An early review of energy-related American Recovery 

and Reinvestment Act Efforts. PERI Working Paper Series Report 338.

Carley, S., Desai, S., Bazilian, M., Kammen, D. 2012. Energy-based economic development: Prioritizing 

opportunities for developing countries. FEEM Working Paper 25.2012.

Baldwin, L.*, Carley, S., Gardner, W.*, June 2011. “Demand-side Management and Energy Efficiency in 

Indiana: A Comparison of Policy Instruments.” Policy brief prepared for the Indiana Utility Regulatory 

Commission.

The Nicholas Institute, 2009. “An Evaluation of Utah’s Greenhouse Gas Reduction Options.” Technical 

policy report prepared for the state of Utah. [Contributing researcher].

Carleyolsen, S., Voss, S., 2006. “Recommendations for the Governor’s Taskforce on a Wisconsin 

Bioindustry Strategy.” White Paper prepared for the Wisconsin’s Bioindustry Consortium Taskforce.
RTI International Peer Review of EPA’s RSE Report 17

Carleyolsen, S., Rude, J., Jenkins, A., 2006. “IGCC: A Cost-Benefit Analysis.” White Paper prepared for the 

Wisconsin Public Service Commission and the Wisconsin IGCC Governor’s Taskforce.

Carleyolsen, S., Meyer, T., Scott, I., Rude, J., 2005. “Estimating Economic Value of Jefferson County Parks, 

Trails, and Open Space.” White Paper given to the Jefferson County Board of Supervisors. Jefferson County, 

WI.

MEDIA PUBLICATI ONS

Carley, S. October 2017. Op-Ed: Mandates help motorists, economy in the long run. Printed in McClatchy 

papers.

Carley, S., Konisky, D. March 2017. Op-Ed: Changes to Indiana’s Solar Policy Misguided. The HeraldTimes (as well as numerous other outlets).

Jasinowski, J., Carley, S. 2014. Op-Ed: Sustainable Manufacturing Makes Cents. Manufacturing Leadership 

Journal. 

Carley, S., Hyman, M. January 12, 2012. Op-Ed: “'Green energy' is the best route to profitable public 

investment.” Printed in McClatchy papers, including the Miami Herald, Kansas City Star, and the 

Sacramento Bee (Also printed in 37 other U.S. news outlets).

OTHER PUBLICATI ONS

Carley, S. January 2017. How states are grappling with solar panels, net energy metering, and the evolving 

electric utility industry. Scholars Strategy Network Brief.

Graham, J. D., Cisney, J.*, Carley, S., Rupp, J. 2014. No time for pessimism about electric cars. Issues in 

Science & Technology.

Carley, 2014. Response to Pollin, R. 2014. A Clean Energy Program for the United States. Boston Review. 

July/August Issue.

Carley, S. 2012. Electric vehicles: Public acceptance, infrastructure and policy. USAEE Dialogue 20(3).

Graham, J., Carley, S., Messer, N.*, Hartman, D.* February, 2011. Plug-in Electric Vehicles: A Practical 

Plan for Progress. SPEA Insights.

Carley, S. May, 2011. National clean energy standards: Experience from the states. SPEA Insights.

SELECTED WORKS IN PROGRESS

Siddiki, S., Carley, S., Zirogiannis, N., Duncan, D., Graham, J. Does dynamic federalism yield compatible 

policies? A study of federal and state vehicle standards. Revise and Resubmit Status at Publius: The Journal 

of Federalism.

Lane, B., Carley, S., Siddiki, S., Dumortier, J., Clark-Sutton, K.*, Krause, R., Graham, J. D. All electric 

vehicles are not the same: An intent to purchase analysis accounting for heterogeneity among vehicle types.

Revise and Resubmit Status at Transportation Research Part D: Transport and Environment.

Wendling, Z.*, Warren, D.*, Rubin, B., Carley, S., Richards, K. An Energy-Economy Econometric Model 

for Conducting State-Level Energy Policy Analysis. Revise and Resubmit Status at Energy Policy.
RTI International Peer Review of EPA’s RSE Report 18

Carley, S., Zirogiannis, N., Duncan, D., Siddiki, S., Graham, J. D. An analysis of the macroeconomic effects 

of 2017-2025 federal fuel economy and greenhouse gas emissions standards. Manuscript under review.

Carley, S., Nicholson-Crotty, S. Energy Policy Learning and Information Channels in the American States.

Manuscript under review.

Carley, S., Yahng, L. Willingness to pay for sustainable beer. Manuscript under review.

Carley, S., Davies, L., Spence, D., Zirogiannis, N. Renewable Portfolio Standards, Renewable Energy 

Markets, and the Importance of Policy Design. Manuscript under review.

Ross, J., Carley, S. Efficient Siting of Nuisance Facilities Under Regulatory and Fiscal Decentralization: 

Empirical Evidence from the Effect of Political Borders on Wind Farms Location. Manuscript under review.

Duncan, D., Zirogiannis, N., Carley, S., Siddiki, S., Graham, J. D. The effect of CAFE standards on vehicle 

sales projections: A total cost of ownership approach. Manuscript under review.

Carley, S., Evans, T., Konisky, D. Vulnerability and the U.S. Energy Transition. Manuscript under review.

Jenn, A., Hardman, S., Carley, S., Zirogiannis, N., Duncan, D., Graham, J. D. Cost implications for 

automakers’ compliance with emission standards from Zero Emissions Vehicle mandate. Working paper.

Baldwin, E., Carley, S., Nicholson-Crotty, S. The global diffusion of renewable energy policies. Working 

paper.

Zirogiannis, N., Carley, S., Duncan, D., Siddiki, S., Graham, J. D. Overcoming the shortcomings of U.S. 

plug-in electric vehicle policies. Working paper.

Siddiki, S., Carley, S., Zirogiannis, N., Duncan, D., Graham, J. D. Policy compatibility by design: The case 

of U.S. vehicle emissions standards. Working paper.

Alcorn, J., Carley, S. Exploring Renewable Energy Certificate market dynamics: What role do markets play 

in renewable energy growth and development? Working paper.

CONFERENCE PROCEEDINGS, PAPERS, AND POSTERS (CO-AUTHOR PRESENTED WORK NOT LISTED)

“A Macroeconomic Study of Federal and State Auto Regulations with Recommendations for Analysts, 

Regulators, and Legislators.” Paper presented at the Annual Conference, U.S. Association of Energy 

Economics, Houston, TX, November, 2017.

“A Macroeconomic Study of Federal and State Auto Regulations with Recommendations for Analysts, 

Regulators, and Legislators.” Paper presented at the 39th Annual Research Conference, Association for 

Public Policy Analysis and Management, Chicago, IL, November, 2017.

“The Global Diffusion of Renewable Energy Policies.” Paper presented at the 38th Annual Research 

Conference, Association for Public Policy Analysis and Management, Washington, D.C., November, 2016.

“Exploring renewable energy certificate market dynamics: What role do markets play in renewable energy 

growth and development?” Paper presented at the International Association of Energy Economics 

Conference, Bergen, Norway, June, 2016.
RTI International Peer Review of EPA’s RSE Report 19

“Policy Learning in the Context of State Energy Policy.” Paper presented at the 74th annual Midwest Political 

Science Association Conference, Chicago, IL, April, 2016.

“Exploring renewable energy certificate market dynamics: What role do markets play in renewable energy 

growth and development?” Paper presented at the 37th Annual Research Conference, Association for Public 

Policy Analysis and Management, Miami, FL, November, 2015.

“The Electric Vehicle Attitude-Behavior Gap: Moving Beyond the Early Adopters.” Paper presented at the 

34th Annual Conference, U.S. Association of Energy Economics, Pittsburgh, PA, October, 2015.

“Global Renewable Energy Generation: An Analysis of Renewable Energy Drivers Across Gross National 

Income Categories.” Paper presented at the 36th Annual Research Conference, Association for Public Policy 

Analysis and Management, Albuquerque, NM, November, 2014.

“Global expansion of renewable energy generation: An evaluation of policy instruments.” Paper presented at 

the 35th Annual Research Conference, Association for Public Policy Analysis and Management, Washington, 

D.C., November, 2013.

“Social learning and policy diffusion: adoption, reinvention, and amendment of the renewable portfolio 

standard.” Paper presented at the Energy systems in Transition Conference, Karlsruhe, Germany, October, 

2013.

“Global expansion of renewable energy generation: An evaluation of policy instruments.” Paper presented at 

the 32nd U.S. Association of Energy Economists/International Association for Energy Economists 

Conference, Anchorage, AK, July, 2013.

“Global expansion of renewable energy generation: An evaluation of policy instruments.” Paper presented at 

the annual Transatlantic Policy Consortium, The Hague, Netherlands. May 2013. 

“Intent to purchase a plug-in electric vehicle: A survey of early impressions in large U.S. cites.” Conference 

proceeding presented at the 31st U.S. Association of Energy Economists/International Association for Energy 

Economists Conference, Austin, TX. November, 2012.

“Power for development: An analysis of on-the-ground experiences of distributed generation in the 

developing world.” Paper presented at the 33rd Annual Research Conference, Association for Public Policy 

Analysis and Management, Washington, D.C., November, 2011.

“NGOs and collaborative energy service provision in developing countries.” Paper presented at the American 

Political Science Association Conference, Seattle, WA, September 4, 2011.

“Energy-based Economic Development: From Fad to Sustainable Discipline?” Paper presented at the 

Seventh International Conference on Environmental, Cultural, Economic and Social Sustainability, 

Hamilton, New Zealand, January 7, 2011.

“Demand-side management: New perspectives for a new era.” Paper presented at the 29th U.S. Association of 

Energy Economists/International Association for Energy Economists Conference, Calgary, October, 2010.

“Demand-side management: New perspectives for a new era.” Paper presented at the SPEA-Speyer 

Workshop, Bloomington, IN, November, 2010. 
RTI International Peer Review of EPA’s RSE Report 20

“State energy policy instruments: Lessons learned from the era of state energy innovation policy.” Paper 

presented at the 32nd Annual Research Conference, Association for Public Policy Analysis and Management, 

Boston, November, 2010.

“Decarbonization of the U.S. electricity sector: Are state energy policy portfolios the solution?” Poster 

presented at the Solar Energy Research Center’s Conference, Solar Fuels and Energy Storage: The Unmet 

Needs, Chapel Hill, NC, January, 2010. 

“Decarbonization of the U.S. electricity sector: Are state energy policy portfolios the solution?” Paper 

presented at the 31st Annual Research Conference, Association for Public Policy Analysis and Management, 

Washington, D.C., November, 2009.

“State renewable energy electricity policy: An empirical evaluation of effectiveness.” Paper presented at the 

30th Annual Research Conference, Association for Public Policy Analysis and Management, Los Angeles, 

CA, November 6, 2008.

“Evaluating the Effectiveness of State Renewable Energy Policies.” Poster presented at the RTEC 

Sustainable Energy Symposium, Raleigh, NC, 2007.

“Tracking Social Capital Indicators using Geographic Information Systems.” Presentation at the Upper 

Midwest Regional Planning Conference, MN, 2005. Received Best Student Presentation award.

INVITED TALKS, LECTURES, WEBINARS, OR PANEL PRESENTATIONS

2017: Innovation, Property Rights, and the Structures of Energy, Property and Environment Research 

Center (PERC), Bozeman, MT; Environmental Protection Agency, Ann Arbor; Environmental 

Protection Agency, Washington D.C.; Electricity Dialogue, Northwestern University; Association of 

Public Policy Analysis and Management Webinar, Washington D.C.; Workshop on Durability and 

Adaptability in Energy Policy, Resources for the Future; Earth and Mineral Sciences Energy 

Institute, Pennsylvania State University

2016: 2016 Austin Electricity Conference, University of Texas; U.S. Association of Energy Economics, 

dual plenary session on Transportation; South Carolina Journal of International Law and Business 

Symposium; University of Texas at Austin, Regional Challenges and Opportunities in Energy 

Transformations Workshop.

2015: Panel on National Science Foundation funding, Indiana University; U.S. Association of Energy 

Economics, session on Energy Economics Education; Workshop on Manufacturing and Public 

Policy; Mini University, Indiana University; Richard G. Lugar Center for Renewable Energy; 

University of Utah, S.J. Quinney College of Law, 20th Annual Stegner Symposium; University of 

North Carolina at Chapel Hill, Odum Institute. 

2014: Kelley School of Business, Indiana University; Martin School of Public Policy and Administration, 

University of Kentucky; Ford School of Public Policy, University of Michigan.

2013: ARPA-E; Centre for Energy Economics and Policy, ETH Zurich; Global Mini-Conference, Indiana 

University; Energy Student Leaders Association, Indiana University; Energy and Climate Seminar 

Series, Georgetown University; International Public Affairs Association, Indiana University; 13th

Annual Association of SPEA Ph.D. Students Conference, Indiana University; Kelley School of 

Business Renaissance Week, Indiana University.

2012: Center for Local, State, and Urban Policy, University of Michigan; School of Public and 

Environmental Affairs Dean’s Council Meeting, Indiana University; Kelley School of Business 

Renaissance Week, Indiana University; Policy Lecture Series, UNC-Chapel Hill Department of 

Public Policy.

2011: Mini University, Indiana University; Ph.D. Student Research Seminar, School of Public and 

Environmental Affairs, Indiana University;
RTI International Peer Review of EPA’s RSE Report 21

2010 Ph.D. Student Research Seminar, School of Public and Environmental Affairs, Indiana University; 

Ph.D. Student Research Seminar, School of Public and Environmental Affairs, Indiana University; 

University Research Day, University of North Carolina at Chapel Hill; Carolina Institute for the 

Environment Board of Visitors, University of North Carolina at Chapel Hill.

2006: Wisconsin’s Bioindustry Consortium Taskforce, Madison, WI.

2005: Jefferson County Board of Supervisors, Jefferson County, WI.

PANEL CHAIR OR MODERATOR:

2017: Environmental Politics & Governance conference; Association for Public Policy Analysis and 

Management conference; U.S. Association of Energy Economics conference

2016: Midwest Political Science Association conference; U.S. Association of Energy Economics annual 

conference; Association for Public Policy Analysis and Management conference

2015: U.S. Association of Energy Economics conference; Association for Public Policy Analysis and 

Management conference

2014: Association for Public Policy Analysis and Management conference

2013: Association for Public Policy Analysis and Management conference; International Public Affairs 

Association conference; U.S. Association of Energy Economics conference

2011: U.S. Association of Energy Economics conference; Association for Public Policy Analysis and 

Management conference

2010: Association for Public Policy Analysis and Management conference

2006: Wisconsin Public Utility Institute conference 

GUEST SEMINAR PRESENTATIONS AT INDIANA UNIVERSITY

2016: SPEA, Experience Day; SPEA, V680 Research Design

2015: SPEA, Experience Day; SPEA, E574 Energy Markets and Analysis; SPEA, V680 Research Design; 

Statistics, S690 Statistical Consulting

2014: SPEA, V680 Research Design

2013: SPEA, V680 Research Design; SPEA, Experience Day; Kelley School of Business, L302 

Sustainability Law & Policy.

2012: SPEA, Experience Day. 

2011: SPEA, V680 Research Design; SPEA, V669 Economic Development; SPEA, V160 National and 

International Policy; SPEA, Experience Day.

2010: SPEA, Advanced Math Camp

GRANTS

“Toward the Diffusion of Sustainable Technologies: The Case of Electric Vehicles” Co-PI with Sean 

Nicholson-Crotty and Saba Siddiki. National Science Foundation. $184,996. 2016-2018.

“The Siting of Energy Infrastructure: Public Perceptions and Public Finance Impacts” Co-PI with David 

Konisky and Steven Ansolabehere. Alfred P. Sloan Foundation. $259,900. 2016-2018.

“The U.S. Energy and Climate Transition: Aggregated Impacts of Policy on Vulnerable Populations” PI with 

Co-PIs Tom Evans and David Konisky. Indiana University Collaborative Research Grant. Office of the Vice 

Provost of Research, Indiana University. $63,437. 2016-2017.

“Consumer Willingness to Pay for Sustainability: The Case of the Brewing Industry” PI. Office of the Vice 

Provost of Research Award for Research Methods and Collaboration, Indiana University. $4,942. 2016.
RTI International Peer Review of EPA’s RSE Report 22

“Study of the macro-economic impact of the light-duty vehicle corporate average fuel economy, greenhouse 

gas and zero-emission vehicle standards: Phases II and III” Co-PI with John Graham, Denvil Duncan, Saba 

Siddiki, and Nikos Zirogiannis. Alliance for Automobile Manufacturers. $590,000. 2016-2017.

“Study of the macro-economic impact of the light-duty vehicle corporate average fuel economy, greenhouse 

gas and zero-emission vehicle standards: Phase I” Co-PI with John Graham, Denvil Duncan, and Saba 

Siddiki. Alliance for Automobile Manufacturers. $202,723. 2015-2016.

“Informing Energy Policy Choices in Indiana using an Econometric and Technology Model.” PI with Barry 

Rubin. Faculty Research Support Program, Indiana University. $72,341. 2012-2013.

“Power for Development: Sustaining Small-Scale Electricity Implementation in Africa.” PI with Jennifer 

Brass and Lauren MacLean. Faculty Research Support Program, Indiana University. $74,484. 2012-2013.

“Exploratory Study of Risks, Benefits, and Costs of DEF and Alternatives.” PI with John Graham. Navistar. 

$89,509. 2011-2012.

“NGO Involvement in Sustainable Energy Programs for International Development.” PI with Jennifer N. 

Brass. Mitsui Environment Fund, Mitsui & Co., Ltd. $59,706. 2011-2012.

“Collaborative Provision of Low-Carbon Distributed Energy in Developing Countries.” PI with Jennifer N. 

Brass. Sustainability Research Development Grant, Indiana University Office of Sustainability. $15,000. 

2011-2012.

“Energy-based Economic Development.” Co-PI with Adrienne Brown (PI) and Sara Lawrence (PI). RTI 

International R&D Grant, RTI International. $63,000. 2009-2010.

Conference Travel Grant. GPSF Travel Award, University of North Carolina at Chapel Hill. $400. 2009

Conference Travel Grant Department of Public Policy, University of North Carolina at Chapel Hill. $600. 

2008.

Grant awarded for travel to Ghana, West Africa, to establish an environmental study abroad program for an 

East coast consortium of colleges. Environmental Studies Grant, Swarthmore College. $10,500. 2001.

HONORS AND AWARDS

Campus Catalyst Excellence in Teaching Award, Indiana University Office of Sustainability, 2017.

George I. Treyz Award for Excellence in Economic Analysis, Best Paper Award, Regional Economic 

Modeling, Inc. 2017. 

Most Personable Faculty Award, Student Choice Award, School of Public and Environmental Affairs, 

Indiana University. 2016.

Outstanding Junior Faculty Award, Office of the Vice Provost for Faculty and Academic Affairs and the 

Office of the Vice Provost for Research, Indiana University-Bloomington. $14,500 research grant. 

2013.

IU Trustees Teaching Award, Indiana University-Bloomington. 2012.

Spot Award, Research Triangle Institute International, RTP, NC. 2009.

Progress Energy Fellow, University of North Carolina at Chapel Hill. 2006-2010.

Future Faculty Fellowship, University of North Carolina at Chapel Hill. 2008.

American Planning Association Best Student Presentation Winner, Upper Midwest American Planning 

Association Conference. 2005.

Morris Udall Scholar, Swarthmore College. 2002.
RTI International Peer Review of EPA’s RSE Report 23

Phillip Barley Memorial Scholar, Swarthmore College. 2002.

SELECTED PUBLICITY AND MEDIA MENTIONS

Forward Kentucky, November 13, 2017. “Did environmental rules kill mining? For coal country, that’s 

yesterday’s debate.” 

Science Daily, October 27, 2017. “Efforts to revive coal industry unlikely to work, may slow job growth.” 

(Similar story printed in Science Newsline: Nature & Earth, Common Dreams, IWW Environmental 

Unionism Caucus, and the Indiana Daily Student)

Greenwire, August 2017. “EPA gathers consumer data as it rethinks GHG standards.”

CNBC, Washington Times, IU Newsroom, March 2017. “IU research shows mileage regulations bring longterm benefits but short-term economy lag.” (Reprinted in over 150 other media outlets).

Indianapolis Star, March 2017. “Solar energy in crossroads in Indiana” (Reprinted in 24 other sources).

Inside EVs, March 2016. “U.S. Cities Ranked for Plug-in Electric Car Readiness—Portland takes Top Spot.” 

Similar news reports appear in Autocarr, Fleet Management Weekly, and Greener Ideal.

Herald Times, February 19, 2016. “IU Researchers Urge Review of Fuel Economy Standards.”

WalletHub, July, 2015. “2015 Most & Least Energy-Expensive States.” (Statements quoted in three 

subsequent news outlets)

CQ Researcher, April 2015. “Sustainability.”

Indiana University Press, November 13, 2013. “Survey: Most Americans unaware of financial advantages of 

owning an electric car” (Reprinted by four other media outlets across the country).

Society for Risk Analysis, Press Release, October 30, 2013. “Residents weigh global benefits and local risks 

in views of climate change measures.” (Reprinted by 289 other media outlets across the country).

Freakonomics, July 24, 2013. “How Politicians Plug Electric Cars.”

Indiana University Press, July 17, 2013. “Economy edges out environment for governments plugging electric 

vehicles” (A variant of the article was also published in Domestic Fuel, Green Autoblog, Blog and 

Batter Chargers, EV World, Environmental Leader, Earth Techling, and The Green Optimistic).

Indiana Daily Student, April 4, 2013. “Awards granted to outstanding junior faculty.” 

Inside Higher Ed and WAMC, Northeast Radio, Academic Minute, March 14, 2013. “Dr. Sanya Carley, 

Indiana University—Consumer Attitude and Electric Cars.”

CBS, January 7, 2013. “American Drivers Not Interested in Electric Cars.”

International New York Times, International Herald Tribune, January 7, 2013. “Will 2013 be the Year of the 

Electric Car?”

New York Times, December 26, 2012. “Car Buyers Lack Interest in Electric Cars, Study Says.”

Indianapolis Business Journal, January 2, 2013. “Report: Plug-in vehicles slow to spark interest in Indy.”

Indiana University Press, December 27, 2012. “IU Study: Consumer intent to purchase electric vehicles is 

low, varies by city.” (Reprinted in a variety of other online outlets).

Indiana University Press, September 18, 2012. “Indiana University Study: Support for Carbon Capture is 

extensive but not strong.” (Reprinted in Science Daily, among a variety of other online outlets).

WTIU “Weekly Special,” September 15, 2011. “Early Adopters.”

WTIU News, July 19, 2011. “Next-Generation Electric Vehicle Appears in Bloomington.”

SPEA Podcast, May, 2011. “The Future of Electric Cars.”

Indiana University Press, May 23, 2011. “Journal article examines the effectiveness of state-level energy 

policies.” (Reprinted in Indiana Ag Connection, UtilityProducts.com, Newswise, Indiana Valuation).

AOL Autos, February 23, 2011. “Are Obama’s Million EV’s Just Science Fiction?”

Kokomo Tribune, September 9, 2010. “19% rate hike coming: Duke Energy plans increase to help pay for 

$2.88B coal plant.”

Indiana University Press, August 20, 2010. “Energy-based Economic Development: A Fad of Here to Stay?” 

(Reprinted in News Blaze, Newswise, Renewable Energy Sources, World.org)
RTI International Peer Review of EPA’s RSE Report 24

TEACHING EXPERIENCE 

V674: Energy Economics and Policy (Graduate level)

School of Public and Environmental Affairs, Indiana University.

Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016

V600: Capstone (Graduate level)

School of Public and Environmental Affairs, Indiana University.

Spring 2016, Spring 2018

V550: Energy Policy Seminar (Masters and Ph.D. level)

School of Public and Environmental Affairs, Indiana University.

Fall 2015, Fall 2017

V450: Research Design (Undergraduate level)

School of Public and Environmental Affairs, Indiana University.

Fall 2015, Fall 2017

E574: Energy Analysis and Markets (Graduate level)

School of Public and Environmental Affairs, Indiana University.

Fall 2010, Fall 2011, Fall 2013

V680: Research Design (Ph.D. level)

Co-instructor of a team-led course, School of Public and Environmental Affairs, Indiana University.

Fall 2011, Fall 2013, Fall 2014, Fall 2015, Fall 2016

E555: Energy Resources and Policy (Undergraduate level)

Teaching Fellow, The Department of Public Policy and the Institute for the Environment, University 

of North Carolina at Chapel Hill.

Spring 2009

E190H: Honors Freshman Seminar on Energy and Society (Undergraduate level)

Co-instructor, Institute for the Environment, University of North Carolina at Chapel Hill.

Spring 2009

PROFESSIONAL SERVICE

Referee and Reviewer Service: 

American Journal of Political Science

Climate Policy 

Ecological Economics 

Economic Development Quarterly 

Energies 

Energy Economics

Energy Policy

Energy Journal

Energy Research & Social Science

Environmental and Resource Economics 

Environmental Practice 

Environmental Science & Technology

Ethics, Policy & Environment

Evaluation Review

Geography

Global Environmental Change

International Journal of Business and Economics

IEEE Transactions on Power Systems 

J of the Assoc of Envir and Res Economists

J of Environmental Economics and Management

J of Geography and Regional Planning

J of Policy Analysis and Management

J of Policy History

J of Politics

J of Public Admin Research and Theory

National Science Foundation

Nature Energy

Nature Climate Change

PLOS One

Policy Sciences

Policy & Society

Policy Studies Journal

Public Administration Review

Publius: The Journal of Federalism

Regulation & Governance

Review of Policy Research 

Renewable and Sustainable Energy Reviews

Springer Publishing

SPEA Insights

State and Local Government Review

Sustainability: Science, Practice & Policy

Transportation Letters
RTI International Peer Review of EPA’s RSE Report 25

Transportation Research Part D: Transport and 

Environment

Utilities Policy

Professional, National, and State Service:

Managing Editor, Journal of Policy Analysis and Management, 2017-present.

Peer Review Committee Member, EPA Response Surface Methodology, 2017.

VP for Academic Affairs, U.S. Association of Energy Economics, 2018.

Secretary/Treasurer, U.S. Association of Energy Economics, 2015-2016.

Editorial Board, Energy Research and Social Science, 2017-2019.

Editorial Board, Public Administration Review, 2015-2017.

Executive Committee, Richard G. Lugar Center for Renewable Energy, 2017-present.

Editorial Board, State and Local Government Review, 2014-2016.

Conference student paper judge, U.S. Association of Energy Economics, 2014, 2015, 2017.

Conference poster judge, Association of Public Policy Analysis and Management, 2016, 2017.

Conference poster judge, U.S. Association of Energy Economics, 2016, 2017.

Guest editor, Special issue on the American Recovery and Reinvestment Act of 2009, Review of Policy 

Research, 2015-2016.

Finance Committee, Chair, U.S. Association of Energy Economics, 2015-2016.

Chair, Natural Resource Security, Energy, and Environmental Policy Conference Program Committee, 

Association of Public Policy Analysis and Management, 2015-2016.

Committee Member, Natural Resource Security, Energy, and Environmental Policy Conference Program 

Committee, Association of Public Policy Analysis and Management, 2013-2016.

Conference Program Committee, U.S. Association of Energy Economics, 2015-2016.

Council Member, U.S. Association of Energy Economics, 2014.

Presidential Advisor, U.S. Association of Energy Economics, 2013.

Academic Affiliate, National Renewable Energy Laboratory, 2013.

Member, Policy Choices Energy and Environment Commission, IU Public Policy Institute, 2011-2012.

University Service:

Member, Bloomington Faculty Council Research Affairs Committee, 2015-2016.

Co-Chair, Energy and the Built Environment, Indiana University, 2013-2016.

Advisory Committee, Workshop in Methods, 2013-2016.

Member, Academic Initiatives Working Group, Indiana University, 2011-2013.

Steering committee, Student Summit on a Green Economy, Indiana University, 2010.

School Service:

Chair, Policy Analysis and Public Finance Faculty Group, 2016-present.

Promotion and Tenure Committee, Indiana University Northwest, 2017.

MPA Curriculum Committee, 2017-2018.

Chair, Policy Committee, School for Public and Environmental Affairs, Indiana University, 2015-2016.

Ph.D. Public Affairs Program Committee, School for Public and Environmental Affairs, Indiana 

University, 2015-2016.

Chair, Environmental Policy Search Committee, School for Public and Environmental Affairs, Indiana 

University, 2014-2015.

Policy Committee, School for Public and Environmental Affairs, Indiana University, 2014-2016.

Budgetary Affairs Committee, School for Public and Environmental Affairs, Indiana University, 2014-

2016.

Faculty Advisor, Energy Student Leaders Association, Indiana University, 2012-present.

MPA Selection Committee, School for Public and Environmental Affairs, Indiana University, 2013, 

2014.
Sanya Carley Peer Reviewer Resume Last Updated 7/19/17 

RTI International Peer Review of EPA’s RSE Report 26

Environmental Policy Ph.D. Exam Committee, School for Public and Environmental Affairs, Indiana 

University, 2013-2014.

Faculty Hiring Committee, Industrial Ecology and Life-Cycle Assessment, School for Public and 

Environmental Affairs, Indiana University, 2011-2012.

Faculty Hiring Committee, MPA Program Director, School for Public and Environmental Affairs, 

Indiana University, 2011-2012.

Member, Hiring Priorities Committee, Policy Analysis and Public Finance faculty group, School for 

Public and Environmental Affairs, Indiana University, 2011-2012.

Faculty Hiring Committee, Energy Policy, School for Public and Environmental Affairs, Indiana 

University, 2010-2011.

Committee Member, Energy Concentration, School for Public and Environmental Affairs, Indiana 

University, 2010-2011.

Ph.D. Dissertation Committee Member:

Adam Abelkop, 2017; Yu Zhang, 2017; Jose Iracheta, 2017; Dave Warren, 2017; Sojin Jang, 

2017; Jessica Alcorn, 2016; Zach Wendling, 2016; Elizabeth Baldwin, 2015; Shuang Zhao, 

2015.

Ph.D. Program Committee Member, School of Public and Environmental Affairs, Indiana University:

Arthur Ku, 2017; Michelle Lee, 2016; Yu Zhang, 2014; Jessica Alcorn, 2014; Ben Inskeep, 

2013; Naveed Paydar, 2013; Chris Miller, 2012; Elizabeth Baldwin, 2012; Dave Warren, 2011; 

Zach Wendling, 2011; Shuang Zhao, 2010.

Honors Undergraduate or Graduate Thesis Committee Member:

Damon Smith, Indiana University, 2015; Chip Gaul, Department of Public Policy, University of 

North Carolina-Chapel Hill, 2011; Elinor Benami, Department of Economics, University of 

North Carolina-Chapel Hill, 2010; Rachel Escobar, Depart. of International Studies, University 

of North Carolina-Chapel Hill, 2009; Jessie Prentice-Dunn, Depart. of Public Policy, University 

of North Carolina-Chapel Hill, 2007.

Junior Faculty Hiring Committee, University of North Carolina-Chapel Hill, Department of Public 

Policy, 2008.

Student representative, University of North Carolina-Chapel Hill, Department of Public Policy, 2007-

2008.

Facilitator, Environmental Studies in Ghana, University of Ghana-Legon and Swarthmore College, 2001.

PROFESSIONAL MEMBERSHIP

Association for Public Policy Analysis & Management (APPAM)

Association of Collegiate Schools of Planning (ACSP)

Brewers Association (BA)

Midwest Political Science Association (MPSA)

International Association of Energy Economists (IAEE)

United States Association for Energy Economics (USAEE)
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VITA

SUJIT DAS

12305 Fort West Drive (865) 789-0299

Knoxville, Tennessee 37934 Email: Dass@ornl.gov

 

EDUCATION

MBA Management Science and Computer Science, University of Tennessee 1984 

MS Metallurgical Engineering, University of Tennessee, 1982 

B. Tech Metallurgical Engineering, Indian Institute of Technology, Kharagpur, India, 1979. 

Ranked 2nd in class with Honors.

PROFESSIONAL EXPERIENCE

Sr. Research Staff Member, Energy and Transportation Science Division, Oak Ridge National 

Laboratory, December 1984-present.

Program manager of the cost modeling of lightweight materials and clean energy manufacturing programs 

for the U.S. Department of Energy. Develop, manage and lead projects for the DOE Office of Vehicle 

Technologies and Advanced Manufacturing Office. Responsible for a total annual budget of more than 

$750K consistently over the past several years and managing a team of 1-6 people per project depending 

on the project type. Develop cost models of advanced materials and transportation technologies and 

decision-making tools for several resource markets. Provide market assessments of energy efficient

technologies including environmental implications for both domestic and international markets. 

Developed expertise in several multi-disciplinary research areas including:

 Life Cycle Assessment of Aluminum Intensive Vehicles for the Aluminum Association 

 Next generation materials with energy/emissions reduction potential in the U.S. industry for 

DOE Advanced Manufacturing Office 

 Manufacturing process modeling of high temperature stationary fuel cell systems in the 350-

400 kW power range for DOE Fuel Cell Technologies Program

 Life cycle modeling of alternative lightweight engine design options for the DOE Propulsion

Materials Program

 Market potential and infrastructure assessment of ethanol and hydrogen as alternative 

transportation fuels 

Cost modeling and life cycle analysis of advanced vehicles and lightweight materials 

Technologies for DOE Office of Vehicle Technologies

Material technology assessments related to Partnership for A New Generation of Vehicles 

(PNGV)/Freedom Cooperative Automotive Research (FreedomCAR)

Potential of renewable energy technologies in rural Bangladesh

 Biomass refinery analysis

 Economic analysis of advanced power electronics, electric motors, and intelligent

 transportation systems
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 Energy efficiency of distribution transformers

 Cost of alternative fuels

 Forecasting of petroleum and uranium supplies

 Estimation of flood-stage economic damages

 The economic viability of plastics and automobile recycling

 Environmental implications of privatization of the power sector in India

 Market assessments of energy efficient technologies such as home refrigerators in India

 Inspection and Maintenance of two-wheeler vehicles in India

 Assessment of uranium resources

Visiting Fellow, Tata Energy Research Institute (TERI), New Delhi, India, October 1992-June 1993.

Developed a comprehensive, computerized, and PC-based Energy-Economic-Environment database for 

TERI -- the first of its kind in India and provided technical support in their ongoing energy and economic 

modeling activities.

Research Assistant, Energy and Economic Analysis Section, Oak Ridge National Laboratory, 

September 1982-December 1984.

Documented and evaluated several EIA, DOE maintained computers models, i.e., Headwater Benefit 

Energy Gains Model and the Petroleum Allocation Model. Developed a computer software "BIOCUT" 

for Economic Evaluation Model for Wood Energy Plantations.

LIST OF PUBLICATIONS

BOOK/CHAPTERS PUBLISHED

Two book chapters published in “Advanced Composite Materials for Automotive Applications: 

Structural Integrity and Crashworthiness,” Edited by Ahmed Elmarakbi, Univ. of 

Sunderland, UK and published by Wiley & Sons (Aug.’13)

Chapter 3: Low Cost Carbon Fibre for Automotive Applications (Part 1: Low Cost Carbon Fibre 

Development); 

Chapter 17: Low Cost Carbon Fibre for Automotive Applications (Part 2: Applications, 

Performance and Cost Reduction Models)

“Recycling and Life Cycle Issues for Lightweight Vehicles,” A Book Chapter in Materials, Design and 

Manufacturing for Lightweight Vehicles, edited by P.K. Mallick, Woodhead Publishing Limited, 

pp. 309-330, 2010

“Material Use in Automobiles.” A Book Chapter in Encyclopedia of Energy, published by Elsevier Inc., 

Vol. 3, pp. 859-869, 2004.

"Plastic Wastes: Management, Control, Recycling, and Disposal." Noyes Data Corporation, NJ (CoAuthored with U.S. Environmental Protection Agency and T. R. Curlee), 1991.
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SELECTED REFERRED ARTICLES/PRESENTATIONS (Out of 60+ articles)

“Cost of Ownership and Well-to-Wheels Carbon Emissions/Oil Use of Alternative Fuels and 

Advanced Light-Duty Vehicle Technologies,” Energy for Sustainable Development, 

17(2013), pp. 626-641

Served as one of the expert reviewers for the following three recent U.S. DOT/U.S. EPA reports

Mass Reduction for Light-Duty Vehicles for Model Years 2017-2025, EDAG/The George 

Washington University Report, Apr. 2012

Light-Duty Technology Cost Analysis Pilot Study, FEV Draft Report, Sept. 3, 2009

An Assessment of Mass Reduction Opportunities for a 2017-2020 Model Year Vehicle Program, 

Lotus Engineering Inc., Mar. 2010

“Lightweighting Opportunities in the Global Automotive Industry,” invited presentation at the

2011 International Automotive Lightweight Materials Development Forum, held in 

Chongqing, China, on Mar. 24-25,’11.(Also at the 12th IUMRS International Conference 

on Advanced Materials, held in Qingdao, China on Sept. 22-28, 2013)

"Importance of Economic Viability Assessment of Automotive Lightweight Materials" invited

presentation at the 3rd Annual Advanced Lightweight Materials for Vehicles conference 

held on Aug. 11-12, ’10, Detroit, MI.

“Analysis of Fuel Ethanol Transportation Activity and Potential Distribution Constraints,” 

Transportation Research Record: Journal of the Transportation Research Board, No. 

2168, Transportation Research Board of the National Academies, Washington, DC, 2010, 

pp. 136-145.

“Reducing GHG Emissions in the United States’ Transportation Sector” Energy for Sustainable

Development, 15 (2011) 117–136, May 11.

“Life Cycle Assessment of Carbon Fiber-Reinforced Polymer Composites,” Intl. Journal of Life Cycle 

Assessment, Volume 16, Issue 3, pp. 268-282, 2011

“Battle Green,” an interview article published in American Metal Market, Oct. 2010, pp. 36-40.

“Shedding Pounds On a Magnesium Diet,” Automotive Engg. International, Apr. 6, 2010, pp. 34-36, 

interview article by Steven Ashley.

“Analysis of Fuel Ethanol Transportation Activity and Potential Distribution Constraints,” 

Transportation Research Record: Journal of the Transportation Research Board, No. 2168, 

Transportation Research Board of the National Academies, Washington, DC, 2010, pp. 136-145.

“Low-Carbon Fuel Standard – Status and analytic issues,” Energy Policy, vol. 38, No.1, Jan. 2010, 

pp. 580-591.

“Importance of Economic Viability Assessment of Automotive Lightweight Materials,” invited 

presentation at the 3rd Annual Advanced Lightweight Materials for Vehicles,” held in Detroit, MI 

on Aug. 11-12, 2010.
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“A Comparative Life Cycle Assessment of Magnesium Front End Parts,” SAE Paper No. 2010-01-0275, 

Society of Automotive Engineers, Warrendale, PA.

“Primary Magnesium Production Costs for Automotive Applications,” Journal of Metals, Vol. 60, No. 11, 

2008, pp. 51-58.

“A Systems Approach to Life Cycle Truck Cost Estimation,” SAE Paper No. 2006-01-3562, Society of 

Automotive Engineers, Warrendale, PA.

“Automotive Lightweighting Materials Benefit Evaluation,” ORNL/TM-2006/545, Oak Ridge National 

Laboratory, Oak Ridge, TN, Nov. 2006

“Lightweight Opportunities for Fuel Cell Vehicles,” SAE Paper No. 2005-01-0007, Society of 

Automotive Engineers, Warrendale, PA.

"A Comparative Assessment of Alternative Powertrains and Body-in-White Materials for Advanced 

Technology Vehicles,” SAE Paper No. 2004-01-0573, Society of Automotive Engineers, 

Warrendale, PA.

“Back To Basics? The Viability of Recycling Plastics by Tertiary Approaches,” Working Paper #5, 

Program on Solid Waste Policy, School of Forestry and Environmental Studies, Yale University, 

New Haven, CT, September 1996. (with T. R. Curlee)

AWARDS & PROFESSIONAL ACTIVITIES

Awarded 2004 Journal of Metals Best Paper by the Mineral, Metals, and Materials Society (TMS)

Chair of Society of Automotive Engineering (SAE) Sustainable Program Development Committee 

(2013-2014)

Member of Transportation Research Board (TRB) Committees (2008- Present)

Transportation Economics

Alternative Transportation Fuels and Technologies 

Invited Speaker on the Life Cycle Assessment of Materials by Beijing University of Technology, China

Conference Session Organizers for SAE and TRB

Peer Reviewers for Several Energy and Environmental Related Journals

Past peer reviewers for the EPA and NHTSA draft reports on the vehicle mass reduction and cost analysis

of light-, medium-, and heavy-duty vehicles including:

(i) 2014 EPA Light-Duty Pickup Truck

(ii) 2015 NHTSA Costs of Medium- and Heavy-Duty Vehicle Fuel Efficiency Emission 

Reduction Technologies for MY 2019-2022

(iii) 2016 NHTSA Mass Reduction for Light-Duty Vehicles for MY 2017-2025
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ARIZONA STATE UNIVERSITY

INDUSTRIAL ENGINEERING

DOUGLAS C. MONTGOMERY

Regents’ Professor of Industrial Engineering and Statistics

ASU Foundation Professor of Engineering

EDUCATION AND EXPERIENCE

Degrees

Ph.D. Virginia Polytechnic Institute, 1969

M.S.I.E. Virginia Polytechnic Institute, 1967

B.S.I.E. Virginia Polytechnic Institute, 1965

Academic Experience

1988 – PresentRegents’ Professor of Industrial Engineering and ASU Foundation Professor of 

Engineering, School of Computing, Informatics and Decision Systems Engineering (Program in Industrial 

Engineering), Arizona State University.

1984 - 1988 John M. Fluke Distinguished Professor of Engineering, Director of 

Industrial Engineering, Professor of Mechanical Engineering, Department of Mechanical Engineering, 

University of Washington.

1978 - 1984 Professor, School of Industrial and Systems Engineering, Georgia Institute of Technology.

1972 - 1978 Associate Professor, School of Industrial and Systems Engineering, Georgia 

Institute of Technology.

1969 - 1972 Assistant Professor, School of Industrial and Systems Engineering, Georgia 

Institute of Technology.

1967 - 1969 Instructor, Department of Industrial Engineering and Operations Research, 

Virginia Polytechnic Institute.

Industrial Experience

1966 Manufacturing/Development Engineer, Eli Lilly, Inc. Creative Packaging 

Division.

1963 - 1964 Process Engineer, Union Carbide Corporation.

Professional Interests

 Engineering statistics, including design and analysis of experiments, statistical methods for process 

monitoring, control, and optimization, and the analysis of time-oriented data. The application of statistics to 

industrial problems, including engineering design, product and process development, and manufacturing.

Consulting Experience

Extensive consulting assignments involving projects with over 100 organizations. General area of 

professional experience focused on engineering applications of statistics and operations research methods. 

Projects have involved design of experiments and response surface methods, implementation of statistical 

process control, process development including characterization and optimization, time series analysis and 

the design of forecasting systems, empirical model building, and the design and analysis of physical 

distribution systems. Specific industry experience includes semiconductors and electronics, medical 

devices, biotechnology, consumer products, chemical and process industries, aerospace, and the service 

industries. Some consulting clients include Pfizer, Procter and Gamble, Intel, Motorola, AT&T, Boeing, 

IBM, The Coca-Cola Company, Lucent Technologies, Dial Corporation, Dow Chemicals, Amoco, GeorgiaPacific, Monsanto Chemicals, Hercules, Alcoa, and Eli Lilly.
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HONORS AND AWARDS

1. Spring 2016 Outstanding Professor Award, given by the Vice Rector of Online Education Programs, 

Technologico de Monterrey, Mexico.

2. Spring 2015 Outstanding Professor Award, given by the Vice Rector of Online Education Programs, 

Technologico de Monterrey, Mexico.

3. 2015 ASU President’s Award for Innovation, as Member, Vietnam Higher Engineering Education 

Alliance Program (HEEAP) 

4. Honorary Member, American Society for Quality (at time of election, the 25th Honorary Member)

5. Fellow, American Statistical Association

6. Fellow, Royal Statistical Society

7. Fellow, Institute of Industrial and Systems Engineers

8. Elected Member, International Statistical Institute

9. Academician, International Academy for Quality

10. ASQ Reliability Division 2013 Award for Best Reliability Paper in Quality Engineering for the 

article “Experiments for Reliability Achievement” 

11. Distinguished Service Medal, 2013. Given by the American Society for Quality.

12. Best paper in 2012 award from the Emerald press, for the paper, “Deploying Lean Six Sigma in a 

Global Enterprise – Project Identification”, by B. Duarte, D.C. Montgomery, J. Fowler and J. 

Konopka, published in the International Journal of Lean Six Sigma, Vol. 3, No. 3, pp. 187-205.

13. Greenfield Medal, 2010. Given by the Royal Statistical Society, for “Contributions to the effective 

application of statistical methods, particularly process monitoring and optimization, quality 

improvement and design and analysis of experiments, and for his influential and accessible 

expository work.”

14. American Statistical Association, 2010 Excellence in Continuing Education Course Recognition 

award for the course “Modern Design and Analysis of Experiments” presented at the 2009 JSM.

15. Arizona Society of Professional Engineers (Engineer’s Week, 2010), Engineering Lifetime 

Achievement Award, 2010

16. George Box Medal, 2008. Given by the European Network for Business and Industrial Statistics 

(ENBIS) for lifetime contributions to the development and application of statistical methods in 

European business and industry.

17. Deming Lecture Award, American Statistical Association, presented at the Joint Statistical 

Meetings, Salt Lake City, 31 July, 2007. The presentation given accompanying the award was 

entitled “A Modern Framework for Enterprise Excellence”. 

18. Conference Honoree, Quality and Productivity Research Conference (American Statistical 

Association), Santa Fe, New Mexico, 4-6 June, 2007. 

19. Testimonial Award, American Society for Quality, 2007, for distinguished service as Chair of the 

Shewhart Medal Committee, 2005-2007.

20. Lloyd S. Nelson Award, 2005. Given by the Statistics Division of the American Society for Quality 

for the Journal of Quality Technology paper having the greatest impact for professional 

practitioners.

21. ASU Outstanding Doctoral Mentor Award, 2004.
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22. Shewell Award, 2001. Given by the Chemical and Process Industries Division of the ASQC for the 

best technical paper at the ASQC/ASA Fall Technical Conference, 2000.

23. Shewhart Medal, 1997. Awarded by the American Society for Quality Control for Outstanding 

Technical Leadership in the Field of Modern Quality Control.

24. William G. Hunter Award, 1996. Given by the Statistics Division of the American Society for 

Quality Control. This award is given for excellence in technical innovation and in the integration 

of statistics with other disciplines.

25. Brumbaugh Award, 1994. Given by the American Society for Quality Control for the best paper in 

a journal of the Society.

26. Shewell Award, 1993. Given by the Chemical and Process Industries Division of the ASQC for the 

best technical paper at the ASQC/ASA Fall Technical Conference, 1992.

27. Ellis R. Ott Award, 1992. Given by the Ellis R. Ott Foundation for the best paper on quality 

engineering during a two-year period.

28. Inagural W.L. Gore Lecture in Management Science, The Alfred Lerner College of Business and 

Economics, The University of Delaware, “Design of Experiments: New Methods and How to Use 

Them in Design, Development and Decision-making”, 16 March 2011.

29. Invited Keynote Address, “Innovation, Statistics and Quality Technology”, Forth International 

Conference on Lean Six Sigma, Glasgow, Scotland, 26-27 March, 2012.

30. Invited Keynote Address, "The Industrial Engineer and the Quality Improvement Sciences: Have 

We Missed an Opportunity?", 8th Israeli Industrial Engineering Research Conference, Beer Sheva, 

Israel, May 1994

31. W. J. Youden Memorial Address, “A Perspective on Models and the Quality Sciences: Some 

Challenges and Future Directions”, presented at the 42nd Annual ASQC/ASA Fall Technical 

Conference October 1998.

32. Inyong Ham Distinguished Lecturer, Department of Industrial and Manufacturing Engineering, 

Pennsylvania State University, “Statistical Methods for Process Robustness Studies”, November 

11, 1999. 

33. Invited Keynote Address, “Experimental Design for Process and Product Design and Development” 

Royal Statistical Society, Glasgow Scotland, 11 September 1998.

34. Invited Keynote Address, “The Future of Industrial Statistics”, South African Statistical Association 

Annual Meeting, University of the Witswaterstrand, Johannesburg, South Africa, 10 November 

2000.

35. Invited Keynote Address, “Some Opportunities and Challenges for Industrial Statisticians”, 

Industrial Statistics in Action 2000, conference at the University of Newcastle-Upon-Tyne, United 

Kingdom, 8-10 September, 2000.

36. Isobel Loutit Invited Plenary Address on Business and Industrial Statistics, “The Modern Practice 

of Statistics in Business and Industry”, 33rd Annual Meeting of the Statistical Society of Canada, 

Halifax, NS, 8-11 June 2003. This was the inaugural Isobel Loutit Address. 

37. Invited Keynote Address, “Statistics and Statisticians in Today’s Business World”, Royal Statistical 

Society Conference on Business Improvement through Statistical Thinking, 21-22 April 2004, 

Coventry, UK. 

38. Invited Keynote Address, “Statistics and the Transformation of Science, Business and Industry”, 

5

th Annual ENBIS Conference, University of Newcastle, Newcastle-Upon-Tine, UK, 14-16 

September, 2005.

39. Invited Keynote Address, “The Modern Practice of Statistics in Business and Industry” Swiss 

Statistics Meeting, Lucerne, Switzerland, 14-16 November, 2007.
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40. Invited Keynote Address, “Modern Experimental Design and its Impact on Design for Six Sigma”, 

Third International Conference on Six Sigma, Edinburgh, Scotland, 15-16 December, 2008.

41. Testimonial Award from the Board of Directors of the American Society for Quality Control, 2000, 

for Leadership and Distinguished Service as Chair of the Brumbaugh Award Committee from 1996-

2000.

42. Testimonial Award from the Board of Directors of the American Society for Quality Control, 1998, 

for Leadership and Distinguished Service as Editor of the Journal of Quality Technology, 1994-

1997. 

43. Pritsker Award - Annual Teaching Award, Department of Industrial and Management Systems 

Engineering, Arizona State University, 1997

44. University Distinguished Visitor, University of Manitoba, Fall, 1994.

45. Distinguished Alumnus, Department of Industrial and Systems Engineering, Virginia Tech 

(Awarded 1994).

46. College of Engineering and Applied Sciences, Arizona State University, Teaching Excellence 

Award (Graduate), 1994.

47. Pritsker Award - Annual Teaching Award, Department of Industrial and Management Systems 

Engineering, Arizona State University 1994.

48. Anderson Teaching Award, Department of Industrial and Management Systems Engineering, 

Arizona State University, 1993.

49. Pritsker Award - Annual Teaching Award, Department of Industrial and Management Systems 

Engineering, Arizona State University, 1992.

50. Engineer of the Year, 1987, Puget Sound Engineering Council.

51. Industrial Engineer of the Year, 1986, Puget Sound Region Institute of Industrial Engineers.

52. Alpha Pi Mu/AIIE Outstanding Teacher Award, School of ISyE, Georgia Tech, 1976-1977.

53. Listed in Who’s Who in the American South and Southwest, American Men and Women of Science, 

Who's Who in Engineering.

54. Phi Kappa Phi

55. Sigma Xi

56. Alpha Pi Mu

57. Mu Rho Sigma (Honorary Member, Va. Tech Chapter 1995)

PROFESSIONAL ACTIVITIES

1. Editor, Quality & Reliability Engineering International, 2000-present

2. Editor, Journal of Quality Technology, 1994-1997

3. Member, Technical Advisory Board, United States Golf Association, 1997-2007

4. American Society for Quality, Honorary Member

a. Chair, Shewhart Medal Committee, 2004

b. Chair, Brumbaugh Award Committee, 1997

c. Member, Brumbaugh Award Committee, 1993 – 2000

d. Member, Shewhart Medal Committee, 1997 - present

e. Chair, Statistics Division, 1981-1982

f. Statistics Technical Committee, 1976-1979

g. Publications Management Board, 1977-1980, 1994-1997, 2000-present

h. Secretary, PBM, 1979-1980
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5. American Statistical Association, Fellow

a. Founding member, Committee on Statistics in Quality and Productivity

b. Advisory Board, Section on Physical and Engineering Sciences, 1981-1983.

6. Royal Statistical Society, Fellow

7. The Institute of Industrial and Systems Engineers, Fellow

a. Region IV Chair, Production Planning and Control Division, 1971-1972

b. Research and Publications Chair, Production Planning and Control Division, 1972-1973

c. Director-Elect, Production Planning and Control Division, 1972-1973

d. National Director, Production Planning and Control Division, 1973-1974

e. Research Chair, Production Planning and Control Division, 1975-1976

f. Advisory Board Member, Production Planning and Control Division, 1975-1976

g. Program Chair, Quality Control and Reliability Division, 1975-1976

h. Region IV Chair, Operations Research Division, 1974-1978

8. Elected Member, International Statistical Institute

9. National Academy of Science, Committee on Applied and Theoretical Statistics, Panel on Research 

Needs in Statistical Quality Control, 1982-1984

10. Advisory Editor in Engineering, John Wiley & Sons, Inc., 1979-1983

11. Department Editor, Applied Probability and Statistics, IIE Transactions, 1980-1986.

12. Book Review Editor, Journal of Quality Technology, 1980-1982

13. Associate Editor, Naval Research Logistics Quarterly, 1982-1988

14. Associate Editor, Journal of Forecasting, 1981-1983

15. Associate Editor, Revue Francaise d'Automatique, d'Informatique et de Recherche Operationnelle, 1980-

1991

16. Department Editor, Quality & Reliability Engineering, IIE Transactions, 1992-1994

17. Editorial Board Member, Journal of Quality Technology, 1980-1982, 1987-present

18. Editorial Board Member, Quality and Reliability Engineering International, 1994-present

19. Editorial Board Member, Quality Engineering, 1997- present

20. Editorial Board Member, Journal of Applied Statistics, 2000 – present

21. Editorial Board Member, International Journal of Experimental Design and Process Optimization, 2009-

present

22. Advisory Editor, Quality Technology and Quantitative Management, 2005-present

23. Editorial Board Member, International Journal of Production Research, 1997- 2009 

24. Editorial Board Member, Total Quality Management, 2000-present

25. Advisory Editor, Journal of Probability and Statistical Science, 2002-present

26. Associate Editor, Naval Research Logistics, 2003-2010.

27. Referee for various journals, including: Technometrics, Operations Research, Management Science, IIE 

Transactions, Operational Research Quarterly, Naval Research Logistics Quarterly, Journal of the Royal 

Statistical Society, Computational Statistics and Data Analysis, The American Statistician, American 

Institute of Chemical Engineers Transactions, Communications in Statistics, Journal of Statistical 

Computation and Simulation, The Engineering Economist, Computers in Industrial Engineering, 

Transportation Research, Journal of the American Statistical Association, and IEEE Transactions on 

Semiconductor Manufacturing.

28. Technical manuscript reviewer for John Wiley and Sons, McGraw-Hill, Holden-Day, Marcel Dekker and 

Duxbury. 

29. Research Proposal Reviewer for the National Science Foundation, various divisions, 1987-present

30. Chair and organizer of technical paper sessions at several conferences of IIE, ASQ, ORSA/TIMS, and 

the Winter Simulation Conference, 1970-1985
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31. Co-chair for the 12th Annual Quality and Productivity Research Conference, Co-sponsored by ASU and 

Motorola Semiconductor Products, May 17-19, 1995

32. Co-chair for the 2002 Industrial Engineering Research Conference, Orlando, FL, May 2002

33. Chair for the 18th Annual Quality and Productivity Research Conference, Tempe, AZ, June, 2002

34. Invited short course presenter at the Joint Statistical Meetings, the ASQ/ASA Fall Technical Conference, 

the U.S. Army Design of Experiments Conference, and the Army Conference on Applied Statistics.

35. Speaker for numerous local chapters of IIE, ASA, and ASQ, 1969-present

36. Participant in several seminar programs for international visitors to Georgia Tech, 1969-1984

SERVICE ACTIVITIES

1. Arizona State University

Chair Search Committee, Industrial Engineering, 1993-94

Department Personnel Committee, 1988-1997, Chair 1996-97

Department Faculty Recruiting Committee, 1989-1993, 1996-97

Department Graduate Committee, 1996-present, Chair 1996-2001

Engineering College Personnel Committee, 1998-present

Engineering College Core Curriculum Committee, 1989-1991

Engineering College Graduate Committee, 1989-1993

Engineering College Bylaws Committee, 1995-1997

University Council on Research and Creative Activities, 1995-1998

Ira A. Fulton School of Engineering Personnel Committee, 1997-2005

Chair Search Committee, Industrial Engineering, 2003-04

SCIDSE Personnel Committee, 2011-2012

2. Georgia Institute of Technology

Advisory Committee, School of ISyE, 1980-82

Chair, Graduate Committee, School of ISyE, 1982-83

M.S. Comprehensive Exam Committee, School of ISyE, 1977-1978, 1982-1983

Undergraduate Curriculum Committee, School of ISyE, 1975-76

Chair, M.S. Comprehensive Exam Committee, School of ISyE, 1974-1975

Research Evaluation Committee, School of ISyE, 1973-1974, 1974-1975

Computer Engineering Committee, College of Engineering, 1972

Chair, Probability and Statistics Interest Group, 1971-1973

Chair, Computers and Simulation Interest Group, 1971-1972

Computer Coordinator, School of ISyE, 1970-1971

Graduate Committee, School of ISyE, 1970-1973, 1977-1979

3. Professional Development Courses Taught (Georgia Institute of Technology)

Design of Production-Inventory Systems

Design of Experiments

Materials Handling

Simulation Techniques (academic administrator, 1973)

Industrial Engineering Review (P.E. Exam)

Traffic Engineering

Statistical Methods

Statistical Design and Analysis

Design and Analysis of Experiments (academic administrator, 1978-1984)

Applied Regression Analysis (academic administrator, 1978-1984)

Sampling Methods and Statistical Analysis in Power Systems Load Research (co-administrator, 1982-

1984)

4. Professional Development Courses Taught (University of Washington)
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Applied Regression Analysis (academic administrator)

Design and Analysis of Experiments (academic administrator)

Statistical Process Control (academic administrator)

Process Optimization & Response Surfaces (academic administrator)

5. Professional Development Courses Taught (Arizona State University)

Instructor in ASU Master Black Belt Certification Program

Training the Trainer in Experimental Design

Introduction to Design of Experiments (also presented over NTU)

Developed on-line courses in design of experiments, regression analysis, and six sigma methods, certificate 

program in six sigma methods/industrial statistics, participated in numerous global outreach programs to 

organizations in the US and abroad

INTERNATIONAL ACTIVITIES AND CONFERENCE PARTICIPATION 

1. Academic Program Reviewer, National University of Singapore, Department of Industrial and Systems 

Engineering, 2014, 2009.

2. Invited keynote address, “Innovation, Statistics and Quality Technology”, Fourth International 

Conference on Lean Six Sigma, Glasgow, Scotland, 26-27 March, 2012.

3. Invited speaker, “Generating and Assessing Exact G-optimal Designs”, Iassic Newton Institute for 

Mathematical Sciences, Cambridge, Design and Analysis of Experiments Workshop, 30 August – 2 

September, 2011.

4. Invited keynote Address, “Modern Experimental Design and its Impact on Design for Six Sigma”, Third 

International Conference on Six Sigma, Edinburgh, Scotland, 15-16 December, 2008.

5. Invited Keynote Address, “The Modern Practice of Statistics in Business and Industry” Swiss Statistics 

Meeting, Lucerne, Switzerland, 14-16 November, 2007.

6. Invited Keynote Address, “Statistics and the Transformation of Science, Business and Industry”, 5th

Annual ENBIS Conference, University of Newcastle, Newcastle-Upon-Tine, UK, 14-16 September, 

2005.

7. Invited Keynote Address, Royal Statistical Society Conference on Business Improvement through 

Statistical Thinking, 21-22 April 2004, Coventry, UK. 

8. Isobel Loutit Invited Plenary Address on Business and Industrial Statistics, 33rd Annual Meeting of the 

Statistical Society of Canada, Halifax, NS, 8-11 June 2003. This was the inaugural Isobel Loutit Address. 

9. Invited Speaker, 6th International Conference on Teaching Statistics, Cape Town South Africa, July 2002.

10. Invited Keynote Address, South African Statistical Association Annual Meeting, University of the 

Witswaterstrand, Johannesburg, South Africa, November 2000.

11. Invited Keynote Address, Industrial Statistics in Action 2000 Conference at the University of NewcastleUpon-Tyne, United Kingdom, September 2000.

12. Invited Keynote Speaker, Royal Statistical Society, Glasgow, Scotland, September 1998.

13. Invited Speaker, Congress of the International Federation of Nonlinear Analysts, Athens, Greece, July 

1996.

14. Invited Keynote Speaker, 8th Israeli Industrial Engineering Conference, Beer Sheva, Israel, 1994

15. Invited Keynote Speaker, International Quality Forum, University of Texas El Paso, El Paso, Texas, 

1992.
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16. Invited Speaker, International Industrial Engineering Research Symposium, Monterey Institute of 

Technology, Monterey, Mexico 1991.

17. Chairman, Regression Methodology Session, International Forecasting Symposium, Quebec, May 1981.

18. Invited Speaker, International Symposium on Industrial Engineering, University of Regiomontana, 

Monterrey, Mexico, September 1980.

19. Co-Chairman and organizer, Forecasting Session; 24th International TIMS Conference, Honolulu, 

Hawaii, June 1979.

20. Consultant to Coca-Cola Export Corporation (includes: Coca-Cola Europe, Coca-Cola Latin America), 

1974-1984.

21. Invited Speaker, Joint Meeting of Japan Operations Research Society, Japan Industrial Management 

Association, and Kansai Institute for Information Systems, Osaka, Japan, August 1977.

22. Invited Speaker, 6th Management Science Colloquium, Osaka University, Japan, August 1977.

23. Invited Speaker, 23rd International Management Science Conference, Athens, Greece, July 1977.

24. Invited Speaker, 2nd Interamerican Conference on Systems and Information Engineering, Mexico City, 

November 1974.

25. Visiting Professor of Engineering, Monterrey Institute of Technology, Monterrey, Mexico, spring quarter 

1972; co-sponsored by the organization of American States.

FUNDED RESEARCH

1. Design Rules for Vertical Paper-based Immuno-Diagnostic System, Defense Threat Reduction Agency, 

joint with the University of Arizona School of Medicine and the University of Utah-Reno School of 

Medicine, 2016-2017, co-principal investigator, $188,500. 

2. Science of Test, Department of Defense, 2014-2018, co-principal investigator, $485,000.

3. Science of Test, Department of Defense, 2011-2014, co-principal investigator, $385,000.

4. Collaborative Research: Efficient Experimentation for Product and Process Reliability Improvement, 

NSF, 2009-2012, co-principal investigator, $348,000.

5. Web-Based Active Learning Modules for Teaching Statistical Quality Control, NSF, 2009-2011, coprincipal investigator, $245,000.

6. Advanced Techniques in Design of Experiments for Computational and Physical Multivariate 

Experiments, NASA, 2008, principal investigator, $50,000.

7. Collaborative Research: Hierarchical Modeling of Yield and Defectivity to Improve Factory Operations, 

NSF/SRC/ISMT, 2004-2007, co-principal investigator, $300,000.

8. Collaborative Research: Monitoring Product and Product Quality Profiles, NSF, 2004-2005, co-principal 

investigator, $100,000.

9. Generalized Linear Model-Based Process Control for Multivariate Measurements, National Science 

Foundation, 1999-2003, co-principal investigator, $211,000.

10. NSF IUERC in Quality & Reliability Engineering, 1997-2001, co-director and co-principal investigator. 

ASU share of annual center funding was approximately $150,000. 

11. Research in Industrial Statistics (projects/graduate student research) sponsored by various organizations 

including Chrysler Electronics, Kellogg Corporation, SGS Thompson, and the Dial Corporation, 1996-

1998, co-principal investigator, $231,000.
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12. Funding for Editorial Operation of JQT, ASQC, 1994-1997, principal investigator, $315,000.

13. Graduate Education in Engineering for Women and Minorities, National Science Foundation, 1992-1997, 

co-principal investigator, $850,000.

14. Process Control Methodology for the Hall Process, Alcoa, 1991, principal investigator, $65,000.

15. Implementation of Design of Experiments, Allied-Signal Aerospace, 1989, principal investigator, 

$40,000.

16. Statistical Methods for Quality and Process Improvement, IBM Corporation, 1985-1988, principal 

investigator, $450,000.

17. Process Control and Optimization Studies for Substrate Manufacturing, IBM Corporation, 1985, principal 

investigator, $67,000.

18. Quality and Process Control in the Factory of the Future, Boeing Electronics Company, 1986, principal 

investigator, $25,000.

19. Statistical Modeling and Analysis in Quality Assurance, Office of Naval Research, 1979-1985, principal 

investigator, $320,000.

20. Determination of International Differences in Reported Fire Losses: Update and Extensions, National 

Fire Data Center, U.S. fire Administration, 1981, co-principal investigator, $50,000.

21. Factor Screening Designs in Computer Simulation Experiments, Office of Naval Research, 1978, 

principal investigator, $25,000.

22. Studies in Support of the Application of Statistical Methodology to the Design and Evaluation of 

Operational Tests, Department of the Army, Harry Diamond Laboratories, 1977, co-principal 

investigator, $45,000.

23. Studies in Support of the Application of Statistical Theory and Methodology to the Design and Evaluation 

of Operational Tests, Department of the Army, Harry Diamond Laboratories, 1976, co-principal 

investigator, $56,000.

24. Research Support on Method-Model Development, U.S. Army Material Systems Agency, 1976, coprincipal investigator, $15,000.

25. Operational Testing of Complex Command and Control Systems, Department of the Army, Harry 

Diamond Laboratories, 1974-1975, principal investigator, $15,000.

PUBLICATIONS

Textbooks

1. Montgomery, D. C. (2017), Design and Analysis of Experiments, 9th edition, Wiley, Hoboken, NJ. (1st

edition, 1976, 2nd edition, 1984, 3rd edition, 1991, 4th edition, 1997, 5th edition, 2001, 6th edition, 2005, 

7

th edition, 2009, 8th edition, 2012)

2. Myers, R. H., Montgomery, D.C. and Anderson-Cook, C.M. (2016), Response Surface Methodology: 

Process and Product Optimization Using Designed Experiments, 4

th edition, John Wiley & Sons, New 

York (Probability and Statistics Series; 1st edition, 1995, 2nd edition, 2002, 3rd edition, 2009). 

3. Montgomery, D. C., Peck, E. A., and Vining, G. G. (2012), Introduction to Linear Regression Analysis, 

5

th edition, John Wiley & Sons, New York. (Probability and Statistics Series; 1st edition, 1983, 2nd

edition, 1992, 3rd edition, 2001, 4th edition, 2006).

4. Montgomery, D. C. (2013), Introduction to Statistical Quality Control, 7th edition, Wiley, Hoboken, NJ.

(1st edition, 1985, 2nd edition, 1991, 3rd edition 1996, 4th edition 2001, 5th edition, 2005, 6th edition, 

2009).
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5. Montgomery, D.C., Jennings, C.L. and Kulahci, M. (2015), Introduction to Forecasting and Time Series 

Analysis, 2nd edition, Wiley (Series in Probability and Statistics, 1st edition 2009), Hoboken, NJ.

6. Montgomery, D. C. and Runger, G.C. (2014), Applied Statistics and Probability for Engineers, 6th

edition, John Wiley & Sons, New York (1st edition, 1994, 2nd edition, 1999, 3rd edition, 2003, 4th edition, 

2006, 5th edition, 2011).

7. Montgomery, D.C., Jennings, C.L. and Pfund, M.E. (2011), Managing, Controlling and Improving 

Quality, Wiley, Hoboken NJ.

8. Kowalski, S.M. and Montgomery, D.C. (2011), Minitab Companion to Design and Analysis of 

Experiments, 7th edition, Wiley, Hoboken, NJ. 

9. Montgomery, D. C., Runger, G. C. and Hubele, N. F. (2011), Engineering Statistics, 5th edition, John 

Wiley & Sons, New York (1st edition, 1998, 2nd edition 2001, 3rd edition 2004, 4th edition 2007).

10. Myers, R. H., Montgomery, D. C., Vining, G. G. and Robinson, T.J. (2010), Generalized Linear Models 

with Applications in Engineering and the Sciences 2

nd edition, John Wiley & Sons, New York (Probability 

and Statistics Series; 1st edition 2002).

11. Hines, W. W., Montgomery, D.C., Goldsman, D. M. and Borror, C. M. (2003), Probability and Statistics 

in Engineering, 4th edition, John Wiley & Sons, New York (1st edition 1972, 2nd edition 1980, 3rd edition 

1990).

12. Montgomery, D. C., Johnson, L. A. and Gardiner, J.S. (1990), Forecasting and Time Series Analysis, 2nd

edition, McGraw-Hill, New York. (1st edition 1976).

13. Johnson, L. A. and Montgomery, D.C, (1974), Operations Research in Production Planning, Scheduling, 

and Inventory Control, John Wiley & Sons, New York.

Research Books and Edited Volumes

1. Coleman, S., Greenfield, T., Stewardson, D., and Montgomery, D. C. (editors) (2008), Statistical Practice 

in Business and Industry, Wiley, Hoboken, NJ.

2. Burdick, R. K., Borror, C. M., and Montgomery, D. C. (2005), Design and Analysis of Gauge R&R 

Studies: Making Decisions with Confidence Intervals in Random and Mixed ANOVA Models, ASA-SIAM 

Series on Statistics and Applied Probability, SIAM, Philadelphia, PA, and ASA, Alexandria, VA.

3. Calado, V. and Montgomery, D. C. (2003), Planejamendo de Experimentos Usando o Statistica, E-Papers

Serviços Editoriais Ltda., Rio de Janeiro, Brazil. 

4. Beichelt, F. E. and Montgomery, D.C. (editors) (2003), Wahrscheinlichkeitstheorie, Stochastische 

Prozesse, Mathematische Statistik, B. G. Teubner Verlag, Weisbaden.

5. Keats, J. B. and Montgomery, D.C. (editors) (1996), Statistical Applications in Process Control, Marcel 

Dekker, New York.

6. Keats, J. B. and Montgomery, D.C. (editors) (1991), Statistical Process Control in Manufacturing, 

Marcel Dekker, New York.

7. George, M. L., Gooch, J. and Montgomery, D.C. (1986), America Can Compete, SMU Press, Dallas, TX.

Refereed Journal Publications

267. Stone, B.B., Montgomery, D.C., Silvestrini, R.T. and Jones B. (2017), “No-confounding designs 

with 24 runs for 7 – 12 factors”, International Journal of Experimental Design and Process 

Optimization, Vol. 5, No. 3, pp. 151-171. 
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266. Montgomery, D.C. and Borror, C.M. (2017), “Systems for modern quality and business 

improvement”, Quality Technology and Quantitative Management, Vol. 14, No. 4, pp. 343-352.

265. Jones, B. and Montgomery, D.C. (2017), “Partial Replication of Small Two-Level Factorial 

Designs”, Quality Engineering, Vol. 29, No. 3, pp. 190-195.

264. Jones, B., Schoen, E.D., and Montgomery, D.C. (2016), “A Comparison of Two-level Designs to 

Estimate All Main Effects and Two-Factor Interactions”, Quality Engineering, Vol. 28, No. 4, pp. 

369-380.

263. Mancenido, M., Pan, R., and Montgomery, D.C. (2016),”Analysis of Subjective Ordinal 

Responses in Mixture Experiments”, Journal of Quality Technology”, Vol. 48, No. 2, pp. 196-

208.

262. Lu, Y., Steptoe, M., Burke, S., Wang, H., Tsai, J.Y., Davulcu, H., Montgomery, D., Corman, S.R., 

and Maciejewski, R. (2016), “Exploring Evolving Media Discourse Through Event Cueing”, 

IEEE Transactions on Visualization and Computer Graphics, Vol 22(1), pp.220-229.

261. Kennedy, K., Silvestrini, R.T., Montgomery, D.C., and Jones, B. (2015), “Prediction Variance 

Properties of Bridge Designs”, International Journal of Experimental Design and Process 

Optimisation”, Vol. 4, pp. 234-255.

260. Krishnamoorthy, A., Montgomery, D.C., Jones, B., and Borror, C.M. (2015), “Analyzing Noconfounding Designs using the Dantzig Selector”, International Journal of Experimental Design 

and Process Optimisation”, Vol. 4, pp. 183-205

259. Jones, B., Silvestrini, R.T., Montgomery, D.C. and Steinberg, D.M. (2015), “Bridge Designs for 

Modeling Systems with Low Noise”, Technometrics, Vol. 57, No. 2, pp. 155-163

258. Montgomery, D.C. (2015), “Discussion of ‘The Case Against Normal Probability Plots of Effects’ 

by R.V. Lenth”, Journal of Quality Technology, Vol. 47, No. 2, pp. 105-106.

257. Jones, B., Shinde, S.M., and Montgomery, D.C. (2015), “Alternatives to Resolution III Regular 

Fractional Factorial Designs for 9-14 Factors in 16 Runs”, Applied Stochastic Models in Business 

and Industry, Vol. 31, pp. 50-58.

256. Krueger, D.C. and Montgomery, D.C. (2014), “Modeling and Analyzing Semiconductor Yield 

with Generalized Linear Mixed Models”, Applied Stochastic Models in Business and Industry, 

Vol. 30, No. 6, pp. 691 – 707.

255. Shinde, S.M., Montgomery, D.C., and Jones, B. (2014), “Projection Properties of NoConfounding Designs for Six, Seven, and Eight Factors in 16 Runs”, International Journal of 

Experimental Design and Process Optimization, Vol. 4, No. 1, pp. 1-26.

254. Park, Y.J., Pan, R., Borror, C.M., Montgomery, D.C., and Lee, G.B. (2014), “Simultaneous 

Improvement of Energy Efficiency and Product Quality in PCB Lamination Process”, 

International Journal of Precision Engineering and Manufacturing – Green Technology, Vol. 1, 

No. 3, pp. 247-256.
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253. Stone, B. B., Montgomery, D.C., Hassler, E., and Silvestrini, R.T. (2014), “An Expected Cost 

Methodology for Screening Design Selection”, Quality Engineering, Vol. 26, No. 2, pp. 139-153.

252. Adibi, A., Montgomery, D.C. and Borror, C.M. (2014), “Phase II Monitoring of Linear Profiles 

using a P-value Approach”, International Journal of Quality Engineering and Technology, 

Special Issue on Monitoring and Control, Vol 4, No. 3, p. 97- 106. 

251. Laungrungrong, B., Borror, C.M., and Montgomery, D.C. (2014), “A One-Sided MEWMA 

Control Chart for Poisson-Distributed Data”, International Journal of Data Analysis Techniques 

and Strategies, Vol. 6, No. 1, pp.15-42.

250 Adibi, A., Borror, C.M. and Montgomery, D.C. (2014), “A P-value Approach for Phase II 

Monitoring of multivariate Profiles”, International Journal of Quality Engineering and 

Technology, Special Issue on Monitoring and Control, Vol 4, No. 3, p. 133-143.

249. Montgomery, D.C. (2014), “Stu Hunter's Contributions to Experimental Design and Quality 

Engineering”, Quality Engineering, Vol. 26, No.1, pp. 5-15.

248. Woodall, W.H and Montgomery, D.C. (2014), “Some Current Trends in the Theory and Application 

of Statistical Process Monitoring”, Journal of Quality Technology, Vol. 46, No. 1, pp. 78-94.

247. Silvestrini, R.T., Montgomery, D.C. and Jones, B. (2013), “Comparing Computer Experiments for 

the Gaussian Process Model Using Integrated Prediction Variance”, Quality Engineering, Vol. 25, 

No. 2, pp. 164-174.

246. Rigdon, S.E., Englert, B. R., Lawson, I.A., Borror, C.M., Montgomery, D.C. and Pan, R. (2013), 

“Experiments for Reliability Achievement”, Quality Engineering, Vol. 25, No. 1, p. 54-72.

245. Abelson, R., Lane, J.K., Rodriguez, R., Johnston, P., Angjeli, E., Ousler, G., and Montgomery, 

D.C. (2012), “A single-center study evaluating the effect of the controlled adverse environment 

(CAEsm) model on tear film stability,” Clinical Ophthalmology, Vol. 6, pp. 1865–1872.

244. Duarte, B., Montgomery, D.C., Fowler, J., and Konopka, J. (2012), “Deploying Lean Six Sigma in 

a Global Enterprise – Project Identification”, International Journal of Lean Six Sigma, Vol. 3, No. 

3, pp. 187-205.

243. Capehart, E.R., Keha, A.B., Kulahci, M., and Montgomery, D.C. (2012), “Generating Blocked 

Fractional Factorial Split-Plot Designs using Integer Programming”, International Journal of 

Experimental Design and Process Optimization, Vol. 3, No. 2, pp. 111-132.

242. Rodriguez-Sifuentes, M., Montgomery, D.C., and Borror, C.M. (2012), “Prediction Variance 

Performance of Combined Array Designs”, International Journal of Experimental Design and 

Process Optimization, Vol. 3, No. 1, pp. 1-32.

241. Abelson, R., Lane, K.J., Rodriguez, J., Johnston, P., Angjeli, E., Ousler, G., and Montgomery, 

D.C. (2012), “Validation and Verification of the OPI 2.0 System”, Clinical Ophthalmology, Vol. 

6, pp. 613-622.

240. Anderson-Cook, C.M., Lu, L., Clark, G., DeHart, S.P., Hoerl, R., Jones, B., MacKay, J., 

Montgomery, D.C., Parker, P.A., Simpson, J.R., Snee, R.D., Steiner, S.H., Van Mullekom, J., 
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Vining, G.G., and Wilson, A.G. (2012), “Statistical Engineering – Forming the Foundations”, 

Quality Engineering,Vol. 24, No. 2, pp. 110-132.

239. Anderson-Cook, C.M., Lu, L., Clark, G., DeHart, S.P., Hoerl, R., Jones, B., MacKay, J., 

Montgomery, D.C., Parker, P.A., Simpson, J.R., Snee, R.D., Steiner, S.H., Van Mullekom, J., 

Vining, G.G., and Wilson, A.G. (2012), “Statistical Engineering – Roles for Statisticians and the 

Path Forward”, Quality Engineering,Vol. 24, No. 2, pp. 110-132.

238. Antony, J., Bhuller, A.S., Kumar, M., Mendibil, K., and Montgomery, D.C. (2012), “Application 

of Six Sigma DMAIC Methodology in a Transactional Environment”, International Journal of 

Quality and Reliability Management, Vol. 29, no. 1, pp. 31 – 53.

237. Cho, T.-Y., Montgomery, D.C. and Borror, C.M. (2012),”A Case Study Involving MixtureProcess Variable Experiments within a Split-Plot Structure”, Quality Engineering, Vol. 24, No. 1, 

pp. 80-93.

236. Johnson, R.T., Hutto, G.T., Simpson, J.R. and Montgomery, D.C. (2012), “Designed Experiments 

for the Defense Community”, Quality Engineering, Vol. 24, No. 1, pp. 60-79

235. Antony, J., Coleman, S., Montgomery, D.C., Anderson, M.J., and Silverstrini, R.T. (2011), 

“Design of Experiments for Non-manufacturing Processes: Benefits, Challenges and Some 

Examples”, Journal of Engineering Manufacture, Vol. 225, No. 11, pp. 2088-2095.

234. Broyles, J.R., Cochran, J.K., and Montgomery, D.C. (2011), “A Markov Decision Process to 

Dynamically Match Hospital Inpatient Staffing to Demand, IIE Transactions on Healthcare 

Systems Engineering, Vol. 1, No. 2, pp. 116-130

233. Kumar, N., Mastrangelo, C. and Montgomery, D.C. (2011), “Hierarchial Modeling using 

Generalized Linear Models”, Quality and Reliability Engineering International, Vol. 27, No. 6, 

pp. 835-842.

232. Borror, C.M., Beechy, T., Shunk, D., Gish, M., and Montgomery, D.C. (2011), “TASER’s 

Roadmap to Quality”, The Quality Management Forum, Vol. 37, No. 3, pp. 13-18.

231. Abelson, R., Lane, K.J., Angjeli, E., Johnston, P., Ousler, G., and Montgomery, D.C. (2011), 

“Measurement of Ocular Surface Protection Under Natural Blink Conditions”, Clinical 

Ophthalmology, Vol. 5, pp. 1349-1357.

230. Laungrungrong, B., Borror, C.M. and Montgomery, D.C. (2011), “EWMA Control Charts for 

Multivariate Poisson-Distributed Data”, International Journal of Quality Engineering and 

Technology, Vol. 2, No. 3, pp. 185-211.

229. Monroe, E.M., Pan, R., Anderson-Cook, C.M., Montgomery, D.C. and Borror, C.M. (2011), “A 

Generalized Linear Model Approach to Designing Accelerated Life Test Experiments”, Quality 

and Reliability Engineering International, Vol. 27, No. 4, pp. 595-607.

228. Johnson, R.T., Montgomery, D.C. and Jones, B.A. (2011), “An Expository Paper on Optimal 

Design”, Quality Engineering, Vol. 23, No. 3, pp. 276-301.
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227. Cho, T.-Y., Borror, C.M. and Montgomery, D.C. (2011), “Mixture-Process Variable Experiments 

Including Control and Noise Variables Within a Split-Plot Structure”, International Journal of 

Quality Engineering and Technology, Vol. 2, No. 1, pp. 1-28.

226. Krueger, D.C., Montgomery, D.C. and Mastrangelo, C.M. (2011), “Application of Generalized 

Linear Models to Predict Semiconductor Yield Using Defect Metrology Data”, IEEE 

Transactions on Semiconductor Manufacturing, Vol. 24, No. 1, pp. 44-58.

225. Capehart, S.R., Keha, A., Kulahci, M. and Montgomery, D.C. (2011), “Designing Fractional 

Factorial Split-plot Experiments Using Integer Programming”, International Journal of 

Experimental Design and Process Optimisation, Vol. 2, pp. 34-57.

224. Johnson, R.T., Montgomery, D.C. and Jones, B. (2011), “An Empirical Study of the Prediction 

Performance of Space-filling Designs”, International Journal of Experimental Design and 

Process Optimisation, Vol. 2, pp. 1-18.

223. Shinde, S.M., Orozco, C., Brengues, M., Lenigk, R., Montgomery, D.C. and Zenhausern, F. 

(2011), “Optimization of a Microfluidic Mixing Process for Gene Expression–Based BioDosimetry”, Quality Engineering, Vol 23, pp. 59–70.

222. Broyles, J. R., Cochran, J. K. and Montgomery, D. C. (2010), “A Statistical Markov Chain 

Approximation of Transient Hospital Inpatient Inventory”, European Journal of Operational 

Research, Vol. 207, No. 3, pp. 1645-1657.

221. Johnson, R.T. and Montgomery, D.C. (2010), “Designing Experiments for Nonlinear Models –

An Introduction”, Quality and Reliability Engineering International, Vol. 26, No. 5, pp. 431-441.

220. Chen, J.Y., Pfund, M.E., Fowler, J.W., Montgomery, D.C. and Callarman, T.E. (2010), “Robust 

Scaling Parameters for Composite Dispatching Rules”, IIE Transactions, Vol. 42, No. 11, pp. 

842-853.

219. Jones, B. and Montgomery, D.C. (2010), “Alternatives to Resolution IV Screening Designs in 16 

Runs”, International Journal of Experimental Design and Process Optimisation, Vol. 1, No. 4, 

pp. 285-295.

218. Gupta, S., Kulahci, M., Montgomery, D.C. and Borror, C.M (2010), “Analysis of SignalResponse Systems using Generalized Linear Mixed Models”, Quality and Reliability Engineering 

International, Vol. 26, No. 4, pp. 375-385.

217. Montgomery, D.C. (2010), “A Modern Framework for Achieving Enterprise Excellence”, 

International Journal of Lean Six Sigma, Vol. 1, No. 1, pp. 56-65.

216. Laungrungrong , B., Mobasher, B., Montgomery, D.C. and Borror, C.M. (2010), “Hybrid Control 

Charts for Active Control and Monitoring of Concrete Strength”, Journal of Materials in Civil 

Engineering, Vol. 2, No. 1 (January), pp. 77-87.

215. Monroe, E.M., Pan, R., Anderson-Cook, C.M., Montgomery, D.C. and Borror, C.M. (2010), 

“Sensitivity Analysis of Optimal Designs for Accelerated life Testing”, Journal of Quality 

Technology, Vol. 42, No. 2, pp. 121-135.

214. Johnson, R.T., Montgomery, D.C., Jones, B. and Parker, P.A. (2010), “Comparing Computer 

Experiments for Fitting High-Order Polynomial Models”, Journal of Quality Technology, Vol. 42, 

No. 1, pp. 86-102.
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213. Rodriguez, M., Jones, B., Borror, C.M. and Montgomery, D.C. (2010), “Generating and 

Assessing Exact G-Optimal Designs”, Journal of Quality Technology, Vol. 42, No. 1, pp. 3-29.

212. Hoskins, D.S., Colbourn, C.J. and Montgomery, D.C. (2009), “D-optimal Designs with 

Interaction Coverage”, Journal of Statistical Theory and Practice, Vol. 3, No. 4, pp. 817-830.

211. Cho, T-Y., Borror, C.M. and Montgomery, D.C. (2009) “Graphical Evaluation of Mixture-Process 

Variable Designs Within a Split-Plot Structure’, International Journal of Quality Engineering and 

Technology, Vol. 1, No. 1, pp. 2–26.

210. Rodriguez, M., Montgomery, D.C. and Borror, C.M. (2009), “Generating Experimental Designs 

Involving Control and Noise Variables using Genetic Algorithms”, Quality and Reliability 

Engineering International”, Vol. 25, No. 8, pp. 1045-1065.

209. Chung, P.J., Goldfarb, H.B., Montgomery, D.C. and Borror, C.M. (2009), “Optimal Designs for 

Mixture-Process Experiments Involving Continuous and Categorical Noise Variables”, Quality 

Technology and Quantitative Management, Vol. 6, No. 4, pp. 451-470.

208. Li, J., Liang, L., Borror, C.M., Anderson-Cook, C.M. and Montgomery, D.C. (2009), “Graphical 

Summaries to Compare Prediction Variance Performance for Variations of the Central Composite 

Design for 6 to 10 Factors”, Quality Technology and Quantitative Management, Vol. 6, No. 4, pp. 

433-449.

207. Johnson, R.T. and Montgomery, D.C. (2009), “Choice of Second-Order Response Surface Designs 

for Logistic and Poisson Regression Models”, International Journal of Experimental Design and 

Process Optimization, Vol. 1, No. 1, pp. 2-23.

206. Montgomery, D.C. (2009), “A Conversation with Stu Hunter”, Quality Engineering, Vol. 21, No. 

3, pp. 233-240.

205. Almimi, A.A., Kulahci, M. and Montgomery, D.C. (2009), “Checking the Adequacy of Fit of 

Models from Split-Plot Designs”, Journal of Quality Technology, Vol. 41, No. 3, pp. 272-284.

204. Johnson, R.T., Parker, P.A., Montgomery, D.C., Cutler, A.D., Danehy, P.M. and Rhew, R.D. 

(2009), “Design Strategies for the Response Surface Models for the Study of Supersonic 

Combustion”, Quality and Reliability Engineering International, Vol. 25, pp. 365-377.

203. Anderson-Cook, C.M., Borror, C.M. and Montgomery, D.C. (2009), “Response Surface Design 

Evaluation and Comparison” (with discussion), Journal of Statistical Planning and Inference, Vol. 

139, pp. 629-674.

202. Montgomery, D. C. and Woodall, W.H. (2008), “An Overview of Six Sigma”, International 

Statistical Review, Vol. 76, No. 3, pp. 329–346.

201. Steinberg, D.M., Bisgaard, S., Doganaksoy, N., Fisher, N., Gunter, B., Hahn, G., Keller-McNulty, 

S., Kettenring, J., Meeker, W.G., Montgomery, D.C. and Wu, C.F.J. (2008), “The Future of 

Industrial Statistics: A Panel Discussion”, Technometrics, Vol. 50, No. 2, pp. 103 - 127.

200. Kumar, M., Antony, J., Madu, C.N., Montgomery, D.C., and Park, S.H. (2008), “Common Myths 

of Six Sigma Demystified”, International Journal of Quality & Reliability Management, Vol. 25 

No. 8, pp. 878-895.

199. Boushell, T.G, Fowler, J.W., Keha, A., Knutson, K., and Montgomery, D.C. (2008), “Evaluation 

of Heuristics for a Class-constrained Lot-to-Order Matching Problem in Semiconductor 

Manufacturing”, International Journal of Production Research, Vol. 46, No. 12, pp. 4143-4166.
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198. Almimi, A.A., Kulahci, M., and Montgomery, D.C. (2008), “Follow-Up Designs to Resolve 

Confounding in Split-Plot Experiments”, Journal of Quality Technology, Vol. 40, No. 2, pp. 154-

166.

197. Montgomery, D. C. (2008), “Discussion of ‘Must a Process Be in Statistical Control Before 

Conducting Designed Experiments?’”, by Soren Bisgaard, Quality Engineering, Vol. 20, pp. 165-

168.

196. Elias, R.J., Montgomery, D.C., Low, S.A. and Kulahci, M. (2008), “Demand Signal Modeling: A 

Short-range Panel Forecasting Algorithm for Semiconductor Firm Device-level Demand”, 

European Journal of Industrial Engineering, Vol. 2, No. 3, pp. 253–278.

195. Almimi, A.A., Kulahci, M, and Montgomery, D.C. (2008), “Estimation of Missing Observations in 

Two-level Split-plot Designs”, Quality and Reliability Engineering International, Vol. 24, pp. 127-

152.

194. Montgomery, D. C. (2008), “Discussion of ‘An Overview of the ShaininTM System for Quality 

Improvement’”, by Stefan H. Steiner, R. Jock MacKay, and John S. Ramberg, Quality 

Engineering, Vol. 20, pp. 36-37. 

193. Jearkpaporn, D., Borror, C.M., Runger, G.C., and Montgomery, D.C. (2007), “Process Monitoring 

for Mean Shifts for Multiple Stage Processes”, International Journal of Production Research, 

Vol. 45, No. 23, pp. 5547-5570.

192. Lawson, C. and Montgomery, D.C. (2007), “A Logistic Regression Modeling Approach to 

Business Opportunity Assessment”, International Journal of Six Sigma and Competitive 

Advantage, Vol. 3, No. 2, pp. 120-136.

191. Perry, L.A., Montgomery, D.C., and Fowler, J.W. (2007), “A Partition Experimental Design for a 

Sequential Process with a Large Number of Variables”, Quality and Reliability Engineering 

International, Vol. 23, pp. 555-564

190. Chung, P. J., Goldfarb, H. B., and Montgomery, D. C. (2007), “Optimal Designs for MixtureProcess Experiments with Control and Noise Factors”, Journal of Quality Technology, Vol. 39, 

No. 3, pp. 179-190.

189. Holcomb, D.R., Montgomery, D. C., and Carlyle, W.M. (2007), “The use of Supersaturated 

Experiments in Turbine Engine Development”, Quality Engineering, Vol. 19, No. 1, pp. 17-27.

188. Lawson, C. and Montgomery, D. C. (2006), “Logistic Regression Analysis of Customer 

Satisfaction Data”, Quality and Reliability Engineering International, Vol. 22, No. 8, pp. 971-

984.

187. Kumar, N., Kennedy, K., Gildersleeve, K., Abelson, R., Mastrangelo, C. M., and Montgomery, D. 

C. (2006), “A Review of Yield Modeling Techniques for Semiconductor Manufacturing”, 

International Journal of Production Research, Vol. 44, No. 23, 5019-5036.

186. Kowalski, S. M., Vining, G. G., Montgomery, D. C., and Borror, C. M. (2006), “Modifying a 

Central Composite Design to Model the Process Mean and Variance when there are Hard-toChange Factors”, Journal of the Royal Statistical Society C (Applied Statistics), Vol. 55, Part 5, 

pp. 615-630.

185. Skinner, K. R., Runger, G. C., and Montgomery, D. C. (2006), “Process Monitoring for Multiple 

Count Data Using a Deleted-Y Statistic”, Quality Technology and Quantitative Management, Vol. 

2, No. 3, pp. 247-262.

184. Vadde, K. K., Syrotiuk, V. R., and Montgomery, D. C. (2006), “Optimizing Protocol Interaction 

using Response Surface Methodology,'' IEEE Transactions on Mobile Computing, Vol. 5, No. 6, 

pp. 627-639.
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183. Gupta, S., Montgomery, D. C., and Woodall, W. H. (2006), “Performance Evaluation of Two 

Methods for Online Monitoring of Linear Calibration Profiles”, International Journal of 

Production Research, Vol. 44, No. 10, pp. 1927-1942.

182. Elias, R.J., Montgomery, D.C. and Kulahci, M. (2006), “An Overview of Short-Term Statiatical 

Forecasting Methods”, International Journal of Management Science and Engineering 

Management, Vol. 1, No. 1, pp. 17-36.

181. Park, Y.-J., Montgomery, D.C., Fowler, J. W. and Borror, C.M. (2006), “Cost-constrained Gefficient Response Surface Designs for Cuboidal Regions”, Quality and Reliability Engineering 

International, Vol. 22, No. 2, pp. 121-139.

180. Robinson, T. J., Wulff, S. S., Montgomery, D. C. and Khuri, A. I. (2006), “Robust Parameter 

Design using Generalized Linear Models”, Journal of Quality Technology, Vol. 38, No. 1, pp. 65-

75.

179. Burdick, R. K., Park, Y.-J., Montgomery, D. C. and Borror, C. M. (2005), “Confidence Intervals 

for Misclassification Rates in a Gauge R&R Study”, Journal of Quality Technology, Vol. 37, No. 

4, pp. 294-303.

178. Houston, D., Ferreira, S. and Montgomery, D. C. (2005), “Using Unreplicated 2k-p Designs for 

Characterizing Moderately Dimensioned Deterministic Computer Models”, Quality and 

Reliability Engineering International, Vol. 21, No. 8, pp. 809-824.

177. Park, Y.-J., Richardson, D. E., Montgomery, D. C., Ozol-Godfrey, A., Borror, C. M. and 

Anderson-Cook, C. M. (2005), “Prediction Variance Properties of Second-Order Designs for 

Cuboidal Regions”, Journal of Quality Technology, Vol. 37, No. 4, pp. 253-266.

176. Ozol-Godfrey, A., Anderson-Cook, C. M. and Montgomery, D. C. (2005), “Fraction of Design 

Space Plots for Examining Model Robustness”, Journal of Quality Technology, Vol. 37, No. 3, 

pp. 223-235.

175. Mason, S.J., Fowler, J.W., Carlyle, W.M. and Montgomery, D.C. (2005), “Heuristics for 

Minimizing Total Weighted Tardiness in Complex Job Shops”, International Journal of 

Production Research, Vol. 43, No. 10, pp. 1943-1963.

174. Montgomery, D.C., Burdick, R.K., Lawson, C.A., Molnau, W.E., Zenzen, F., Jennings, C.L., 

Shah, H.K., Sebert, D.M., Bowser, M.D. and Holcomb, D.R. (2005), “A University-Based SixSigma Program”, Quality and Reliability Engineering International, Vol. 21, No. 3, pp. 243-248.

173. Vining, G. G., Kowalski, S. M. and Montgomery, D. C. (2005), “Response Surface Designs 

Within a Split-Plot Structure”, Journal of Quality Technology, Vol. 37, No. 2, pp. 115-129.

172. Jearkpaporn, D., Montgomery, D. C., Runger, G. C. and Borror, C. M. (2005), “Model-Based 

Process Monitoring using Robust Generalized Linear Models”, International Journal of 

Production Research, Vol. 43, No. 7, pp. 1337–1354.

171. Montgomery, D. C., Myers, R. H., Carter, W. H. Jr. and Vining, G. G. (2005), “The Hierarchy 

Principal in Designed Industrial Experiments”, Quality and Reliability Engineering International, 

Vol. 21, No. 2, pp. 197-201.

170. Suryanarayanan, S., Montgomery, D. C., and Heydt, G. T. (2005), “Considerations for 

Implementing Tag Schedules in Transmission Circuits”, IEEE Transactions on Power Systems, 

Vol. 20, No. 1, pp. 523-524.

169. Kowalski, S. M., Borror, C. M., and Montgomery, D. C. (2005), “A Modified Path of Steepest 

Ascent for Split-Plot Experiments”, Journal of Quality Technology, Vol. 37, No. 1, pp. 75-83.

168. Goldfarb, H. B., Borror, C. M., Montgomery, D. C., and Anderson-Cook, C. M. (2005), “Using 

Genetic Algorithms to Generate Mixture-Process Experimental Designs Involving Control and 

Noise Variables”, Journal of Quality Technology, Vol. 37, No. 1, pp. 60-74.
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167. Skinner, K. M., Montgomery, D. C., and Runger, G. C. (2004), “Generalized Linear Model-Based 

Control Charts for Discrete Semiconductor Data”, Quality and Reliability Engineering 

International, Vol. 20, pp. 777-786.

166. Anderson-Cook, C. M., Goldfarb, H. B., Borror, C. M., Montgomery, D. C., Canter, K. K., and 

Twist, J. N. (2004), “Mixture and Mixture-Process Variable Experiments for Pharmaceutical 

Applications”, Pharmaceutical Statistics, Vol. 3, pp. 247-260.

165. Drain, D., Carlyle, W. M., Montgomery, D. C., Borror, C. M., and Anderson-Cook, C. M. (2004), 

“A Genetic Algorithm Hybrid for Constructing Optimal Response Surface Designs”, Quality and 

Reliability Engineering International, Vol. 20, pp. 637-650.

164. Schaefer, L. A., Montgomery, D. C., and Wolfe, P. M. (2004), “A Flow Metamodel for Delivery 

Agents Over a Spatial Area”, IIE Transactions, Vol. 36, pp. 1055-1065.

163. Woodall, W. H., Spitzner, D. J., Montgomery, D. C., and Gupta, S. (2004), “Using Control Charts 

to Monitor Process and Product Profiles”, Journal of Quality Technology, Vol. 36, No. 3, pp. 309-

320.

162. Heredia-Langner, A., Montgomery, D. C., Carlyle, W. M., and Borror, C. M. (2004), “ModelRobust Optimal Designs: A Genetic Algorithm Approach”, Journal of Quality Technology, Vol. 

36, No. 3, pp. 263-279.

161. Goldfarb, H. B., Borror, C. M., Montgomery, D. C., and Anderson-Cook, C. M. (2004), 

“Evaluating Mixture-Process Designs with Control and Noise Variables”, Journal of Quality 

Technology, Vol. 36, No. 3, pp. 245-262.

160. Robinson, T. J., Myers, R. H., and Montgomery, D. C. (2004), “Analysis Considerations in 

Industrial Split-Plot Experiments with Non-Normal Responses”, Journal of Quality Technology, 

Vol. 36, No. 2, pp. 180-192.

159. Goldfarb, H. B., Anderson-Cook, C. M., Borror, C. M., and Montgomery, D. C. (2004), “Fraction 

of Design Space Plots to Assess the Prediction Capability of Mixture and Mixture-Process 

Designs”, Journal of Quality Technology Vol. 36, No. 2, pp. 169-179.

158. Shah, H. K., Montgomery, D. C., and Carlyle, W. M. (2004), “Response Surface Modeling and 

Optimization in Multiresponse Experiments using Seemingly Unrelated Regressions”, Quality 

Engineering, Vol. 16, No. 3, pp. 387-397.

157. Myers, R. H., Montgomery, D. C., Vining, G. G., Borror, C. M., and Kowalski, S. M. (2004), 

“Response Surface Methodology: A Retrospective and Literature Survey”, Journal of Quality 

Technology, Vol. 36, No. 1, pp. 53-77.

156. Goldfarb, H. B., Borror, C. M., Montgomery, D. C., and Anderson-Cook, C. M. (2004), “ThreeDimensional Variance Dispersion Graphs for Mixture-Process Experiments”, Journal of Quality 

Technology, Vol. 36, No. 1, pp. 109-124.

155. Jearkpaporn, D., Montgomery, D. C., Runger, G. C., and Borror, C. M. (2003), “Process 

Monitoring for Correlated Gamma Distributed Data Using Generalized Linear Model Based 

Control Charts”, Quality and Reliability Engineering International Vol. 19, No. 6, pp. 477-491.

154. Burdick, R. K., Borror, C. M., and Montgomery, D. C. (2003), “A Review of Methods for 

Measurement Systems Capability Analysis”, Journal of Quality Technology, Vol. 35, No. 4, pp. 

342-354.

153. Goldfarb, H. B., Borror, C. M., and Montgomery, D. C. (2003), “Mixture-Process Variable 

Experiments with Noise Variables”, Journal of Quality Technology, Vol. 35, No. 4, pp. 393-405.

152. Borror, C. M., Keats, J. B., and Montgomery, D. C. (2003), “Robustness of the Time Between 

Events CUSUM”, International Journal of Production Research, Vol. 41, No. a5, pp. 3435-3444.
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151. Wisnowski, J.W., Simpson, J.R., Montgomery, D.C., and Runger, G. C. (2003). "Resampling 

Methods for Variable Selection in Robust Regression," Computational Statistics and Data 

Analysis, Vol. 43, No. 3, pp. 341-355.

150. Skinner, K. R., Montgomery, D. C., and Runger, G. C. (2003), “Process Monitoring for Multiple 

Count Data Using Generalized Linear Model Based Control Charts”, International Journal of 

Production Research, Vol. 41, No. 6, pp. 1167-1180.

149. Holcomb, D. R., Montgomery, D. C., and Carlyle, W. M. (2003), “Analysis of Supersaturated 

Designs”, Journal of Quality Technology, Vol. 35, No. 1, pp. 13-27.

148. Heredia-Langner, A., Carlyle, W. M., Montgomery, D. C., Borror, C. M., and Runger, G. C. 

(2003), “Genetic Algorithms for the Construction of D-Optimal Designs”, Journal of Quality 

Technology, Vol. 35, No. 1, pp. 28-46.

147. Lanning, J. W., Montgomery, D. C., and Runger, G. C. (2002-03), “Monitoring a Multiple Stream 

Filling Operation Using Fractional Samples”, Quality Engineering, Vol. 15, No. 2, pp. 183-195.

146. Skinner, K. R., Montgomery, D. C., Runger, G. C., Fowler, J. W., McCarville, D. R., Rhoads, T. 

R., and Stanley, J. D. (2002), “Multivariate Statistical Methods for Modeling and Analysis of 

Wafer Probe Test Data”, IEEE Transactions on Semiconductor Manufacturing, Vol. 15, No. 4, 

pp. 523-530.

145. Perry, L. A., Montgomery, D. C., and Fowler, J. W. (2002), “Partition Experimental Designs for 

Sequential Processes: Part II - Second-Order Models”, Quality and Reliability Engineering 

International, Vol. 18, No. 5, pp. 373-382.

144. Somerville, S. E., Montgomery, D. C., and Runger, G. C. (2002), “Filtering and Smoothing 

Methods for Mixed Particle Count Distributions”, International Journal of Production Research, 

Vol. 40, No. 13, pp. 2991-3013.

143. Wisnowski, J. W., Simpson, J. R., and Montgomery, D. C. (2002) "An Improved Compound 

Estimator for Robust Regression," Communications in Statistics: Simulation and Computation,

Vol. 31, No. 4, pp. 653-772.

142. Montgomery, D. C., Loredo, E. N., Jearkpaporn, D., and Testik, M. C. (2002), “Experimental 

Designs for Constrained Regions”, Quality Engineering, Vol. 14, No. 4, pp. 587-601.

141. Heredia-Langner, A., Montgomery, D. C., and Carlyle, W. M. (2002), “Solving a Multistage 

Partial Inspection Problem using Genetic Algorithms”, International Journal of Production 

Research, Vol. 40, No. 8, pp. 1923-1940.

140. Wisnowski, J. W., Simpson, J. R., and Montgomery, D. C. (2002), “A Performance Study for 

Multivariate Location and Shape Estimators”, Quality and Reliability Engineering International, 

Vol. 18, No. 2, pp. 117-129.

139. Borror, C. M., Heredia-Langner, A., and Montgomery, D. C. (2002), “Generalized Linear Models 

in the Analysis of Industrial Experiments”, Journal of Propagations in Probability and Statistics 

(International Edition), Vol. 2, No. 2, pp. 127-144.

138. Fowler, J. W., Phojanamongkolkij, N., Cochran, J. K., and Montgomery, D. C. (2002), “Optimal 

Batching in a Wafer Fabrication Facility Using a Multi-product G/G/c Model with Batch 

Processing”, International Journal of Production Research, Vol. 40, No. 2, pp. 275-292.

137. Borror, C. M., Montgomery, D. C., and Myers, R. H. (2002), “Evaluation of Statistical Designs 

for Experiments Involving Noise Variables”, Journal of Quality Technology, Vol. 34, No. 1, pp. 

54-70.

136. Canter, K. G., Kennedy, D. J., Keats, J. B., Montgomery, D. C., and Carlyle, W. M. (2002), 

“Screening Stochastic Environmental Life Cycle Assessment Inventory Models”, International 

Journal of Life Cycle Assessment, Vol. 7, No. 1, pp. 18-26.
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135. Montgomery, D. C. (2001), “The Future of Industrial Statistics”, Orion, Vol. 16, No. 1, pp. 1-21.

134. Perry, L. A., Montgomery, D. C., and Fowler, J. W. (2001), “Partition Experimental Designs for 

Sequential Processes: Part I - First-Order Models”, Quality and Reliability Engineering 

International, Vol. 17, No. 6, pp. 429-438. 

133. Houston, D. X., Ferreira, S., Collofello, J. S., Montgomery, D. C., Mackulak, G. T., and Shunk, 

D. L (2001), “Behavorial Characterization: Finding and Using the Influential Factors in Software 

Process Simulation Models”, Journal of Systems and Software, Vol. 59, pp. 259-270.

132. Molnau, W. E., Runger, G. C., Montgomery, D. C., Skinner, K. R., Loredo, E. N., and Prabhu, S. 

S. (2001), “A Program for ARL Calculation for Multivariate EWMA Control Charts”, Journal of 

Quality Technology, Vol. 33, No. 4, pp. 515-521.

131. Montgomery, D. C., Lawson, C., Molnau, W. E., and Elias, R. (2001), Invited discussion of “Six 

Sigma Black Belts: What Do They Need to Know?”, by R. W. Hoerl, Journal of Quality 

Technology, Vol. 33, No. 4, pp. 407-409.

130. Montgomery, D. C. and Borror, C. M. (2001), Invited discussion of “Factor Screening and 

Response Surface Exploration”, by S.-W. Cheng and C. F. J. Wu, Statistica Sinica, Vol. 11, No. 3, 

pp. 591-595.

129. Lewis, S. L., Montgomery, D. C. and Myers, R. H. (2001), “Confidence Interval Coverage for 

Designed Experiments Analyzed with Generalized Linear Models”, Journal of Quality 

Technology, Vol. 33, No. 3, pp. 279-292.

128. Lewis, S. L., Montgomery, D. C. and Myers, R. H. (2001), “Examples of Designed Experiments 

with Nonnormal Responses”, Journal of Quality Technology, Vol. 33, No. 3, pp. 265-278.

127. Heredia-Langner, A., Montgomery, D. C., Runger, G. C., Borror, C. M., and Post, R. I. (2001), 

“Performance of Customized Control Charts to Detect Process Disturbances”, Quality and 

Reliability Engineering International, Vol. 17, No. 3, pp. 205-218.

126. Wisnowski, J. W., Montgomery, D. C., and Simpson, J. R. (2001), “A Comparative Analysis of 

Multiple Outlier Detection Procedures in the Linear Regression Model”, Computational Statistics 

and Data Analysis, Vol. 36, No. 3, pp 351-382.

125. Montgomery, D. C. (2001), “Opportunities and Challenges for Industrial Statisticians”, Journal of 

Applied Statistics, Vol. 28, Nos. 3&4, pp. 427-439.

124. Molnau, W. E., Montgomery, D. C., and Runger, G. C. (2001), “Statistically Constrained 

Economic Design of the Multivariate Exponentially Weighted Moving Average Control Chart”, 

Quality and Reliability Engineering International, Vol. 17, No. 1, pp. 39-49.

123. Montgomery, D. C., Keats, J. B., Yatskievitch, M., and Messina, W. S. (2000), “Integrating 

Statistical Process Monitoring with Feedforward Control”, Quality and Reliability Engineering 

International, Vol. 16, No. 6, pp. 515-525.

122. Woodall, W. H. and Montgomery, D. C. (2000-01), “Using Ranges to Estimate Variability”, 

Quality Engineering, Vol. 13, No. 2, pp. 211-217.

121. O’Neill, J. C., Borror, C. M., Eastman, P. Y., Fradkin, D. G., James, M. P., Marks, A. P., and 

Montgomery, D. C. (2000), “Optimal Assignment of Samples to Treatments for Robust Design”, 

Quality and Reliability Engineering International, Vol. 16, No. 5, pp. 417-421.

120. Nelson, B. J., Montgomery, D. C., Elias, R. J., and Maass, E. (2000), “A Comparison of Several 

Design Augmentation Strategies”, Quality and Reliability Engineering International, Vol. 16, No. 

5, pp. 435-449.

119. Heredia-Langner, A., Loredo, E. N., Montgomery, D. C., and Griffin, A. H. (2000), “Optimization 

of a Bonded Leads Process using Statistically Designed Experiments”, Robotics and Computer 

Integrated Manufacturing, Vol. 16, No. 5, pp. 377-382.
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118. Zimmer, L. S., Montgomery, D. C., and Runger, G. C. (2000), “Guidelines for the Application of 

Adaptive Control Charting Schemes”, International Journal of Production Research, Vol. 38, No. 

9, pp. 1977-1992.

117. Borror, C. M. and Montgomery, D. C. (2000), “Mixed Resolution Designs as Alternatives to 

Taguchi Inner/Outer Array Designs for Robust Design Problems”, Quality and Reliability 

Engineering International, Vol. 16, No. 2, pp. 117-127.

116. Carlyle, W. M., Montgomery, D. C., and Runger, G. C. (2000), “Optimization Problems and 

Methods in Quality Control and Improvement” (with Discussion), Journal of Quality Technology, 

Vol. 32, No. 1, pp. 1-31.

115. Montgomery, D. C., Keats, J. B., Perry, L. A., Thompson, J. R., and Messina, W. S. (2000), 

“Using Statistically Designed Experiments for Process Development and Improvement: An 

Application in Electronics Manufacturing”, Robotics and Computer Integrated Manufacturing, 

Vol. 16, No. 1, pp. 55-63.

114. Lewis, S. L., Montgomery, D. C., and Myers, R. H. (1999-2000), “The Analysis of Designed 

Experiments with Nonnormal Responses”, Quality Engineering, Vol. 12, No. 2, pp. 225-244.

113. Wisnowski, J. W., Runger, G. C., and Montgomery, D. C. (1999-2000), “Analyzing Data From 

Designed Experiments: A Regression Tree Approach”, Quality Engineering, Vol. 12, No. 2, pp. 

185-198.

112. Woodall, W.H. and Montgomery, D. C. (1999), “Research Issues and Ideas in Statistical Process 

Control”, Journal of Quality Technology, Vol. 31, No. 4, pp. 376-386.

111. Silknitter, K. O., Wisnowski, J. W., and Montgomery, D. C. (1999), “The Analysis of Covariance: 

A Useful Technique for Analyzing Quality Improvement Experiments”, Quality and Reliability 

Engineering International, Vol. 15, No. 4, pp. 303-316.

110. Runger, G. C., Keats, J. B., Montgomery, D. C., and Scranton, R. D. (1999), “Improving the 

Performance of the Multivariate EWMA Control Chart”, Quality and Reliability Engineering 

International, Vol. 15, No. 3, pp. 161-166.

109. Borror, C. M., Montgomery, D. C., and Runger, G. C. (1999), “Robustness to Normality of the 

EWMA Control Chart”, Journal of Quality Technology, Vol. 31, No. 3, pp. 309-316.

108. Montgomery, D. C. (1999), “Experimental Design for Product and Process Design and 

Development” (with commentary), Journal of the Royal Statistical Society Series D (The 

Statistician), Vol. 48, Part 2, pp. 159-177.

107. Catlin, A. E., Bauer, K. W., Jr., Mykytka, E. W., and Montgomery, D. C. (1999), “System 

Comparison Procedures for Automatic Target Recognition Systems”, Naval Research Logistics, 

Vol. 46, No. 4, pp. 357-371.

106. Hauck, D. J., Runger, G. C., and Montgomery, D. C. (1999), “Multivariate Statistical Process 

Monitoring and Diagnosis with Grouped Regression-Adjusted Variables”, Communications in 

Statistics: Simulation and Computation, Vol. 28, No. 2, pp. 309-328.

105. Montgomery, D. C. (1999), “Some Comments on Future Directions in RSM”, Invited discussion 

of papers by G. E. P. Box and R. H. Myers, Journal of Quality Technology, Vol. 31, No. 1, pp. 

45-46.

104. Simpson, J. R. and Montgomery, D. C. (1998), “A Performance-Based Assessment of Robust 

Regression Methods”, Communications in Statistics: Simulation and Computation, Vol. 27, No. 4, 

pp. 1031-1049.

103. Simpson, J. R. and Montgomery, D. C. (1998), “The Development and Evaluation of Alternative 

Generalized M-estimation Techniques,” Communications in Statistics: Simulation and 

Computation, Vol. 27, No. 4, pp. 999-1018.
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102. Stanley, J. D., McCarville, D. R., and Montgomery, D. C. (1998), “A Time Series Approach for 

Compensating Errors in Complex Gauge Systems”, Quality and Reliability Engineering 

International, Vol. 14, No. 4, pp. 273-280.

101. Sebert, D. M., Montgomery, D. C., and Rollier, D. A. (1998), “A Clustering Algorithm for 

Identifying Multiple Outliers in Linear Regression”, Computational Statistics and Data Analysis, 

Vol. 27, pp. 461-484.

100. Simpson, J.R., and Montgomery, D.C. (1998), “A Robust Regression Technique Using 

Compound Estimation”, Naval Research Logistics, Vol. 45, No. 2, pp. 125-139.

99. Zimmer, L. S., Montgomery, D. C., and Runger, G.C. (1998), “Evaluation of a Three-State 

Adaptive Sample Size X-Bar Control Chart”, International Journal of Production Research, Vol. 

36, No. 3, pp. 733-743.

98. Borror, C. M., Montgomery, D. C., and Runger, G. C. (1997), “Confidence Intervals on Variance 

Components from Gauge Capability Studies”, Quality and Reliability Engineering International, 

Vol. 13, No. 6, pp. 361-369.

97. Kennedy, D. J., Montgomery, D. C., Rollier, D. A. and Keats, J. B. (1997), “Assessing Input Data 

Uncertainty in Life Cycle Assessment Inventory Models”, International Journal of Life Cycle 

Assessment, Vol. 2, No. 4, pp. 229-239.

96. Bowles, M.L., and Montgomery, D.C. (1997-98), “How to Formulate the Ultimate Margarita: A 

Tutorial on Experiments with Mixtures”, Quality Engineering, Vol. 10, No. 2, pp. 239-253.

95. Montgomery, D. C., Borror, C. M., and Stanley, J. D. (1997-98), “Some Cautions in the Use of 

Plackett-Burman Designs”, Quality Engineering, Vol. 10, No. 2, pp. 371-381.

94. Andere-Rendon, J., Montgomery, D. C., and Rollier, D. A. (1997), “Design of Mixture 

Experiments Using Bayesian D-Optimality”, Journal of Quality Technology, Vol. 29, No. 4, pp. 

451-463.

93. Schaub, D.A., Chu, K.-R., and Montgomery, D.C.(1997), “Optimizing Stereolithography 

Throughput”, Journal of Manufacturing Systems, Vol. 16, No. 4, pp. 290-303. 

92. Myers, R. H., and Montgomery, D. C. (1997), “A Tutorial on Generalized Linear Models”, 

Journal of Quality Technology, Vol. 29, No. 3, pp. 274-291.

91. Runger, G.C., and Montgomery, D.C. (1997), “Multivariate and Univariate Process Control: 

Geometry and Shift Directions”, Quality and Reliability Engineering International, Vol. 13, No. 

3, pp. 153-158.

90. Prabhu, S. S., Runger, G. C., and D. C. Montgomery (1997), “Selection of the Subgroup Size and 

Sampling Interval for a CUSUM Control Chart”, IIE Transactions, Vol. 29, No. 6, pp. 451-457. 

89. Schaub, D. A., and Montgomery, D. C. (1997), “Using Experimental Design to Optimize the 

Stereolithography Process”, Quality Engineering, Vol. 9, No. 4, pp. 575-585. 

88. Montgomery, D. C., Keats, J. B., Fowler, J. W., Runger, G. C., and Rajavelu, G. (1997), 

“Statistical Monitoring Techniques for Contamination Data”, Journal of the Institute of 

Environmental Sciences, Vol. XL, No. 2, pp. 23-30.

87. Prabhu, S. S., Montgomery, D. C., and G. C. Runger (1997), “Economic-Statistical Design of an 

Adaptive 

x Control Chart”, International Journal of Production Economics, Vol. 49, pp. 1-15. 

86. Lawson, C., Keats, J. B., and Montgomery, D. C. (1997), “Comparison of Robust and Least 

Squares Regression in Computer-Generated Probability Plots”, IEEE Transactions on Reliability, 

Vol. 46, No. 1, pp. 108-115. 

85. Montgomery, D. C., and Woodall, W. H. (1997) (editors), “A Discussion on Statistically-Based 

Process Monitoring and Control”, Journal of Quality Technology, Vol. 29, No. 2, pp. 121-162.
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84. Simpson, J.R., and Montgomery, D. C. (1996), “A Biased-Robust Regression Technique for the 

Combined Outlier-Multicollinearity Problem”, Journal of Statistical Computation and Simulation, 

Vol. 56, pp. 1-22.

83. Kennedy, D. J., Montgomery, D. C., and Quay, B. H. (1996), “Stochastic Environmental Life 

Cycle Assessment Modeling”, International Journal of Life Cycle Assessment, Vol. 1, No. 4, pp. 

199-207.

82. McCarville, D. R., and D. C. Montgomery (1996), “Optimal Guard Bands for Gauges in Series,” 

Quality Engineering, Vol. 9, No. 2, pp. 167-177.

81. Somerville, S. E. and D. C. Montgomery (1996), “Process Capability Indices and Nonnormal 

Distributions,” Quality Engineering, Vol. 9, No. 2, pp. 305-316.

80. Rhoads, T. R., D. C. Montgomery, and C. M. Mastrangelo (1996), “A Fast Initial Response 

Scheme for the Exponentially Weighted Moving Average Control Chart,” Quality Engineering, 

Vol. 9, No. 2, pp. 317-327.

79. Del Castillo, E., Grayson, J. M., Montgomery, D. C., and G. C. Runger (1996), “A Review of 

Statistical Process Control Techniques for Short-Run Manufacturing Systems,” Communications 

in Statistics-Theory and Methods, Vol. 25, No. 11, pp. 305-316.

78. Runger, G. C., Alt, F. B., and D. C. Montgomery (1996), “Controlling Multiple Stream Processes 

with Principal Components,” International Journal of Production Research, Vol. 34, No. 11, pp. 

2991-2999.

77. Keats, J.B., Montgomery, D.C., Runger, G.C., and W.S. Messina (1996), “Feedback Control and 

Statistical Process Monitoring”, International Journal of Reliability, Quality and Safety 

Engineering, Vol. 3, No. 3, pp. 231-241.

76. Montgomery, D. C. and G. C. Runger (1996), “Foldovers of 2k-p Resolution IV Designs,” Journal 

of Quality Technology, Vol. 28, No. 4, pp. 446-450.

75. Runger, G. C., Alt, F. A., and D. C. Montgomery (1996), “Contributors to a Multivariate 

Statistical Process Control Signal”, Communications in Statistics - Theory and Methods, Vol. 25, 

No. 10, pp. 2203-2213.

74. Shumate, D. A. and D. C. Montgomery (1996), “Development of a TiW Plasma Etch Process 

Using a Mixture Experiment and Response Surface Methodology,” IEEE Transactions on 

Semiconductor Manufacturing, Vol. 9, No. 3, pp. 335-343.

73. Mastrangelo, C. M., Runger, G. C. and D. C. Montgomery (1996) “Statistical Process Monitoring 

with Principal Components,” Quality and Reliability Engineering International, Vol. 12, No. 3, 

pp. 203-210.

72. Cornell, J. A. and D. C. Montgomery (1996), “Interaction Models as Alternatives to Low-Order 

Polynomials,” Journal of Quality Technology, Vol. 28, No. 2, pp.163-176.

71. Del Castillo, E., Montgomery, D. C., and D. R. McCarville (1996), “Modified Desirability 

Functions for Multiple Response Optimization,” Journal of Quality Technology, Vol. 28, No. 3, 

pp. 337-345.

70. Cornell, J. A. and D. C. Montgomery (1996), “Fitting Models to Data: Interaction Versus 

Polynomial? Your Choice!”, Communications in Statistics-Theory and Methods, Vol. 25, No. 11, 

pp. 2531-2555.

69. Scranton, R., G. C. Runger, J. B. Keats and D. C. Montgomery (1996), “Efficient Shift Detection 

Using Multivariate EWMA Control Charts and Principal Components,” Quality and Reliability 

Engineering International, Vol. 12, No. 3, pp.165-172.

68. Del Castillo, E., P. Mackin, and D. C. Montgomery (1996), "Multiple Criteria Optimal Design of 

Control Charts," IIE Transactions, Vol. 28, No. 6, pp. 467-474.
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67. Del Castillo, E. and D. C. Montgomery (1996), "A General Model for the Optimal Economic 

Design of Charts Used to Control Short or Long Run Processes," IIE Transactions, Vol. 28, No. 

3, pp. 193-201.

66. Lowry, C. A., and D. C. Montgomery (1995), "A Review of Multivariate Control Charts," IIE 

Transactions, Vol. 27, No. 6, pp. 800-810.

65. Grayson, J. M., G. C. Runger, and D. C. Montgomery (1995-96), "Average Run Length 

Performance of the U-Chart with Control Limits Based on the Average Sample Size," Quality 

Engineering, Vol. 8, No. 1, pp. 117-128.

64. Annadi, H.P., J. B. Keats, G. C. Runger, and D. C. Montgomery (1995), "An Adaptive Sample 

Size CUSUM Control Chart," International Journal of Production Research, Vol. 33, No. 6, pp. 

1605-1616.

63. Torng, J. C-C., J. K. Cochran, D. C. Montgomery, and F. P. Lawrence (1995), “Implementing 

Statistically Constrained Economic EWMA Control Charts,” Journal of Quality Technology, Vol. 

27, No. 3, pp. 257-264.

62. Montgomery, D. C., J. C-C. Torng, J. K. Cochran, and F. P. Lawrence (1995), “Statistically 

Constrained Economic Design of the EWMA Control Chart,” Journal of Quality Technology, 

Vol. 27, No. 3, pp. 250-256.

61. Mastrangelo, C. M. and D. C. Montgomery (1995), "SPC with Correlated Observations for the 

Chemical and Process Industries," Quality and Reliability Engineering International, Vol. 11, No. 

2, pp. 79-89.

60. Del Castillo, E. and D. C. Montgomery (1995), "A Kalman Filtering Process Control Scheme with 

an Application to Semiconductor Short-Run Manufacturing," Quality and Reliability Engineering 

International, Vol. 11, No. 2.

59. Prabhu, S. S., D. C. Montgomery, and G. C. Runger (1995), "A Design Tool to Evaluate Average 

Time to Signal Properties of Adaptive Control X-Bar Charts," Journal of Quality Technology,

Vol. 27, No. 1, pp. 74-83.

58. Heinsman, J. A. and D. C. Montgomery (1995), "Optimization of a Household Product 

Formulation Using a Mixture Experiment," Quality Engineering, Vol. 7, No. 3, pp. 583-600.

57. Torng, James C., D. C. Montgomery and J. K. Cochran (1994), "Economic Design of the EWMA 

Control Chart," Economic Quality Control, Vol. 9, No. 1, pp. 3-23.

56. Prabhu, S. S., D. C. Montgomery, and G. C. Runger (1994), "A Combined Adaptive Sample Size 

and Adaptive Sampling Interval Control Scheme," Journal of Quality Technology, Vol. 26, No. 3, 

pp. 164-176.

55. Del Castillo, E. and D. C. Montgomery (1994), "Short-Run Statistical Process Control: Q-Chart 

Enhancements and Alternatives," Quality and Reliability Engineering International, Vol. 10, No. 

2, pp. 87-97.

54. Montgomery, D. C., J. B. Keats, G. C. Runger, and W. S. Messina (1994), "Integrating Statistical 

Process Control and Engineering Process Control," Journal of Quality Technology, Vol. 26, No. 

2, pp. 79-87.

53. Montgomery, D. C. and S. R. Voth (1994), "Multicollinearity and Leverage in Mixture 

Experiments," Journal of Quality Technology, Vol. 26, No. 2, pp. 96-108.

52. Del Castillo, E. and D. C. Montgomery (1993), "Optimal Design of Control Charts for Finite –

Length Production Runs," Economic Quality Control, Vol. 8, No. 4, pp. 225-240.

51. Del Castillo, E. and D. C. Montgomery (1993), "A Nonlinear Programming Solution to the Dual 

Response Problem," Journal of Quality Technology, Vol. 25, No. 3, pp. 199-204.
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50. Montgomery, D. C. and G. C. Runger (1993), "Gage Capability and Designed Experiments, Part 

II: Experimental Design Models and Variance Component Estimation," Quality Engineering, Vol. 

6, No. 2, pp. 289-305.

49. Montgomery, D. C. and G. C. Runger (1993), "Gage Capability and Designed Experiments, Part 

I: Basic Methods," Quality Engineering, Vol. 6, No. 1, pp. 115-135.

48. Runger, G. C. and D. C. Montgomery (1993), "Adaptive Sampling Enhancements for Shewhart 

Control Charts," IIE Transactions, Vol. 25, No. 3, pp. 41-51.

47. Montgomery, D. C. and D. J. Friedman (1993), "Prediction Using Regression Models with 

Multicollinear Predictor Variables," IIE Transactions, Vol. 25, No. 3, pp. 73-84.

46. Coleman, D. E. and D. C. Montgomery (1993), "A Systematic Approach to Planning for a 

Designed Industrial Experiment," (with discussion), Technometrics, Vol. 35, No. 1, pp. 1-27.

45. Montgomery, D. C. (1992), "The Use of Statistical Process Control and Design of Experiments in 

Product and Process Development,” IIE Transactions, Vol. 24, No. 5, pp. 4-17.

44. Chua, M. K. and D. C. Montgomery (1992), "Investigation and Characterization of a Control 

Scheme for Multivariate Quality Control," Quality and Reliability Engineering International, Vol. 

8, no. 1, pp. 37-44.

43. Hubele, N. F., D. C. Montgomery and W. H. Chin (1991-1992), "An Application of Statistical 

Process Control in Jet-Turbine Engine Component Manufacturing," Quality Engineering, Vol. 4, 

No. 2, pp. 197-210.

42. Chua, M. K. and D. C. Montgomery (1991), "A Multivariate Quality Control Scheme," 

International Journal of Quality and Reliability Management, Vol. 8, No. 6, pp. 29-46.

41. Montgomery, D. C. and C. M. Mastrangelo (1991), "Some Statistical Process Control Methods for 

Autocorrelated Data," (with discussion), Journal of Quality Technology, Vol. 23, No. 3, pp. 179-

193.

40. Yourstone, S. A. and D. C. Montgomery (1991), "Detection of Process Upsets – Sample 

Autocorrelation Control Chart and Group Autocorrelation Control Chart Applications," Quality 

and Reliability Engineering International, Vol. 7, pp. 133-140.

39. Montgomery, D. C. (1990-91), "Using Fractional Factorial Designs for Robust Process 

Development," Quality Engineering, Vol. 3, No. 2, pp. 193-205.

38. Yourstone, S. A. and D. C. Montgomery (1989), "A Time-Series Approach to Discrete Real-Time 

Process Quality Control," Quality and Reliability Engineering International, Vol. 5, No. 4, pp. 

309-317.

37. Montgomery, D. C. (1988), "Experimental Design for Product and Process Optimization," SAE 

Transactions, Fall Volume, pp. 145-155.

36. Gardiner, J. S., Friedman, D. J. and D. C. Montgomery (1987), "A Note on the Average Run 

Length of Cumulative Sum Control Charts for Count Data," Quality and Reliability Engineering 

International, Vol. 3, No.1, pp. 53-55.

35. Gardiner, J. S. and D. C. Montgomery (1987), "Using Statistical Control Charts for Software 

Quality Control," Quality and Reliability Engineering International, Vol. 3, No. 1, pp. 15-20.

34. Montgomery, D. C. and R. H. Storer (1986), "Economic Models and Process Quality Control," 

Quality and Reliability Engineering International, Vol. 2, No. 4, pp. 221-228.

33. Montgomery, D. C., Panagos, M., and R. G. Heikes (1985), "Economic Design of 

x

Control 

Charts for Two Manufacturing Process Models," Naval Research Logistics Quarterly, Vol. 32. 

No. 4, pp. 631-647.
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32. Montgomery, D. C. (1985), "The Effect of Non-normality on Variables Sampling Plans," Naval 

Research Logistics Quarterly, Vol. 32, No. 1, pp. 34-41.

31. Friedman, D. J. and D. C. Montgomery (1985), "Evaluation of the Predictive Performance of 

Biased Regression Estimators," Journal of Forecasting, Vol. 4, No. 2, pp. 153-163.

30. Askin, R. G. and D. C. Montgomery (1984), "An Analysis of Constrained Robust Regression 

Estimators," Naval Research Logistics Quarterly, Vol. 31, No. 2, pp. 283-296.

29. Montgomery, D. C. and L. Greene (1983), "Validation of Computer Simulation Models of Missile 

Systems," Journal of Spacecraft and Rockets, Vol. 20, No. 3, pp. 272-278.

28. Montgomery, D. C. (1982), "Economic Design of an

x

Control Chart," Journal of Quality 

Technology, Vol. 14, No. 2, 99. 40-43.

27. Saniga, E. W. and D. C. Montgomery (1981), "Economical Quality Control Policies for a SingleCause System," AIIE Transactions, Vol. 13, No. 3, pp. 258-264.

26. Montgomery, D. C. and R. G. Askin (1981), “Problems of Nonnormality and Multicollinearity for 

Forecasting Methods Based on Least Squares," AIIE Transactions, Vol. 13, No. 2, pp. 102-15.

25. Montgomery, D. C. and G. Weatherby (1980), "Modeling and Forecasting Time Series Using 

Transfer Function and Intervention Methods," AIIE Transactions, Vol. 12, No. 4, pp. 289-307.

24. Askin, R. G. and D. C. Montgomery (1980), "Augmented Robust Estimators," Technometrics,

Vol. 22, No. 3, pp. 333-341.

23. Montgomery, D. C., E. W. Martin and E. A. Peck (1980), "Interior Analysis of the Observations 

in Multiple Linear Regression," Journal of Quality Technology, Vol. 12, No. 3, pp. 163-172.

22. Montgomery, D. C. and R. G. Conard (1980), "Comparison of Simulation and Flight-Test Data 

for Missile Systems," Simulation, Vol. 31, pp. 63-72.

21. Montgomery, D. C. (1980), "The Economic Design of Control Charts: A Review and Literature 

Survey," Journal of Quality Technology, Vol. 12, No. 2, pp. 75-87.

20. Stewart, R. D., D. C. Montgomery and R. G. Heikes (1978), "Choice of Double Sampling Plans 

Based on Prior Distributions and Costs," AIIE Transactions, Vol. 10, No. 1, pp. 19-30.

19. Montgomery, D. C. and V. M. Bettencourt, Jr. (1977), "Multiple Response Surface Methods in 

Computer Simulation," Simulation, Vol. 29, No. 4, pp. 113-121.

18. Montgomery, D. C. (1977), "Discussion of 'Studies in a Simulated Job Shop'" by S. Eilon and I. 

Chowdhury, Invited Discussion, Proceedings of the Institution of Mechanical Engineers, London, 

Vol. 189, pp. 185-186.

17. Montgomery, D. C. and L. E. Contreras (1977), "A Note on Forecasting with Adaptive Filtering," 

Operational Research Quarterly, Vol. 28, No. 1, pp. 87-91.

16. Montgomery, D. C. and R. G. Heikes (1976), "Process Failure Mechanisms and Optimal Fraction 

Defective Control Charts," AIIE Transactions, Vol. 8, No. 4, pp. 467-472.

15. Heikes, R. G., D. C. Montgomery, and R. L. Rardin (1976), "Using Common Random Numbers 

in Simulation Experiments--An Approach to Statistical Analysis," Simulation, Vol. 27, No. 3, pp. 

81-85.

14. Montgomery, D. C. and D. M. Evans, Jr. (1975), “Second-Order Response Surface Designs in 

Computer Simulation”, Simulation, Vol. 26, pp. 169-178.

13. Montgomery, D. C., R. G. Heikes and J. F. Mance (1975), "Economic Design of Fraction 

Defective Control Charts," Management Science, Vol. 21, No. 11, pp. 1272-1284

12. Heikes, R. G., D. C. Montgomery, and J. Young (1974), "Alternative Process Models in the 

Economic Design of T

2 Control Charts," AIIE Transactions, Vol. 6, No. 1, pp. 55-61.

X
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11. Horwitz, J. S. and D. C. Montgomery (1974), "A Computer Simulation Model of a Rubella 

Epidemic," Computers in Biology and Medicine, Vol. 4, No. 2, pp. 189-198.

10. Montgomery, D. C., M. S. Bazaraa and A. J. Keswani (1973), "Inventory Models with a Mixture 

of Backorders and Lost Sales," Naval Research Logistics Quarterly, Vol. 20, No. 2, pp.255-263.

9. Montgomery, D. C. and P. J. Klatt (1972), "Economic Design of T

2 Control Charts to Maintain 

Current Control of a Process," Management Science, Vol. 19, No. 1, pp. 76-89.

8. Montgomery, D. C., J. J. Talavage and C. J. Mullen (1972), "A Response Surface Approach to 

Improving Traffic Signal Settings in a Street Network," Transportation Research, Vol. 6, pp. 69-

80.

7. Sipper, D. and D. C. Montgomery (1972), "Probability Zones in Stochastic Project Networks," 

Journal of Systems Management, Vol. 23, No. 8, pp. 36-42. 

6. Montgomery, D. C. and P. J. Klatt (1972), "Minimum Cost Multivariate Quality Control Tests," 

AIIE Transactions, Vol. 4, No. 2, pp. 103-110 This paper also appeared in Proceedings of the 

AIIE Annual Conference, Anaheim, California, June 1972.

5. Ghare, P. M., D. C. Montgomery and W. C. Turner (1971), "Optimal Interdiction Policy for a 

Flow Network" Naval Research Logistics Quarterly, Vol. 18, No. 1, pp. 37-45.

4. Montgomery, D. C. (1970), "Adaptive Control of Exponential Smoothing Parameters by 

Evolutionary Operation," AIIE Transactions, Vol. 2, No. 3, pp. 268-269. 

3. Dickey, J. W. and D. C. Montgomery (1970) "A Simulation-Search Technique: An Example 

Application for Left-Turn Phasing," Transportation Research, Vol. 4, pp. 339-347.

2. Montgomery, D. C. (1969), "An Application of Statistical Forecasting Techniques in an Inventory 

Control Policy," Production and Inventory Management, Vol. 10, No. 1, pp. 66-73.

1. Montgomery, D. C. (1968), "An Introduction to Short-Term Forecasting," The Journal of 

Industrial Engineering, Vol. 11, No. 10, pp. 500-504. This paper was also selected for inclusion 

in Mathematical Models in Marketing, edited by Robert G. Murdick, International Textbook Co. 

(1971).

Chapters in Books

18. Hassler, E., Montgomery, D.C. and Silvestrini, R.T. (2015), “Bayesian D-Optimal Design Issues 

for Binomial Generalized Linear Model Screening Designs”, in Frontiers in Statistical Quality 

Control 11, edited by S. Knoth and W. Schmid, Springer AG, Switzerland, pp. 337-353.

17. Johnson, R.T., Montgomery, D.C. and Kennedy, K.S. (2012), “Hybrid Space-Filling Designs for 

Computer Experiments”, in Frontiers in Statistical Quality Control 10, edited by H.-J. Lenz, W. 

Schmid and P.-T. Wilrich, Springer, New York, pp. 287-301.

16. Englert, B.R., Rigdon, S.E., Borror, C.M., Montgomery, D.C., and Pan, R. (2012), “Optimal 

Designs for Multifactor Experiments for Exponentially Distributed Lifetimes”, in Frontiers in 

Statistical Quality Control 10, edited by H.-J. Lenz, W. Schmid and P.-T. Wilrich, Springer, New 

York, pp. 303-317.

15. Goldfarb, H. B. and Montgomery, D. C. (2006), “Graphical Methods for Comparing Response 

Surface Designs for Experiments with Mixture Components”, Chapter 14 in Response Surface 

Methodology and Related Topics, edited by André I. Khuri, World Scientific Publishing Co., 

Singapore.
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14. Montgomery, D. C. and Jennings, C. L. (2006), “An Overview of Industrial Screening 

Experiments”, Chapter 1 in Screening: Methods for Experimentation in Industry, Drug Discovery, 

and Genetics, Angela Dean and Susan Lewis, Editors, Springer, NY, pp. 1-20.

13. Montgomery, D. C. and Jennings, C. L. (2003), “Statistische Methoden in der Prozesskontrolle”, 

in Wahrscheinlichkeitstheorie, Stochastische Prozesse, Mathematische Statistik, F.E. Beichelt and 

D. C. Montgomery, Editors, B. G. Teubner Verlag, Weisbaden.

12. Mastrangelo, C. M. and Montgomery, D. C. (1999), “Process Monitoring with Autocorrelated 

Data”, in Statistical Process Monitoring and Optimization, edited by G. G. Vining and Sung H. 

Park, pp. 139-160, Marcel Dekker, Inc., New York.

11. Montgomery, D. C., Peck, E.A., and Simpson, J.R.(1998), “Multicollinearity and Biased 

Estimation in Regression”, in Handbook of Statistical Methods for Engineers and Physical 

Scientists, 2nd edition, edited by H.M. Wadsworth, Jr., McGraw-Hill, pp. 16.1-16.27.

10. Mastrangelo, C. M. and D. C. Montgomery (1996), "Time Series Analysis," Encyclopedia of 

Operations Research and Management Science, Kluwer Academic Publishers, Norwell, MA.

9. McCarville, D. R. and D. C. Montgomery (1996), “Optimizing Defect Levels and Losses from 

Gage Errors,” in Statistical Applications in Process Control, edited by J. B. Keats and D. C. 

Montgomery, Marcel Dekker, New York.

8. Montgomery, D. C. and G. C. Runger (1996), “Experimental Design Models with Random 

Components,” in Statistical Applications in Process Control, edited by J. B. Keats and D. C. 

Montgomery, Marcel Dekker, New York.

7. Montgomery, D. C. (1996), “Some Practical Guidelines for Designing an Industrial Experiment,” 

in Statistical Applications in Process Control, edited by J. B. Keats and D. C. Montgomery, 

Marcel Dekker, New York.

6. Messina, W. S., Montgomery, D. C., Keats, J. B. and G. C. Runger (1996), “Strategies for 

Statistical Monitoring of Integral Control for the Continuous Process Industries,” in Statistical 

Applications in Process Control, edited by J. B. Keats and D. C. Montgomery, Marcel Dekker, 

New York.

5. Montgomery, D. C. (1991), "Analyzing Location and Dispersion Effects from Designed 

Experiments: Same Examples," Statistical Process Control in Manufacturing, J. B. Keats and D. 

C. Montgomery, editors, Marcel Dekker, New York.

4. Montgomery, D. C. and E. A. Peck (1990), "Multicollinearity in Regression," Handbook of

Statistical Methods for Engineers, McGraw-Hill Book Co., H.M. Wadsworth, Jr., Editor, 

McGraw-Hill, New York.

3. Montgomery, D. C. and D. J. Friedman (1989), "Statistical Process Control in a ComputerIntegrated Manufacturing Environment," Statistical Process Control in Automated Manufacturing, 

J. B. Keats and N. F. Hubele, editors, Marcel Dekker, New York.

2. Johnson, L. A. and D. C. Montgomery (1979), "Forecasting with Exponential Smoothing and 

Related Methods," Forecasting, Vol. 12, TIMS Studies in the Management Sciences, NorthHolland, Amsterdam.

1. Johnson, L. A. and D. C. Montgomery (1978), "Production and Inventory Control," The 

Encyclopedia of Computer Science and Technology, Vol. 10, Marcel Dekker, New York.

Papers in Conference Proceedings
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37. Montgomery, D.C. (2014), “The Contribution of Six Sigma to the Development of Statistical 

Thinking in the Workplace”, Proceedings of the 9th International Conference on Teaching 

Statistics, Flagstaff, Arizona, July, the International Statistics Institute, pp. 1 – 5.

36. Shaukat, K., Montgomery, D.C. and Syrotiuk, V.R. (2011), “Adaptive Overhead Reduction via 

MEWMA Control Charts”, Proceedings of the 14th ACM International Conference on Modeling, 

Analysis and Simulation of Wireless and Mobile Systems (MSWiM'11), Miami, Florida, U.S.A., 

October 31-November 4, pp. 205-212. 

35. Johnson, R.T., Montgomery, D.C., Jones, B., and Fowler, J.W. (2008), “Comparing Designs for 

Computer Simulation Experiments,” Proceedings of the Winter Simulation Conference, pp. 463-

470, Miami, FL, Dec. 7-10.

34. Chatlani, V., Tylavsky, D. J., Montgomery, D.C., and Dyer, M, (2007), “Statistical Properties of 

Diversity Factors for Probabilistic Loading of Distribution Transformers,” 2007 North American 

Power Symposium, September, pp. 581-587.

33. Lin, Y.K., Pfund, M.E., Fowler, J.W., and Montgomery, D.C. (2006), “Classification of Parallel 

Machine Environments under Various Correlation Structures”, 36th International Conference on 

Computers and Industrial Engineering, Taipei, Taiwan, R.O.C., June 20-23, pp. 1253-1261

32. Hoskins, D.S., Colbourn, C.J., and Montgomery, D.C. (2005), “Software Performance Testing 

using Covering Arrays, Efficient Screening Designs with Categorical Factors”, Proceedings of the 

5

th International Workshop on Software Performance, WOPS 05, pp. 131-136.

31. Montgomery, D. C., Borror, C. M., and Lewis, S. L. (1997), “Analysis of Designed Experiments 

using SAS PROC GENMOD”, Proceedings of the Western Users of SAS Software, Universal City, 

CA, 22-24 October.

30. Rhoads, T. R. and D. C. Montgomery (1996), “Process Monitoring with Principal Components and 

Partial Least Squares,” Proceedings of the Industrial Engineering Research Conference, 

Minneapolis, MN.

29. Montgomery, D. C., Keats, J. B., and G. Rajavelu (1996), “Statistical Monitoring Techniques for 

Contamination Data,” Institute of Environmental Sciences-Proceedings, Orlando, Fl.

28. Montgomery, D. C. and M. L. Bowles (1995), “Multiple Response Optimization Methods,”

Proceedings of the Annual Conference on Applied Statistics, Atlantic City, NJ.

27. Myers, R. H. and D. C. Montgomery (1994), “Robust Design and Response Surface 

Methodology,” Proceedings of the 50th Conference on Applied Statistics, Atlantic City, NJ.

26. Montgomery, D. C. (1994), "Strategies for Integrating Statistical Process Control and Engineering 

Process Control", Proceedings of the Rutgers Conference on Computer Integrated Manufacturing 

in the Process Industries, Piscataway, NJ.

25. Mastrangelo, C. M., and D. C. Montgomery (1994), "Shift Detection Properties of MovingCenterline EWMA Control Schemes", Proceedings of the Industrial Engineering Research 

Conference, Atlanta, GA.

24. Montgomery, D. C., and J. E. Taggart (1993), "Selection of a Second Order Response Surface 

Design," Proceedings of the SAS Users Group International, New York, NY.

23. Montgomery, D. C., C. M. Mastrangelo, and C. A. Lowry (1993), "Statistical Process Monitoring 

for Dynamic Systems," Proceedings of the Industrial Engineering Research Conference, San 

Francisco, CA.

22. Lowry, C. A. and D. C. Montgomery (1992), "Multivariate Quality Control: Review and 

Enhancement," Proceedings of the Institute for Decision Sciences Conference, San Francisco, CA..

21. Montgomery, D. C. (1992), "Some Problems in Computer-aided Design of Experiments," 

Proceedings of the SAS Users Group International, Honolulu, HI.
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20. Montgomery, D. C. and C. M. Mastrangelo (1991), "Basic Properties of the EWMA," Proceedings 

of the Arizona State University Conference on Statistical Control and Design, Tempe, AZ.

19. Montgomery, D. C. (1991), "Experimental Design in Engineering Design and Development," 

Proceedings of the SAS Users Group International, New Orleans, LA.

18. Montgomery, D. C. (1989), "Rational Subgroups and Control Charts," Proceedings of the Arizona 

State University Conference on Statistical Control and Design, Tempe, AZ. 

17. Montgomery, D. C., Gardiner, J. S., and B. A. Pizzano (1987), "Statistical Process Control 

Methods for Detecting Small Process Shifts," Frontiers in Statistical Quality Control, H. J. Lenz et 

al., editors, Physica-Verlag, Heidelberg.

16. Montgomery, D. C. (1984), "Biased Estimation and Robust Regression," Proceedings of the 

American Association of Physicists in Medicine Midyear Topical Symposium, Mobile, AL.

15. Montgomery, D. C. (1983), "Statistical Consulting: Some Comments on Training of Statisticians," 

Proceedings of the SAS Users Group International, New Orleans, LA.

14. Montgomery, D. C., Heikes, R. G., and M. R. Scheffler (1981), "Probability Models for the 

Occurrence of Defects," Frontiers in Statistical Quality Control, H. J. Lenz, G. B. Wetherill and 

P.Th. Wilrich, Editors, Physica-Verlag, Vienna.

13. Montgomery, D. C. (1981), "Cost Based Acceptance Sampling Plans and Process Control 

Schemes," Proceedings of the AIIE Fall Conference, Washington, D. C.

12. Montgomery, D. C. and E. A. Peck (1980), "The Multicollinearity Problem in Regression," 

Proceedings of the Southeast Decision Sciences Conference, Orlando, FL.

11. Montgomery, D. C. and G. Weatherby (1979), "Factor Screening Methods in Computer 

Simulation," Proceedings of the Winter Simulation Conference, San Diego, CA.

10. Simms, E. D. and D. C. Montgomery (1977), "The Use of Discriminant Analysis for Risk 

Assessment in Operational Testing," Proceedings of the 16th Annual U.S. Army Operations 

Research Symposium, Ft. Lee, VA.

9. Russ, S. W., Jr., D. C. Montgomery, and H. M. Wadsworth, Jr., (1977) "A Cost Optimal Approach 

to Selection of Experimental Designs for Operational Testing Under Conditions of Constrained 

Sample Size," Proceedings of the 16th Annual U.S. Army Operations Research Symposium, Ft. 

Lee, VA.

8. Friese, W. F., Jr., and D. C. Montgomery (1977), "A Cost-Optimal Approach to Selecting a 

Fractional Factorial Design," Proceedings of the 16th Annual U.S. Army Operations Research 

Symposium, Ft. Lee, VA.

7. Brown, E. L. and D. C. Montgomery (1975), "An Application of Network Simulation to 

Operational Testing and Evaluation," Proceedings of the 14th Annual U. S. Army Operations 

Research Symposium, Ft. Lee, VA., November.

6. Montgomery, D. C., J. F. Mance and R. G. Heikes (1974), "An Economic Model of the Fraction 

Defective Control Chart with Multiple Assignable Causes," Transactions of the ASQC, Boston, 

MA.

5. Johnson, L. A. and D. C. Montgomery (1974), "On Dynamic Production Planning Models," 

Proceedings of the Southeast IDS Meeting, New Orleans, LA.

4. Marsh, J. D. and D. C. Montgomery (1973), "Optimal Procedures for Scheduling Jobs with 

Sequence Dependent Changeover Times on Parallel Processors," Proceedings of the AIIE Annual 

Conference, Chicago, IL.

3. Montgomery, D. C. and H. M. Wadsworth (1972), "Some Techniques for Multivariate Quality 

Control Applications," Transactions of the ASQC, Washington, D.C.
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2. Ghare, P. M. and D. C. Montgomery (1970), "Flow Management in Transportation Networks," 

Proceedings of the Fifth International Conference on Operations Research, Venice, Italy, June 

1969, Tavistock Publishers, Ltd..

1. Montgomery, D. C. (1971), "Stochastic Capacity Decision Models for Production Facilities," 

Proceedings of the AIIE Annual Conference, Boston, MA.

Papers and Presentations at Meetings

193. Hill, R. R., Ahner, D, Dillard, D. and Montgomery, D. C. (2017), “Examining Potential 

Reductions in Wind Tunnel Testing Data Requirements”, Presented at the Quality and 

Productivity Research Conference, Storrs, CT, June, 2017.

192. Hassler, E., Montgomery, D.C., and Silvestrini, R. (2016), “Design of Experiments for 

Generalized Linear Models with Random Blocks”, Invited presentation at the Quality and 

Productivity Research Conference, Tempe, AZ 13-16 June, 2016.

191. Burke, S.E., Anderson-Cook, C.M., Borror, C.M., and Montgomery, D.C., (2016), “A Layered 

Pareto Front Approach to Search for the Top N Subpopulations in a Stockpile”, Invited 

presentation at the Quality and Productivity Research Conference, Tempe, AZ 13-16 June, 

2016.

190. Montgomery, D.C. (2016), “Modern Experimental Design or The Flight of The Phoenix”, 

Invited plenary address at the JMP Discovery Summit Europe, Amsterdam, 15-17 March 

2016.

189. Mancenido, M.V., Montgomery, D.C., and Pan, R. (2015), “Performance of Standard Mixture 

Designs in Modeling Ordinal Responses”, presentation at the 2015 INFORMS annual meeting, 

Philadelphia.

188. Montgomery, D.C. (2015), “Design of Experiments: A Key to Successful Innovation”, Invited 

presentation at the 59th Annual Fall Technical Conference, Houston, TX. 8-9 October.

187. Burke, S.E., Montgomery, D.C., Borror, C.M., and Silvestrini, R.T. (2015), “Optimal Designs 

for Dual Response Systems”, Invited presentation at the 59th Annual Fall Technical 

Conference, Houston, TX. 8-9 October.

186. Weese, M.L., Montgomery, D.C. and Ramsey, P.J. (2015), “Analysis Strategies for Definitive 

Screening Designs”, Invited presentation at the 59th Annual Fall Technical Conference, Houston, 

TX. 8-9 October.

185. Montgomery, D.C. (2015), “Modern Experimental Design or The Flight of The Phoenix”, 

Invited plenary address at the JMP Discovery Summit, San Diego, CA, 14-17 September 2015.

184. Ramsey, P., Weese, M., and Montgomery, D.C. (2015), “Model Selection Strategies for 

Definitive Screening Designs Using JMP Pro and R”, Invited presentation at the JMP 

Discovery Summit, San Diego, CA, 14-17 September 2015.

183. Montgomery, D.C. (2015), “Teaching Design of Experiments to Engineers and Scientists”, 

invited presentation at the Design and Analysis of Experiments Conference 2015, Cary NC, 4-

6 March 2015.
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182. Mancendito, M., Montgomery, D.C., and Pan, R. (2014), “Modeling Ordered Categorial 

Responses in Mixture Expoeriments”, Invited presentation at the 58th Annual Fall Technical 

Conference, Richmond, Va., 2-3 October.

181. Stone, B.B., Montgomery, D.C., Silvestrini, R., and Jones, B. (2014), “No Confounding 

Designs of 20 and 24 Runs: Alternatives to Resolution IV Screening Designs”, Invited 

presentation at the 58th Annual Fall Technical Conference, Richmond, Va., 2-3 October.

180. Montgomery, D.C. (2014), “The Contribution of Six Sigma to the Development of Statistical 

Thinking in the Workplace”, Invited presentation at the 9th International Conference on 

Teaching Statistics, Flagstaff, AZ, 13-18 July.

179. Montgomery, D.C. (2014), “Innovation, Six Sigma and Quality Technology”, Invited 

presentation at the 2014 Joint Research Conference, Seattle, Washington, 24-26 June.

178. Shinde, S.M., Montgomery, D.C. and Jones, B. (2013), “Projection Properties of NoConfounding Designs for Six, Seven, and Eight Factors in Sixteen Runs”, invited presentation 

at the 57th annual Fall Technical Conference, San Antonio, Texas, 18 October 2013.

177. Montgomery, D.C. (2013), “Stu Hunter’s Contributions to Statistics and Quality Engineering”, 

invited presentation at the 57th annual Fall Technical Conference, San Antonio, Texas, 17 

October 2013.

176. Jones, B. and Montgomery, D.C. (2013), invited short course, “Recent Developments in 

Design of Experiments”, presented at the 57th annual Fall Technical Conference, San Antonio, 

Texas, 16 October 2013.

175. Jones, B. and Montgomery, D.C. (2013), “Stu Hunter’s Contributions to Statistics and Quality 

Engineering”, invited presentation in the Technometrics session, Joint Statistical Meetings, 

Montreal, Canada, August 2013.

173. Montgomery, D.C. (2013), “Stu Hunter’s Contributions to Statistics and Quality Engineering”, 

invited presentation at the conference honoring Stu Hunter’s 90th birthday, Amsterdam, March 

2013.

172. Chen, Y., Montgomery, D.C., Fowler, J., and Pfund, M. (2013), “Using Regression Splines to 

Parameterize Composite Dispatching Rules”, presented at the 43rd International Confernce on 

Computers and Industrial Engineering, 16-18 October, 2013, Hong Kong.

171. Hassler, E., Montgomery, D.C., and Silvestrini, R. (2013), “Bayesian D-Optimal Design Issues 

for Generalized Linear Models”, invited presentation at the 11th Workshop on Intellegent 

Statistical Quality Control, Sydney, Australia, 20-23 August, 2013.

170. Rigdon, S., Pan, R., Montgomery, D.C. and Borror, C.M. (2012), “Design of Experiments for 

Reliability Improvement”, invited presentation at the 56th Annual Fall Technical Conference, 

St. Louis, MO, 3-4 October, 2012.

169. Timmer, D., Gonzalez, Montgomery, D.C. and Borror, C.M. (2012), “DOE Education 

Strategies”, invited presentation at the 56th Annual Fall Technical Conference, St. Louis, MO, 

3-4 October, 2012.

168. Montgomery, D.C. (2012), “Methods and Applications of Generalized Linear Models”, oneday short course presented at the 56th Annual Fall Technical Conference, St. Louis, MO, 3 

October, 2012.
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167. Montgomery, D.C. (2012), “Experiments with Physical and Resource Constraints”, Invited 

presentation at the 2012 Joint Statistical Meetings, 28 July – 2 August, San Diego, CA.

166. Montgomery, D.C. (2012), “Innovation, Statistics and Quality Technology”, Invited keynote 

address presented at the Fourth International Conference on Lean Six Sigma, Glasgow, 

Scotland, 26-27 March, 2012

165. Timmer, D., Gonzalez, M., Borror, C. and Montgomery, D.C. (2011), “Web-Based Active 

Learning Laboratories for Teaching Control Charts”, presented at the 55th Annual Fall 

Technical Conference, Kansas City, October 13-14, 2011.

164. Krueger, D. and Montgomery, D.C. (2011). “Integrating CART and Generalized Linear 

Models for Improving process Understanding”, presented at the 55th Annual Fall Technical 

Conference, Kansas City, October 13-14, 2011.

163. Montgomery, D.C. (2011), “Generating and Assessing Exact G-optimal Designs” , Invited 

presentation at the Iassic Newton Institute for Mathematical Sciences, Cambridge, Design and 

Analysis of Experiments Workshop, 30 August – 2 September, 2011.

162. Montgomery, D.C. (2011), “Design of Experiments: New Methods and How to Use Them in 

Design, Development and Decision-making”, Inaugural W.L. Gore lecture at the Alfred Lerner 

College of Business and Economics, The University of Delaware, 16 March 2011. 

161. Shinde, S. and Montgomery, D.C. (2010), “Analysis Methods for Non-regular Fractional 

Factorial Designs, presented at the INFORMS Annual Meeting, Austin Texas, 7 – 10 

November.

160. Monroe, E., Pan, R., Montgomery, D.C., Borror, C.M., and Anderson-Cook, C.M. (2010), 

“Sensitivity Analysis of Optimal Designs for Accelerated Life Testing”, Journal of Quality 

Technology invited paper session, INFORMS Annual Meeting, Austin Texas, 7 – 10 

November.

159. Jones, B. and Montgomery, D.C. (2010), “Workshop on Modern Experimental Design 

Methods”, presented at the 2010 Army Conference on Applied Statistics” Cary, NC, 18-19 

October.

157. Johnson, R.T., Montgomery, D.C., Jones, B., and Parker, P.A. (2010), “Comparing Computer 

Experiments for Fitting High-Order Polynomial Models”, invited presentation at the Journal of 

Quality Technology Session at the 54th Annual Fall Technical Conference, Birmingham, 

Alabama, 7-8 October.

156. Fish, B.R., Rigdon, S.E., Borror, C.M., Montgomery, D.C. and Pan, R. (2010), “Optimal 

Designs for Multifactor Life Testing Experiments”, invited presentation at the 10th

International Workshop on Intelligent Statistical Quality Control, Seattle, WA 18-20 August.

155. Johnson, R.T., Montgomery, D.C. and Kennedy, K.S. (2010), “Hybrid Space-Filling Designs 

for Computer Experiments”, invited presentation at the 10th International Workshop on 

Intelligent Statistical Quality Control, Seattle, WA 18-20 August.

154. Rigdon, S.E., Montgomery, D.C., Pan, R., and Borror, C.M. (2010), “Optimal Design for 

Multi-Factor Life-Testing Experiments”, presented at the Joint Statistical Meetings, 

Vancouver BC, Canada, 31 July-5 August.
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153. Montgomery, D.C. (2010), “A New Framework for Teaching Design of Experiments”, Invited 

Panel Discussion on Future Developments in Experimental Design at the Joint Statistical 

Meetings, Vancouver BC, Canada, 31 July-5 August.

152. Laungrungrong, B., Borror, C.M., and Montgomery, D.C. (2009), “Multivariate PoissonDistributed Control Charts”, 53rd Annual Fall Technical Conference, Indianapolis, 7-10 

October.

151. Capehart, S.R., Kulahci, M, Keha, A., and Montgomery, D.C. (2009), “Designing Fractional 

Factorial Split-Plot Experiments using Integer Programming”, 53rd Annual Fall Technical 

Conference, Indianapolis, 7-10 October.

150. Montgomery, D.C. (2009), “Critical Components of a Quality and Reliability Engineering 

Graduate Program”, presentation and panel discussion at the INFORMS annual meeting, San 

Diego CA, 11-14 October.

149. Montgomery, D.C. (2009),”Panel Discussion: Information and Messages from Editors of QSR 

Journals”, presented at the INFORMS annual meeting, San Diego CA, 11-14 October.

148. Montgomery, D.C. (2009), “Generating and Assessing Exact G-Optimal Designs: (Is it worth

it?)”, invited presentation at the Joint Statistical Meetings, 3 August, Washington, DC.

147. Montgomery, D.C. (2009), “Modern Experimental Design methods and Their Impact on 

Business and Industry”, invited tutorial session at the INFORMS Regional Conference, 24 April, 

Tempe AZ.

146. Jones, B. Johnson, R.T., and Montgomery, D.C. (2009), “Comparing Space Filling Designs for 

Gaussian Process Models”, presented at the INFORMS Regional Conference, 24 April, Tempe 

AZ.

145. Broyles, J.R., Cochran, J.K., and Montgomery, D.C. (2009), “A Markov Decision Process for 

Hospital Inpatient Staffing”, presented at the INFORMS Regional Conference, 24 April, Tempe 

AZ.

144. Montgomery, D.C. (2008), “Modern Experimental Design and its Impact on Design for Six 

Sigma”, Invited keynote Address at the Third International Conference on Six Sigma, 

Edinburgh, Scotland, 15-16 December, 2008.

143. Johnson, R.T., Montgomery, D.C., and Jones, B. (2008), “Comparing Designs used for Fitting 

Gaussian process Models”, invited presentation at the 52nd Annual Fall Technical Conference, 

Phoenix, AZ, 9-10 October, 2008.

142. Krueger, D. and Montgomery, D.C. (2008), “Semiconductor Yield Modeling using Generalized 

Linear Models”, invited presentation at the 52nd Annual Fall Technical Conference, Phoenix, 

AZ, 9-10 October, 2008.

141. Montgomery, D. C. (2008), “Some Experiences with Designing Experiments”, Friday 

Luncheon Address, 52nd Annual Fall Technical Conference, Phoenix, AZ, 9-10 October, 2008.

140. Montgomery, D.C. (2008), “Statistical Design Techniques for Robust Design”, invited 

presentation at the European Conference on Design of Experiments, Antwerp, Belgium, 

January, 2008.

139. Montgomery, D.C. (2007), “A Modern Framework for Enterprise Excellence”, Deming 

Lecture, presented at the Joint Statistical Meetings, Salt Lake City, 31 July.
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138. Montgomery, D.C. (2007), “Statistics and Science, Business and Industry”, Invited keynote 

presentation at the JMP Users’ Conference, Cary NC, 13 June 2007.

137. Montgomery, D. C. (2007), “Teaching DOX: Some Adventures and Lessons Learned”, Invited 

Plenary Presentation at the ASA Quality and Productivity Research Conference, Santa Fe, 

New Mexico, 4 June 2007.

136. Montgomery, D. C. (2006), “Logistic Regression”, invited short course given at the 50th

Annual ASA/ASQ Fall Technical Conference, Columbus OH, October, 2006

135. Montgomery, D. C. (2006), “Comparison and Evaluation of Designs”, invited presentation at 

the Joint Statistical Meetings, Seattle, WA, August, 2006.

134. Montgomery, D. C. (2006), “The Impact of Statistics on Science, Business and Industry”, 

invited Keynote Address, meeting of the International Statistical Institute, Lima, Peru, January, 

2006.

133. Montgomery, D. C. (2005), “Some Trends in Six-Sigma Education”, invited presentation at 

the 49th Annual ASA/ASQ Fall Technical Conference, St. Louis MO, 20-21 October, 2005.

132. Lawson, C. A. and Montgomery, D. C. (2005), “Business Process Characterization using 

Categorical Data Models”, invited presentation at the 49th Annual ASA/ASQ Fall Technical 

Conference, St. Louis MO, 20-21 October, 2005.

131. Montgomery, D. C. (2005), “Criteria for Designing Experiments: Some Practical 

Considerations”, invited presentation at the Los Alamos National Labs Design and Analysis of 

Experiments Conference, Santa Fe, NM,.11-14 October, 2005. 

130. Montgomery, D. C. and Goldfarb, H. B. (2005),”Graphical Methods for the Evaluation of 

Mixture and Mixture-Process Designs”, invited presentation in the 50th anniversary of mixture 

experiments session, Joint Statistical Meetings, Minneapolis, MN, 7-11 August, 2005.

129. Montgomery, D. C. (2005), “Statistics and the Transformation of Science, Business and 

Industry”, invited keynote presentation at the 5th Annual ENBIS Conference, University of 

Newcastle, Newcastle-Upon-Tine, UK, 14-16 September, 2005.

128. Park, You-Jin, Richardson, D. E., Borror, C. M., Anderson-Cook, C. M., and Montgomery, D. 

C. (2004), “Prediction Variance Properties of Second-Order Response Surface Designs for 

Cuboidal Regions”, invited paper presented at the 48th ASA/ASQ Fall Technical Conference, 

Roanoke, VA, 14-15 October. 

127. Robinson, T. J., Wulff, S. S., Montgomery, D. C., and Kurhi, A. I (2004), “A Response 

Surface Approach to Robust Parameter Design using Generalized Linear Models”, invited 

paper presented at the 48th ASA/ASQ Fall Technical Conference, Roanoke, VA, 14-15 

October.

126. Heredia-Langner, A., Montgomery, D. C., Carlyle, W. M., and Borror, C. M. (2004), “ModelRobust Optimal Designs: A Genetic Algorithm Approach”, invited paper presented in the 

Journal of Quality Session at the 48th ASA/ASQ Fall Technical Conference, Roanoke, VA, 14-

15 October.

125. Chung, J., Goldfarb, H. B., and Montgomery, D. C. (2004), “Statistical Designs for MixtureProcess Variable Experiments with Control and Noise Variables”, invited paper presented at 

the 48th ASA/ASQ Fall Technical Conference, Roanoke, VA, 14-15 October.

124. Holcomb, D. R., Montgomery, D. C., and Lurponglukana, N. (2004), “A Bootstrap Method for 

Determining Active factors in Unreplicated Factorial Designs”, invited paper presented at the 

48th ASA/ASQ Fall Technical Conference, Roanoke, VA, 14-15 October.
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123. Woodall, W. H., Spitzner, D., Montgomery, D. C., and Gupta, S. (2004), “Using Control 

Charts to Monitor Product and Process Quality Profiles”, invited presentation at the Joint 

Statistical Meetings, Toronto, CA 8-12 August.

122. Kowalski, K. M., Vining, G. G., Montgomery, D. C. and Borror, C. M. (2004), Modeling the 

Process Mean and Variance from a CCD”, contributed paper presentation at the Joint 

Statistical Meetings, Toronto, CA 8-12 August.

121. Montgomery, D. C., Jearkpaporn, D., Runger, G. C., and Borror, C. M. (2004), “Monitoring 

Mean Shifts for Multistage Processes using Generalized Linear Models”, invited presentation 

at the Joint Statistical Meetings, Toronto, CA 8-12 August.

120. Montgomery, D. C. (2004), “Six Sigma: New Directions for DOX”, invited presentation at the 

2004 Quality and Productivity Research Conference, 19-21 May, Durham, NC.

119. Montgomery, D. C. (2004), “Designing Experiments: Some Adventures and Lessons 

Learned”, invited seminar at the Industrial Statistics Research Center, University of 

Newcastle-Upon-Tyne, 23 April, Newcastle-Upon-Tyne, UK.

118. Montgomery, D. C. (2004), “Statistics and Statisticians in Today’s Business World”, keynote 

address, Royal Statistical Society Conference on Business Improvement Through Statistical 

Thinking, 21-22 April, Coventry, UK.

117. Goldfarb, H. B., Anderson-Cook, C. M., Borror, C. M., and Montgomery, D. C. (2003), 

“Graphical Methods to Assess the Prediction Capability of Mixture and Mixture-Process 

Designs”, invited presentation at the 47th Annual ASA/ASQ Fall Technical Conference, El 

Paso, TX, 16-17 October 2003.

116. Drain, D. C., Borror, C. M., Montgomery, D. C., and Anderson-Cook, C. M. (2003), “The 

Effect of Correlated Noise Variables on Designed Experiments”, invited presentation at the 

47th Annual ASA/ASQ Fall Technical Conference, El Paso, TX, 16-17 October 2003.

115. Burdick, R. K., Borror, C. M., and Montgomery, D. C. (2003), “A Review of Methods for 

Measurement Systems Capability Analysis”, invited presentation at the Journal of Quality 

Technology session, 47th Annual ASA/ASQ Fall Technical Conference, El Paso, TX, 16-17 

October.

114. Kowalski, S., Vining, G.G., Montgomery, D.C., and Borror, C.M. (2003), Modifying a Central 

Composite Design to Model the Mean and Variance Within a Split-Plot Structure”, invited 

presentation at the Joint Statistical Meetings, San Francisco, CA 3-7 August 2003.

113. Robinson, T. J., Myers, R. H., and Montgomery, D. C. (2003), “Analysis Considerations in 

Industrial Split-Plot Experiments when the Responses are Non-normal”, invited presentation at 

the Joint Statistical Meetings, San Francisco, CA, 3-7 August 2003.

112. Montgomery, D.C., Burdick, R. K., Sebert, D.M., Shah, H. K., Molnau, W., Lawson, C., 

Zenzen, F., and Holcomb, D. R. (2003), “Teaching Six-Sigma Concepts in a University 

Setting”, invited presentation at the Joint Statistical Meetings, San Francisco, CA 3-7 August 

2003.

111. Drain. D. C., Montgomery, D. C., and Borror, C.M. (2003), “The Application of Hybrid 

Heuristic Optimization in Design of Experiments”, invited presentation at the Joint Statistical 

Meetings, San Francisco, CA 3-7 August 2003.

110. Montgomery, D. C. (2003), “The Modern Practice of Statistics in Business and Industry”, the 

Isobel Loutit Invited Plenary Address on Business and Industrial Statistics, 33rd Annual 

Meeting of the Statistical Society of Canada, Halifax, NS, 8-11 June 2003.

109. Jearkpaporn, D., Eastman, S. A., Gonzalez-Altamirano, G., Holcomb, D. R., Heredia-Langner, 

A., Borror, C. M., and Montgomery, D. C. (2003), “Using Supersaturated Designed 
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Experiments for Factor Screening and Robustness Analysis in the Design of a Semiconductor 

Clock Circuit “, contributed paper presented at the 2003 ASA Quality and Productivity 

Research Conference, Yorktown Heights, NY, 21-23 May 2003.

108. Anderson-Cook, C. M., Ozol, A., Myers, R. H., and Montgomery, D. C. (2003), “Fraction of 

Design Space Plots for Generalized Linear Models”, invited paper presented at the 2003 ASA 

Quality and Productivity Research Conference, Yorktown Heights, NY, 21-23 May 2003.

107. Goldfarb, H. B., Borror, C. M., Montgomery, D. C., and Anderson-Cook, C. M. (2003), 

“Graphical Methods to Assess the Prediction Capability of Mixture and Mixture-Process 

Designs”, invited paper presented at the 2003 ASA Quality and Productivity Research 

Conference, Yorktown Heights, NY, 21-23 May 2003.

106. Montgomery, D. C. (2003), “Research Needs in Experimental Design”, Invited Presentation at 

the Journal Editors’ Session, INFORMS National Meeting, San Jose, CA, 18-20 November 

2002.

105. Janakirim, M. and Montgomery, D.C. (2002), “Integrating Engineering Process Control and 

Statistical Process Control for Effective APC for Semiconductor Processes”, Invited 

Presentation at the ASQ/ASA Fall Technical Conference, Valley Forge, PA, 17-18 October 

2002.

104. Jearkpaporn, D., Montgomery, D. C., Runger, G. C., and Borror, C. M. (2002), “Process 

Monitoring for Correlated Gamma Distributed Variables using GLM Based Control Charts”, 

Invited Presentation at the ASQ/ASA Fall Technical Conference, Valley Forge, PA, 17-18 

October 2002.

103. Montgomery, D. C. (2002), “Education of Future (Industrial) Statistical Consultants”, Invited 

Presentation at the Joint Statistical Meetings, New York, 11-15 August 2002.

102. Kowalski, S., Borror, C. M., and Montgomery, D. C. (2002), “The Path of Steepest Ascent in 

Split-Plot Experiments”, Contributed Presentation at the Joint Statistical Meetings, New York, 

11-15 August 2002.

101. Montgomery, D. C. (2002), “Teaching Experimental Design to Engineers: Some Experiences 

and Advice”, Invited Presentation at the 6th International Conference on Teaching Statistics, 

Cape Town, South Africa, 7-12 July 2002. 

100. Fowler, J. A. and Montgomery, D. C. (2002), “The Future of the IERC”, Presentation at the 

Industrial Engineering Research Conference, Orlando, Florida, 18-19 May 2002.

99. Montgomery, D. C. (2002), “Some Thoughts About Research”, Invited Presentation at the 

First IIE Doctoral Colloquium, Industrial Engineering Research Conference, Orlando, Florida, 

18-19 May 2002.

98. Montgomery, D. C. (2002), “A Retrospective on Response Surface Methodology”, invited 

presentation at the Virginia Tech Conference on RSM in Honor of Professor Raymond H. 

Myers, Blacksburg, Virginia, 19-20 April 2002.

97. Wisnowski, J. W., Runger, G. C., and Montgomery, D. C. (2001), “Enhanced Analysis of 

Factorial Designs with Regression Trees”, Invited Presentation at the 45th Annual Fall 

Technical Conference, 18-19 October, Toronto, Canada.

96. Skinner, K. R., Runger, G. C., and Montgomery, D. C. (2001), Multivariate Control Charts for 

Discrete Data”, Invited Presentation at the 45th Annual Fall Technical Conference, 18-19 

October, Toronto, Canada.

95. Montgomery, D. C. (2001), Invited Panelist for the Session “The 50th Anniversary of Response 

Surface Methodology”, 45th Annual Fall Technical Conference, 18-19 October, Toronto, 

Canada.
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94. Holcomb, D. R., Jr. and Montgomery, D. C. (2001), “Some Difficulties in Analyzing PlackettBurman Design with Interactions”, Invited Presentation at the 45th Annual Fall Technical 

Conference, 18-19 October, Toronto, Canada.

93. Heredia-Langner, A., Carlyle, W. M., and Montgomery, D. C. (2001), “Genetic Algorithms for 

the Construction of D-Efficient Designs”, Invited Presentation at the 45th Annual Fall 

Technical Conference, 18-19 October, Toronto, Canada.

92. Rejavelu, G., Montgomery, D. C., and Vining, G. G. (2001), “Graphical Design Evaluation 

Techniques for Constrained Mixture Experiments”, Invited Presentation at the 45th Annual 

ASQ/ASA Fall Technical Conference, 18-19 October, Toronto, Canada.

91. Heredia-Langner, A., Carlyle, W. M., and Montgomery, D. C. (2001), “Model-Robust Optimal 

Designs Using Genetic Algorithms”, Invited Presentation at the Joint Statistical Meetings, 5-9 

August, Atlanta GA.

90. Myers, R. H. and Montgomery, D. C. (2001), “Analysis of Designed Experiments using 

GLMs”, Invited presentation at the Joint Statistical Meetings, Atlanta GA, 5-9 August.

89. Myers, R. H. and Montgomery, D. C. (2001), “Generalized Linear Models and Response 

Surface Methods”, Invited presentation at the ASA Quality and Productivity Research

Conference, Austin TX, 22-25 May.

88. Holcomb, D. R., Montgomery, D. C., and Carlyle, W. M. (2000), “Supersaturated Designs in 

Product Design and Development”, Invited presentation at the 44th Annual ASQC/ASA Fall 

Technical Conference, Minneapolis, MN, 12-13 October, 2000.

87. Somerville, S. E., Montgomery, D. C., and Runger, G. C. (2000), “Filtering and Smoothing 

Methods for Mixed Particle Count Distributions”, Invited presentation at the 44th Annual 

ASQ/ASA Fall Technical Conference, Minneapolis, MN, 12-13 October, 2000.

86. Wisnowski, J. W., Simpson, J. R., Montgomery, D. C., and Runger, G. C. (2000), “Regressor 

Variable Selection for Contaminated Data Sets”, Invited presentation at the 44th Annual 

ASQ/ASA Fall Technical Conference, Minneapolis, MN, 12-13 October, 2000.

85. Montgomery, D. C., Loredo, E. N., Jearkpaporn, D., and Testik, M. C. (2000), “Experimental 

Designs for Constrained Regions”, Invited presentation at the 44th Annual ASQ/ASA Fall 

Technical Conference, Minneapolis, MN, 12-13 October, 2000.

84. Montgomery, D. C. (2000), “Some Opportunities and Challenges for Industrial Statisticians” 

(Invited Keynote Address), Industrial Statistics in Action 2000, conference at the University of 

Newcastle-Upon-Tyne, United Kingdom, 8-10 September, 2000.

83. Lewis, S. M., Montgomery, D. C. and Myers, R. H. (1999), “The Analysis of Designed 

Experiments using Generalized Linear Models”, Invited presentation at the 43rd Annual Fall 

Technical Conference, Houston Texas, October 14-15.

82. Carlyle, W. M., Montgomery, D. C. and Runger, G. C. (1999), “Optimization Problems and 

Methods in Quality Control and Improvement”, Journal of Quality Technology session -

invited presentation at the 43rd Annual Fall Technical Conference, Houston Texas, October 14-

15.

81. Borror, C. M., Keats, J. B. and Montgomery, D. C. (1999), “Control Charts for Low Rates of 

Process Nonconformance”, Invited presentation at the 43rd Annual Fall Technical Conference, 

Houston Texas, October 14-15.

80. Lanning, J., Montgomery, D. C. and Runger, G. C. (1999), “Adaptive Methods for Monitoring 

Fractionally Sampled Multiple Stream Processes”, Invited presentation at the 43rd Annual Fall 

Technical Conference, Houston Texas, October 14-15.
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79. Wisnowski, J. W., Montgomery, D. C. and Simpson, J. R. (1999), “A Comparative Analysis of 

Multiple Outlier Detection Procedures in the Linear Regression Model”, Invited presentation 

at the 43rd Annual Fall Technical Conference, Houston Texas, October 14-15.

78. Montgomery, D. C. (1999), “Statistical Methods for Process Robustness Studies”, Inyong Ham 

Distinguished Lecture, Department of Industrial and Manufacturing Engineering, Pennsylvania 

State University, November 11. 

77. Montgomery, D. C. (1999), “Statistical Methods for Achieving Six-Sigma Results”, Invited 

presentation at the Pharmaceutical and Medical Device Industries Conference on Six-Sigma, 

Institute of International Research, Philadelphia, PA, September 23-24.

76. Vining, G. G., Kowalski, S. L. and Montgomery, D. C. (1999), “Hard-to-Change Design 

Variables in a Response Surface Setting”, Invited presentation at the Joint Statistical Meetings, 

Baltimore, MD, August 8-12

75. Montgomery, D. C. (1999), “Multiple Response Optimization Methods”, Invited presentation 

at the Joint Statistical Meetings, Baltimore, MD, August 8-12.

74. Montgomery, D. C. (1998), “A Perspective on Models and the Quality Sciences: Some 

Challenges and Future Directions”, W. J. Youden Memorial Address presented at the 42nd 

Annual ASQ/ASA Fall Technical Conference, 22-23 October, Corning, NY.

73. Zimmer, L. S., Montgomery, D. C., and Runger, G. C. (1998), “Some Guidelines for the 

Application of Adaptive Control Charts”, Invited presentation at the 42nd Annual ASQC/ASA 

Fall Technical Conference, 22-23 October, Corning, NY.

72. Borror, C. M., Montgomery, D. C., and Myers, R. H. (1998), “Optimal Design Strategies for 

Experiments Involving Noise Variables”, Invited presentation at the 42nd Annual ASQC/ASA 

Fall Technical Conference, 22-23 October, Corning, NY.

71. Montgomery, D. C. (1998), “Some Challenges and Opportunities for Industrial Statisticians” 

Invited presentation at a Panel Discussion on Emerging Issues and Directions in Quality 

Improvement, INFORMS, Seattle Washington, 27 October.

70. Montgomery, D. C. (1998), “Designed Experiments for Product and Process Development: 

Some Examples”, Invited presentation at INFORMS, Seattle Washington, 27 October.

69. Montgomery, D. C. (1998), “Experimental Design for Process and Product Design and 

Development” Invited Keynote Address, Royal Statistical Society, Glasgow Scotland, 11 

September.

68. Montgomery, D. C. and Vining, G. G. (1998), “Methods and Applications of Generalized 

Linear Models”, Invited Short Course presented for the Section on Engineering and Physical 

Sciences, Joint Statistical Meetings, Dallas, TX, 11 August.

67. Montgomery, D. C. (1998), “Some Challenges for Industrial Statisticians”, Invited 

Presentation at the Joint Statistical Meetings, Dallas, TX, 10 August.

66. Montgomery, D. C. (1997), “Generalized Linear Models and Designed Experiments”, Invited 

plenary presentation at the Applied Probability and Statistics Day, Johns Hopkins University 

Applied Physics Laboratory, 18 October, Laurel, MD. 

65. Sebert, D. M., Montgomery, D. C., and D. A. Rollier (1997), “Identifying Multiple Outliers 

and Influential Subsets in Linear Regression: A Clustering Approach”, presented at the 41st

Annual ASQ/ASA Fall Technical Conference, 16-17 October, Baltimore, MD. 

64. Montgomery, D. C. (1997), “Some Aspects of Generalized Linear Models for Designed 

Experiments”, Plenary Address, Nineteenth Annual Midwest Biopharmaceutical Statistics 

Workshop, Ball State University, Muncie, Indiana. 
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63. Montgomery, D. C. (1997), “Response Surface Methodology”, invited tutorial presented at the 

Southern California American Statistical Association Applied Statistics Workshop, Long 

Beach, California.

62. Montgomery, D. C., and G. C. Runger (1996), “Multivariate Control Charts and Process 

Monitoring”, invited short course at the ASQ/ASA Fall Technical Conference, Scottsdale, AZ, 

(sponsored by the Statistics Division of ASQ).

61. Montgomery, D. C. (1996), “Multiple Response Optimization Methods,” invited presentation 

at the 2nd Congress of the International Federation of Nonlinear Analysts, Athens, Greece.

60. Montgomery, D. C. (1995), “Response Surface Methods and Designs,” invited short course at 

the ASQC/ASA Fall Technical Conference, St. Louis, MO, (sponsored by the Statistic 

Division of ASQC).

59. Montgomery, D. C. (1994), "Regression Analysis," invited short course at the ASQC/ASA 

Fall Technical Conference, Birmingham, Alabama, (sponsored by the Statistics Division of 

ASQC).

58. Montgomery, D. C. (1994), "Design of Experiments," invited short course at the 40th U.S. 

Army Design of Experiments Conference, U.S. Military Academy, West Point, NY.

57. Montgomery, D. C. (1994), "Statistical Process Control for the Process Industries," invited 

short course at the Joint Statistical Meetings, Toronto, Canada, (sponsored by the Quality and 

Productivity Section of ASA).

56. Mastrangelo, C. M., and D. C. Montgomery (1994), "Shift Detection Properties of MovingCenterline EMWA Control Schemes", presented at the IIE Research Conference, Atlanta, GA.

55. Montgomery, D. C. (1994), "The Industrial Engineer and the Quality Improvement Sciences: 

Have We Missed an Opportunity?", invited Keynote Address at the 8th Israeli Industrial 

Engineering Conference, Beer Sheva, ISRAEL.

54. Montgomery, D. C. (1994), "Strategies for Integrating Statistical Process Control and 

Engineering Process Control", presented at the Conference on Computer Integrated 

Manufacturing in the Process Industries, Rutgers University.

53. Montgomery, D. C. (1993), "Planning, Conducting, and Analyzing Industrial Experiments," 

invited presentation at the 49th Annual Conference on Applied Statistics, Atlantic City, NJ.

52. Montgomery, D. C. (1993), "Solutions for Customer-Driven Quality Problems with Design of 

Experiments", invited tutorial at the Fall ORSA/TIMS Conference.

51. J. B. Keats, D. C. Montgomery, G. C. Runger, and W. S. Messina (1993), "Strategies for 

Integrating Statistical Process Control with Feedback (PID) Controllers", presented at the 

ASQ/ASA Fall Technical Conference, Rochester, NY.

50. Del Castillo, E. and D. C. Montgomery (1993), "Methods for Finite-Horizon Process Control: 

"Q" Charts and Alternative Techniques", presented at the ASQ/ASA Fall Technical 

Conference, Rochester, NY.

49. Montgomery, D. C. and J. A. Heinsman (1993), "Optimization of Product Formulation Using 

Mixture Experiments," invited presentation at the ORSA/TIMS Conference, Chicago, IL.

48. Montgomery, D. C., C. M. Mastrangelo and C. A. Lowry (1993), "Statistical Process 

Monitoring for Aluminum Smelting," invited presentation at the 10th Annual Quality and 

Productivity Research Conference, Knoxville, TN.

47. Montgomery, D. C., C. M. Mastrangelo and C. A. Lowry (1992), 'Statistical Process 

Monitoring for Dynamic Systems," invited presentation at the IIE Research Conference, Los 

Angeles, CA.
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46. Montgomery, D. C. and J. E. Taggart (1993), "Selection of a Second-Order Response Surface 

Design," invited presentation at the SAS Users Group International Conference, New York.

45. Coleman, D. E. and D. C. Montgomery (1992), "A Systematic Approach to Planning for a 

Designed Industrial Experiment," invited paper, Technometrics Session, ASQ/ASA Fall 

Technical Conference, Philadelphia, PA.

44. Mastrangelo, C. M. and D. C. Montgomery (1992), "Characterization of a Moving Centerline 

EWMA Control Chart,” invited presentation at the ASQCASA Fall Technical Conference, 

Philadelphia, PA.

43. Montgomery, D. C. and S. R. Voth (1991), "Some Practical Aspects of Designing Mixture 

Experiments." Invited presentation at the ASQ/ASA Fall Technical Conference, Lexington, 

KY.

42. Montgomery, D. C. and C. M. Mastrangelo (1990), "Statistical Process Control Methods for 

Autocorrelated Data." Invited paper, JQT Session, ASQ/ASA Fall Technical Conference, 

Richmond, VA.

41. Montgomery, D. C. (1984), "Economic Models and Statistical Process control," invited 

presentation at the Joint Statistical Meetings, Philadelphia, PA.

40. Montgomery, D. C. (1984), "Design of Experiments in Development and Manufacturing 

Engineering," invited presentation at the 3rd Annual IBM Corporate Quality Conference, 

Austin, TX.

39. Montgomery, D. C. (1984), "Improving Quality and Productivity in Manufacturing with 

Design of Experiments," invited presentation at the 10th Annual IBM Design of Experiments 

Conference, Lexington, Kentucky.

38. Montgomery, D. C. (1984), "Sampling Procedures for Monitoring Service Contracts," invited 

paper given at the Spring ORSA Meeting, San Francisco, Calif.

37. Montgomery, D. C. and F. D. Baker (1983), "Statistical Modeling of Soybean Growth," 

Workshop on Crop Simulation, University of Illinois, Urbana-Champaign, Illinois.

36. Montgomery, D. C. (1983), "The Effect of Nonnormality on Acceptance Sampling Plans for 

Variables," presented at a Meeting of the National Academy of Science, Washington, D.C.

35. Montgomery, D. C. and D. J. Friedman (1982), "An Evaluation of Biased Estimators for 

Prediction," invited paper given at the Joint Statistical Meetings, Cincinnati, Ohio.

34. Montgomery, D. C. (1982), "Some Hazards of Using Regression Analysis as a Statistical Tool 

for Load Research," invited paper given at the AEIC Load Research Conference, Atlanta, 

Georgia.

33. Montgomery, D. C. (1981), "Cost Based Acceptance Sampling Plans and Process Control 

Schemes," invited paper presented at the AIIE Fall Technical Conference, Washington, D. C., 

also in Conference Proceedings.

32. Montgomery, D. C. (1981), "Regression Analysis - Some Aspects of its Use in Load 

Research," invited paper given at the AEIC Load Research Conference, Atlanta, Georgia.

31. Montgomery, D. C. and E. A. Peck (1981), "The Multicollinearity Problem in Regression," 

invited tutorial session at the Southeast Institute for Decision Sciences Meeting, Orlando, 

Florida, February 1980; also in Conference Proceedings.

30. Montgomery, D. C. and G. Weatherby (1979), "Factor Screening Methods in Computer 

Simulation," presented at the Winter Simulation Conference, San Diego, Calif., also in 

Conference Proceedings.
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29. Montgomery, D. C. (1979), "Methods for Combining Forecasts," presented at the 24th 

International TIMS Conference, Honolulu, Hawaii.

28. Johnson, L. A. and D. C. Montgomery (1979), "Forecasting Methods in Production and 

Operations Management," invited paper presented at the 24th International TIMS Conference, 

Honolulu, Hawaii.

27. Simms, E. D. and D. C. Montgomery (1977), "The Use of Discriminant Analysis for Risk 

Assessment in Operational Testing," Presented at the 16th Annual U.S. Army Operations 

Research Symposium, Ft. Lee, Virginia.

26. Russ, S. W., Jr., D. C. Montgomery, and H. M. Wadsworth, Jr. (1977), "A Cost Optimal 

Approach to Selection of Experimental Designs for Operational Testing Under Conditions of 

Constrained Sample Size," Presented at the 16th Annual U.S. Army Operations Research 

Symposium, Ft. Lee, Virginia.

25. Friese, W. F., Jr., and D. C. Montgomery (1977), "A Cost-Optimal Approach to Selecting a 

Fractional Factorial Design," presented at the 16th Annual U.S. Army Operations Research 

Symposium, Ft. Lee, Virginia, also in Conference Proceedings.

24. Montgomery, D. C. (1977), "Procedures for Optimizing and Integrating Production and 

Distribution Operations," invited paper presented at the 6th Management Science Colloquium, 

Osaka University, Osaka, Japan.

23. Johnson, L. A. and D. C. Montgomery (1977), "Forecasting with Prediction Limits," invited 

paper presented at the 23rd International TIMS Conference, Athens, Greece.

22. Montgomery, D. C. and V. M. Bettencourt, Jr. (1976), "A Review of Multiple Response 

Surface Methods in Computer Simulation," invited paper presented at the Fall ORSA/TIMS 

National Meeting, Miami, Florida.

21. Brown, E. L. and D. C. Montgomery (1975), "An Application of Network Simulation to 

Operational Testing and Evaluations," presented at the 14th Annual U.S. Army Operations 

Research Symposium, Ft. Lee, Virginia, also in Conference Proceedings.

20. Johnson, L. A. and D. C. Montgomery (1975), "Forecasting and Time Series Analysis," 

seminar presented at the 3rd Annual AIIE Fall Systems Engineering Conference, Las Vegas.

19. Johnson, L. A. and D. C. Montgomery (1975), "Planning Lot Size Production for Inventory," 

invited paper presented at the Fall ORSA/TIMS National Meeting, Las Vegas.

18. Gearing, D. V., R. G. Heikes and D. C. Montgomery (1975), "Development of an Economic 

Model of Moving Average Control Charts," presented at the Fall 1975 ORSA/TIMS National 

Meeting, Las Vegas.

17. Montgomery, D. C., R. G. Heikes, and Y. G. Yap (1975), "A Comparison of Two Adaptive 

Forecasting Systems," presented at the 47th National ORSA Meeting, Chicago, Illinois.

16. Cummings, J. M., B. B. McCra, D. C. Montgomery and R. G. Heikes (1974), "Repairing 

Response Surface Designs to Minimize Bias," presented at the 46th National ORSA Meeting, 

San Juan, Puerto Rico.

15. Montgomery, D. C. (1974), "Experimental Design Techniques for Computer Simulation," 

invited paper at the Second Interamerican Conference on Information and Systems 

Engineering, Mexico City.

14. Marsh, J. D. and D. C. Montgomery (1974), "Scheduling Jobs with Sequence Dependent Setup 

Times on Parallel Machines," presented at the 45th National ORSA Meeting, Boston, 

Massachusetts.
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13. Johnson, L. A. and D. C. Montgomery (1974), "On Dynamic Production Planning Models," 

invited paper presented at the Distinguished Scholars Seminar, Southeast Institute of Decision 

Sciences Meeting, New Orleans, Louisiana, also in Conference Proceedings.

12. Montgomery, D. C. and C. K. Hudson (1973), "Use of Equiradial Designs in Response Surface 

Methodology," presented at the 44th National ORSA Meeting, San Diego, California.

11. Heikes, R. G., D. C. Montgomery and J. Young (1973), "Alternate Process Models in the 

Economic Design of T2 Control Charts," presented at the 44th National ORSA Meeting, San 

Diego, California, subsequently published in AIIE Transactions.

10. Alt, F. B., J. J. Goode, D. C. Montgomery and H. M. Wadsworth (1973), "Variable Control 

Charts for Multivariate Data," invited paper presented at the American Statistical Association 

Meeting, New York.

9. Marsh, J. D and D. C. Montgomery (1973), "Optimal Procedures for Scheduling Jobs with 

Sequence-Dependent Changeover Times on Parallel Processors," invited paper presented at the 

AIIE Annual Conference, Chicago, Illinois.

8. Montgomery, D. C. and D. M. Evans (1972), "Second Order Response Surface Designs in 

Digital Simulation," invited paper presented at the 41st National ORSA Meeting, New 

Orleans, Louisiana, a revised version of this paper was subsequently published in Simulation.

7. Montgomery, D. C. and P. J. Klatt (1972), "Minimum Cost Multivariate Quality Control 

Tests," invited paper presented at the AIIE Annual Conference, Anaheim, California, also in 

Conference Proceedings and subsequently published in AIIE Transactions.

6. Montgomery, D. C. and H. M. Wadsworth (1972), "Some Techniques for Multivariate Quality 

Control Applications," invited paper presented at the American Society for Quality Control 

Annual Conference, Washington, D.C., also in Conference Proceedings.

5. Montgomery, D. C. (1971), "Stochastic Capacity Decision Models for Production Facilities," 

invited paper presented at the AIIE Annual Conference, Boston, Massachusetts, also in 

Conference Proceedings.

4. Montgomery, D. C. (1970), "Expectations of Young Engineers from Their Employers and 

Professional Societies," invited paper presented at the 13th International Meeting of APICS, 

Cincinnati, Ohio.

3. Fabrycky, W. J., V. Chachra and D. C. Montgomery (1970), "A Simulation Study of Three 

Classes of Job-Shop Sequencing Rules," invited paper presented at the 13th International 

Meeting of APICS, Cincinnati, Ohio.

2. Ghare, P. M. and D. C. Montgomery (1969), "Flow Management in Transportation Networks," 

invited paper presented at the 5th International Conference on Operations Research, Venice, 

Italy, also in Conference Proceedings.

1. Montgomery, D. C. (1970), "Evolutionary Operation and Machine Center Capacity Control in 

Job-Shop Systems," contributed paper presented at the 11th American Meeting of TIMS, Los 

Angeles, California.

Other Publications

59. Montgomery, D.C. and Anderson-Cook, C.M. (2016), “In Memory of Connie M. Borror”, 

Obituary in Quality Engineering, Vol. 28, No. 3, pp. 247-248.

58. Montgomery, D.C. (2016), “Why Do Lean Six Sigma Projects Sometimes Fail?”, editorial in 

Quality and Reliability Engineering International, Vol. 32, No. 4, pp. 1279.
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57. Montgomery, D.C. (2016), “Collecting Data”, editorial in Quality and Reliability Engineering 

International, Vol. 32, No. 2, pp. 333.

56. Montgomery, D.C. (2015), “Show Me the Money”, editorial in Quality and Reliability 

Engineering International, Vol. 31, No. 8, pp. 1303.

55. Montgomery, D.C. (2015), “Robert Vincent (Bob) Hogg”, editorial in Quality and Reliability 

Engineering International, Vol. 31, No. 4, pp. 555.

54. Montgomery, D.C. (2015), “A.V. Feigenbaum”, editorial in Quality and Reliability Engineering 

International, Vol. 31, No. 2, pp. 163.

53. Montgomery, D.C. (2014), “Big Data and the Quality Profession”, editorial in Quality and 

Reliability Engineering International, Vol. 30, No. 4, pp. 447.

52. Montgomery, D.C. (2014), “Lean Six Sigma and Promoting Innovation”, editorial in Quality and 

Reliability Engineering International, Vol. 30, No. 1, pp. 1.

51. Montgomery, D.C. (2013), “Lean Six Sigma and Quality Management”, editorial in Quality and 

Reliability Engineering International, Vol. 29, No. 7, pp. 935.

50. Montgomery, D.C. (2013), “2013: The International Year of Statistics”, editorial in Quality and 

Reliability Engineering International, Vol. 29, No. 3, pp. 305.

49. Montgomery, D.C. (2013), “The Quality, Reliability and Statistical Engineering Profession in the 

21st Century”, editorial in Quality and Reliability Engineering International, Vol. 29, No. 1, pp. 

1.

48. Montgomery, D.C. (2012), “Giants of Quality – W. Edwards Deming”, editorial in Quality and 

Reliability Engineering International, Vol. 28, No. 3, pp. 247-248.

47. Montgomery, D.C. (2011), “Giants of Quality – Walter Shewhart”, editorial in Quality and 

Reliability Engineering International, Vol. 27, No. 8, pp. 979.

46. Montgomery, D.C. (2011), “Innovation and Quality Technology”, editorial in Quality and 

Reliability Engineering International, Vol. 27, No. 6, pp. 733-734.

45. Montgomery, D.C. (2011), The Principles of Testing”, The ITEA Journal, invited editorial, Vol 

32, No. 3, pp. 231-234.

44. Montgomery, D.C. (2010), “The 25th Anniversary Volume of Quality and Reliability Engineering

International”, editorial in Quality and Reliability Engineering International, Vol. 26, No. 1, pp. 

1-2.

43. Montgomery, D.C. (2009), “Computer Modelling”, editorial in Quality and Reliability 

Engineering International, Vol. 25, No. 6, pp. 645.

42. Montgomery, D.C. (2009), “It’s a Great Time to be a Statistician”, editorial in Quality and 

Reliability Engineering International, Vol. 25, No. 4, pp. 379-380.

41. Tiwari, M.K., Antony, J., and Montgomery, D. C. (2008), “Editorial Note for the Special Issue on 

Effective Decision Support to Implement Lean and Six Sigma Methodologies in the 

Manufacturing and Service Sectors”, International Journal of Production Research, Vol. 46, No. 

23, pp. 6563-6566.

40. Montgomery, D.C. (2008), “Applications of Design of Experiments in Engineering”, editorial in 

Quality and Reliability Engineering International, Vol. 24, pp. 501-502. 

39. Montgomery, D.C. (2008), “Does Six Sigma Stifle Innovation?”, editorial in Quality and

Reliability Engineering International, Vol. 24, pp. 249.
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38. Montgomery, D.C. (2008), “A Retrospective on Volume 23 of Quality and Reliability 

Engineering International”, editorial in Quality and Reliability Engineering International, Vol. 

24, pp. 1-2.

37. Montgomery, D.C. (2007), “SPC Research – Current Trends”, editorial in Quality and 

Reliability Engineering International, Vol. 23, pp. 515-516. 

36. Montgomery, D. C. (2006), “Designed Experiments in Process Improvement”, editorial in 

Quality and Reliability Engineering International, Vol. 22, No. 8, pp. 863-864.

35. Montgomery, D. C. (2006), “Analyzing and Improving Measurement Systems: A Key to 

Effective Decision-Making”, editorial in Quality and Reliability Engineering International, Vol. 

22, No. 3, pp. 237-238.

34. Montgomery, D. C. and Brombacher, A.C. (2006), “Carol J. Feltz and David Newton”, editorial 

in Quality and Reliability Engineering International, Vol. 22, No. 2, pp. i.

33. Brombacher, A. C. and Montgomery, D. C. (2005), “News from Newcastle: Product Quality 

from a Customer Perspective”, editorial in Quality and Reliability Engineering International, 

Vol. 21, No. 8, pp. iii.

32. Montgomery, D. C. (2005), “Generation III Six Sigma”, editorial in Quality and Reliability 

Engineering International, Vol. 21, No. 6, pp. iii-iv.

31. Montgomery, D. C. (2005), “Changing of the Guard”, editorial in Quality and Reliability 

Engineering International, Vol. 21, No. 1, pp. iii.

30. Montgomery, D. C. (2004), “Selecting the Right Improvement Projects”, editorial in Quality

and Reliability Engineering International, Vol. 20, No. 7, pp. iii-iv.

29. Montgomery, D. C. (2004), “Improving Business Performance: Project-by-Project”, editorial in 

Quality and Reliability Engineering International, Vol. 20, No. 4, pp. iii.

28. Montgomery, D. C. (2003), “Corporate Ethics and Quality”, editorial in Quality and Reliability 

Engineering International, Vol. 19, No. 6, pp. iii-iv.

27. Montgomery, D. C. (2003), “Quality Improvement and Economic Growth”, editorial in Quality 

and Reliability Engineering International, Vol. 19, No. 3, pp. iii.

26. Montgomery, D. C. (2003), review: The Mahalanobis-Taguchi Strategy, G. Taguchi and R. 

Jugulum, in: Journal of Quality Technology, Vol. 35, No. 2.

25. Montgomery, D. C. (2003), “Education for Industrial Statisticians”, editorial in Quality and 

Reliability Engineering International, Vol. 19, No. 1.

24. Montgomery, D. C. (2002), “Changing Roles for the Industrial Statistician”, editorial in Quality 

and Reliability Engineering International, Vol. 18, No. 5.

23. Montgomery, D. C. (2002), “Research in Industrial Statistics – Part II”, editorial in Quality and 

Reliability Engineering International, Vol. 18, No. 2.

22. Montgomery, D. C. (2001), “Research in Industrial Statistics – Part I”, editorial in Quality and 

Reliability Engineering International, Vol. 17, No. 6.

21. Montgomery, D. C. (2001), “Beyond Six-Sigma”, editorial in Quality and Reliability 

Engineering International, Vol. 17, No. 4.

20. Montgomery, D. C. (2001), “Some Thoughts on ISO/QS Registration”, editorial in Quality and 

Reliability Engineering International, Vol. 17, No. 1.

19. Borror, C. M., Montgomery, D. C., and Runger, G. C. (2000), “Statistical Experimental Design 

– Some Recent Advances and Applications”, editorial in Quality and Reliability Engineering 

International, Vol. 16, No. 5.
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18. Montgomery, D. C. (2000), “The Present State of Industrial Statistics”, editorial in Quality and 

Reliability Engineering International, Vol. 16, No. 4.

17. Montgomery, D. C. (2000), “A Meeting for Industrial Statisticians”, editorial in Quality and 

Reliability Engineering International, Vol. 16, No. 2.

16. Montgomery, D. C. (1988), "Experimental Design and Product and Process Development," 

Manufacturing Engineering, Vol. 101, No. 3, September.

15. Heikes, R. G. and D. C. Montgomery (1981), "Productivity is Enhanced by Statistical Quality 

Control," Industrial Engineering, Vol. 13, No. 3.

14. Montgomery, D. C. (1981), review: Dynamic Regression: Theory and Algorithms, M. H. 

Pearson and L. J. Slater, in: Journal of Quality Technology, Vol. 13, No. 1.

13. Montgomery, D. C. (1980), review: Practical Experiences with Modeling and Forecasting 

Time Series, G. M. Jenkins, In: Journal of Quality Technology, Vol. 12, No. 1.

12. Montgomery, D. C. (1977), review: Statistical Methods for Digital Computers, K. Enslein, A. 

Ralstan and H. S. Wolf, eds., in: TIMS Interfaces.

11. Montgomery, D. C. (1977), review: Fundamentals of Finite Mathematics, R. L. Childress, in: 

Interfaces, Vol. 8, No. 1.

10. Montgomery, D. C. (1975), review: Engineering Mathematics, A. C. Bajapi, L. R. Mustoe and 

D. Walker, In: Industrial Engineering, Vol. 7, No. 1.

9. Montgomery, D. C. (1975), review: Industrial Systems: Planning, Analysis, and Control, 

David D. Bedworth, in: Industrial Engineering, Vol. 7, No. 1.

8. Johnson, L. A. and D. C. Montgomery (1973), review: An Introduction to Production and 

Inventory Control, and Production and Inventory Control: Theory and Practice, R. W. Van 

Ness and W. Monhemius, in: Industrial Engineering, Vol. 5, No. 12.

7. Montgomery, D. C. (1970), review: An Illustrated Guide to Linear Programming, S.I. Gass, in: 

Industrial Engineering, Vol. 12, No. 8.

6. Montgomery, D. C. (1970), review: Theory of Games and Strategies, Levin and DesJardins, in: 

Industrial Engineering, Vol. 2, No. 7.

5. Montgomery, D. C. (1969), review: Queuing Theory, J. A. Panico, in: Industrial Engineering, 

Vol. 1.

4. Montgomery, D. C. (1968), review: Fundamentals of Operations Research, Ackoff and Sasieni, 

The Journal of Industrial Engineering, Vol. 19, No. 7.

3. Montgomery, D. C. (1968), review: Operations Research and the Design of Management 

Information Systems, J. F. Pierce, ed., The Journal of Industrial Engineering, Vol. 19, No. 4.

2. Montgomery, D. C. (1971), "Simulation Predicts Product Behavior," Machine Design.

1. Montgomery, D. C. and W. L. Berry (Editors) (1974), Production Planning and Control: 

Concepts, Techniques and Systems, Production Planning and Control Division Monograph No. 

1, American Institute of Industrial Engineers.

RESEARCH STUDENTS SUPERVISED

Ph.D. Dissertations

68. Sarah E. Burke, “Optimal Design of Experiments for Dual-Response Systems”
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67. Michelle V. Mancenido, “Categorical Responses in Mixture Experiments”, co-advisor with Rong 

Pan

66. Edgar Hassler, “Bayesian D-Optimal Design Issues and Optimal Design Construction for 

Generalized Linear Models with Random Effects”, co-advisor with Rachel Silvestrini

65. Azadeh Adibi, “A P-Value Approach for Phase II Profile Monitoring”, co-advisor with Connie M. 

Borror.

64. Brian B. Stone, “No-Confounding Designs of 20 and 24 Runs for Screening Experiments and a 

Design Selection Methodology”, co-advisor with Rachel Silvistrini.

63. Kathryn S. Kennedy, “Bridging the Gap Between Space-Filling Designs and Optimal Designs: 

Designs for Computer Experiments”, co-advisor with Rachel Silvistrini.

62. Shilpa M. Shinde, “Projection Properties and Analysis Methods for Six-to-Fourteen Factor NoConfounding Designs in 16 Runs”.

61. Richard B. Abelson, “The Development of a Validated Clinically Meaningful Endpoint for the 

Evaluation of Tear Film Stability as a Measure of Ocular Surface Protection in the Diagnosis and 

Treatment of Dry Eye Disease”.

60. Joseph M. Juarez, “Accelerated Life Testing of Electronic Circuit Boards with Applications in 

Lead-Free Design”.

59. Brett Duarte, “An Analytical Approach to Lean Six Sigma Deployment Strategies: Project 

Identification and Prioritization", co-advisor with John Fowler.

58. Dana C. Krueger, “Semiconductor Yield Modeling using Generalized Linear Models”.

57. Busaba Laungrungrong, “Multivatiate Charts for Multivariate Poisson-Distributed Data”.

56. Shilpa Gupta, “Profile Monitoring – Control Chart Schemes for Monitoring Linear and Low-Order 

Polynomial Profiles”.

55. Tae-Yeon Cho, “Mixture-Process Variable Design Experiments with Control and Noise Variables 

within a Split-Plot Structure”.

54. James R. Broyles, “Markovian Model of Patient Throughput in Hospitals: A Regression and 

Decision Process Approach”, co-advisor with Jeff Cochran.

53. Eric M. Monroe, “Optimal Experimental Designs for Accelerated Life Tests with Censoring and 

Constraints”, co-advisor with Rong Pan.

52. Rachel T. Johnson, “Experimental Designs for Computer Experiments”, co-advisor with John 

Fowler.

51. Capehart, S. R., “Designing Fractional Factorial Split-Plot Experiments Using Integer 

Programming”, co-advisor with Murat Kulahci.

50. Myrta Rodriguez, “Evaluation and Construction of Optimal Experimental Designs for Fitting 

Response Surface Models”, co-advisor with Connie Borror.

49. Ashraf Almimi, “Split-Plot Designs: Follow-Up Experiments, Missing Observations, and Model 

Adequacy Checking”, co-advisor with Murat Kulahci.

48. Russell Elias, “Demand Model Management: A Model-Based Expert System for the Forecasting of 

Semiconductor Product”.

47. Peter J. Chung, “Mixture-Process Experiments with Continuous and Categorical Noise Variables”.

46. Cathy Lawson, “Business Process Characterization using Categorical Data Models”.

45. You-Jin Park, “Application of Genetic Algorithms in Response Surface Optimization Problems”.
Douglas Montgomery Peer Reviewer Resume Last Updated 9/5/17

RTI International Peer Review of EPA’s RSE Report 78

44. Duangporn Jearkpaporn, “Multivariate Process Monitoring using Generalized Linear Models”, coadvisor with G. C. Runger.

43. David C. Drain, “Response Surface Methods for Experiments Involving Correlated Noise 

Variables”.

42. Heidi B. Goldfarb, “Mixture-Process Experiments with Control and Noise Variables”.

41. Katina R. Skinner, “Multivariate Process Control for Discrete Data”, co- advisor with G. C. Runger.

40. Alejandro Heredia-Langner, “Genetic Algorithms in Quality Control Problems”, co- advisor with 

W. M. Carlyle. 

39. Elvira N. Loredo, “Annual Electrical Peak Load Forecasting Methods with Measures of Prediction 

Error”.

38. Mani Janakiram, “Statistical and Engineering Process Control Integration Strategies for 

Constrained Controllers”.

37. Harendra Shah, “Impact of Correlated Responses on the Desirability Function”, co- advisor with 

W. M. Carlyle. 

36. Geetha Rajavelu, “Graphical Design Evaluation Techniques for Constrained Mixture 

Experiments”.

35. Lisa Custer, “Augmenting Experimental Designs: Approaches and Comparisons”.

34. Kelly G. Canter, “Screening Methods for Life Cycle Inventory Models”, co-advisor with W. M. 

Carlyle.

33. Don R. Holcomb, Jr., “Supersaturated Experiments for use in Product Development”, co-advisor 

with W. M. Carlyle.

32. Leonard A. Perry, “Partition Experimental Designs for Sequential Processes”.

31. Teri Reed Rhoads, “An Investigation of Strategies for Multivariate Monitoring of Continuous 

Processes”.

30. Daniel R. McCarville, “Test Guard Band Probability Models and Strategies for Optimization”.

29. Wisnowski, James W., “Multiple Outliers in Regression: Detection Methods, Robust Estimation, 

and Variable Selection”, co-advisor with G. C. Runger.

28. Steven E. Somerville, “Filtering and Monitoring Methods for Univariate and Multiple Stream 

Processes”, co-advisor with G. C. Runger.

27. Jeffrey W. Lanning, “Methods for Monitoring Fractionally Sampled Multiple Stream Processes”, 

co-advisor with G. C. Runger.

26. Connie M. Borror, “Response Surface Methods for Experiments Involving Noise Variables”.

25. Sharon L. Lewis, “Analysis of Designed Experiments using Generalized Linear Models”.

24. Wade E. Molnau, “Economic Statistically Constrained Design of the Multivariate Exponentially 

Weighted Moving Average Control Chart”, co-advisor with G. C. Runger.

23. Lora S. Zimmer, “Contributions to Adaptive Control Charts”, co-advisor with G. C. Runger.

22. Daryl J. Hauck, “Extensions to Regression Adjustment Techniques in Multivariate Process 

Monitoring”, co-advisor with G. C. Runger.

21. Dale J. Kennedy, “Development and Analysis of Stochastic Environmental Life Cycle Assessment 

Inventory Modeling”, co-advisor with D. A. Rollier.

20. David M. Sebert, “A Clustering Approach to Finding Multiple Outliers in Linear Regression”.
Douglas Montgomery Peer Reviewer Resume Last Updated 9/5/17

RTI International Peer Review of EPA’s RSE Report 79

19. Carole Shlaes, “Use of Chance-Constrained Programming Techniques to Determine Optimal 

Insurance Deductible Levels”, co-advisor with J. B. Keats.

18. James R. Simpson, “New Methods and Comparative Evaluations for Robust and Biased-Robust 

Regression Estimation”.

17. Jose Andere-Rendon, “Computer-Aided Robust Design of Mixture Experiments Based on 

Bayesian D-Optimality,” co-advisor with D. A. Rollier.

16. Diane Schaub, "Experimental Design Strategy: An Application to Rapid Prototyping of Aerospace 

Parts”.

15. Sharad S. Prabhu, "Adaptive Sample Size and Adaptive Sampling Interval Schemes for an X-Bar 

Control Chart”.

14. Christina M. Mastrangelo, "Statistical Process Monitoring for Autocorrelated Data".

13. Enrique Del Castillo, "Some Models and Methods for Statistical Process Control in Short-Run 

Manufacturing Systems".

12. William S. Messina, "Strategies for the Integration of Engineering Process Control and Statistical 

Process Control," co-advisor with J. B. Keats.

11. James C. C. Torng, "The Economic Statistical Design of Variables Control Charts with an 

Application to Exponentially Weighted Moving Average Charts," co-advisor with J. K. Cochran.

10. D. Y. Kim, "Economic Statistical Design and Analysis for the Poisson CUSUM Control Chart," 

co-advisor with J. B. Keats.

9. M. K. Chua, "A Control Scheme for Multivariate Quality Control".

8. John S. Gardiner, "Statistical Process Control Methods for Detecting Small Process Shifts with 

Applications to Integrated Circuit Manufacturing".

7. Steven A. Yourstone, "Real-Time Process Quality Control in a Computer-Integrated 

Manufacturing Environment".

6. David J. Friedman, "An Evaluation of Biased Estimators in Prediction".

5. Rickey A. Kolb, "Robustness of Current Methodologies for the Analysis of Contingency Tables 

with Respect to Small Expected Cell Values".

4. Ginner Weatherby, "Aggregation, Disaggregation and Combination of Forecasts".

3. Joseph D. Marsh, Jr., "Scheduling Parallel Processors”.

2. Michael P. Deisenroth, "On Simulation Methodology in Vehicular Traffic Flow".

1. Ronald G. Askin, "The Combination of Biased and Robust Estimation Techniques in Multiple 

Regression Models”.

M.S. Theses and MS Statistics Projects

48. Archana Krishnamoorthy, “Analysis of No-Confounding Designs using the Dantzig Selector.”

47. Jeanne Huddleston, “Harm During Hospitalizations for Heart Failure: Adverse Events as a 

Reliability Measure of Hospital Policies and Procedures”, co-advisor John Fowler.

46. Dean S. Hoskins, “D-Optimal Designs with Interaction Coverage”, MS Statistics Project.

45. Shilpa Madhavan Shinde, “Statistical Analysis and Optimization of a Microfluidic Mixing Process 

Based on the Bubble-Induced Acoustic Microstreaming Principle for a Gene Expression Assay.”
Douglas Montgomery Peer Reviewer Resume Last Updated 9/5/17

RTI International Peer Review of EPA’s RSE Report 80

44. Andrea M. Archer, “Bootstrap Confidence Regions on the Optimum Point of a Quadratic 

Response Surface”, MS Statistics Project.

43. Jiahong Li, “Comparisons of Prediction Variance Properties of Six-to Ten-Factor CCD and CCD 

Min Res V Response Surface Designs”, MS Statistics Project.

42. Nuttha Lurponglukana, “Using a Bootstrap Method to Determine Active Factors in an 

Unreplicated Factorial Experiment”.

41. Amy K. Volpe, “Fitting Response Surface Models with Mixed Effects using Generalized Linear 

Mixed Models”, MS Statistics Project.

40. Diane E. Richardson, “Variance Dispersion Studies of Second Order Response Surface Designs in 

Cuboidal Regions”, MS Statistics Project.

39. Russell J. Elias, “Demand Signal Modeling: A Model-Based Approach to the Forecasting of 

Future Product Demand”, co - advisor J. B. Keats.

38. Duangporn Jearkpaporn, “Using Half-Normal Plots to Identify Important Factors in Screening 

Experiments Analyzed with the Generalized Linear Model”.

37. Sharlyn R. Stocker, “Measurement Capability with Wear Out”, co-advisor, George C. Runger.

36. John A. Druyor, “An Investigation of Linear Screening and Prediction Accuracy in Constrained 

Mixture Experiments”.

35. Pamela A. Okamoto, “Parameter Estimation from Hazard Plots using Robust Regression 

Techniques”, co-advisor with J. B. Keats.

34. M. J. Yatskievych, “Integrating Statistical Process Control with Feedforward Control,” co-advisor 

with J. B. Keats.

33. Richard D. Scranton, “Enhancements to the Multivariate Exponentially Weighted Moving 

Average Control Chart,” co - advisor J. B. Keats.

32. Steven E. Somerville, "Process Capability Ratios and Non-normal Distributions".

31. Daniel R. McCarville, "Defect Levels and Losses due to Gauge Error".

30. James E. Taggart, "A Comparison of Some Second-Order Response Surface Designs Based on 

Rotability and Prediction Variance".

29. Nora B. Peterschmidt, "Comparison of Regression Estimates in Mixture Experiments".

28. Sheila R. Voth, "Leverage, Multicollinearity and Bias in Response Surface Designs for Mixtures".

27. Robert Gilby, "A Wald Sequential Ratio Test, Modified to Ignore Small Shifts in the Process 

Variable".

26. Christina M. Mastrangelo, "Statistical Process Control Methods for Autocorrelated Data".

25. Deborah Garner, "Regression Diagnostics for Influence with Many Influential Cases".

24. Timothy G. Fields, "Nonlinear Programming Techniques for the Multiple Response Problem".

23. Beth Quay, "Investigation of Methods for Determining Influential Data Points in Regression 

Analysis".

22. Ronda K. Martin, "Interactive Regression Diagnostics".

21. Margaret Panagos, "Economic Design of the 

x Chart for Two Process Models".

20. Richard Matteson, "Minimum Bias Estimation of the Slope of a Response Surface".

19. Joseph F. Mance, "Economic Design of Fraction Defective Control Charts to Maintain Current 

Control of a Process".

18. Phillip J. Klatt, "Design of Control Charts for the Mean Vector of a Multivariate Normal Process".
Douglas Montgomery Peer Reviewer Resume Last Updated 9/5/17

RTI International Peer Review of EPA’s RSE Report 81

17. A. K. Keswani, "Single Item Inventory Models for Back Orders and Lost Sales".

16. Mari Krista Jones, "A Comparison of Direct Smoothing with Short Term Forecasting Techniques 

for Periodic Data".

15. James M. Jerkins, "Some Algorithms for Nonlinear Regression".

14. William F. Friese, "A Cost Optimal Approach to Selecting a Fractional Factorial Design".

13. Claude K. Hudson, "Use of the Class of Equiradial Designs as Second Order Response Surface 

Designs".

12. David E. Ferguson, "The Development of an Adaptive Prediction and Control System".

11. Daniel M. Evans, Jr., "The Use of Second Order Response Surface Designs in Digital Simulation".

10. Joseph M. Cummings, "Repairing Response Surface Experiments to Minimize Bias".

9. Geneveive M. Cruz, "A Statistical Approach to the Combination of Forecasts".

8. Philip V. Coyle, "An Adaptation of Bayesian Statistical Methods to the Determination of Optimal 

Sample Sizes for Operational Testing".

7. Elwyn L. Brown, "An Application of Simulation Networking Techniques in Operational Test 

Design and Evaluation".

6. Jimmie K. Boles, "A GPSS II Simulation of an Air Defense Problem".

5. Joan S. Horwitz, "A Mathematical Model of an Epidemic Process".

4. Richard Harris, "Economic Design of T2 Control Charts for Multivariate, Multi-State Processes".

3. Timothy G. Fields, "Nonlinear Programming Techniques for the Multiple Response Problem".

2. Robert M. Baker, "An Application of Bayesian Statistical Methods in the Determination of 

Sample Size for Operational Testing in the U.S. Army".

1. Frank B. Alt, "Some Aspects of Multivariate Statistical Control Charts".
Douglas Montgomery Peer Reviewer Resume Last Updated 9/5/17

RTI International Peer Review of EPA’s RSE Report 82

DOUGLAS C. MONTGOMERY

Biographical Data

Dr. Douglas C. Montgomery is Regents’ Professor and the ASU Foundation Professor of Engineering at 

Arizona State University. He held the John M. Fluke Distinguished Chair in Engineering, was the Director 

of Industrial Engineering and Professor of Mechanical Engineering at the University of Washington in 

Seattle. He was a Professor of Industrial & Systems Engineering at the Georgia Institute of Technology. 

He holds BSIE, MS and Ph.D. degrees from Virginia Polytechnic Institute.

Professor Montgomery's professional interests are in industrial statistics, including design of experiments, 

quality control, applications of linear models, and time series analysis and forecasting. He also has interests 

in operations research and statistical methods applied to modeling and analyzing manufacturing systems. 

He has lectured extensively throughout the Americas, Europe and the Far East. He was a Visiting 

Professor of Engineering at the Monterey Institute of Technology in Monterey, Mexico, and a University 

Distinguished Visitor at the University of Manitoba. Professor Montgomery has conducted basic research 

in empirical stochastic modeling, process control, and design of experiments. The Department of Defense, 

the Office of Naval Research, the National Science Foundation, the United States Army, and private 

industry have sponsored his research. He has supervised 66 doctoral dissertations and more than 40 MS 

theses and MS Statistics projects. 

Professor Montgomery is an author of thirteen textbooks that have appeared in over 35 English editions and 

numerous foreign languages, including Design and Analysis of Experiments, 8

th edition (2012), 

Introduction to Statistical Quality Control, 7th edition (2012), Generalized Linear Models,2

nd edition (2010, 

with R. H. Myers, G. G. Vining and T.J. Robinson), Engineering Statistics, 5th edition (2011, with G. C. 

Runger and N. F. Hubele), Applied Statistics and Probability for Engineers, 5

th edition (2011, with G.C. 

Runger), Introduction to Linear Regression Analysis, 5

th edition (2012, with E. A. Peck and G. G. Vining), 

and Response Surface Methodology,3

rd edition (2009, with R. H. Myers and C.M. Anderson-Cook). He has 

edited or coauthored seven other research books or edited volumes. His research papers have appeared in 

many journals, including the Journal of Quality Technology, Technometrics, Management Science, Naval

Research Logistics Quarterly, IIE Transactions, Journal of the Royal Statistical Society, Communications 

in Statistics, IEEE Transactions on Reliability, Quality and Reliability Engineering International, IEEE 

Transactions on Semiconductor Manufacturing, Quality Engineering, Operational Research Quarterly, 

International Journal of Production Research, Journal of Spacecraft and Rockets, and Transportation 

Research. He is a past Editor of the Journal of Quality Technology and one of the current chief editors of 

Quality and Reliability Engineering International. He has served as the Applied Probability and Statistics 

Department Editor and as the Quality and Reliability Engineering Department Editor for IIE Transactions.

He is a member of the Editorial Board of the Journal of Quality Technology, the Journal of Applied 

Statistics, Quality Engineering, the Journal of Probability and Statistical Science, and Quality Technology 

and Quantitative Management.

Professor Montgomery's industrial experience includes engineering assignments with Union Carbide 

Corporation and Eli Lilly and Company. He also has extensive consulting experience with many national 

and international organizations. 

Professor Montgomery is an Honorary Member of the American Society for Quality, a Fellow of the 

American Statistical Association, a Fellow of the Royal Statistical Society, a Fellow of the Institute of 

Industrial Engineers, an Elected Member of the International Statistical Institute, and an Elected 

Academician of the International Academy for Quality. He has held several national offices in ASQ, ASA, 

and IIE. He is a member of the honorary societies Phi Kappa Phi, Sigma Xi, Mu Sigma Rho, and Alpha 

Pi Mu. He received the Deming Lecture Award from the American Statistical Association. He is a 

recipient of the Shewhart Medal, the Distinguished Service Medal, the William G. Hunter Award, the 

Brumbaugh Award, the Lloyd S. Nelson Award, and two Shewell Awards from the American Society for 

Quality, the George Box Medal from ENBIS, the Greenfield Medal from the Royal Statistical society, and 

the Ellis R. Ott Award. He has also received several outstanding teaching awards, including the Arizona 
Douglas Montgomery Peer Reviewer Resume Last Updated 9/5/17

RTI International Peer Review of EPA’s RSE Report 83

State University Engineering College Graduate Teaching Excellence Award in 1994. He was named an 

ASU Outstanding Doctoral Mentor in 2004. 
RTI International Peer Review of EPA’s RSE Report 84

Appendix B. Charge Letter
RTI International Peer Review of EPA’s RSE Report 85

October 30, 2017

Dr. Sanya Carley 

School of Public and Environmental Affairs

Indiana University 

1315 E. 10th Street

Bloomington, IN 47405

SUBJECT: Peer Review of EPA’s Response Surface Equation Report

Dear Dr. Carley, 

RTI International has been contracted by EPA to facilitate a peer review of their Response Surface 

Methodology Report. You have been selected to participate on this panel and your conflict of interest 

evaluation is complete. RTI will compensate you $3,000 for your services. 

This charge letter contains specific questions to guide you in your review, the review schedule, and details 

about the materials we would like you to send us by the end of the three-week review period. 

Additionally, you should receive the peer review materials in the same email that delivered this letter. 

Background 

EPA’s Office of Transportation and Air Quality has developed a statistical approach to access results 

from the Advanced Light-Duty Powertrain and Hybrid Analysis (ALPHA) model. To demonstrate the 

credibility of the methodology and gain acceptance in the light-duty automotive community, EPA has 

decided to initiate an independent peer review. 

The ALPHA model is a full vehicle simulation model which is used to assess the effectiveness of 

different technology packages in vehicles. Effectiveness values from ALPHA act as robust inputs to the 

Optimization Model for Reducing Emissions of Greenhouse Gases from Automobiles (OMEGA) as well 

as the overall rulemaking process. 

Because operating the ALPHA model in real time to conduct full vehicle simulations is cost- and timeprohibitive, EPA has developed a method of deriving the necessary effectiveness values using a statistical 

methodology known as a Response Surface Model (RSM). An RSM is used to computationally 

synthesize a large set of simulation outputs to derive response surface equations (RSE). The derived RSEs 

can then be used in place of running the ALPHA model in real time.
RTI International Peer Review of EPA’s RSE Report 86

Charge Questions

For this review, EPA is looking for the reviewer’s opinion of the use of response surface modeling to 

access the results of the ALPHA model for use in the OMEGA model. The ALPHA and OMEGA models 

themselves are not part of the review and no independent data analysis is required.

EPA would like you to consider the questions below to help define the scope of the review. You are not 

expected to respond to the questions individually; instead, they should be considered a guide for your 

response. 

General Questions and Issues to Consider 

1. EPA’s overall approach to applying response surface modelling to accessing ALPHA model 

results and whether the resulting response surface equations provide accurate and robust inputs 

for the OMEGA model.

2. Reasonableness of any assumptions, implicit or explicit, contained in EPA’s execution of the 

methodology.

3. Clarity, completeness and accuracy of the technical application of response surface modelling; 

and 

4. Any recommendations for specific improvements to the functioning or the quality of the 

methodology.

In your review, please identify any recommendations that would improve the methodology, clearly 

distinguishing between specific improvements that can be readily made using available data and 

literature, and improvements that are more theoretical or exploratory, which would rely on data or 

literature not readily available to the EPA. Comments should be detailed enough that EPA readers or 

others familiar with the report can understand the comments’ relevance to the Response Surface Equation 

Report.

Schedule 

The schedule for this peer review is as follows: 

• October 30th, 2017: Charge letter distributed to reviewers

• October 31st, 2017: Peer Review Kick-Off Call

• Date TBD: Mid-review conference call

• November 22nd, 2017: Comment/review due via email to Kyle Clark-Sutton at kcs@rti.org. 

Materials 

Upon completion of your review, you should submit your report under a cover letter that states: 

1) Your name
RTI International Peer Review of EPA’s RSE Report 87

2) The name and address of your organization

3) A statement of any real or perceived conflict(s) of interest. 

Should you have any questions or concerns, feel free to contact me via phone at 919-541-5874 or by 

email. In addition, the EPA project manager for this effort is Jeff Cherry and he may be reached at 734-

214-4371 or cherry.jeff@epa.gov. 

For any questions about the review process itself, please contact Ruth Schenk in EPA’s Quality Office, 

National Vehicle and Fuel Emissions Laboratory at 734-214-4017 or schenk.ruth@epa.gov. 

Thanks for your participation! 

Sincerely, 

Kyle Clark-Sutton

Research Economist, RTI International

(919) 541-5784

kcs@rti.org
RTI International Peer Review of EPA’s RSE Report 88

Appendix C. Sanya Carley Comments
RTI International Peer Review of EPA’s RSE Report 89

November 16, 2017

Kyle Clark-Sutton

Research Economist

RTI International

RE: Peer Review of EPA’s Response Surface Equation Report

Dear Mr. Clark-Sutton,

Thank you for the invitation to conduct a peer review of the EPA’s Response Surface Equation 

Report. I am an associate professor in the School of Public and Environmental Affairs at Indiana 

University. My work address is presented below. 

Please find a summary of my review enclosed in this submission package. These comments and 

recommendations are based on my understanding of Response Surface Methodology, costeffectiveness of different vehicle technology packages, the use of input parameters and the 

operation of the OMEGA model, and U.S. fuel economy and greenhouse gas emissions standards 

for light-duty vehicles between 2017 and 2025.

To the best of my knowledge, I have no real or perceived conflicts of interest in conducting this 

review. I have conducted research with colleagues on the macroeconomic implications of U.S. 

fuel economy and greenhouse gas emissions standards. As part of this effort, my colleagues and I 

recreated the EPA’s OMEGA model and used it to generate estimates of vehicle prices. This 

research was funded by the Alliance of Automobile Manufacturers but the work was conducted 

independently of the funding organization. I disclosed this potential conflict of interest to the 

EPA when they were in the process of seeking peer reviewers and it was determined at the time 

to not be a conflict.

Please do not hesitate to contact me with further questions about my review, or if there are other 

questions that I could address that would assist with the process.

Sincerely,

Sanya Carley

Associate Professor

Chair, Policy Analysis and Public Finance

School of Public and Environmental Affairs

Indiana University

1315 E. 10th St., Bloomington, IN 47408, Room 353

812-856-0920

scarley@indiana.edu

Enclosure: A summary of review comments and recommendations
RTI International Peer Review of EPA’s RSE Report 90

PEER REVIEW

EPA RESPONSE SURFACE METHODOLOGY REPORT

Sanya Carley

School of Public and Environmental Affairs

Indiana University

November 16, 2017

I. SUMMARY

This document summarizes my review of the “EPA Report on the Implementation of Response 

Surface Methods to Reproduce ALPHA Modeling Results in the OMEGA Model Preprocess” 

and all supporting modeling outputs provided in the review package. The proposed response 

surface method (RSM) will be used to replicate simulation modeling in a manageable time 

frame, and generate technology effectiveness estimates to be used in the Environmental 

Protection Agencies’ (EPA) Optimization Model for reducing Emissions of Greenhouse gases 

from Automobiles (OMEGA). The OMEGA model, in turn, is an optimization model used to 

generate light-duty vehicle technology cost estimates that comply with emissions standards, as 

used for the Final Rulemaking for the 2017-2025 greenhouse gas emissions standards (OMEGA 

v1.4.1) and more recently the Technical Assistance Report of 2022-2025 standards (OMEGA 

v1.4.56).

After a thorough review of the report and supporting documentation, my general impression is 

that response surface statistical methods are an appropriate and efficient approach to generate 

data needed to populate the OMEGA model. The RSM is an analysis tool that is increasingly 

accepted in engineering and other disciplines, and subjected to rigorous peer review. An analysis 

of the model performance in this specific case also leads me to believe that the RSM approach is 

highly accurate, and capable of generating results that match the significantly more timeintensive ALPHA simulations.
RTI International Peer Review of EPA’s RSE Report 91

II. OVERVIEW OF APPROACH

The general approach to the use of RSM is as follows. The modeler uses the ALPHA model to 

evaluate all combinations of engines, transmissions, road loads, and vehicle types to produce a 

design of experiments. The design of experiments is then entered into the response surface 

equation modeling program, a standard statistical modeling software program, JMP from SAS. 

The response surface equations are then generated using this statistical program. Using four 

different input parameters—mass reduction, aero drag reduction, rolling resistance reduction, 

and transmission type—and the response surface equations, the modeler then compares the 

results between the RSM outputs and the design of experiments from the ALPHA simulations, 

and assesses the quality of the fitted model as well as the accuracy of the model to predict the 

experimental results. The output of the RSM is converted into a spreadsheet of vehicle 

effectiveness to be used in the OMEGA model, as designed to match the former spreadsheets 

used with the lumped parameter model.

All of these steps are clearly described, and in greater detail, in the “EPA Report on the 

Implementation of Response Surface Methods to Reproduce ALPHA Modeling Results in the 

OMEGA Model Preprocess” report. This process, as outlined, is appropriate and matches 

standard procedures.

III. APPROPRIATENESS OF THE APPROACH 

A. RSM as an Accepted Approach

RSM is a set of mathematical and statistical techniques that allows one to fit a 

polynomial model to data. RSM can account for several different independent variables 

(also referred to as factors or operating parameters), that can vary at the same time over a 

set of experimental runs. RSM can be used to develop the functional relationship between 

an outcome of interest (or a “response”) and several different independent variables, so as 

to simultaneously optimize the values of these variables. The errors in RSM are assumed 

to be random.

Response Surface Methodology was first introduced by Box and Wilson in 1951 (Box 

and Wilson, 1951). Since its inception, but particularly beginning it in the early 1970s, it 

has been applied to a range of complex topics, such as automobiles and impact load 

conditions (e.g., Avelle et al. 2002), water desalination (e.g., Boubakri et al., 2014), and 

food industry processes (see Yolmeh and Jafari, 2017 for a comprehensive review of this 

literature), among many other topics. 

Figure 1 graphs the number of different types of studies that have used RSM since it was 

first introduced, according to a search within the Scopus database. This graph 

demonstrates that engineering studies are the most common applications of the method, 

followed closely by biochemistry and agricultural/biological sciences. Figure 2 shows the 

same data but over time, between the 1961 and 2016. This figure demonstrates that the 

majority of studies that used RSM in the earliest years of the methodology were 

immunology and microbiology studies. The use of the methodology has grown 
RTI International Peer Review of EPA’s RSE Report 92

significantly over time within various chemistry disciplines as well as, more recently, 

engineering disciplines.

Figure 1. Number of Published RSM Articles by General Category of Study, 1951-2017

Figure 2. Published RSM Articles over Time, by General Category of Study, 1961-2016

0 2000 4000 6000 8000 10000 12000 14000

Engineering

Biochemistry, Genetics and Molecular Biology

Agricultural and Biological Sciences

Environmental Science

Physics and Astronomy

Computer Science

Pharmacology, Toxicology and Pharmaceutics

Earth and Planetary Sciences

Business, Management and Accounting

Neuroscience

Dentistry

Nursing

Psychology

Economics, Econometrics and Finance

Number of Published Articles

0

1000

2000

3000

4000

5000

6000

7000

1961196419671970197319761979198219851988199119941997200020032006200920122015

Number of Published Articles

Year

Engineering

Chemistry

Biochemistry, Genetics and Molecular

Biology

Chemical Engineering

Agricultural and Biological Sciences

Medicine

Environmental Science

Materials Science

Physics and Astronomy

Immunology and Microbiology
RTI International Peer Review of EPA’s RSE Report 93

B. Timing and Practical Considerations

The execution time of the RSM is similar to that associated with the former lumped 

parameter model, and significantly faster than running the ALPHA model. This modeling 

efficiency allows for real-time input parameter generation for the OMEGA model, and 

also allows the EPA—and others that choose to replicate EPA results—to use standard 

computing equipment. These conditions also have implications for the EPA budget, since 

running the RSM will not require additional financial resources.

C. Response Surface Modeling Process

To perform the RSM analysis, the modelers use a standard software package for this 

purpose, the JMP Classical Response Surface Design Model. This software is highly 

flexible, able to generate equations with the appropriate functional form, and assess 

which independent variables should be included. 

IV.VALIDATION OF THE MODELING OUTPUTS

A. Validation of Modeling Results

a. Model Performance

A standard test that is used to evaluate the performance of the model is a 

goodness-of-fit estimate. The EPA has confirmed that they obtained sufficiently 

high R2values for all of their model runs.

b. Comparison of Output to ALPHA Modeling Output

There are a variety of performance metrics that one could use to assess response 

surface equation accuracy and adequacy. For this review, I evaluated the size of 

the residuals, the percent error, and the distribution of the residuals. 

Table 1 displays the first two performance metrics, along with additional detail 

about the model runs. The first column designates the vehicle type, of which there 

are six. The second column designates the powertrain category, of which there are 

four. The combination of six vehicle types of four powertrain categories results in 

24 different categories of model runs. The observation count is the number of 

ALPHA runs with all allowable combinations of the independent variables that 

stay within parameter bounds. The next four columns provide statistics for the 

residual (the difference between the dependent variable of the response surface 

equation and the ALPHA simulation). The final column displays the percent 

error, or the deviation between the experimental ALPHA values and the predicted 

RSM values for a determined set of conditions.

These statistics confirm that the predicted values have excellent accuracy. The 

average residual is 0.0013 and the average percent error is -0.0004 percent. All 

combinations of vehicle type and powertrain perform similarly. The combination 

that has the highest residual is the High Power/Weight 2014 Atkinson.
RTI International Peer Review of EPA’s RSE Report 94

Table C-1. RSM Performance Metrics

Vehicle Type Powertrain 

Category

Obs Average 

Residual

Residual 

Standard 

Deviation

Residual 

Min

Residual 

Max

% Error

Low Power/Weight -- Low 

Road Load

2014 GDI 375 -2.5E-10 0.19 -0.51 0.60 -2.4E-08

Low Power/Weight -- Low 

Road Load

2014 Atkinson 371 6.5E-03 0.20 -0.51 0.41 -2.9E-05

Low Power/Weight -- Low 

Road Load

2020 Atkinson 375 2.3E-10 0.18 -0.52 0.40 -3.1E-08

Low Power/Weight -- Low 

Road Load

2020 24 Bar Turbo 375 3.9E-10 0.18 -0.65 0.50 7.7E-08

Medium Power/Weight --

Low Road Load

2014 GDI 375 6.1E-11 0.14 -0.40 0.50 4.8E-08

Medium Power/Weight --

Low Road Load

2014 Atkinson 375 5.7E-10 0.26 -0.57 0.69 -3.7E-07

Medium Power/Weight --

Low Road Load

2020 Atkinson 375 3.1E-11 0.18 -0.43 0.48 -4.0E-07

Medium Power/Weight --

Low Road Load

2020 24 Bar Turbo 375 5.0E-11 0.28 -0.73 0.55 -8.9E-07

High Power/Weight 2014 GDI 351 -1.1E-02 0.25 -0.58 0.57 4.1E-05

High Power/Weight 2014 Atkinson 313 0.42 0.35 -0.69 0.70 -1.8E-04

High Power/Weight 2020 Atkinson 340 8.1E-04 0.32 -0.65 0.64 5.8E-07

High Power/Weight 2020 24 Bar Turbo 375 3.0E-11 0.23 -0.69 0.68 3.3E-07

Lower Power/Weight --

High Road Load

2014 GDI 325 2.5E-10 0.17 -0.49 0.59 1.2E-08

Lower Power/Weight --

High Road Load

2014 Atkinson 274 2.5E-10 0.17 -0.38 0.41 7.3E-08

Lower Power/Weight --

High Road Load

2020 Atkinson 285 -1.6E-12 0.11 -0.29 0.33 4.7E-08

Lower Power/Weight --

High Road Load

2020 24 Bar Turbo 375 5.5E-11 0.15 -0.43 0.35 4.2E-07

Medium Power/Weight --

High Road Load

2014 GDI 332 9.7E-05 0.21 -0.52 0.50 2.5E-06

Medium Power/Weight --

High Road Load

2014 Atkinson 280 -3.4E-11 0.23 -0.58 0.65 -2.9E-08

Medium Power/Weight --

High Road Load

2020 Atkinson 282 1.1E-10 0.21 -0.60 0.63 2.2E-08

Medium Power/Weight --

High Road Load

2020 24 Bar Turbo 363 -9.9E-03 0.28 -0.59 0.65 4.7E-05

Truck 2014 GDI 357 -3.5E-03 0.27 -0.58 0.58 7.4E-06

Truck 2014 Atkinson 315 -1.4E-02 0.38 -0.77 0.78 6.2E-05

Truck 2020 Atkinson 336 -1.2E-03 0.38 -0.77 0.86 1.4E-05

Truck 2020 24 Bar Turbo 358 2.2E-02 0.36 -0.74 0.78 -6.8E-05

All vehicle types All powertrain 

categories

8257 1.3E-03 0.25 -0.77 0.86 -4.1E-06
RTI International Peer Review of EPA’s RSE Report 95

I also plotted the residuals to see if they fit a normal distribution, as suggested by 

Bezerra et al. (2008). Figure 3 presents a histogram of all residuals across the 8,257 

model runs. The distribution appears normal. I also looked at the histograms for all 

vehicle types, powertrain technologies, and vehicle type-powertrain combinations

separately (not shown here). These plots provide no cause for concern.

Figure 3. Histogram of Residuals

B. The Design of Experiments

The RSM output is compared to the ALPHA modeling output, which assumes that the 

ALPHA output (the design of experiments) is accurate. I have no reason to believe that 

this is cause for concern, however, since the ALPHA model has already gone through 

thorough rigorous peer review (see 

https://nepis.epa.gov/Exe/ZyPdf.cgi?Dockey=P100PUKT.pdf). The peer reviewers found 

the ALPHA model to be highly reliable and accurate.

V. RECOMMENDATIONS

A. Design of Experiments

A future extension of model validation could be an assessment of the RSM output with 

actual testing data. One should assume that the results would be similar to the estimates 

of comparison between ALPHA and RSM, however, since the EPA’s previous work 
RTI International Peer Review of EPA’s RSE Report 96

found that ALPHA estimates were within the margin of 3% error as compared to actual 

vehicle performance testing.

B. Transparency

As stated in the report, one of the benefits of the RSM is “increased transparency regarding 

synthesis of ALPHA simulation into OMEGA modeling”. It is not entirely clear to me how the 

use of RSM will increase transparency. But I strongly encourage and support full transparency of 

modeling inputs, outputs, processes, and supporting information.
RTI International Peer Review of EPA’s RSE Report 97

References

Avalle, M., Chiandussi, G., Belingardi, G. 2002. Design optimization by response surface 

methodology: application to crashworthiness design of vehicle structures. Structural and 

Multidisciplinary Optimization 24, 325-332.

Bezerra, M.A., Santelli, R.E., Oliveira, E.P., Villar, L.S., Escaleira, L.A. 2008. Response surface 

methodology (RSM) as a tool for optimization in analytical chemistry. Talanta 76, 965-977.

Boubakri, A., Hafiane, A., Bouguecha, S.A.T. 2014. Application of response surface 

methodology for modeling and optimization of membrane distillation desalination process. 

Journal of Industrial and Engineering Chemistry 20, 3163-3169.

Box, G.E., Wilson, K. 1951. On the experimental attainment of optimum conditions. Journal of 

the Royal Statistical Society. Series B (Statistical Methodology) 13(1), 1-45.

Yolmeh, M., Jafari, S.M. 2017. Applications of response surface methodology in the food 

industry processes. Food Bioprocess Technology 10, 413-433. 
RTI International Peer Review of EPA’s RSE Report 98

Appendix D. Sujit Das Comments
RTI International Peer Review of EPA’s RSE Report 99

Sujit Das 

12305 Fort West Drive, Knoxville, TN 37934 

Knoxville, TN 37934, USA 

(865)789-0299 dass@ornl.gov 

November 17, 2018 

Kyle Clark-Sutton 

Research Economist, RTI International 

3040 Cornwallis Road 

PO Box 12194, Research Triangle Park, NC 27709 

RE: Peer Review of EPA’s Response Surface Equation Report 

Dear Mr. Clark-Sutton: 

Thank you for inviting me to conduct a peer review of EPA’s Response Surface Equation Report.

I have completed the review. 

Enclosed with this letter is a summary of my review comments and recommendations. These 

comments are made on the basis of the current state of science as I understand it. To the best of 

knowledge, I have no real or perceived conflicts of interest in conducting this review. 

Please feel free to contact me should you have any questions or need additional regarding this 

review. 

Sincerely, 

Sujit Das 

Enclosure: A summary of review comments and recommendations 
RTI International Peer Review of EPA’s RSE Report 100

PEER REVIEW COMMENTS

EPA RESPONSE SURFACE MODEL (RSM)

Sujit Das

Oak Ridge National Laboratory

12305 Fort West Drive, Knoxville, TN 37934

November 22, 2017
RTI International Peer Review of EPA’s RSE Report 101

Peer Review of EPA Response Surface Model (RSM)

1. The industry standard statistical software JMP from SAS has the design of experiments and 

design generation capabilities besides being interactive and visual and thereby justifies its use 

towards the development of Response Surface Equations. Unlike SAS (which is commanddriven), JMP has a graphical user interface to explore data visually. JMP is the tool of choice for 

scientists, engineers and other data explorers in almost every industry and government sector. It 

combines dynamic data visualization with powerful statistics, in memory and on the desktop. 

Interactive and visual, JMP reveals insights that raw tables of numbers or static graphs tend to 

hide.

2. RSM approach has demonstrated clearly an effective use of the large scale simulations from 

the already validated full vehicle simulation model ALPHA. It definitely serves the intended 

appropriate and accurate means of assessing technology packages by means of the efficient 

transposition of full-vehicle simulation results into OMEGA inputs. RSM design concept is very 

similar to the design of LPM which was used for each Light-duty Greenhouse Gas rulemaking, 

from the 2009 FRM through the 2016 Proposed Determination. The RSEs allows any ALPHA 

run to be derived at a similar speed as the current spreadsheet LPM. A similar user-friendly and 

execution time LPM front end used for RPM is definitely an advantage, but it needs to be 

customized for RPM which is limited to only a combination of few technology options (i.e., for 

specific vehicle type and powertrain model with user-specific inputs for mass reduction, aero 

drag reduction, rolling resistance reduction, and transmission type) compared to LPM, for a large 

number of RPM users.

3. Response surface methodology (RSM) explores the relationships between several explanatory 

variables and one or more response variables. A sequence of designed experiments (DOE) was 

used, i.e., the main idea of RSM to obtain an optimal response. A DOE used in this case was 

based on an automated process that is configured to produce a complete set of ALPHA results 

for all combinations of engines, transmissions, roadloads, and vehicle types to be used in the 

OMEGA analysis. It is a relatively easy statistical model to estimate and apply, even when little 

is known about the process. It maximizes the production of a special substance by optimization 

of operational factors. A factorial experiment or a fractional factorial design generally used to 
RTI International Peer Review of EPA’s RSE Report 102

estimate RSE process has generated as series of equations from a complete set of ALPHA data 

for each vehicle type and powertrain model. A second-degree RSE polynomial model was 

developed for each 24 vehicle cases based on a combination of 6 vehicle types and 4 powertrain 

types in the present analysis.

4. A comparison of CO2 results between RSE and ALPHA has confirmed the validity of the data 

transfer between these two models thereby proving the accuracy of the technical application of 

response surface modeling. A total of 21 results (only 2020_TURB24 was available for 

LPW_LRL vehicle) out of total 24 vehicle types were examined for the RSM validation. 

Residuals were found to be between a narrow range of -1.0 and 1.0 gCO2/mile in all cases. The 

line slope of the plot of results of ALPHA and RSE was also found to be 45o and thus has 

ensured the validity of data transfer between them. In addition, as the physics behind the Mass, 

Aero, and Roll are quite linear in reality, and so CO2 emission impacts of any values between the 

range of these parameters were also found to be reasonable using the RSE results.

5. Section 6. Baseline Vehicle Adaptation needs further details in terms of the necessary process 

steps for adjusting the effectiveness of a baseline vehicle to match the ALPHA model. The 

adjustment approach for the baseline vehicle adaptation is an interesting one as it allows ~ 50 

alternative options to consider in a baseline vehicle.

6. Since the RSE final output is CO2 emissions provided to the OMEGA model with the 

technology alternatives necessary to produce the most cost-effective path for compliance, a short 

discussion of it will be useful for unfamiliar users.

7. A description of three different transmission types considered and denoted by numerals (i.e., 

2, 4, & 5) would be useful. An appropriate justification needs to be included why other two 

types, i.e., 1 and 3 were not considered for the RSM DOE analysis.

8. It is unclear why the assumed vehicle mass reduction value is not actually reflected in the 

ALPHA spreadsheets provided, e.g., for 2020 TURB24 vehicle, 3109.15 lbs and 2961.3 lbs Test 

Weight have been assumed for a mass reduction of 5% and 10%, respectively, for a baseline 

vehicle Test Weight of 3257 lbs? Similar level of difference was found in all 21 different vehicle 

type/powertrain considered for RSM.
RTI International Peer Review of EPA’s RSE Report 103

9. The draft report mentions about six vehicle types in OMEGA analysis and four powertrain 

categories in the ALPHA. It is unclear about the consistency in the number of vehicle types and 

powertrain categories between these two tools and thereby to what extent does the current RSM 

cover the overall analysis scope of the OMEGA technology options?

10. In spite of the fact that there are four independent variables, i.e., mass reduction, 

aerodynamic drag reduction, rolling resistance reduction, and transmission type have been used 

for the development of RSE equations, but +50 ALPHA data variables have been included in the 

several vehicle spreadsheets provided. It’d be good to provide the description of each of the 

ALPHA variables for an understanding of impacts of the four major dependent variables 

considered.

11. As the RSE “Effectiveness” implementation is expanded beyond the currently limited six 

vehicles, four powertrains, and three transmission type options provided, the user-friendliness in 

terms of inputs should be kept in mind. Using the current framework provided as an example, it 

is difficult for a novice user to perform a quick analysis. Specifically, a discussion on the 

“Baseline Vehicle Adaptation” procedure needs to be included in the documentation, when all 

original LPM technology options are also available for RSM for the baseline vehicle adaptation. 

Some Comments/Warning should be included if the results are invalid for transmission cases 1 & 

3 as is the case now. The inputs for Vehicle Type, Model, and Transmission in Column A should 

be interlinked with the corresponding numeric value in Column B on this worksheet.

12. It’d be useful for the EPA draft report completeness to provide some background information 

on the models and tools used in EPA’s light-duty Greenhouse Gas (GHG) rulemakings for 

unfamiliar audience. 

13. Not sure whether any model validation was done in terms of using the model to predict the 

response for one or more combinations of design factors that were not used to build the RSM 

models? What agreements between the two results were found for such a validation?

14. Overall, the quality of RSE methodology appears to be reasonable for the four independent 

variables considered. The validity of this methodology need to reexamined if it is expanded to a 

higher number of independent variables in the future.
RTI International Peer Review of EPA’s RSE Report 104

Appendix E. Doug Montgomery Comments
RTI International Peer Review of EPA’s RSE Report 105

Douglas C. Montgomery

3841 East Talowa Street

Phoenix AZ 85044

480.496.8872

30 November 2017

Kyle Clark-Sutton 

Research Economist, RTI International 

3040 Cornwallis Road 

PO Box 12194, Research Triangle Park, NC 27709 

RE: Peer Review of EPA’s Response Surface Equation Report 

Dear Mr. Clark-Sutton: 

Thank you for asking me to participate in a peer review of EPA’s Response Surface Equation 

Report. I have completed the requested review. 

Previously I have sent you a summary of my review comments and recommendations. These 

comments are made on the basis of the current state of science as I understand it. To the best of 

my knowledge, I have no real or perceived conflicts of interest in conducting this review. 

Please feel free to contact me should you have any questions or need additional regarding this 

review. 

Sincerely,

Douglas C. Montgomery
RTI International Peer Review of EPA’s RSE Report 106

ALPHA is the acronym for the Advanced Light-Duty Powertrain and Hybrid Analysis full vehicle 

simulation model developed to study greenhouse gas emissions from vehicle internal combustion engines. 

This is a validated model that has been shown to provide accurate prediction of emissions for various 

combinations of engines and vehicle types. However, running the ALPHA model is very time-consuming. 

In situations like this a standard industry practice is to replace the computer model with a statistical model 

that can be executed more quickly but which has comparable accuracy in prediction. Such a statistical 

model is usually called a metamodel. 

A widely used approach to creating the statistical metamodel is to conduct a designed experiment on the 

computer model investigating factors of interest to the analysts and then fit the model to the data resulting 

from the experiment. Response surface methodology (RSM) is a standard technique for this purpose.

This report is a review of the RSM models produced for the ALPHA simulation model. There are 24 

models representing a range of powertrain and vehicle types. For each RSM model I was furnished with 

a spreadsheet that contained the designed experiment that was performed on that configuration of the 

ALPHA model, along with the observed responses, the predicted responses from the RSM model, and the 

residuals. For these experiments the inputs factors for the design include Mass Reduction, Aero Drag 

Reduction, Rolling Resistance Reduction, and Transmission type. The experiments used were variations 

of standard factorial designs. Response surface models were fit to the experimental results to produce the 

spreadsheet outputs that I was given.

I investigated the adequacy of the RSM models by first analyzing the residuals from these models in the 

spreadsheets that were provided. I constructed normal probability plots of the residuals and plots of the 

residuals versus the predicted response. These plots investigate the normality of the response variable 

and the equality of variance assumption, both of which are standard RSM assumptions. The normality 

assumption is of only moderate importance since the underlying statistical methodology is robust to all 

but severe departures from normality. The equal variance assumption is more important, and moderate to 

large departures from this assumption may require remedial measure such as the use of variancestabilizing transformation. A few of the normal probability plots exhibited very small potential 

departures from normality but nothing severe enough to call model validity into question. Similarly, 

some of the plots of residuals versus the predicted response exhibited a non-random pattern, but none of 

the patterns were serious enough to question the equal variance assumptions. It is also worth noting that 

the model residuals ae extremely small as all models provide extremely good fits to the data obtained 

from the simulation model.

I selected a subset of the 24 models for further investigation. I loaded the experimental designs for these 

models into JMP PRO V 13 and performed my own RSM analysis, fitting the standard second-order 

model. The results for one of these RSM model from spreadsheet HPW 1026 2017a tab 2014 GDI are 

discussed below. This is typical of the results I obtained for all models that I investigated.
RTI International Peer Review of EPA’s RSE Report 107

Plot of actual versus predicted response:

The points in this plot lie almost exactly along a straight line, indicating excellent agreement between 

the simulation model output and the predicted value from the second-order RSM model.

Summary of fit and analysis of variance for the RSM model:

The R

2

statistic for the model exceeds 0.99, indicating that most of the variability in the sample data (in 

excess of 99%) is explained by the RSM model. Also, the R

2

-adjusted statistic is also in excess or 0.99. 

R

2

-adjusted is a reflection of potential overfitting; that is including terms not really important in the 

model just to inflate the ordinary R

2

. When these two statistics are in close agreement as they are here 

there is likely to be no substantial issue with overfitting. The analysis of variance indicates that the 

model contains at least one statistically significant term.

RSM Model Parameters Estimates:
RTI International Peer Review of EPA’s RSE Report 108

The second –order model contains 15 parameters; an intercept, four main effects, six 2-factor 

interactions, and four quadratic terms. The parameter estimates display indicates that all but two of 

these terms are statistically significant at the 0.05 level. However, in RSM we usually think that it’s the 

order of the model that is most important so we often do not remove non-significant terms from the 

model unless there are many of them. That is not the case here.

The PRESS Statistic

In model validation it is important that the model both fit the sample and that it provide good 

predictions of new data. The PRESS (Prediction Error Sum of Squares) statistic, reported below, is a 

standard one-sample-at-a-time cross-validation used to assess potential prediction performance.

Notice that the PRESS statistic is very similar to the residual sum of squares from the analysis of 

variance. An R

2

-like prediction error statistic can be computed from PRESS simply by replacing the 

residual sum of squares in the equation for R2 by PRESS. This gives:

2

Prediction

22.449 1 1 0.9999

273428

PRESS R

TotalSS

    

We would expect the RSM model to explain in excess of 99% of the variability in data produced by the 

simulation model. This is excellent validation of potential prediction performance.

Summary of Conclusions

I conclude that the RSM approach has produced statistical metamodels that are an excellent alternative 

to the APLHA simulation model. So long as they are used to interpolate over the ranges of the four 

factor used in their construction I expect that they will be excellent alternatives to the ALPHA simulation 

procedure.
RTI International Peer Review of EPA’s RSE Report 109

Appendix F. Response Surface Report
RTI International Peer Review of EPA’s RSE Report 110

EPA Report on the Implementation of Response 

Surface Statistical Methods to Reproduce 

ALPHA Modeling Results in the OMEGA 

Model Preprocess

J. Cherry

March 2017
RTI International Peer Review of EPA’s RSE Report 111

1 Background:

For the Light-Duty (LD) Greenhouse Gas (GHG) rulemakings created by the Environmental 

Protection Agency (EPA) including the MY 2012-2016 and MY 2017-2025 Final Rules, estimates for the 

effectiveness of vehicle technologies have played an important role as a robust input into the overall 

rulemaking analysis process and as input to EPA’s Optimization Model for reducing Emissions of 

Greenhouse gases from Automobiles (OMEGA). 

For each Light-duty Greenhouse Gas rulemaking, from the 2009 FRM through the 2016 Proposed 

Determination, EPA has applied a combination of full-vehicle simulation modeling and a Lumped 

Parameter Model (LPM). The LPM methodology has been continuously developed, refined, and 

calibrated throughout each of these rulemakings to reflect the latest technology developments and 

comments received regarding the application of the LPM. The National Academy of Sciences (NAS) 

reviewed the application of the LPM in their 2011 and 2015 reports on technologies available for 

reducing fuel consumption and found the LPM to be robust and to accurately predict the overall 

effectiveness of combinations of technology. While EPA continues to believe that the LPM is an 

appropriate and accurate means of assessing technology packages, the efficient transposition of fullvehicle simulation results into OMEGA inputs has historically required many hours of manual 

calibration, that has not been well understood by our stakeholders.

In response to comments received from stakeholders and in an effort to reduce the manual 

interpretation and calibration of the LPM, EPA is considering replacing the LPM with an industry 

standard statistical methodology. This methodology, commonly known as a Response Surface Model 

(RSM), computationally is able to synthesize large numbers of simulations and distill the outputs into an 

equation which represents the effectiveness of technology packages. This latest process to reproduce these 

technology effectiveness estimates in real time for OMEGA is the subject of this report. First, some 

history of the process for reference.

1.1 History:

One method for determining the effectiveness value for a vehicle technology package required for an 

OMEGA analysis would be to run a validated full vehicle simulation. In practice, robust full vehicle 

simulations require a considerable set of data and a finite amount of time to execute for each simulation. 

During a typical analysis cycle, many thousands of simulations are performed. For example, 

preprocessing data for an OMEGA run requires approximately one million technology package results 

that would require several days to execute on a modern computer. This situation along with the lack of a 

complete set of engine maps, transmission maps, and other validated data required for such simulations 

during the analysis for the MY 2012-2016 Final Rule required an alternative solution. 

In response to this need, EPA combined an extensive library of full vehicle simulation data, test data, 

and public literature to create the Lumped Parameter Model (LPM). Historically, the LPM has been 

implemented as a spreadsheet method to provide vehicle technology package effectiveness values in the 

preprocessing phase for the OMEGA model. The LPM was originally based on the techniques for 

combining (lumping) various vehicle technologies into their various loss categories as detailed in a SAE 

paper by General Motors (2002-01-0628). This lumping process results in a first-principles energy 
RTI International Peer Review of EPA’s RSE Report 112

balance accounting for the various synergies and disynergies as the technologies are merged to reduce 

double counting and missed efficiencies. The result is a final effectiveness value to represent the changed 

efficiency of the vehicle as the result of the additional (or subtracted) technologies. An example of these 

loss categories is shown in Figure 6 and the original Excel version of the LPM is shown in Figure 7. 

FIGURE 6 – EXAMPLE LOSS CATEGORIES IN A LIGHT DUTY VEHICLE
RTI International Peer Review of EPA’s RSE Report 113

FIGURE 7 - ORIGINAL LPM SPREADSHEET

The LPM user interface consisted of selecting a vehicle type and various technology combinations 

(see arrows in Figure 7) to calculate the final result in percent. In practice the spreadsheet was used 

twice, first to calculate a percentage effectiveness improvement using the technologies on a baseline 

vehicle, and second to calculate a percentage effectiveness improvement with additional technologies 

applied to the same baseline vehicle. This process was automated to provide approximately fifty 

improvements for each baseline vehicle as input to the OMEGA model.

The LPM was validated for present day vehicles and technologies by comparing the results to test 

data from various EPA databases and a contract with Ricardo Inc. provided another set of simulation 

results for validating present and future technologies. For the regulatory activities associated with the 

EPA Staff Deliberative Materials--Do Not Quote or Cite

Vehicle type: Standard Car Description: Technology picklist

Family Package: Z

Heat 

Lost To

Vehicle Parasitics Gearbox, Exhaust &

Mass Drag Tires T.C. Coolant

Inertia Aero Rolling Access Trans Friction Pumping Ind Eff Second

Load Load Load Losses Losses Losses Losses Losses Law Check

13.0% 4.0% 4.0% 1.8% 4.2% 6.6% 4.4% 32.0% 30.0% 100.0% OK

0% 16% 8% 64% 33% 16% 75%

13.0% 3.4% 3.7% 0.8% 3.3% 5.6% 1.1% 31.8% 30%

Indicated Mech Brake Drivetrain Fuel Road

Efficiency Efficiency Efficiency Efficiency Efficiency Loads

Baseline 38.0% 71.1% 27.0% 77.8% 21.0% 100.0%

New 38.2% 82.5% 31.5% 87.2% 27.5% 95.4%

PMEP Brake

72.9% Fuel Consumption Original friction/brake ratio Losses Efficiency

27.1% FC Reduction Based on PMEP/IMEP >>>> 11% 27%

37.2% FE Improvement (GM study) =71.1% mech efficiency

N/A Diesel FC Reduction

Independent User Picklist

Technology FC Estimate Loss Category Implementation into estimator Include? (0/1) Gross FC Red

Aero Drag Reduction 3.0% Aero 16% aero (cars), 10.5% aero (trucks) 1 3.0%

Rolling Resistance Reduction 1.5% Rolling 8% rolling 1 1.5%

Low Fric Lubes 0.5% Friction 2% friction 1 0.5%

EF Reduction 2.0% Friction 8.5% friction 1 2.0%

ICP 2.0% Pumping 12% pumping, 38.2% IE, -2% fric 0 0.0%

DCP 3.0% total VVT Pumping 18.5% pumping, 38.2% IE, -2% fric 0 0.0%

CCP 3.0% total VVT Pumping 18.5% pumping, 38.2% IE, -2% fric 1 3.0%

Deac 6.0% Pumping, friction 39% pumping 0 0.0%

DVVL 4.0% Pumping 30% pumping, -3% friction 1 4.0%

CVVL 5.0% Pumping 37% pumping, -3% friction 0 0.0%

Camless 10.0% Pumping 76% pumping, -5% friction 0 0.0%

GDI 1.5% Ind Eff 38.6% Ind Eff 0 0.0%

Turbo/Dnsize 6.0% Pumping 39% pumping 0 0.0%

5-spd 2.5% Trans, pumping 22% pumping, -5% trans 0 0.0%

CVT 6.0% Trans, pumping 46% pumping, -5% trans 0 0.0%

ASL 1.5% Pumping 9.5% pumping 1 1.5%

Agg TC Lockup 0.5% Trans 2.5% trans 1 0.5%

6-spd auto 5.5% Trans, pumping 42% pumping, -5% trans 1 5.5% Or #44/45

AMT 6.5% Trans 35% trans (increment) 1 6.5%

42V S-S 7.5% F, P, A 13% friction, 19% pumping, 38% access 1 7.5%

12V acc + Imp alt 1.5% Access 18% access 0 0.0% Or #53

EPS 1.5% Access 18% access 1 1.5%

42V acc + imp alt 3.0% Access 36% access 1 3.0% Or #51

HCCI dual-mode 11.0% Ind. Eff, pumping 41% IE, 25% pumping 0 0.0%

GDI (lean) 10.5% Ind. Eff, pumping 40% IE, 38% pumping 0 0.0%

Diesel - LNT 30.0% over gas Ind Eff, pumping 48% IE, 85% pumping, -13% friction 0 0.0%

Diesel - SCR 30.0% over gas Ind Eff, pumping 46% IE, 80% pumping, -13% friction 0 0.0%

Opt. E25 8.5% Ind. Eff, pumping 39% IE, 40% pumping 0 0.0%

33.6%

% of original fuel

Pick one or 

6-spd

Pick one

Vehicle Energy Effects Estimator

Pick one

Pick one

Current Results

Indicated Energy

Brake Energy Engine Friction

Road Loads

Baseline % of fuel

Reduction
RTI International Peer Review of EPA’s RSE Report 114

Light-duty Greenhouse Gas Midterm Evaluation (MTE), the LPM has also been calibrated with results 

from EPA’s full-vehicle simulation model, ALPHA. 

2 Development of ALPHA:

After the completion of the MY 2017-2025 LD Final Rule, EPA began an extensive project to 

benchmark a wide variety of engines, transmissions, and vehicles to create the Advanced Light-Duty 

Powertrain and Hybrid Analysis (ALPHA) full vehicle simulation model based on the existing GEM 

model used for heavy duty compliance purposes. The intent of this project was for the ALPHA model to 

be fully functional, validated, and peer reviewed for use during the MY 2017-2025 LD Final Rule 

Midterm Evaluation (MTE) process. 

As the MTE progressed, the ALPHA model matured and was capable of providing most of the 

technology package effectiveness values needed for the OMEGA analysis. With the ALPHA model 

results being applied widely across EPA’s analyses, the LPM quickly became less a model and more a 

repeater of ALPHA model results. For the Proposed Determination phase of the MTE, EPA recognized 

that a more efficient and less complex method could be developed to access ALPHA results directly. In 

addition, EPA now has the capability to perform large scale simulation using ALPHA and the application 

of these large-scale simulation results requires a more streamlined and less manual process. EPA 

considered several alternatives for its future analyses with respect to the application of simulation results. 

Figure 8 illustrates some possible methods to accomplish this task.

FIGURE 8 – POSSIBLE ALTERNATIVES TO ACCESS ALPHA RESULTS

Discussion for each alternative:

• Maintain LPM:

o The LPM was originally designed to generate values - not to match several models 

simultaneously resulting in a significant increase in complexity and maintenance. While 

EPA feels that the LPM continues to be a robust and accurate tool, the calibration and 

maintenance of the tool is manually intensive.

• Run ALPHA in Real-Time:

o Running ALPHA in real-time would be the ideal solution, however, as discussed earlier, 

the execution time would be prohibitive. 

• Create full ALPHA Matrix of results:
RTI International Peer Review of EPA’s RSE Report 115

o The execution time would be prohibitive initially unless significant budget and resources 

are dedicated for access to a complex cloud or supercomputing system. This process 

would have to be repeated for any additions or changes to the ALPHA model and would 

impact sensitivity analyses depending on scenario.

• Create Response Surface Equations (RSEs) from ALPHA results:

o The RSEs allow any ALPHA run to be derived at a similar speed as the current 

spreadsheet LPM.

o Requires no programming or calibration – A simple check sheet verifies RSE alignment 

with ALPHA results.

o An overnight batch job producing several thousand ALPHA results is sufficient to create 

a set of RSEs.

o This method was chosen and is described in the following sections.

3 New Method for OMEGA Preprocess:

Given the above discussion along with the ALPHA model now capable of providing most of the 

needed effectiveness data, the LPM is no longer required and can be replaced with industry standard 

Response Surface Equations. This technique allows any combination of the ALPHA full vehicle 

simulations to be accessed in real time to assemble the necessary effectiveness data for the OMEGA 

Model. 

This process begins by instructing the ALPHA model to execute a Design Of Experiments (DOE) to 

provide the necessary inputs to the RSE. The DOE used for this task is an automated process that is 

configured to produce a complete set of ALPHA results for all combinations of engines, transmissions, 

roadloads, and vehicle types to be used in the OMEGA analysis. The DOE generates thousands of 

modeling results to populate the statistical RSE generation tool. EPA adheres to a “Performance Neutral” 

methodology for all rulemaking simulation work as described in Chapter 2 of the Technical Support 

Document (TSD) of the Proposed Determination (PD) phase of the MTE1. Several ALPHA runs are 

executed per table point and the run closest to the same performance as the base vehicle is selected to 

ensure the DOE is populated with “Performance Neutral” results. For this example, the inputs to the RSE 

include Mass Reduction, Aero Drag Reduction, Rolling Resistance Reduction, and Transmission type. A

small sample of the 21,000+ ALPHA results used to generate the RSE for vehicle type MPW_LRL is 

shown in Table 2.

 

1 https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P100Q3L4.pdf
RTI International Peer Review of EPA’s RSE Report 116

Mass

Reduction

Aero

Reduction

Rolling

Reduction

Transmission

Type

CO2

g/mi

5% 10% 15% 4 215.7

5% 10% 20% 4 213.4

5% 15% 0% 4 219.9

5% 15% 5% 4 217.9

5% 15% 10% 4 215.6

5% 15% 15% 4 213.3

5% 15% 20% 4 211.1

5% 20% 0% 4 217.6

5% 20% 5% 4 215.4

5% 20% 10% 4 213.2

5% 20% 15% 4 210.9

5% 20% 20% 4 208.8

0% 0% 0% 5 222.8

0% 0% 5% 5 220.4

0% 0% 10% 5 218.3

0% 0% 15% 5 215.9

0% 0% 20% 5 213.7

0% 5% 0% 5 220.5

0% 5% 5% 5 218.1

0% 5% 10% 5 215.9

0% 5% 15% 5 213.8

0% 5% 20% 5 211.3

0% 10% 0% 5 218.2

0% 10% 5% 5 215.9

0% 10% 10% 5 213.7

0% 10% 15% 5 211.4

0% 10% 20% 5 209.0

0% 15% 0% 5 216.0

0% 15% 5% 5 213.8

0% 15% 10% 5 211.3

0% 15% 15% 5 208.8

0% 15% 20% 5 207.0

0% 20% 0% 5 213.5

0% 20% 5% 5 211.2

0% 20% 10% 5 208.9

0% 20% 15% 5 206.6

0% 20% 20% 5 204.6

TABLE 2 – SMALL SAMPLE OF ALPHA RESULTS FOR VEHICLE TYPE MPW_LRL
RTI International Peer Review of EPA’s RSE Report 117

4 RSE Generation:

The next phase of the RSE process generates a series of equations from a complete set of ALPHA 

data for each vehicle type and powertrain model. The sets of data used in this process are shown in 

Figure 11. EPA used the industry standard statistical software JMP from SAS2to create the response 

surface equations. The complete table of ALPHA results for a particular vehicle type and powertrain 

model is entered and an example Response Surface Equation result is shown in Figure 9.

220.667785899048+-14.9927683931428*((Mass-0.1)/0.1)+-4.89195006285714*((Aero-0.1)/0.1)+-

4.37358584114285*((Roll-0.1)/0.1)+-18.64483848*((Trans-3)/2)+((((Mass-0.1)/0.1)*(Aero-0.1))/0.1)*-

0.0912061119999999+((((Mass-0.1)/0.1)*(Roll-0.1))/0.1)*0.405110426666666+((((Aero-0.1)/0.1)*(Roll0.1))/0.1)*0.0391345333333341+((((Mass-0.1)/0.1)*(Trans-3))/2)*1.52171338742856+((((Aero0.1)/0.1)*(Trans-3))/2)*0.17252074057143+((((Roll-0.1)/0.1)*(Trans-3))/2)*0.22308929142857+((((Mass0.1)/0.1)*(Mass-0.1))/0.1)*-0.158016769523809+((((Aero-0.1)/0.1)*(Aero0.1))/0.1)*0.0321151047619073+((((Roll-0.1)/0.1)*(Roll-0.1))/0.1)*0.00802461333332157+((((Trans3)/2)*(Trans-3))/2)*-1.66503986133335

FIGURE 9 – EXAMPLE RSE FOR VEHICLE TYPE = MPW_LRL AND MODEL = 2014_GDI

Throughout this process, the ALPHA results are compared to the RSE results as shown in Figure 10

to ensure the validity of the data transfer from ALPHA and RSE equation implementation. An added 

benefit of this comparison is the verification that the ALPHA model results are smooth and predictable as 

expected.

 

2 https://www.jmp.com/en_us/software/data-analysis-software.html
RTI International Peer Review of EPA’s RSE Report 118

FIGURE 10 – ALPHA VS RSE CO2 VALUES

5 Practical Implementation:

The practical implementation of the RSE method uses a similar effectiveness spreadsheet and user 

interface format as before replacing the LPM with methods described in this document. This similar 

format and user interface avoided disruptive modifications to the existing OMEGA preprocess and 

continues the practicality of a visual tool for verification purposes and transparency for stakeholders. The 

new effectiveness tool has been reduced to a single spreadsheet tab labeled “Effectiveness” as the LPM 

and associated equations no longer exist. 

5.1 RSE Layout

The current OMEGA analysis consists of six vehicle types based on power to weight ratio and road 

loads:

• Low Power/Weight – Low Road Load (Typical Small Car)

• Medium Power/Weight – Low Road Load (Typical Standard Car)

• High Power/Weight (Typical Large Car)

• Low Power/Weight – High Road Load (Typical Small SUV)

• Medium Power/Weight – High Road Load (Typical Large SUV)

• Truck (Typical Full Size Pickup)

170

180

190

200

210

220

230

240

250

260

170 180 190 200 210 220 230 240 250 260

RSE CO2 (g/mile)

ALPHA CO2 (g/mile)

Alpha vs RSE CO2 Values
RTI International Peer Review of EPA’s RSE Report 119

The current ALPHA model consists of four powertrain categories:

• 2014 GDI (2014_GDI)

• 2014 Atkinson (2014_ATK2)

• 2020 Atkinson (2020_ATK2)

• 2020 24 Bar Turbo (2020_TURB24)

The six vehicle types combined with the four model categories result in twenty-four RSEs as shown 

in Figure 11.

FIGURE 11 – RSE LAYOUT

5.2 Process Summary

A summary of the process is shown in Figure 7. Simply stated, the inputs are applied to the 

selected RSE and the corresponding ALPHA CO2 value is generated.

FIGURE 12 – RSE IMPLEMENTATION SUMMARY
RTI International Peer Review of EPA’s RSE Report 120

5.3 Example Case

An example case of the “Effectiveness” RSE in shown in Figure 13. The Vehicle Type selection and 

Model selection determine the appropriate RSE to be used. In this example the RSE equation in Figure 9

(MPW_LRL 2014_GDI) has been selected with the inputs:

• Mass Reduction = 5% 

• Aero Drag reduction = 15% 

• Rolling Resistance reduction = 5% 

• Transmission = TRX21 (4)

THE RESULTING VALUE FROM THE RSE IS CALCULATED AS SHOWN IN FIGURE 13 CLOSELY 

MATCHING THE ALPHA VALUE FROM THE RSE INPUT TABLE IN TABLE 3.

FIGURE 13 – EXAMPLE CASE

TABLE 3 – EXAMPLE ALPHA RESULT FROM RSE INPUT TABLE

Mass

Reduction

Aero

Reduction

Rolling

Reduction

Transmission

Type

CO2

g/mi

5% 10% 15% 4 215.7

5% 10% 20% 4 213.4

5% 15% 0% 4 219.9

5% 15% 5% 4 217.9

5% 15% 10% 4 215.6
RTI International Peer Review of EPA’s RSE Report 121

Comparisons of execution time has shown the RSE method is similar to the LPM including the 

overhead of the automation system. The added benefits include:

• Elimination of LPM programming and calibration

• Increased transparency regarding synthesis of ALPHA simulation into OMEGA modeling

• Real-Time extraction of ALPHA results with the ability to quickly represent the latest available 

benchmarking and simulation data in greenhouse gas analyses.

• Ability for stakeholders to readily reproduce the RSEs based on ALPHA simulations and/or their 

own large-scale simulation results.

• Vast speed improvement over executing ALPHA in Real-Time allowing the OMEGA analysis to 

run on standard EPA computing equipment without additional resources or budget.
RTI International Peer Review of EPA’s RSE Report 122

6 Conclusion

EPA’s Lumped Parameter Model has been a robust tool for estimating the effectiveness of light-duty 

vehicle technology packages to reduce greenhouse gas emissions. While EPA continues to believe that 

the continued application of the LPM would provide accurate assessments, we also recognize that the 

required manual calibration of the LPM and the associated interpretation of ALPHA full-vehicle 

simulation could be improved. EPA considered several alternatives in considering the future 

development or replacement of the LPM. EPA has found the most efficient approach is to replace the 

LPM with statistically derived Response Surface Equations. EPA believes this change in methodology 

will allow the agency to more readily access large-scale simulation results, improve the robustness of the

analyses, and improve transparency in the OMEGA process.