Decision support flexible manufacturing systems

The justification of flexible manufacturing systems (FMS) is a topic that has gained a lot of attention due to the strategic effect it has on the competitive stature of industrial firms. The decision to convert to FMS should follow a pattern of feasibility assessments, cost/benefit analysis where consideration of issues such as increased quality, productivity and capability as well as tangible benefits takes place. Difficulties, however, arise in the modeling of the behavior of the proposed FMS. This paper introduces the design of a decision support framework that aids the strategic planner in simulating the performance of proposed FMS and in determining the parameters that affect the costs and benefits, tangible and intangible, of such a system. The decision support system (DSS) allows the user to use subjective evaluations of benefits accruing from intangible considerations of new product design, faster turnaround on design-to-market cycles and new marketing strategies in fragmented markets. The DSS enhances the examination of issues such as changing demand, varied tasks and routings, job and machine flexibility, etc. which affect costs and benefits. It performs these important functions by allowing for the development of scenarios that aid in the evaluation of the effect of the conversion from non-flexible to flexible manufacturing on the organization's financial position.     

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.     

The Design of a Knowledge-Based Decision Support System to Support the Information Analyst in Determining Requirements

Decision support system (DSS) researchers and designers continue to look for unstructured organizational tasks where there is a critical need for intelligent computer-based support. One such decision task is information requirements determination. Requirements determination is recognized as the most crucial phase of the systems development life cycle. Unfortunately, most methodologies and CASE tools focus only on how to specify the requirements once they are determined. There is very little computer support for the process of determining requirements. This paper discusses the conceptual design and development of a knowledge-based DSS to support information analysts in the critical decision task of determining requirements for the design of effective information systems. The expert modeling support system has the expertise to assist the analyst in studying the organization as a whole and in modeling the system under study in the context of the overall organization's goals and needs. The focus of the paper is on the problems associated with building the knowledge base component of the intelligent decision support system. A prototype implementation of the system is described.     

The Determinants of DSS Success: An Integrated Model*

The results of a field study investigating the determinants of decision support systems (DSS) success are presented. A multivariate model was developed and tested using multiple regression hierarchical analysis on responses from 118 DSS users. Several specific hypotheses are also proposed and tested. Data analyses indicate that DSS success, as measured by DSS satisfaction and perceived benefits, depends on several factors: previous user experience with DSS, user involvement, user training, top management support, information sources, the level of managerial activity, and task structure, difficulty and interdependence. Based on the research results, several suggestions are made to improve the likelihood of successful DSS implementation.     

A network of management support systems

Based on the decision-making process that actually happens in most organizations, a management support system (MSS) or an integrated decision support systems (DSS) is defined as a network of subsystem interfaces, as opposed to management information system (MIS) being a network of information flows. Three modes for subsystem interfaces are discussed. To help the operationalization of the (computerized) support system, we also propose a design architecture which consists of the continuum of conceptual constructs, operational constructs and implementational constructs.     

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.     

The Influences of User Characteristics on Performance in a Logistics DSS Application

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.     

The Effectiveness of Decisional Guidance: An Empirical Evaluation

Decisional guidance is defined as how a decision support system (DSS) influences its users as they structure and execute the decision‐making process. It is assumed that decisional guidance has profound effects on decision making, but these effects are understudied and empirically unproven. This paper describes an empirical, laboratory‐experiment‐based evaluation of the effectiveness of deliberate decisional guidance and its four types. We developed and used a comprehensive model consisting of four evaluation criteria: decision quality, user satisfaction, user learning, and decision‐making efficiency. On these criteria, we compared decisional guidance versus no guidance, informative versus suggestive decisional guidance, and predefined versus dynamic decisional guidance. We found that deliberate decisional guidance was more effective on all four criteria; suggestive guidance was more effective in improving decision quality and user satisfaction, and informative guidance was more effective in user learning about the problem domain, whereas dynamic guidance was more effective than predefined guidance in improving decision quality and user learning; and both suggestive guidance and dynamic guidance reduced the decision time.     

The Positive Effects of Easy Goals on Decision Quality and Risk Propensity in an MCPLP Task

In this study the potential performance benefits of easy goals were examined within the multiple cue probability learning paradigm (MCPLP). Specifically, the effects of varying levels of goal difficulty on performance and risk propensity (used to define a form of commitment) were investigated. With few exceptions, previous studies demonstrated support for difficult goals. In this study, contrary to the majority of past evidence, as goals became easier decision quality significantly improved. Moreover, risk propensity increased with easier goals and, as suggested by a post-hoc analysis, had more direct impact on decision makers’ behaviors than goal levels. Goal-related behavior in complex MCPLP tasks appears to significantly differ from the majority of goal evidence in other types of research due to the tendency for subjects to view complex task properties in the same context as goal levels.     

The Effects of Task Information and Outcome Feedback on Individuals' Insight into Their Decision Models

When making business decisions, people generally receive some form of guidance. Often, this guidance might be in the form of instructions about which inputs to the decision are most important. Alternatively, it might be outcome feedback concerning the appropriateness of their decisions. When people receive guidance in making difficult judgments, it is important that they do not confuse this guidance with insight into their own decision models. This study examined whether people confuse their actual decision model with task information and outcome feedback. Subjects predicted the likelihood that various hypothetical companies would experience financial distress and then reported the decision models they believed they had used. Their reported models were compared with their actual models as estimated by a regression of the subjects' predictions on the inputs to their decisions. In a 2times2 factorial design, some subjects were provided with task information regarding the relative importance of each input to their decisions while others were not. Some subjects were provided with outcome feedback regarding the quality of their decisions while others were not. The subjects tended to confuse the task information and outcome feedback with their actual decision models. Implications for the results are discussed.     

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.     

BPR决策支持系统分布式求解模型的研究

基于多Ａｇｅｎｔ规划技术,提出并建立了支持企业过程重组（ＢＰＲ）的决策支持系统的分布式协同求解模型,包括企业过程的分层规划理论、多Ａｇｅｎｔ协同模式分析、状态空间模型以及任务分解算法等四部分内容．它的提出为企业过程重组的工具支持提供了理论依据和技术方法．实践证明,应用该技术可以有效地指导ＢＰＲＤＳＳ的开发     

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 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.     

Decision support system for the batching problems of steelmaking and continuous-casting production

This paper investigates two batching problems for steelmaking and continuous-casting (SCC) production in an integrated iron and steel enterprise. The tasks of the problems are to make the decisions as how to consolidate ordered slabs into charges, and then how to group charges into casts. The effective decisions on these batching problems can help to balance the requirements of materials in downstream production lines, improve the customer satisfaction levels, and reduce production costs (including reduction of open ordered slabs, less slabs quality upgrading, reduction of steel-grade changeovers, and reduction of inefficient utilization of tundishes lives). We first formulate the problems as integer-programming models by consider practical constraints and requirements, and then develop the two heuristic algorithms for the corresponding batching problems. By embedding above models and algorithms, we develop decision support system (DSS) software with interactive planning editor. The DSS has been tested by using practical data set collected from the steelmaking plant in Baosteel which is one of the most advanced iron and steel enterprises in China. Computational experiments demonstrate that the models and algorithms developed can generate the satisfactory solutions when they work together with the planning editor in the DSS.     

User cognitive types for decision support systems

This paper describes and presents the results of an experiment that was used to investigate the relationships between cognitive types, information presentation and their effect on decision making performance within the context of an MIS/DSS. The experimental task required subjects to specify production levels for future periods given information on prior results. Ninety-six subjects performed the experiment, with 24 in each of the four Jungian cognitive type groups. All subjects received the same information for making decisions but different information presentation modes. The information presentation modes included raw and statistically summarized data and graphical and tabular layouts. The subjects were provided their results after each of their ten decision ‘runs’ via hard copy output. Decisions were entered at CRT terminals by the subjects. Experimental results demonstrated significant differences in performance by cognitive types. Decision performance rankings were partially supported by the results. Hypothesized pairings of cognitive types and information presentation modes were also partially supported. Implications of the results for MIS/DSS design are explored.     

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.     

Presenting Geographic Information: Effects of Data Aggregation,Dispersion, and Users' Spatial Orientation

Geographic information systems (GIS) have taken on an increasingly important role supporting decision making in many organizations. GIS have been used to support a breadth of tasks including oil and mineral exploration, facility location, logistics support, and facilities management decisions. The effectiveness of GIS as a decision support tool comes primarily from the visual display of data in the form of maps. When presenting information as a geographic map, the level of data aggregation potentially affects aspects of task complexity such as information load and the potential for pattern recognition by the user. Other task attributes expected to be related to data aggregation effects include problem size, the degree of data dispersion, and users' spatial orientation skills. We conducted an experiment to study these effects and their interactions. Subjects used a GIS including map-based information characterized by different levels of problem size, data dispersion, and data aggregation. Spatial orientation skill was examined as a covariate in the experimental treatments. The results indicate that all of these geographic information characteristics have significant impacts on decision performance. Moreover, many interactions are present among the factors. We evaluate these interactions in order to derive implications for practice and for future research.     

DUAL-METRIC MEASUREMENT OF THE IMPACT OF COMPLEXITY ON DECISION QUALITY*

Both sophisticated and unsophisticated subjects were provided either a simple or a complex cue set at random on which to base decisions. A dual-metric approach that consisted of measuring interrater consistency and cross-item variance for each subject category was used to compare the decision quality of the subjects. Although complexity and decision quality were inversely related, the sophisticated subjects performed relatively better using the complex cue set; the unsophisticated subjects performed better using the simple cue set. These findings support the notion that decision-maker and task compatibility are important to the assessment of decision quality.     

Supply Chains in Sub‐Saharan Africa: A Decision Support System for Small‐Scale Seed Entrepreneurs*

It is necessary to infuse a consistent supply of improved seed varieties into local sub‐Saharan African crop production to improve low crop yields. The best distribution channel for the improved seed varieties may be small‐scale commercial seed companies, but local entrepreneurs struggle to determine whether such businesses are viable. Using a multi‐echelon supply chain approach, a decision support system (DSS) was designed to help African seed entrepreneurs make informed decisions about small‐scale seed chain businesses. Specifically, entrepreneurs make decisions about where to locate seed enterprises, with which farmers to contract, and where to store seed. Optimization and simulation modeling are used to evaluate infrastructure variables such as distance, transportation cost, and storage loss and cost in three development level areas. Currently, the decision tool is used in Mozambique, Malawi, Kenya, and Tanzania. The model has supported the start‐up of at least 17 small seed companies that are now introducing improved seed varieties into villages and farms. The DSS applies decision science research in a humanitarian application and offers important managerial implications about supply chain infrastructure to nongovernmental organizations and humanitarian groups. Such applications are vital as groups such as USAID, the Gates Foundation, and the International Crops Research Institute for the Semi‐Arid Tropics (ICRISAT) continue to move toward micro‐enterprise, value chain, and market‐oriented development programs.