bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cellular Versus Functional Layouts Under a Variety of Shop Operating Conditions*
Authors: Shafer Scott M. , Charnes John M. 
Published: June 2007

ABSTRACTThis paper addresses the suitability of cellular manufacturing under a variety of operating conditions. Queueing theoretic and simulation models of cellular and functional layouts are developed for various shop operating environments to investigate several factors believed to influence the benefits associated with a cellular manufacturing layout. The queueing models show how operations overlapping, which is more practical with a cellular layout, becomes more beneficial as the lot size increases. The simulation models are developed to study the performance of cellular and functional layouts in a wide variety of operating environments by varying the levels of four factors: (1) the degree to which natural part families occur, (2) the number of operations required to process the parts, (3) the processing times of the parts at each machine, and (4) the lot size. Two response variables are used to measure shop performance: the average time spent by a batch in the system, and the average work‐in‐process level. Statistically significant reductions in the average time in the system and average work‐in‐process measures were detected for the cellular layouts in all the operating environments studied.
A Game‐Theoretic Approach to Quantity Discount Problems*
Authors: Chiang Wen‐Chyuan , Fitzsimmons James , Huang Zhimin , Li Susan X. 
Published: June 2007

ABSTRACTThe traditional quantity discount problem is analyzed from the perspective of game theory, including both noncooperative and cooperative models. For the noncooperative case, the Stackelberg equilibrium is derived. For the cooperative case, the Pareto Optimality criteria are used to find a group of optimal strategies. Both scenarios are illustrated through an example which quantifies the benefits resulting from cooperation between the buyer and the seller for game‐theoretic solutions using geometric programming.
Reliability and Coherence of Causal, Diagnostic, and Joint Subjective Probabilities
Authors: Wright M. Keith , Stokes Lynne , Dyer James S. 
Published: June 2007

AbstractProbabilistic knowledge is an important input to the analysis of many decisions, and may be required for long‐range forecasting, classical decision analysis, influence diagrams, fault tree analysis, and expert systems. If objective probabilities cannot be calculated, it is vital that decision makers use the best available subjective measures of probability. Standard mathematical theory allows the analyst several choices in framing subjective probability assessments. When assessing cause‐effect probabilities, the three choices are causal, diagnostic, and joint probabilities. Choosing among these is difficult in light of conflicting reports that, due to cognitive heuristics, probability judgment may be biased in various poorly understood ways. For example, it has been reported that, when judging causal probabilities, people are subject to the causal information bias, and thus upwardly revise their prior probabilities more than when judging diagnostic probabilities. On the other hand, it has also been reported that in some cases, people do not take proper account of new evidence, which results in under‐revision of prior probabilities. Furthermore it has been reported that, when assessing joint probabilities, people are subject to the conjunction fallacy, and thus often judge a joint probability to be higher than one of the two corresponding marginal probabilities. Our research compares the relative effects of these biases in a laboratory setting: we present new empirical results comparing the test‐retest reliabilities and under(over) revision rates of causal, diagnostic, and joint probabilities. Our results suggest that the tendency to both under‐revise and over‐revise prior probabilities is greatest when judging diagnostic probabilities, as opposed to either causal or joint probabilities. Furthermore our results suggest that joint probability judgment is more reliable than either causal or diagnostic probability judgment.
Perceived Service Quality and User Satisfaction with the Information Services Function*
Authors: Kettinger William J. , Lee Choong C. 
Published: June 2007

AbstractA constant concern of management information systems (MIS) researchers and practitioners has been improving user satisfaction with the information services function (USISF). Due to the growth of end‐user computing, decentralization, and alternative sources of supply, an organization's information services function (ISF) is now faced with serving customers that possess substantial discretion in their use and purchase of information systems (IS) services. This increasingly market‐oriented environment demands sensitivity to IS customers' expectations and perceived value of ISF services. One important source of guidance in such an IS management environment is to look at marketing literature for frameworks that may permit the ISF to more effectively determine and convey the value of their services. Recognizing the need to improve existing MIS measures of user satisfaction with the ISF, this study adapts the SERVQUAL measure from marketing to provide more specific information about user satisfaction with the information service function. It was found that, while the three original dimensions of the traditional user information satisfaction (UIS) measure remain strong predictors of overall ISF user satisfaction, two aspects of IS service quality, “reliability” and “empathy,” are also significant predictors. The results suggest that the original dimensions of UIS may not be comprehensive enough to capture the more detailed dimensions of ISF service quality in SERVQUAL, and that the reliability and empathy dimensions of service quality may be needed to supplement the traditional UIS measure in determining user satisfaction with the information services function. Implications for research and practice resulting from these findings are discussed.
Using Neural Networks to Determine Internally‐Set Due‐Date Assignments for Shop Scheduling*
Authors: Philipoom Patrick R. , Rees Loren Paul , Wiegmann Lars 
Published: June 2007

AbstractThe production control system for a shop can be viewed as consisting of three sequential stages, the order‐promising stage, the order‐release stage, and the dispatching (or shop floor) stage. The first stage, wherein a customer's job arrives and is assigned a due date, provides the focus for this research. In particular, the performance of six regression‐based due‐date assignment rules found in the literature is compared with due dates determined by a neural network. The purpose is to see whether neural networks hold any promise for application in this area.For the particular shop and the conditions studied, it is found that the neural network outperforms all six conventional rules according to mean‐absolute‐deviation (MAD) and standard‐deviation‐of‐lateness (SDL) criteria, although for one rule on the latter criterion, the difference is not statistically significant. Further analysis indicates that this conclusion generally holds both when the amount of data available is varied and a second, more structured shop is studied. On a third shop with random routings, the neural network outperforms the best conventional method according to the MAD measure, but results are mixed for the SDL criterion. The superior performance of the neural network leads us also to evaluate a regression model nonlinear in its independent variables, a case not considered in the due‐date literature. The nonlinear model generally outperforms the conventional rules on MAD and SDL. The neural network outperforms the nonlinear model on MAD, while the results for SDL are not as clear.
Planning and Control in Multi‐Cell Manufacturing
Authors: Steele Daniel C. , Berry William L. , Chapman Stephen N. 
Published: June 2007

This research compares Material Requirements Planning (MRP), Kanban, and Period Batch Control (PBC) as alternative approaches to the planning and control of multi‐cell manufacturing involving flow cells and assembly. Since previous research on performance of these systems in cellular manufacturing has been primarily conceptual, the experiments reported here provide new insights into their comparative performance.The results show that the production environment is a major factor in system choice. Three operating factors—Master Production Schedule (MPS) volume variation, MPS mix variation, and setup time/lot size—clearly affect system choice. All systems performed well under Justin‐Time (JIT) conditions; there was no advantage to Kanban. Under the mixed conditions of high MPS variation, but small setup time/lot sizes, PBC produced superior performance compared to Kanban and MRP. Under non‐JIT conditions, MRP was seen as clearly more effective. Finally, the results indicate that when conditions permit very small lot sizes relative to requirements, Kanban may perform best, even when MPS variation is high.
Validating Expert Systems: A Demonstration Using Personal Choice Expert, A Flexible Employee Benefit System*
Authors: Sturman Michael C. , Milkovich George T. 
Published: June 2007

A method for validating expert systems, based on validation approaches from psychology and Turing's “imitation game,” is demonstrated using a flexible employee benefits expert system. Psychometric validation has three aspects: the extent to which the system and expert decisions agree (criterionrelated validity), the inputs and processes used by experts compared to the system (content validity), and differences between expert and novice decisions (construct validity). If these criteria are satisfied, then the system is indistinguishable from experts for its domain and satisfies the Turing Test.Personal Choice Expert (PCE) was designed to help employees of a Fortune 500 firm choose benefits in their flexible benefits system. Its recommendations do not significantly differ from those given by independent experts. Hence, if the system‐independent expert agreement (criterion‐related validity) were the only standard, PCE could be considered valid. However, construct analysis suggests that re‐engineering may be required. High intra‐expert agreement exists only for some benefit recommendations (e.g., dental care and long‐term disability) and not for others (e.g., short‐term disability, accidental death and dismemberment, and life insurance). Insights offered by these methods are illustrated and examined.
The Role of Interorganizational and Organizational Factors on the Decision Mode for Adoption of Interorganizational Systems*
Authors: Premkumar G. , Ramamurthy K. 
Published: June 2007

The dramatic growth of interorganizational systems (IOS) has changed the way organizations conduct their business, and has resulted in significant tangible and intangible benefits being realized by participating firms. However, the implementation of these IOS requires the cooperation and commitment of all the participating members. These members may have complex economic and business relationships among themselves that can result in a number of social, political, and economic factors influencing the adoption and implementation of IOS.This study examines the role of interorganizational and organizational factors on the decision mode for adoption of IOS, in the specific context of electronic data interchange (EDI). Four interorganizational factors, based on the socio‐political framework derived from research in marketing, and five organizational factors based on research in IS were used in the study. The data for the study were collected through a large scale field survey. Two respondents, the sales/purchase manager and the IS manager, from 201 firms responded to the survey. The results of discriminant analysis of the data reveal that two interorganizational variables, competitive pressure and exercised power, and two organizational variables, internal need and top management support, are important variables to differentiate firms with proactive decision mode from firms with reactive decision mode.The study also evaluates the differences between proactive and reactive firms on three implementation outcomes. Proactive firms are found to have greater extent of adaptation, more external connectivity with trading partners, and better integration of EDI information in their internal IS applications.
The Influences of User Characteristics on Performance in a Logistics DSS Application
Authors: Swink Morgan 
Published: June 2007

Decision Support Systems (DSS) are widely used in logistics decision applications, and a large number and variety of systems are commercially available. We investigate the contributions of user characteristics including experiences, data preferences, intuition, and effort to decision performance in a logistics DSS context. The study includes a laboratory experiment in which decision makers with varied experiences used a DSS to make facility network design decisions for problems of varying complexity. Two variants of the DSS are utilized in order to examine the interactions of a DSS decision aid with user characteristics.We find that intuition and effort are associated with decision‐making performance. High analytic ability is not related to intuition, however. Education and previous experience are associated with performance. Yet these characteristics are also unrelated to intuition. Decision makers who highly value disaggregated data provided by the DSS tend to perform poorly. Also, the results suggest that the effects of users' experiences and preferences on performance are influenced by an analytical decision aid.
Master Production Rescheduling Policy in Capacity‐Constrained Just‐In‐Time Make‐To‐Stock Environments
Authors: Kern Gary M. , Wei Jerry C. 
Published: June 2007

AbstractThis paper describes a simulation experiment to evaluate the relative effectiveness of rescheduling policies in capacity‐constrained, just‐in‐time (JIT) make‐to‐stock production environments. Three performance measures were analyzed: average finished goods inventory, total units of sales lost, and a measure of schedule instability. Among the three loading methods we studied (level loading, front loading, and back loading), none was superior across all three performance measures. Level loading was the most robust, while back loading had an interaction effect with forecasting model and capacity level. Time fence scenario was found not to be as significant a factor in system performance as reported by previous research. Once an acceptable time fence scenario is selected, attempts to optimize this factor do not appear to offer significant improvement. It was found that higher demand variation, larger forecast errors, and tight capacity lead to the degradation of system performance. Judicious use of slack capacity helps a system to incorporate the periodic schedule changes typical of JIT systems.
Cognitive Limitations and Investment “Myopia”*
Authors: Chi Tailan , Fan Dashan 
Published: June 2007

ABSTRACTOptimization of investment decisions in an uncertain and dynamically evolving environment is difficult due to the limitations of the decision‐maker's cognitive capacity. Thus, actual investment decisions may deviate from the dynamically optimal decision rule. This paper investigates how a potential investment rule bias affects the expected payoff from a project that has an uncertain development time and an uncertain completion cost. The result shows that the presence of a potential bias in the adopted decision rule dissipates project value and that the dissipating effect is greater for a longer term project if the completion cost is an increasing function of the time to completion.
Measuring the Congruence Between Market Requirements and Manufacturing: A Methodology and Illustration*
Authors: Bozarth Cecil C. , Berry William L. 
Published: June 2007

ABSTRACTThis paper presents a measurement methodology, based on existing research in the manufacturing strategy and general strategy areas, for evaluating the congruence between market needs and manufacturing plant capabilities. The proposed methodology has several important advantages over current approaches. First, the methodology uses available data and statistical techniques to measure market‐manufacturing congruence. As a result, it is not dependent on assumptions about “classic” process choices. Second, the proposed methodology can evaluate the strategic fit between market needs and manufacturing in light of one or more performance criteria. Third, the methodology can be used to derive scalar measures of market‐manufacturing congruence, allowing users to evaluate congruence for a large number of products, customers, or market segments.The theoretical basis, research objectives and major steps of the methodology are discussed, followed by an illustrative example from a North American manufacturer. Finally, the applicability of the methodology to a wide range of strategic manufacturing decisions is discussed.
Analysis of Local Decision Rules in a Dual‐Kanban Flow Shop
Authors: Ardalan Alireza 
Published: June 2007

ABSTRACTA recent article reported the results of a study on the effects of two kanban policy variables—the length of withdrawal cycle and the type of priority rule—on average customer wait time and total inventory. This study extends that work by adding two kanban policy variables and two performance criteria. It reports the results of simulation experiments that were conducted to determine how four policy variables—withdrawal cycle, priority rule, status of waiting withdrawal kanbans, and number of kanbans influence four performance criteria—average customer wait‐time, total inventory, and average number of full containers in the input and output stock points of stations. It was found that the information about waiting withdrawal kanbans in sequencing decisions results in the simultaneous improvement in two conflicting objectives—customer wait time and total inventory. Also, the effects of including the information regarding the status of waiting withdrawal kanbans on system performance are larger than the effects associated with the type of priority rule. The results provide insights into determining the level of each policy variable while fully considering the possible interactions among the variables and the levels of other policy variables to improve system performance. These insights allow for setting the levels of policy variables to make the improvement process smooth.
Adaptive, Agile Approaches to Organizational Architecture Utilizing Agent Technology*
Authors: Ottaway Thomas A. , Burns James R. 
Published: June 2007

ABSTRACTIntelligent agent‐based approaches to software support systems are believed to provide an unparalleled technology for integrating people, materials, and capital equipment. Software agents can be used to automate mechanization strategies for reducing the information content of work. As such, software agents diminish the need for utilizing humans to do repetitive, programmable tasks.The purpose of studies of communities of intelligent agents is to ascertain their ability to accommodate flexible, agile organizational structures that can respond rapidly to changes in the marketplace, increase throughput, reduce costs, and improve quality. This paper presents a conceptual prototype for agent‐based structural selfdesign and describes experiments conducted regarding such studies together with the results obtained.
Complexity Factors and Intuition‐based Methods for Facility Network Design
Authors: Swink Morgan , Robinson E. Powell 
Published: June 2007

ABSTRACTLogistics managers frequently utilize decision support systems (DSS) to make facility network design decisions. Many DSS do not provide optimization capabilities, but instead rely on scenario evaluation as a means for developing solutions. We experimentally assessed the performances of decision makers, including experienced managers, who used four variants of a scenario evaluation‐based DSS to solve realistically sized network design problems of varying complexities. Complexity factors included DSS attributes, problem size, network types, and demand dispersion patterns. Decision makers' performances were assessed relative to optimal solutions.Overall, the decision makers generated relatively high‐quality solutions using the DSS variants. The type of design problem solved did not significantly impact problem‐solving performance. However, performance degraded and variability in solution quality escalated as problem size was increased. The availability of incremental solution cost improvement cues in the DSS significantly improved solution quality and reduced performance variability. Iconic graphic enhancements to the DSS did not consistently affect performance. However, significant interactions existed among the effects of DSS graphics capabilities, DSS information cues, and problem attributes.
Managing Delphi Surveys Using Nonparametric Statistical Techniques*
Authors: Schmidt Roy C. 
Published: June 2007

ABSTRACTInformation systems researchers have often turned to a variant of the Delphi survey technique to support their research of key issues in their field. Two particular weaknesses of past studies using this approach have been a lack of a definitive method for conducting the research and a lack of statistical support for the conclusions drawn by the researchers. In this paper, the author presents a method, based on nonparametric statistical techniques, to conduct ranking‐type Delphi surveys, perform analysis, and report results. The author takes this one step further by illustrating an actual analysis of a Delphi survey. The analysis is compared to results that were presented without the benefit of the author's approach. This paper shows that use of the advocated approach can streamline and strengthen studies, improve the validity of results, and thus better serve the consumers of the research findings. Since the ranking‐type Delphi is so popular among information systems researchers, a consistent method is needed to apply to their data collection, analysis, and reporting of results. This paper provides such a method in concise form and illustrates the use of the method in a manner affording comparison between it and previous practice.
The Significance of Information Systems Research on Emerging Technologies: Seven Information Technologies that Promise to Improve Managerial Effectiveness*
Authors: Kendall Kenneth E. 
Published: June 2007

ABSTRACTEmerging information technologies are those excitingly novel advances that decision makers are just beginning to notice. These emerging products or processes may have been invented some time ago, but are only now beginning to reveal practical applications. This paper examines this collection of seven studies exploring emerging technologies, classifying them so the context of their results can be related to other emerging technologies. Once the research is classified and the contribution and managerial significance of the research is identified, it becomes apparent that certain barriers exist, holding back the technology from wider use and appreciation. Common obstacles include resistance to, or difficulty with, using the technology; uncertainty concerning the value of the technology; and the complexities involved with implementation. This paper concludes with recommendations for future research aimed at breaking down the barriers in order to move from the technological emergence phase to the technological sublime phase in which decision makers and other end users understand the technology, realize and appreciate its value, and put the technology to its best use.