NISTIR 7790

Simulation and Analysis for 

Sustainability in Manufacturing 

Processes

Alexander P. Muroyama

Mahesh Mani 

Kevin Lyons

Bjorn Johansson
2 

NISTIR 7790

Simulation and Analysis for 

Sustainability in Manufacturing 

Processes 

Alexander P. Muroyama

Mechanical Engineering Department 

University of Maryland, College Park 

Mahesh Mani

Kevin Lyons

Manufacturing Systems Integration Division 

Engineering Laboratory 

Bjorn Johansson

 Product and Production Development 

Chalmers University of Technology 

 May 2011 

U.S. Department of Commerce 

Gary Locke, Secretary 

National Institute of Standards and Technology 

Pat Gallagher, Director
3 

Table of Contents

1. Introduction ................................................................................................................................. 4

2. Literature Review ........................................................................................................................ 5

3. Technical Approach..................................................................................................................... 6

4. Case Study Scenario .................................................................................................................... 7

4.1. Implementation ............................................................................................................................ 8

4.2. Input Data .................................................................................................................................... 8

4.3. Simulation Experiments ............................................................................................................ 10

4.4. Results& Discussions ................................................................................................................ 10

4.4.1. Throughput ........................................................................................................................ 10

4.4.2. Energy Consumption ......................................................................................................... 11

4.4.3. Material Usage & CO2 Emissions ..................................................................................... 11

5. Conclusion and Future Research ............................................................................................... 12

6. Acknowledgments ..................................................................... Error! Bookmark not defined.

7. References ................................................................................................................................. 13

8. Appendix: .................................................................................................................................. 15

List of Figures

Figure 1 : Technical Approach.......................................................................................7 

Figure 2 : Input-Output Diagram..................................................................................8 

Figure 3 : AutoMod Factory Layout (1 Injection Molding Machine)....................................8 

Figure 4 : AutoMod Factory Layout (2 Injection Molding Machines)........................................9 

Figure 5 : Weekly Throughput...................................................................................11 

Figure 6 : Energy Consumption.................................................................................12

Figure 7 : Annual CO2 Emissions...............................................................................13 

Figure 8. Injection Molding Machine .............................................................................15 

Figure 9. Injection molding of Golf balls.....................................................................15

Figure 9. Runtime snapshot from AutoMod with one injection molding machine...................16 

Figure 10. Runtime snapshot from AutoMod with two injection molding machines....................16 

List of Tables

Table 1: Injection molding data on golf balls................................................................10 

Table 2: Resource data.........................................................................................10 

Table 3: Material usage data..................................................................................13 
4 

Simulation and Analysis for Sustainability in Manufacturing

Processes

Abstract:

Sustainability has become a u biquitous term in almost every field, especially in engineering 

design and manufacturing. Recently, an increased awareness of environmental problems and 

resource depletion has led to an emphasis on environmentally friendly practices. This is 

especially true in the manufacturing industry where energy consumption and the amount of 

waste generated can be high. This requires proactive tools to be developed to carefully analyze 

the cause-effect of current manufacturing practices and to investigate alternative practices. One 

such approach to sustainable manufacturing is the combined use of Discrete Event Simulation 

(DES) and Life Cycle Assessment (LCA) to analyze the utilization and processing of 

manufacturing resources in a f actory setting. On an economic aspect such method can 

significantly reduce the financial and environmental costs by evaluating the system performance 

before its construction or use. This project considers what-if scenarios in a simplified golf ball 

factory, using as close to real-world data as possible, to demonstrate DES and LCA’s ability to 

facilitate decision-making and optimize the manufacturing process. Plastic injection molding, an 

energy-intensive step in the golf ball manufacturing process, is the focus of the DES model. 

AutoMod, a 3-D modeling software, was used to build the DES model and AutoStat was used to 

run the trials and analyze the data. By varying the input parameters such as type and number of 

injection molding machines and material used, the simulation model can output data indicating 

the most productive and energy efficient methods. On a more detailed level, the simulations can 

provide valuable information on bottlenecks or imbalances in the system. Correcting these can 

allow the factory to be both “greener” and more cost-effective. This study illustrates a small but 

important aspect of the supply chain thus demonstrating the systems approach to sustainability.

Keywords: Sustainability, Discrete Event Simulation, Life Cycle Assessment, Injection Molding

1. Introduction 

For many manufacturing companies, the term sustainability has become synonymous with not 

only the preservation of the environment, but importantly cost-savings and efficiency. Discrete

Event Simulation (DES) is a powerful computing technique for understanding the behavior of 

systems. A system is a collection of entities (e.g., people and machines) that interact over time. 

In a factory setting, DES software can be a vital tool in analyzing the efficiency. Through the 

consideration of what-if scenarios, conclusions can be drawn on how to optimize a system’s 

performance before its construction or use. Although DES is widely implemented as an analysis 

and problem-solving tool in factory designs, only recently has environmental sustainability been 

considered in conjunction with DES. Life Cycle Assessment (LCA) is an evaluation of a 

product’s environmental impact during its life cycle. Life Cycle Inventory (LCI) as part of LCA 

involves the data collection, modeling, description and verification of data. The LCI database in 

general encompasses all relevant data that help quantify the material and energy flows into and 
5 

out of the environment for relevant unit processes within study boundaries. When LCA is 

implemented along with DES, environmentally friendly decisions can be made that reduce a 

manufacturing system’s environmental impact to society.

Injection molding is one of the most common manufacturing processes in use today. About a 

third of all plastics by weight are processed through injection molding [1]. The process, which 

entails melting a polymer resin in a barrel and injecting it into a clamped mold, is very energyintensive (See Appendix Figure 8). In addition, the plastics themselves present environmental 

challenges: they are slow to break down in landfills and can release harmful substances during 

use or disposal [2]. At the same time, injection molding provides a straightforward and highly 

automated method of producing large quantities of products. Injection molding is one method of 

manufacturing two-piece golf balls. About a billion golf balls are produced each year, and like 

many manufacturing processes, it takes a s ignificant amount of energy and materials. For this 

reason, a simplified golf ball factory is the focus of our case study. 

 

2. Literature Review

The applications of DES are not limited to manufacturing systems design. Simulation in general 

can be used to evaluate efficiency and profitability in any queuing system, such as hospitals or 

airports. Additionally, 3-D modeling and simulation software are rapidly evolving and becoming 

more capable every year. Today, environmental responsibility is becoming increasingly relevant 

with growing concerns about pollution, resource depletion, global warming, and energy. One 

approach to improving sustainability in manufacturing is combining DES and LCA to analyze 

how changing various parameters affects the waste materials and energy used by the system 

along with the throughput. Few research publications in this area exist, but the area has gained 

popularity in the recent years 

Earlier Solding and Thollander [3] describe how DES could be utilized to reduce electricity 

consumption for foundries. Östergren et al. [4] and Johansson et al. [5] describe how DES could 

be utilized in combination with LCA for decreasing environmental impacts during food 

production. More recently Johansson et al. [6] used a model of a paint shop to demonstrate 

planning a manufacturing setup with an emphasis on sustainability. As far as injection molding 

goes, 3-D modeling has been used to analyze mold designs for efficiency, but has not been used 

to model the process at a systems level [7].

The energy implications of plastic injection molding have been the focus of several papers. More 

specifically, the three types of injection molding machine: all-electric, hydraulic, and hybrid 

have very different power requirements but largely perform the same functions. Hydraulic 

machines are more precise and better for large clamping forces, but all-electric machines are 

much more efficient in terms of energy consumption. Literature suggests that all-electric 

machines can have energy savings between 60 % and 70 % compared to hydraulic [1]. For this 

reason, there has been a recent trend towards all-electric injection molding machines and some 

manufacturers have stopped building hydraulic machines altogether. In the United States, the 

amount of energy consumed by injection molding alone is greater than the whole electric 
6 

production for some developed countries [8]. In the plastics industry, there is still room for 

improvement, especially in growing economies like India and China. 

The plastics production stage is energy intensive and has large emissions. The main metric for 

injection molding is energy consumption. Material waste and cycle times can be reduced with a 

hot runner system, in which the runners are thermally controlled to keep the plastic from 

solidifying [8]. The key parameters that factor into energy consumption are pressure, 

temperature, cycle times, and mass flow rate. For this reason, the amount of energy consumed by 

an injection molding machine can vary greatly depending on the material being processed. Gupta

et al. [2] describe a m ethodology for estimating the energy required to manufacture a part 

through injection molding so that appropriate changes could be made during the design stage. 

Golf balls are highly standardized products so the design cannot be changed much, but 

simulation can accurately estimate the materials used because of this standardization. Past 

literature has shown little emphasis on collecting accurate data to build simulation models [9]; 

this is perhaps because it is rare for companies to publicly release factory data. Note that the golf

ball factory system discussed in the paper is built with some real-world data (literature) and 

estimated data (for purposes of simulation). 

3. Technical Approach

The study aimed to define how best DES and Life Cycle Inventory (LCI) data, as part of LCA, 

could be combined to help analyze the utilization and processing of manufacturing unit 

processes. Figure 1 illustrates the technical approach. In manufacturing, a unit process is a single 

component part of the end-to-end manufacturing process that transforms raw materials into 

finished goods. Additionally, the study sought to determine the implications of such methods and 

evaluations to reduce the financial and environmental costs for energy intensive manufacturing 

processes. In building the model, the current LCI for injection molding was evaluated. The 

following what-if scenarios in a simplified golf ball factory using as close to real-world data as 

possible demonstrate DES and LCA’s ability to facilitate decision-making and optimize energy 

intensive manufacturing processes such as injection molding. 

Figure 1. Technical approach 
7 

4. Case Study Scenario 

The simulated golf ball factory is composed of five steps: Injection Molding, Polishing, Painting, 

Coating, and Packaging. For the purposes of the DES simulation, Injection Molding is split into 

two steps: plasticizing and clamping. The ionomer resin cover material is melted in the barrel 

and injected around the rubber core of the ball, held by pins in the mold (See Appendix Figure 

9). Once it is cooled, it is ejected and the seam and rough spots on t he ball are removed by 

polishing. After the polishing is over, the ball receives two coats of paint and a final clear coat. 

The last step is packaging the balls in boxes before they are ready for transport. One load 

produces 16 golf balls. 

Figure 2. Input-Output diagram for golf ball manufacturing

The input-output diagram of the model (Figure 2) shows the unit processes in the model. Each 

of the steps requires some amount of energy, and most require a material input. The left half is 

devoted entirely to injection molding. The elements in red are not included in the model either 

due to unavailability of information or they were outside the scope of the project. There will also 

be energy losses in almost every step due to heat lost to the environment or inefficiencies within 

the machine. The drying and stamping procedures of golf ball manufacturing are omitted from 

this study.
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4.1. Implementation

Figure 3. Simplified golf ball factory AutoMod simulation with one injection molding machine

Figure 4. Golf ball factory simulation with injection molding machines in parallel

The case study scenario was implemented using AutoMod, a flexible 3-D simulation software 

that is ideal for manufacturing systems. AutoStat, a statistical analysis software for AutoMod, 

was used to run the trials. The above snapshots (Figures 3 & 4) show the set-up of the factory 

and how adding a second injection molding machine would affect it. Figure 3 also shows how 

the polishing machine becomes the bottleneck with the golf balls queuing up. 

4.2. Input Data

The injection molding component of the simulation is largely based on real-world data from 

Thiriez’s thesis on i njection molding [8]. Table 1 shows the injection molding data. For an 

unspecified type of Surlyn, DuPont’s proprietary golf ball cover resin, the energy consumption 

and cycle times are given (Table 1). The two types of ionomer resins examined in the following 

simulation are Surlyn 9320W, which has the lower melting point of 70°C and therefore the 

shorter plasticizing cycle time, and Surlyn 8670 with a melting point of 100°C [10]. Polishing, 

painting, coating, and packaging machine data are estimated using similar equipment 

specifications. Material use is estimated through the standard requirements of golf balls. The 

material inputs are ionomer resin, rubber, paint, coating, and cardboard. Mean Time To Failure 
9 

(MTTF) and Mean Time To Repair (MTTR) were chosen randomly depending on the process 

(see Table 2). 

Table 1. Injection molding data on golf balls (From [8])

Table 2. Resource data used in the simulation

The default settings (Table 2) are for the use of a hydraulic injection molding machine and an 

ionomer resin that requires a shorter cycle time for the plasticizing process. It is assumed that 

production of the ionomer resin and rubber core occurs before the simulation and that a hot 

runner system is used. Energy is based on usage time of the machine and material used is based 

on throughput. 

r triangular triangular

Lower Limit N/A 17 18 14 9 5

Mode 10 20 20 17 10 7

Upper Limit N/A 23 22 20 11 9

Energy (kW)

Busy 7.32 10.98 9 5.97 5.97 7.5

Failures

Distribution Type exponential exponential exponential exponential exponential exponential

MTTF(min) 13 15 5 7 8 10

MTTR (min) 1 1 1 1 1 1

Material Consumed Ionomerresin (cover) Rubber(core) N/A Paint Coating Cardboard

Amountperload 0.2kg 0.4kg 0.0151L 0.0076L 0.15m^2

Unit Manufacturing Processes

Resource

Injection Molding

(Plasticizing)

Injection

Molding

(Clamping) Polishing Painting Coating Packaging

Processing Times (sec)

Cycle Time Distribution Type N/A triangular triangular triangula
10 

4.3. Simulation Experiments

In the AutoMod simulation model, four parameters were varied combinatorially. The parameters 

include: number of injection molding machines in parallel, the type of polymer used (affecting 

plasticizing time), the number of repairmen, and the type of injection molding machine 

(hydraulic, all-electric, or hybrid). Each scenario was run five times with a default time of one 

week, and then averaged. Figures 10 and 11 in the Appendix present the runtime snapshots from 

AutoMod simulation. 

Note that the primary purpose of the simulation is to provide information for requirements of a 

golf ball factory in order to make its design more sustainable. The responses are in terms of 

materials, energy, CO2 emissions, and throughput. The CO2 emissions data is given by an f factor 

for Maryland as described in Ameta et al. [11]. For Maryland, the value of f is 0.620 metric 

tons/MWh. Energy Information Administration reports CO2 emissions in terms of metric tons. 

The factor f is computed using the average energy mix available in each state in the USA and

includes equivalent computations of impacts from emission of CH4 and NOx into CO2. With this

information, decisions can be made that minimize energy usage while maximizing throughput. 

4.4. Results& Discussions

A number of conclusions can be drawn from the information provided by the simulation in terms 

of throughput, energy consumption, and materials. The following subsections discuss them 

specifically.

4.4.1. Throughput

Figure 5. Weekly throughput 

In both the single and parallel injection molding machine setups, the polishing machine was 

identified to be the utilization bottleneck. With one injection molding machine, adding a 

repairman had little effect (see Figure 5). H owever, both a second machine and a second 

repairman can increase the throughput to over 300,000 balls. In other words, a second repairman 

is needed to make the investment of another machine worthwhile. Plasticizing time had almost 

no effect on throughput for two machines, while using the higher melting point polymer for one 

machine compromises the throughput. 
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4.4.2. Energy Consumption 

Figure 6. Energy consumption 

The type of injection molding machine had a significant impact on e nergy consumption. For 

hydraulic machines, injection molding accounted for almost half of the total energy consumed, 

as opposed to about a quarter for all-electric machines. Injection molding was the most energyintensive step of the manufacturing process, unless the machine was all-electric. For the shorter 

plasticizing time and one repairman, the second machine was hardly used as shown by its small 

energy consumption (see Figure 6). In this case, type of machine would have an even greater 

effect on e nergy consumption because an idling hydraulic machine consumes much more 

electricity than an idling all-electric machine.

4.4.3. Material Usage & CO2 Emissions

In this work the CO2 emissions were computed purely as a function of machine energy 

consumption. Changing from a hydraulic to an electric machine could cut down almost a third 

of the total CO2 emissions in every scenario (see Figure 7). However, these were extremely 

modest estimates due to the exclusion of HVAC, lighting, waste materials and transportation 

energy. Though output materials are included in the results (Table 3), a full LCA would include

the environmental impacts of materials from extraction to disposal. 

Figure 7. Material usage emissions
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Table 3. Material Usage Data

Material Usage

Input parameters changed Output data from simulation

# of Injection 

Molding 

Machines

IM Type/ 

Power Draw (kW)

Plasticizing 

time (sec) Repairmen

Rubber 

(kg)

Polymer 

(kg) Paint (L) Coating (L)

Cardboard 

(m^2)

1 Electric (6.3) 20 1 4879 2439.2 184.2 91.8 1829.2

2 Electric (6.3) 20 2 8582 4290.8 324.4 162 3218

Depending on the requirements of the factory, the simulation runs makes it clear what kind of 

trade-offs are worth the investment. For example, if the weekly throughput of the factory must be 

more than 300,000; a second injection molding machine must be invested in. In terms of 

throughput, investing in a second machine is probably only worth it if another repairman is hired 

as well. The percentage increase in throughput is greater than the percentage increase in injection 

molding energy consumption when adding a second machine, provided that there are two 

repairmen and the factory is processing the higher-melting-point ionomer. However, one aspect 

of machine processing that the simulation model does not capture is idle energy consumption. 

While all-electric injection molding machines consume significantly less electricity when idle, in 

hybrid and hydraulic machines especially, idle energy consumption can be quite high. Therefore, 

the disparity between the energy consumptions of the different types of machines and one or two 

injection molding machines would be even greater than what is shown in the data. If the annual 

CO2 emissions must be below 100 metric tons all-electric machines must be used. 

Material waste is a large aspect of LCA and the simulation model gives estimates of material use 

by production yield. The extraction and processing of the raw materials consumes a significant 

amount of energy as does the transportation of those materials. Those aspects of the supply chain 

are beyond the scope of this study, but would increase the CO2 emissions drastically. In fact, the 

polymer production has the highest impact on the environment in the whole life cycle inventory 

of injection molding [8]. Additionally, hydraulic injection molding machines cause 

environmental problems in the form of hydraulic oil waste, which is not measured in this study. 

Coolant waste, which is generally water in a closed loop, is also not accounted for. 

The environmental impact of the paint and coating depend on the volatility of the compounds 

used. The Environmental Protection Agency (EPA) regulates the use of volatile organic 

compounds (VOCs), which pose health risks as well as environmental concerns. Golf ball 

manufacturers can typically save money by using VOCs rather than less-toxic aqueous solvents. 

Water-based coatings are much harder to evaporate, requiring energy-intensive, expensive drying 

ovens [12]. Weighing these trade-offs is something that would require further investigation and a 

more in-depth factory simulation. 

5. Conclusion and Future Research

The main purpose of the study was to illustrate the potential of incorporating LCA with the input 

data in discrete event simulation for manufacturing applications. Regulations on manufacturing 
13 

waste and energy usage are becoming increasingly stringent, and companies are looking for ways 

to reduce their carbon footprint without sacrificing the quality and quantity of their products. By 

designing a discrete event simulation of a factory setting in tandem with sustainability metrics, 

companies can focus their resources more effectively. While renewable energy sources are going 

to be instrumental for a sustainable future, optimization of factories through simulation is an 

immediate answer to resource depletion. 

This project was conducted as part of a Summer Undergraduate Research Fellowship (SURF) 

program at the National Institute of Standards and Technology (NIST). Owing to the duration of 

the project (3 months), there are several shortcomings with the design of this simulation which 

could be addressed in the future. The study could benefit from a systems approach to problem 

solving, where the focus is on the entire factory rather than injection molding in particular. The 

total energy statistic covers only the energy spent by the factory machines. The inclusion of 

energy used by HVAC and lighting systems, extraction, transportation, and processing of 

materials, conveyer energy, etc. would make the output data more applicable. The CO2 emissions 

by the simulated factory are likely a fraction of the emissions by a real-world factory. Future 

work could improve the accessibility of accurate real-world data, or programming sustainability 

metrics within the software itself, as demonstrated in the SIMTER project [13]. The lack of LCI 

data led to a general uncertainty with the results. Uncertainty quantification of the LCI datasets is 

a major work and must be done in the future to ensure reliable results. Finally, the study could 

also benefit from an analysis of the materials wasted during production and how much gets 

recycled. 

Disclaimer 

Mention of commercial products or services in this report does not imply approval or 

endorsement by NIST, nor does it imply that such products or services are necessarily the 

best available for the purpose. 

6. References

[1] Kanungo, A., Swan, E. 2008. A ll-Electric Injection Molding Machines. In 

Proceedings from the Thirteenth Industrial Energy Technology Conference, New 

Orleans, LA. 6-9 May 2008. 

[2] Gupta, S. K., Weissman, A., Ananthanarayanan, A., Sriram, R. 2010. A Systematic 

Methodology for Accurate Design-Stage Estimation of Energy Consumption for Injection 

Molded Parts. In Proceedings of the ASME 2010 International Design Engineering 

Technical Conferences & Computers and Information in Engineering Conference, 

Montreal, Canada. 15-18 August 2010. 

[3] Solding, P., Thollander. P. 2006. Increased Energy Efficiency in a Swedish Iron 

Foundry Through Use of Discrete Event Simulation. In Proceedings of the 2006 Winter 
14 

Simulation Conference, eds. Perrone, L. F., Wieland, F. P., Liu J., Lawson, B. G., Nicol, 

D M., Fujimoto, R. M. Monterey, CA, USA. 

[4] Östergren, K., Berlin, J., Johansson, B. Stahre, J., Tillman, A.M. 2007. A tool for 

productive and environmentally efficient food production management. European 

Conference of Chemical Engineering, Copenhagen, 16-20 September 2007. 

[5] Johansson, B., Stahre, J., Berlin, J., Östergren, K., Sundstrom, B., Tillman, A.M. 

2008. Discrete Event Simulation with Lifecycle Assessment data at a Juice 

Manufacturing System. In Proceedings of the 5th FOODSIM Conference, University 

College Dublin, Ireland. 

[6] Johansson, B., Mani, M., Skoogh, A., Leong, S. 2009. Discrete Event Simulation to 

Generate Requirements Specification for Sustainable Manufacturing Systems Design. In 

Proceedings of the 9th Performance Metrics for Intelligent Systems workshop, 

Gaithersburg, MD. 21-23 September 2009. 

[7] Mattis, J., Sheng, P., DiScipio, W., Leong, K. 1996. A Framework for Analyzing 

Energy Efficient Injection-Molding Die Design, Technical Report (CSM Report-96-09), 

California: University of California, Berkeley, 1996. 

[8] Thiriez, A. 2006. An Environmental Analysis of Injection Molding, Masters

Thesis at Massachusetts Institute of Technology, Cambridge, MA, USA.

[9] Massey, T., Wang, Q., 2007. The Development of a Discrete-Event Simulation Model 

to Aid the Design of Complex Manufacturing Systems. Engineering Letters 16.1 (2007). 

19 Feb. 2008. 

Web http://www.engineeringletters.com/issues_v16/issue_1/EL_16_1_20.pdf [Accessed 

July 16, 2010]. 

[10] Matweb [WWW]. [Cited 7.16.2010]. Available: http://www.matweb.com 

[11] Ameta, G., Mani, M., Rachuri, S., Feng, S. C., Sriram, R. D. and Lyons, K.W. 2009

Carbon weight analysis for machining operation and allocation for redesign', 

International Journal of Sustainable Engineering,2009. 

[12] U.S. Patent Application No. 12/336,728, Publication No. 20100151971 (published 

June 17, 2010) (Robert Wilson, applicant). 

[13] Heilala, J., Saija, V., Tonteri, H., Montonen, J., Johansson, B., Stahre, J., Lind, S. 

2008. Simlation-Based Sustainable Manufacturing System Design, In Proceedings of the 

2008 Winter Simulation Conference, eds. Mason, S.J., Hill, R. R., Piscataway, NJ: 

Institute of Electrical and Electronics Engineers, Inc.
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Appendix:

Figure 8. Injection molding machine 

(Source: http://plastics.inwiki.org/Injection_molding_machine) 

Figure 9. Injection molding of golf balls

(Source: madehow.com) 
16 

Figure 10. Runtime snapshot from AutoMod with one injection molding machine

Figure 11. Runtime snapshot from AutoMod with two injection molding machines in 

parallel