Concepts,Theory, and Techniques DISTRIBUTED COMPUTER SYSTEM DESIGN: A MULTICRITERIA DECISION-MAKING METHODOLOGY*

The design of distributed computer systems (DCSs) requires compromise among several conflicting objectives. For instance, high system availability conflicts with low cost which in turn conflicts with quick response time. This paper presents an approach, based on multi-criteria decision-making techniques, to arrive at a good design in this multiobjective environment. An interactive procedure is developed to support the decision making of system designers. Starting from an initial solution, the procedure presents a sequence of non-dominated vectors to designers, allowing them to explore systematically alternative possibilities on the path to a final design. The model user has control over trade-offs among different design objectives. This paper focuses on the details of the mathematical model used to provide decision support. Accordingly, a formulation of DCS design as a multicriteria decision problem is developed. The exchange search heuristic used to generate nondominated solutions also is presented. We argue that multicriteria models provide a more realistic formulation of the DCS design problem than the single-criterion models used widely in the literature. While obtaining a clear definition of design objectives (single or multiple) is an important activity, by explicitly acknowledging the trade-offs among multiple objectives in the design process, our methodology is more likely to produce a better overall design than methods addressing a single criterion in isolation.     

A heterarchical generic discrete manufacturing simulation model and its application in a world class manufacturing environment

Current factory design and evaluation is very primitive. Factory components are designed in many cases independently. Product and process design are not well integrated. An encompassing framework is needed for iterating through a series of total factory designs, searching for optimal performance. In addition, a vehicle is needed for predicting the performance of a proposed advanced manufacturing system, so that engineers may have a sound means for evaluating such proposals. A heterarchical discrete manufacturing SIMNET II simulation model (SIMCELLS) was developed as a comprehensive methodology for designing and evaluating discrete manufacturing systems. SIMCELLS allows manufacturing systems engineers to experiment with alternative system structures and control strategies while seeking that combination of design features that will produce the desired overall system performance. The model in combination with a modernization programme is enabling a firm to successfully manufacture and sell trucks meeting international standards. The SIMNET II model     

META-DESIGN CONSIDERATIONS IN DEVELOPING MODEL MANAGEMENT SYSTEMS*

This paper examines cognitive considerations in developing model management systems (MMSs). First, two approaches to MMS design are reviewed briefly: one based on database theory and one based on knowledge-representation techniques. Then three major cognitive issues—human limitations, information storage and retrieval, and problem-solving strategies—and their implications for MMS design are discussed. Evidence indicates that automatic modeling, which generates more complicated models by integrating existing models automatically, is a critical function of model management systems. In order to discuss issues pertinent to automatic modeling, a graph-based framework for integrating models is introduced. The framework captures some aspects of the processes by which human beings develop models as route selections on a network of all possible alternatives. Based on this framework, three issues are investigated: (1) What are proper criteria for evaluating a model formulated by an MMS? (2) If more than one criterion is chosen for evaluation, how can evaluations on each of the criteria be combined to get an overall evaluation of the model? (3) When should a model be evaluated? Finally, examples are presented to illustrate various modeling strategies.     

Designing Distribution Systems to Support Vendor Strategies in Supply Chain Management*

The interactions among a firm's distribution strategy, market share, and distribution costs are an important consideration in the design of supply chain networks. However, these interactions are largely ignored by existing distribution system design methodologies, which assume demand is constant regardless of the firm's distribution strategy. This paper describes a multidisciplinary framework that considers these interactions in the design of “profit maximizing” distribution networks. The framework employs two major decision support methodologies: (1) binary logit models for estimating market share considering various demand-influencing parameters such as product price and distribution service, and (2) a mixed-integer programming (MIP) model for finding optimal distribution network designs. We applied the framework to an actual design problem facing a national distributor of industrial chemical products. The test results verify the framework's large-scale capability and the potential benefit of the integrated solution methodology.     

Measuring the Congruence Between Market Requirements and Manufacturing: A Methodology and Illustration*

This 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.     

A Clustering Algorithm for Computer-Assisted Process Organization

Amathematical programming clustering model that forms groups based on total group membership interactions is extended to include precedence relationships, group size limits, and group time limits. The extended clustering model is most appropriate for structured design of information systems as described by the computer-assisted process organization (CAPO), which requires certain ordering and may have limits on development and production capacity. An efficient algorithm for optimizing the CAPO criteria along with computational results is presented. The results show that the method is viable for the CAPO problems reported in the literature.     

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.     

Distinguishing Features of Just-in-Time Systems in the Make-to-Order/Assemble-to-Order Environment*

Few studies of just-in-time (JIT) implementation examine their significance in make-to-order manufacturing environments. This study examines the relative importance of several operating variables that are characteristic features of JIT systems within such environments in North America. The results suggest that the most salient features of JIT for make-to-order and assemble-to-order firms are the elimination of waste (in the form of time and defects), reduced setup time, reduced lotsize, and a smaller pool of suppliers. In addition, the results suggest that the use of non-domestic suppliers can possibly hinder efforts at JIT implementation of materials procurement.     

Efficiency and effectiveness of knowledge management systems in SMEs

This paper proposes a 3D fuzzy logic methodology to assess the efficiency and effectiveness of knowledge management systems (KMSs) adopted by small and medium enterprises (SMEs) and identifies a classification bringing together the behaviour of SMEs when adopting KMSs. The proposed methodology was previously tested on an individual SME and then implemented on a sample of SMEs. The results highlight that there is an ample variety of behaviours related to the nature of knowledge and the KMSs used. Specifically, four typologies of behaviour are identified: the efficient and effective SME, the effective but inefficient SME, the efficient but ineffective SME, and the inefficient and ineffective SME. The results show that only 18% of surveyed SMEs are efficient and effective for both KM-Tools and KM-Practices, the remaining 82% are inefficient or ineffective for KM-Tools and/or KM-Practices. Lastly, the paper shows how this methodology may be used by an SME as a managerial tool to suggest appropriate changes to improve the efficient and effective adoption of KMSs supporting SMEs in the knowledge management processes.     

Redesigning a Cellular Manufacturing System to Handle Long-Term Demand Changes: A Methodology and Investigation*

Cellular manufacturing systems have been proposed as an alternative to the job shop since they provide some of the operational benefits of a flow line production process, while retaining to some extent the flexibility of job shops. However, this must be balanced against the possibility of additional initial investments in equipment to form the cells and a certain loss in manufacturing flexibility, particularly in terms of the ability to deal with long-term demand changes. This paper presents a model-based heuristic cell system redesign methodology to deal with such demand changes. The methodology is validated and applied to system designs generated from several data sets published in the literature. Results show that different kinds of demand changes incur distinct kinds of costs. Further, characteristics of cell designs that can handle long-term demand changes at least cost are identified.     

Construct Measurement in Information Systems Research: An Illustration in Strategic Systems

This study seeks to highlight construct measurement issues in information systems (IS) research. It describes the normative process of construct measurement and identifies the difficult problems involved in measurement and some ways in which these difficulties may be overcome. An illustrative construct-operationalization study in the area of strategic systems outlines how the normative guidelines may be applied to IS. Some specific recommendations for IS include developing a preliminary model of the construct even if there is little previous measurement research, devoting greater attention to predictive validity because a lack of theories in IS precludes the examination of nomological validity, verifying the assumptions underlying the computation of an overall index, and examining the measurement properties of the index.     

FINDING SYNERGY BETWEEN DECISION SUPPORT SYSTEMS AND EXPERT SYSTEMS RESEARCH

This article examines the potential for synergy between decision support systems (DSS) and expert systems research. Three predominant research traditions in DSS (applications, design, and technology) are examined and used as a basis to identify ways in which expert systems and DSS research interrelate. A range of emerging trends in DSS research that focus on these commonalities is discussed.     

Testing for Nonlinear Dependence in Inventory Data

This paper describes the properties of nonlinear dynamical systems in demand data and assesses the implications for inventory management. A test from Brock, Dechert, and Scheinkman (BDS) [6] for detecting nonlinearities is presented. This test assists inventory managers with two issues. First, it can be used to detect departures from the independence and stationarity assumptions of particular inventory models, thereby identifying the need for more suitable models. Second, the BDS test can determine whether forecasting model improvements are possible, and can measure improvements in the forecasting model process. The corresponding reductions in forecast errors improve inventory management and lead to reduced inventory costs. Using actual weekly demand for oil filters, this paper demonstrates the use of the BDS test and the effects of nonlinearities in demand data on inventory model performance.     

Inventory Positioning/Partitioning for Backorders Optimization for a Class of Multi-Echelon Inventory Problems*

Inventory management has undergone significant philosophical changes in recent decades such as the advent of the zero inventory concept. However, as attractive as the concept of minimal inventories may be, it is often unrealistic in application. Attention to basic features of inventory control systems such as order quantities, base stock levels, and reorder points remain crucial to ensure customer service at minimal cost. A nonlinear optimization model for determining base stock levels in a multi-echelon inventory network is presented. Lagrangian relaxation results in (1) newsboy-style relations that provide the optimal solutions, and (2) instantaneous shadow prices for the budget constraint. Sensitivity analysis of this model will facilitate making decisions concerning the desired investment in inventory for the entire system. This model may be solved on standard nonlinear programming software and is generalizable to problems in both production and distribution settings.     

The Selection of Computer-Aided Software Engineering Tools: A Multi-Criteria Decision Making Approach

The selection of the appropriate computer-aided software engineering (CASE) tools to suit the needs of an organization requires the systematic application of the multi-criteria decision methodology (MCDM). In order to make a case for the use of MCDM, the application of the ELECTRE I method to the selection of CASE tools from a possible set of six alternatives is demonstrated. The demonstration helps in gaining a grasp of the MCDM approach and the ELECTRE I method. There is potential for the application of the MCDM approach in other software engineering decisions, especially in the feasibility analysis of the systems life cycle.     

An Efficient Zero-One Formulation of the Multilevel Lot-Sizing Problem

This paper presents a zero-one linear formulation of the multilevel lot-sizing problem for materials requirement planning systems without capacity constraints. The model is an efficient statement of the problem and has a structure that is particularly convenient for research work. In addition, it is demonstrated that the relaxed linear programming solution to this formulation will always be integer. The results of a rather large computational history are reported along with a variable reduction methodology that allows for the solution of reasonably sized research problems.     

ASSESSING INFORMATION SYSTEM VALUE: AN EXPERIMENTAL STUDY

Linear models of information system value that are a function of information attributes are empirically studied by having managers in two firms make assessments of the importance of attributes, the level of satisfaction produced by various levels of each attribute, and the overall value of total systems. The results verify that such models may be quite useful in information system design. It is also shown that ordinally ranked attributes work as well as the interval-scaled attributes in the linear model. Since ordinal measures are easier to obtain, this suggests that “linear ordinal” models may be the most practical method of quantifying information systems value.     

Optimal Acquisition of FMS Technology Subject to Technological Progress*

In order to gain competitive advantage, a firm must link its technology choice to its total manufacturing strategy and business unit's goals. A dynamic model is presented to examine the strategic decision concerning the acquisition of flexible manufacturing systems (FMS) technology. A major contribution of this model is its ability to capture the strategic benefits of FMS with respect to economies of scope and technological progress. Decisions such as the timing and size of new technology acquisition and the scrapping of conventional capacity are explored as a firm plans for the upgrading of its facility to meet future dynamic strategic goals. This model may be used to assist with strategic planning because it identifies the critical relationships and trade-offs between various exogenous forces (such as market growth or decay, the cost of acquiring flexible manufacturing systems, and the rate of technological progress) and the decision variables considered.     

Scheduling with Sequencing Flexibility*

This study examines the effects of sequencing flexibility on the performance of rules used to schedule operations in manufacturing systems. The findings show that taking advantage of even low levels of sequencing flexibility in the set of operations required to do a job results in substantial improvement in the performance of scheduling rules with respect to mean flowtime. Differences in the mean flowtime measure for various rules also diminish significantly with increasing sequencing flexibility. Performance improvements additionally result for such due-date related performance measures as mean tardiness and the proportion of jobs tardy. At high levels of sequencing flexibility, some nonparametric scheduling rules outperform the shortest processing time rule in terms of the mean flowtime criterion. Rules based on job due dates also outperform rules based on operation milestones in terms of tardiness related criteria at high levels of sequencing flexibility. The implications of these findings for the design of manufacturing systems and product design are noted.