CONTINUITY IN MIS/DSS LABORATORY RESEARCH: THE CASE FOR A COMMON GAMING SIMULATOR*

To date, case analyses and field studies have predominated in MIS/DSS research. Laboratory studies seem not to have been given the attention they deserve. This trend is partially due to: (1) the high costs associated with developing decision simulators and related software to support laboratory research and (2) difficult problems with external validity because of unrealistically simple simulated decision environments and DSS software. Since MIS/DSS laboratory research has been most successful when studies have occurred in an interrelated stream, the development of common, portable, and flexible simulators seems desirable. Common simulators would reduce the time and cost of developing experiments, provide for research continuity, comparability, and replication, and facilitate researcher-to-researcher communication. Sufficiently complex simulators and DSS software would reduce problems with external validity by providing more realistic laboratory models. This paper presents a software package that offers considerable potential as a common tool for DSS research.     

USING ARTIFICIAL INTELLIGENCE TECHNIQUES TO ENHANCE THE CAPABILITIES OF MODEL MANAGEMENT SYSTEMS

Decision Support Systems (DSSs) originally were proposed as interactive problem-solving vehicles through which models and analytical techniques could be made available to decision makers. Model management represents a line of research within the DSS field that focuses on the design and implementation of software environments to support the use of DSSs for this purpose. This paper describes the tasks that must be supported within these environments and illustrates how existing prototype Model Management Systems (MMSs) implementations provide this support. The use of artificial intelligence techniques in such implementations are reveiwed, and three scenarios are presented to show how future MMSs could be constructed using these techniques.     

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.     

Applications and Implementation DECISION SUPPORT SYSTEMS: AN EMPIRICAL ASSESSMENT FOR DECISION MAKING

A laboratory experiment examined the effects of applying decision support system (DSS) technology to decision making in ill-structured problem environments under varying information conditions. Marketing executives participated in the experiment which investigated the effects of DSS availability, DSS training, and data availability on dependent variables that included: (1) the number of alternatives considered by a subject during decision making, (2) the period of time spent by a subject to complete the decision-making process, (3) the subject's perceived confidence in the decisions he or she had made, (4) the amount of data considered by a subject's during decision making, (5) the individual subject's decision processing, and (6) the subject's performance overall. Our results indicate that all three factors significantly affect the number of alternatives considered by subjects during the decision-making process. We therefore suggest that DSS training be coordinated with decision training in order to realize the potential of DSSs as described in the DSS literature.     

A Modeling Framework for Supply Chain Simulation: Opportunities for Improved Decision Making*

Owing to its inherent modeling flexibility, simulation is often regarded as the proper means for supporting decision making on supply chain design. The ultimate success of supply chain simulation, however, is determined by a combination of the analyst's skills, the chain members' involvement, and the modeling capabilities of the simulation tool. This combination should provide the basis for a realistic simulation model, which is both transparent and complete. The need for transparency is especially strong for supply chains as they involve (semi)autonomous parties each having their own objectives. Mutual trust and model effectiveness are strongly influenced by the degree of completeness of each party's insight into the key decision variables. Ideally, visual interactive simulation models present an important communicative means for realizing the required overview and insight. Unfortunately, most models strongly focus on physical transactions, leaving key decision variables implicit for some or all of the parties involved. This especially applies to control structures, that is, the managers or systems responsible for control, their activities and their mutual attuning of these activities. Control elements are, for example, dispersed over the model, are not visualized, or form part of the time‐indexed scheduling of events. In this article, we propose an alternative approach that explicitly addresses the modeling of control structures. First, we will conduct a literature survey with the aim of listing simulation model qualities essential for supporting successful decision making on supply chain design. Next, we use this insight to define an object‐oriented modeling framework that facilitates supply chain simulation in a more realistic manner. This framework is meant to contribute to improved decision making in terms of recognizing and understanding opportunities for improved supply chain design. Finally, the use of the framework is illustrated by a case example concerning a supply chain for chilled salads.     

Reverse Auctions with Multiple Reinforcement Learning Agents*

Reverse auctions in business‐to‐business (B2B) exchanges provide numerous benefits to participants. Arguably the most notable benefit is that of lowered prices driven by increased competition in such auctions. The competition between sellers in reverse auctions has been analyzed using a game‐theoretic framework and equilibria have been established for several scenarios. One finding of note is that, in a setting in which sellers can meet total demand with the highest‐bidding seller being able to sell only a fraction of the total capacity, the sellers resort to a mixed‐strategy equilibrium. Although price randomization in industrial bidding is an accepted norm, one might argue that in reality managers do not utilize advanced game theory calculations in placing bids. More likely, managers adopt simple learning strategies. In this situation, it remains an open question as to whether the bid prices converge to the theoretical equilibrium over time. To address this question, we model reverse‐auction bidding behavior by artificial agents as both two‐player and n‐player games in a simulation environment. The agents begin the game with a minimal understanding of the environment but over time analyze wins and losses for use in determining future bids. To test for convergence, the agents explore the price space and exploit prices where profits are higher, given varying cost and capacity scenarios. In the two‐player case, the agents do indeed converge toward the theoretical equilibrium. The n‐player case provides results that reinforce our understanding of the theoretical equilibria. These results are promising enough to further consider the use of artificial learning mechanisms in reverse auctions and other electronic market transactions, especially as more sophisticated mechanisms are developed to tackle real‐life complexities. We also develop the analytical results when one agent does not behave strategically while the other agent does and show that our simulations for this environment also result in convergence toward the theoretical equilibrium. Because the nature of the best response in the new setting is very different (pure strategy as opposed to mixed), it indicates the robustness of the devised algorithm. The use of artificial agents can also overcome the limitations in rationality demonstrated by human managers. The results thus have interesting implications for designing artificial agents in automating bid responses for large numbers of bids where human intervention might not always be possible.     

Modelbase Construction with Object-Oriented Constructs*

This paper proposes modelbase construction mechanisms in a decision support system (DSS) for representing and managing models of diverse management science/operations research modeling paradigms, using object-oriented database management systems (ODBMS) constructs. It focuses on the construction of a modelbase that maintains logical independence among the DSS components including modelbase, database, and solvers, but relieves the mismatching characteristics by facilitating intelligent and stabilized integration of them. As a conceptual framework to build such a modelbase, this research uses generic model concepts, and adopts structured modeling language (SML) as a paradigm-neutral model representation sublanguage. In the modelbase, three model abstraction layers including model type, model structure, and model instance are devised to facilitate the capture of multiple modeling paradigms and specific application models in different instantiation levels. The constructs and methods discussed are flexible enough to be applied to a wide variety of decision-making and problem-solving paradigms. A prototype system is developed under a commercial ODBMS called OBJECTSTORE with the C++ programming language, and diverse model manipulation commands are illustrated by an object-oriented structured query language (OSQL).     

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.     

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.     

Project‐Level Reuse Factors: Drivers for Variation within Software Development Environments*

Systematic reuse can dramatically improve software development productivity and quality even though a software reuse methodology may require substantial investments. Some projects may fail to achieve the targeted amounts of reuse within organizations that are overall successful in employing reuse. To explain such variation, this research explores the effects of project‐level factors in the success of software reuse. A model that relates project factors to project reuse success is developed using an information‐rich case study approach. The results are based on the insights obtained in a nominal group technique session, triangulated with structured interviews and comparative case studies. Success factors identified by the study relate to client influence, project culture, project attributes, and developer reuse experience. An organization that can successfully identify the factors affecting potential software reuse will be able to better target investments for the improvement of its reuse methodology and thus positively affect its software development productivity and quality.     

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.     

Adaptive,Agile Approaches to Organizational Architecture Utilizing Agent Technology*

Intelligent 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.     

Agent-based interbay system control for a single-loop semiconductor manufacturing fab

This study investigates agent-based interbay control of a wafer fabrication facility with a single-loop track. Doing so will simultaneously resolve the production scheduling and vehicle dispatching problems for an interbay over-hoist-transportation system which is commonly employed in the semiconductor manufacturing industry. Two issues are focused on in this study: (i) To design appropriate agent-based control architecture for an interbay system and (ii) to develop a coordination mechanism for the autonomous agents in the interbay system, so that wafer manufacturing tasks can be accomplished efficiently. In this study, we construct a distributed interbay system architecture, in which wafer cassettes, vehicles, and manufacturing cells with stockers are modelled as independent and autonomous agents. This study develops an agent coordination mechanism that is based on a vehicle-initiated contract net protocol. Three types of bidding strategies are proposed and empirically examined via simulation. The performances of the proposed agent-based control mechanism, in terms of cassette sojourn time and throughput, are observed and our experiments have shown promising results.     

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.     

Animation in User Interfaces Designed for Decision Support Systems: The Effects of Image Abstraction,Transition, and Interactivity on Decision Quality*

This paper develops the idea of animation in user interfaces designed for decision support systems (DSS), proposes a framework to investigate the efficacy of animation in these interfaces, and reports on a study that examined the effects of properties of animation specified by the framework. Based on a review of selected background literature, principal properties affecting the efficacy of animation in user interfaces designed for DSS are identified and the effects on decision quality of three of these properties are hypothesized. To evaluate these hypotheses, data was collected in a laboratory experiment involving two different tasks. The results for both tasks indicate that animation in user interfaces designed for DSS should employ parallel as opposed to sequential navigation interactivity techniques. The decision quality of subjects that used a parallel navigation technique was significantly greater than that of those that used a sequential navigation interactivity technique. The results regarding the efficacy of image abstraction and transition effects varied by task. For one task, decision quality was significantly greater for subjects that used realistic as opposed to abstract images, but decision quality did not vary by transition effect. For the other task, decision quality was significantly greater for subjects that used gradual as compared to abrupt transition, but image abstraction had no effect on decision quality.     

Looking Inside Glitch Mitigation Capability: The Effect of Intraorganizational Communication Channels

Manufacturers can reduce the occurrence of glitches in their operations by building capabilities to prevent them, yet mitigation capabilities are also needed to contain the effects of the glitches that will still inevitably occur every now and then. We examine the glitch mitigation capability of a production process from an information‐processing perspective and propose that (i) the impact of operational glitches on delivery performance is contingent on the formalization of intrafirm communication channels and (ii) this effect is stronger when formal communication channels are complemented with informal channels. We test our model in a sample of 163 make‐to‐order production processes and find support for the first hypothesis and partial support for the second hypothesis. The statistical analyses also reveal nonhypothesized empirical regularities, which we explore through an additional qualitative study based on 34 site visits and 30 interviews with production planners. The results have practical implications for the design of intraorganizational communication channels, and they also contribute to the research on organizational resilience and communications by showing that when coping with disruptions, the formal communication channels have advantages that are seldom discussed in the literature or recognized by practitioners.     

Intelligent Procedures for Intra‐Day Updating of Call Center Agent Schedules

For nearly all call centers, agent schedules are typically created several days or weeks before the time that agents report to work. After schedules are created, call center resource managers receive additional information that can affect forecasted workload and resource availability. In particular, there is significant evidence, both among practitioners and in the research literature, suggesting that actual call arrival volumes early in a scheduling period (typically an individual day or week) can provide valuable information about the call arrival pattern later in the same scheduling period. In this paper, we develop a flexible and powerful heuristic framework for managers to make intra‐day resource adjustment decisions that take into account updated call forecasts, updated agent requirements, existing agent schedules, agents' schedule flexibility, and associated incremental labor costs. We demonstrate the value of this methodology in managing the trade‐off between labor costs and service levels to best meet variable rates of demand for service, using data from an actual call center.     

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.     

A Cooperative Savings Game Approach to a Time Sensitive Capacity Allocation and Scheduling Problem

We consider a competitive scheduling setting with arbitrary number of agents each having the option to utilize two parallel resources to satisfy its demand: (i) an in‐house resource dedicated to process only the tasks of each specific agent, and (ii) a flexible resource capable of processing all agents' workloads. In a noncooperative setting, each agent would determine how much of its demand it will subcontract to the flexible resource with the objective to deliver its entire demand as quickly as possible subject to the priority rules set by the owner of the flexible resource (i.e., third‐party). In this study, we also allow for agents to coalesce with other agents and update their initial subcontracting decisions to attain rescheduling savings. Evidently, a grand coalition of all agents can coordinate to achieve the maximum savings possible, but the resulting schedule may yield individual losses for a subset of agents (which we refer to as “losers”), thus necessitating a transfer payment scheme to distribute the rescheduling savings among the agents in an equitable way. We model the rescheduling interactions among the agents as a cooperative savings game, and propose savings distribution schemes that invoke the core allocation concept.