Decision support systems for production scheduling tasks- Part I of a case study: Analysis and task redesign

The first three steps of a new design method for decision support systems in production scheduling tasks are applied to a large bulk terminal. The operational characteristics of the company largely correspond with those of typical semiprocess industries. The design model used consists of five steps: production analysis, task analysis, task redesign, decision support design and decision support implementation. The results of the application of the design model up to the task redesign step are described.     

A Computerized Approach to Decision Process Tracing for Decision Support System Design*

We propose the use of computerized process tracing (CPT) tools as an appropriate approach for monitoring the information acquisition and evaluation phase of specific decision processes. CPT tools are unobtrusive and seem particularly relevant for evaluating certain decision tasks that may be supported by decision support systems (DSS). CPT tools can be an important component of DSS development. An information systems research taxonomy developed by previous researchers [29] [36] is used to position research work involving the methodology of CPT. Using a critique suggested by Libby [28], CPT tools are evaluated and compared to alternative process tracing tools. A brief empirical example using CPT is provided, and future uses relative to DSS are suggested. The appendix includes an example of a specific CPT tool.     

A Knowledge Based Decision Support System for Setup Reduction

The issue of setup reduction is important for firms seeking to incorporate advanced procedures and concepts such as flexible manufacturing systems (FMS) and just-in-time (JIT) manufacturing to improve manufacturing productivity. Investing adequate amounts in setup reduction is a complex decision affected by many factors including the existing level of automation, worker characteristics, product features, and the manufacturing environment. The consequences of improper investment (i.e., over- and underinvestment) includes possible disruption of manufacturing operations as well as wasted resources. Much of the knowledge involved in the investment decision is heuristic and experience-based, and takes on greater significance when the form of the setup reduction function is not known a priori. This paper describes the development of a knowledge-based decision support system (KBDSS) that combines both heuristic and procedural knowledge to provide support for the setup reduction investment decision. The system uses codified manufacturing expertise to improve both the accuracy and effectiveness of setup-reduction decision making. Use of the KBDSS is illustrated by two examples based on disparate manufacturing contexts.     

User Perceptions of Decision Support Effectiveness: Two Production Planning Experiments*

Decision support systems continue to be very popular in business, despite mixed research evidence as to their effectiveness. We hypothesize that what-if analysis, a prominent feature of most decision support systems, creates an “illusion of control” causing users to overestimate its effectiveness. Two experiments involving a production planning task are reported which examine decision makers' perceptions of the effectiveness of what-if analysis relative to the alternatives of unaided decision making, and quantitative decision rules. Experiment 1 found that almost all subjects believed what-if analysis was superior to unaided decision making, although using what-if analysis had no significant effect on performance. Experiment 2 found that decision makers were indifferent between what-if analysis and a quantitative decision rule which, if used, would have led to significant cost savings. Thus, what-if analysis did create an illusion of control: decision makers perceived performance differences where none existed, and did not detect large differences when they were present. In both experiments, decision makers exhibited difficulty realizing that their positive beliefs about what-if analysis were exaggerated. Such misjudgments could lead people to continue using what-if analysis even when it is not beneficial and to avoid potentially superior decision support technologies.     

Editorial Another milestone

A Master Production Scheduling Decision Support System within a multi-product medical supplies market has the dual task of providing good customer service levels while maintaining minimum reasonable levels of finished goods stock in the face of considerable internal manufacturing lead time and customer demand uncertainty. This paper examines the critical design parameters within an adaptive model highlighting how the total system orders in the internal pipeline are utilized in the decision-making process for assessing how much to load the internal manufacturing pipeline. Two different methods for tracking manufacturing lead times within the adaptive loop are also considered. Classical control concepts are applied within the Decision Support System (DSS) to avoid any long-term drift in finished stocks. Finally scenario analysis is performed via simulation for a set of design parameters and a range of stimuli typical of company operating situations. An effective decision support system design in terms of architecture and parameter settings is recommended based upon the ability of the model to maintain high customer service levels. The DSS readily interfaces between marketing and production functions to enhance company competitive advantage across a wide range of products.     

An Empirical Investigation of the Effects of Decision Aids on Problem-Solving Processes*

Despite the growing number of investigations examining decision support systems (DSS), relatively few empirical studies have evaluated the effects of DSS on problem-solving processes. This study uses protocol analysis to investigate the impact of a specific decision aid on problem-solving processes in a semi-structured problem. Results indicate that decision aids influence the problem-solving processes of decision makers. The effect of a decision aid is found to be contingent on familiarity with the decision aid, task familiarity, and the interaction of these two factors. Suggestions for incorporating interaction effects and problem-solving processes into future examinations are proposed.     

A Decision Support System for Production/ Distribution Planning in Continuous Manufacturing

This paper presents a decision support system (DSS) for managing production/distribution planning in a continuous manufacturing environment. The vendor has multiple plants and distribution centers (DCs). The trading partners have widely varying independent demand patterns. The DSS is designed for use in a multiproduct environment with overlapping raw materials and processing requirements. The production and distribution lead times at plants may span multiple planning periods. The impact of any manual override of a suggested solution can also be evaluated. The DSS is based on a linear programming model with a rolling horizon and was originally designed for a large process industry. Results of a pilot implementation using actual data are also presented, which show potential for significant savings for the company.     

Decision Making in an Automated Environment: The Effects of Anonymity and Proximity with a Group Decision Support System*

Recent advances in information systems technology have made it possible to support the work of interacting groups using networked personal computers. A laboratory experiment was conducted using a group decision support system to evaluate effects of anonymity and proximity on group process in automated group problem solving. Twenty groups of four persons each performed an idea-generating task using an interactive electronic brainstorming program. This experiment's main findings were: (1) Group members working anonymously and apart generated more comments. (2) Working in the same room increased satisfaction. (3) Highest levels of perceived system effectiveness were reported under anonymity.     

Learning by Objectives for Adaptive Shop-Floor Scheduling*

Effective production scheduling requires consideration of the dynamics and unpredictability of the manufacturing environment. An automated learning scheme, utilizing genetic search, is proposed for adaptive control in typical decentralized factory-floor decision making. A high-level knowledge representation for modeling production environments is developed, with facilities for genetic learning within this scheme. A multiagent framework is used, with individual agents being responsible for the dispatch decision making at different workstations. Learning is with respect to stated objectives, and given the diversity of scheduling goals, the efficacy of the designed learning scheme is judged through its response under different objectives. The behavior of the genetic learning scheme is analyzed and simulation studies help compare how learning under different objectives impacts certain aggregate measures of system performance.     

A Simulation of Partial Information Use in Decision Making: Implications for DSS Design

Information matrices are often the output produced by a decision support system. These matrices are a common method for expressing a decision situation under different decision-making scenarios. The decisions involved in designing a decision support system to generate the information matrix are important and involve several cost and benefit components. A designer needs guidance in making effective design decisions in this context. Such guidance can be provided by considering the relationships among specific design decisions, costs, and benefits. The general objective of this study is to provide a comprehensive framework for this purpose. This study is the first to develop and present a comprehensive cost-benefit framework for evaluating design decisions for a variety of scenarios. The specific objective of this research is to provide guidance regarding the number of available information dimensions to incorporate in a computer-based decision aid. Simulation experiments are conducted with a completely specified model based on the cost-benefit framework (including needed assumptions) to evaluate how many information dimensions to include for a specific information matrix size to achieve a balance between information use costs and decision quality. Based upon extensive simulation analyses for a hypothetical decision maker, the practical guideline found for designers is to include only the top half of the relevant information dimensions in any specific decision support system. Over a large number of repeating choice decisions, the savings in cognitive effort and information gathering costs clearly offset relatively minor losses in decision quality.     

The Application of Neural Networks and a Qualitative Response Model to the Auditor's Going Concern Uncertainty Decision*

An auditor gives a going concern uncertainty opinion when the client company is at risk of failure or exhibits other signs of distress that threaten its ability to continue as a going concern. The decision to issue a going concern opinion is an unstructured task that requires the use of the auditor's judgment. In cases where judgment is required, the auditor may benefit from the use of statistical analysis or other forms of decision models to support the final decision. This study uses the generalized reduced gradient (GRG2) optimizer for neural network learning, a backpropagation neural network, and a logit model to predict which firms would receive audit reports reflecting a going concern uncertainty modification. The GRG2 optimizer has previously been used as a more efficient optimizer for solving business problems. The neural network model formulated using GRG2 has the highest prediction accuracy of 95 percent. It performs best when tested with a small number of variables on a group of data sets, each containing 70 observations. While the logit procedure fails to converge when using our eight variable model, the GRG2 based neural network analysis provides consistent results using either eight or four variable models. The GRG2 based neural network is proposed as a robust alternative model for auditors to support their assessment of going concern uncertainty affecting the client company.     

The Interaction Effects Between General Thinking Skills and an Interactive Graphics-Based DSS to Support Problem Structuring*

An interactive graphics-based problem-structuring aid, GISMO, based on concepts from structural modeling and motivated by research in imagery theory and cognitive psychology was incorporated into a decision support system. A laboratory experiment was conducted to investigate the relationships between three individual general thinking skills (GTS) and the effectiveness of the problem-structuring tool. A theoretical model of the roles of GTS and GISMO in problem formulation and a methodology for testing the model are presented. The model posits that the effectiveness of visual problem-structuring aids in formulating or understanding complex problems is influenced by specific cognitive skills (i.e., visual-thinking, verbal, and logical-reasoning skills). The results indicate GISMO use is related to higher levels of problem-structure understanding, regardless of verbal and logical-reasoning skills. Also, the results indicate a strong interaction effect between GISMO use and visual-thinking skill. Subjects classified as high visual thinkers benefited more from the use of GISMO than the low visual-thinking subjects. The results of the experiment lend support for the findings of a previous study of GISMO reported in the literature, and the image theorist view that the ability to create and use visual, mental images is related to better problem-solving performance.     

Decision Support System of Truck Routing and Refueling: A Dual‐Objective Approach

The variable‐route vehicle‐refueling problem (VRVRP) is a variant of the network‐flow problem which seeks, for a vehicle traveling from origin s to destination d, both the route and the refueling policy (sequence of fuel stations to use between s and d) that jointly minimize the fuel cost of operating the vehicle. Commercial‐grade decision support systems that solve the VRVRP are widely used by motor carriers, but they provide heuristic solutions only. Exact methods are available from the academic side, but because they focus on minimizing costs, they tend to cut fuel costs in exchange for increased vehicle miles (which can increase fuel consumptions and pollutants emission). We propose a new approach to the VRVRP that allows carriers to jointly seek the two possibly conflicting goals; minimizing fuel cost and vehicle miles. Computational testing shows that our approach (i) outperforms the commercial software products in both goals, and (ii) finds solutions that require significantly less vehicle miles than those given by the exact method proposed in the academic literature, without incurring unacceptable increases in fuel cost.     

An Application of Expectancy Theory to Explain a Manager's Intention to Use a Decision Support System

A continuing gap exists between the capabilities of sophisticated computer-based information systems and the extent to which these systems are used by individuals. Studies which have examined the relationship between system utilization and various user, system, implementation, and organizational variables have provided few consistent findings. A new approach to this topic is suggested by a recent study by Davis, Bagozzi, and Warshaw [11], which indicates that individuals' intentions to use a system determine subsequent use. A large body of psychology-based research also supports this relationship between behavioral intentions and subsequent behavior. This study employs expectancy theory, which has often been used to examine behavioral intentions, to explain managers' intentions to use a decision support system (DSS). The results imply that the variables of the expectancy force model are determinants of a manager's behavioral intentions to use a DSS, and the variables of the expectancy valence model are determinants of the attractiveness of using a DSS to a manager.     

The Effects of an Interactive Graphics-Based DSS to Support Problem Structuring

Graphical, interactive, problem-structuring tools were devised and incorporated into a DSS. These tools were based on concepts of structural modeling and motivated by propositions and findings in memory theory and imagery theory. A laboratory study was then conducted to investigate the relationship between decision-maker characteristics and use of the problem-structuring aid. The experiment was designed to determine whether use of the tool to assist in problem formulation led to a better understanding of problem structure. A moderately complex management simulation and its companion DSS were used in the experiment. Eighty-four subjects were divided into two groups: a control group that did not use the Graphical Interactive Structural Modeling Option (GISMO), and an experimental group that did. The results showed a statistically significant effect between cognitive ability and use of GISMO. Field independents (i.e., high cognitive ability) effectively combined GISMO use with image processing skills to understand the structure of the problem domain, but field dependents (i.e., low cognitive ability) were ineffective. There was very little difference in problem understanding in field dependents who used the tool and those who did not.     

Data Driven Modeling: An Application in Scheduling

This paper presents a data driven modeling (DDM) approach to certain types of optimization problems. DDM relinquishes control of the completed model to the user department rather than the operations research (OR) staff. The approach emphasizes development of models that are dependent on data maintained and understood by the users. The data base consists of coded user rules which describe when changes will occur in the problem structure and data which captures the generalization of the problem. Both the rules and data can be updated by user department personnel. These data drive a matrix generator controlled by the rules which uses the data base as input to generate the specific model formulation. This DDM system is designed by OR consultants or staff to allow independence of use along with low-cost and minimal-effort maintenance. The DDM approach is illustrated with an application to a real-world medical scheduling problem.     

Integrating Neural Networks and Semi-Markov Processes for Automated Knowledge Acquisition: An Application to Real-time Scheduling*

Recently, artificial neural networks (ANN) have gained attention as a promising modeling tool for building intelligent systems. A number of applications have been reported in areas varying from pattern recognition to bankruptcy prediction. In this paper, we present a creative methodology that integrates computer simulation, semi-Markov optimization, and ANN techniques for automated knowledge acquisition in real-time scheduling. The integrated approach focuses on the synergy between operations research and ANN in eliciting human knowledge, filtering inconsistent data, and building competent models capable of performing at the expert level. The new approach includes three main components. First, computer simulation is used to collect expert decisions. This step allows expert knowledge to be obtained in a non-intrusive way and minimizes the difficulties involved in interviewing experts, constructing repertory grids, or using other similar structures required for manual knowledge acquisition. The data collected from computer simulation are then optimized using a semi-Markov decision model to remove data redundancies, inconsistencies, and errors. Finally, the optimized data are used to build ANN-based expert systems. The integrated approach is evaluated by comparing it with the human expert and using ANN alone in the domain of real-time scheduling. The results indicate that ANN-based systems perform worse than human experts from whom the data were collected, but the integrated approach outperforms human experts and ANN models alone.     

Multiple Attribute Decision Making with Inexact Value-Function Assessment

A major issue in value-function assessment is the possibility of receiving an indefinite response from a decision maker to a question about preferences. The conventional treatment requires a single, definite response from the decision maker. This approach either assumes that response error is zero or accepts the final solution as only approximate, without knowing how approximate it might be. In this paper we examine the issue of indefinite responses, define it more precisely, and present a method for incorporating it into decision making. The method is adaptive in that it is iterative and interactive with the decision maker who, at each iteration, is provided with information regarding the potential advantage of answering more trade-off questions and/or answering questions more carefully to reduce response error. In contrast to other approaches that “force” rationality and decisiveness, we work with irrationality and indecisiveness on the part of the decision maker. The method is demonstrated for the case of concave, increasing value functions.     

An Empirical Investigation of Information Search Strategies with Implications for Decision Support System Design

Understanding how people search through and combine information before making decisions is an important concern in the study of decision making and in the design of decision support systems (DSS). The purpose of this study is to examine DSS search strategies in relation to the body of empirical research on information load. Prior research is examined in relation to basic empirically-testable hypotheses and compared to the results of this study as a way of validating the procedures used here. The transfer of information load empirical research to a DSS scenario is confirmed statistically. Then, results of an analysis of information search patterns under DSS conditions are described. Finally, implications for DSS design are discussed in terms of possible search support mechanisms for specific search strategies.