Strategic Assessment Model (SAM): A Multiple Criteria Decision Support System for Evaluation of Strategic Alternatives*

The evaluation of strategic alternatives is a particularly difficult task. This difficulty is due to the complexities inherent in the evaluation process and the lack of structured information. The evaluation process must consider a multitude of relevant information from both the internal and external environments of the organization. Various analytical and normative models have helped decision makers utilize large volumes of information in strategic evaluation; however, most of these models have some limitations. We present a multiple criteria decision support system, called strategic assessment model (SAM), that addresses some of the limitations inherent in the existing models. SAM captures the decision maker's beliefs through a series of sequential, rational, and analytical processes. The environmental forces—decomposed into internal, task, general opportunities, and threats—are used along with the analytic hierarchy process (AHP), subjective probabilities, the entropy concept, and utility theory to enhance the decision maker's intuition in evaluating a set of strategic alternatives.     

On flexible product-mix decision problems under randomness and fuzziness

This paper considers several models of product-mix decision problems and production planning problems under uncertain conditions, and shows that these are extensional and versatile models for resolving previous product-mix problems. These proposed models include randomness derived from statistical analysis based on historical data, ambiguity of decision maker's intuition and the quality of received information, and flexibility in accomplishing the original plan. Furthermore, given that the upper limit values of some constraints have flexibility, and given a decision maker's level of satisfaction, we propose a flexible product mix of problems using the theory of constraints (TOC), and develop an efficient solution method. We then provide a numerical example that compares our models with some previous basic models. Efficiency of flexibility is obtained when our proposed models are applied to several conditions, such as measurable changes from the expected value of future returns.     

Use and Abuse of Weights in Multiple Objective Decision Support Models*

This paper examines difficulties with the use of weights to solve multiple objective decision support models: misunderstanding of the meaning of weights, issues of commensurability and, most important, the likely inability of weights alone to isolate the decision maker's most-preferred point. The constraint method is shown to be an attractive alternative.     

Organizational and supervisory justice effects on experienced threat during change: The moderating role of leader in-group representativeness

We explore the complex interplay between organizational justice and supervisory justice when predicting group members' threat perceptions in a context of organizational change. Based on the assumptions of relational models of procedural justice and prior research done in the multifoci justice framework, we hypothesize that the extent to which a supervisor is seen to embody and represent key in-group attributes will moderate the interaction between the supervisor's own interactional justice and the overall organizational procedural justice. Specifically, organizational justice is expected to decrease employees' feelings of threat particularly when the supervisor treats group members fairly and is perceived to be in-group representative rather than non-representative. We found support for this hypothesis across two studies, a cross-sectional survey and a scenario experiment. The findings confirm the particularly powerful role that an in-group representative leader's interactional fairness has in managing group members' responses to fundamental organizational processes. The implications for further research and practice are discussed.     

A case-based distance method for screening in multiple-criteria decision aid

Screening is a process of multiple-criteria decision aid (MCDA) in which a large set of alternatives is reduced to a smaller set that most likely contains the best choice. We propose screening using a distance model calibrated on the basis of the decision-maker's own judgement. Viewing MCDA as preference aggregation based on consequence data, we define consequence and preference expressions (values and weights) and describe how they are aggregated. Then we define screening and explain some of its properties. Using an appropriate definition of distance, our case-based distance method screens a set of alternatives using criterion weights and a distance threshold obtained by quadratic optimization using the decision-maker's selection of alternatives from a test set. This case-based method can elicit the decision maker's preferences more expeditiously and accurately than direct inquiry. An application in water supply planning is used to demonstrate the procedure.     

Flexible reasoning using sensible agent-based systems: A case study in job flow scheduling

Planning and control systems for highly dynamic and uncertain manufacturing environments require adaptive flexibility and decision-making capabilities. Modern distributed manufacturing systems assess the utility of planning and executing solutions for both system goals (e.g. minimize manufacturing production time for all parts or minimize WIP) and local goals (e.g. expedite part A production schedule or maximize machine X utilization). Sensible Agents have the ability to alter their autonomy levels to choose among a set of decision models in order to handle the differences between local and system goals. In this paper, Sensible Agents are applied to a production planning and control problem in the context of job shop scheduling and decision model theory. Sensible Agents provide for trade-off reasoning mechanisms among system and local utilities that are flexible and responsive to an agent's abilities, situational context and position in the organizational structure of the system.     

Organizational processes as determinants of information value

Research on the determinants of information value has not explicitly considered whether the type of organizational process participated in by a decisionmaker has an effect on his/her evaluation of information on which the decision is based. Much important managerial activity in organizations is carried on through formal or informal processes such as budgeting and strategic planning. If process differences are significant determinants of the way in which information is valued, the inclusion of this element into the framework for thinking in this area might add considerably to our ability to prescribe appropriate managerial processes as well as to our understanding of differences in the outcomes of decision processes. This empirical study suggests that organizational process type does have a significant effect on the determination of information value in the context of two generic process types (individual, group) that were evaluated in a strategic decisionmaking context.     

A decision support methodology for strategic planning in maritime transportation

This paper presents a decision support methodology for strategic planning in tramp and industrial shipping. The proposed methodology combines simulation and optimization, where a Monte Carlo simulation framework is built around an optimization-based decision support system for short-term routing and scheduling. The simulation proceeds by considering a series of short-term routing and scheduling problems using a rolling horizon principle where information is revealed as time goes by. The approach is flexible in the sense that it can easily be configured to provide decision support for a wide range of strategic planning problems, such as fleet size and mix problems, analysis of long-term contracts and contract terms. The methodology is tested on a real case for a major Norwegian shipping company. The methodology provided valuable decision support on important strategic planning problems for the shipping company.     

CONSTRAINED DECISION CRITERIA1

The choice of a suitable decision criterion is a difficult decision for many decision makers. In general, each criterion selects a different optimal act, each having its own appealing features. This paper proposes an approach allowing the decision maker to incorporate multiple criteria into an easily solved mathematical programming model whose solution is the optimal act. Optimality is defined by the objective function used in the model. The constraints embody the decision maker's anxieties and minimal desires.     

ASSESSMENT OF MULTIATTRIBUTED MEASURABLE VALUE AND UTILITY FUNCTIONS VIA MATHEMATICAL PROGRAMMING*

A major restriction on the use of decision analysis in practice is the frequent difficulty of determining a decision maker's multiattribute utility function. The assessment process can be complex and tedious and generally involves: (1) identifying relevant independence conditions, (2) assessing conditional utility functions, (3) assessing scaling constants, and (4) checking for consistency. Some of the assessment and modeling complexities encountered include an assessor's inability to respond in a quantitatively meaningful and consistent way to hypothetical gambles and an analyst's problem in selecting an appropriate functional form that accurately characterizes the conditional utility assessments. A simplified procedure that mitigates these difficulties is proposed. This procedure facilitates the determination of scaling constants by obtaining (via mathematical programming) a multiattributed measurable value function which is converted to a multiattributed utility function. The methodology can be developed advantageously to produce an interactive software package for use as an assessment aid.     

Retail category management: State-of-the-art review of quantitative research and software applications in assortment and shelf space management

Retail requires efficient decision support to manage increasing product proliferation and various consumer choice effects with limited shelf space. Our goal is to identify, describe and compare decision support systems for category planning. This research analyzes quantitative models and software applications in assortment and shelf space management and contributes to a more integrated modeling approach. There are difficulties commonly involved in the use of commercial software and the implementation and transfer of scientific models. Scientific decision models either focus on space-dependent demand or substitution effects, whereas software applications use simplistic rules of thumb. We show that retail assortment planning models neglect space-elastic demand and largely also ignore constraints of limited shelf space. Shelf space management streams on the other hand, mostly omit substitution effects between products when products are delisted orout-of-stock, which is the focus of consumer choice models in assortment planning. Also, the problem sizes of the models are often not relevant for realistic category sizes. Addressing these issues, this paper provides a state-of-the-art overview and research framework for integrated assortment and shelf space planning.     

OPTIMAL SAFETY-STOCK INVESTMENT THROUGH SUBJECTIVE EVALUATION OF STOCKOUT COSTS

This paper solves the problem of setting safety stocks by combining a subjective evaluation of the stockout-cost function with the usual holding-cost function. The technique given in this paper for estimating a decision maker's (DM's) disvalue function for stockouts is robust in terms of the shapes permissible for this function and allows for uncertainty on the part of the DM in expressing his/her trade-offs. The estimation technique could be applied to situations other than the safety-stock problem. We provide an optimization routine for the safety-stock problem which is designed to operate in conjunction with the estimation technique. Safety-stock levels are arrived at iteratively.     

Selecting a Portfolio of Technologies: An Application of Decision Analysis

The Department of Energy is faced with the critical and complex decision of selecting technologies for waste site remediation. This research focuses on developing a decision support tool which assists the decision maker to find an optimal portfolio. A portfolio consists of selecting the appropriate processes for a remediation site and, subsequently, selecting a technology for each process so that the decision maker's objectives are achieved. The measures supporting the objectives are risk, life-cycle cost, and time required for remediation. The model uses exponential attribute utility functions with an additive objective function. The model provides the decision maker with estimates of the cost and time distributions, and their associated utility. Cumulative frequency distributions illustrate the dominance of technology choices and the variance in the results. The model permits sensitivity analysis in the form of rainbow and tornado diagrams to display the effects of changes in the values of the input variables. Overall, the model provides a generic technology selection tool that can be used to make better informed decisions and may be easily manipulated to reflect changes in the remediation process.     

Interactive fuzzy goal programming for multi-objective transportation problems

This paper presents an interactive fuzzy goal programming approach to determine the preferred compromise solution for the multi-objective transportation problem. The proposed approach considers the imprecise nature of the input data by implementing the minimum operator and also assumes that each objective function has a fuzzy goal. The approach focuses on minimizing the worst upper bound to obtain an efficient solution which is close to the best lower bound of each objective function. The solution procedure controls the search direction via updating both the membership values and the aspiration levels. An important characteristic of the approach is that the decision maker's role is concentrated only in evaluating the efficient solution to limit the influences of his/her incomplete knowledge about the problem domain. In addition, the proposed approach can be applied to solve other multi-objective decision making problems. The performance of this solution approach is evaluated by comparing its results with that of the two existing methods in the literature.     

AN ADAPTIVE DECISION SUPPORT SYSTEM FOR ACADEMIC RESOURCE PLANNING

Institutions of higher learning are growing increasingly interested in the use of model-based approaches to their resource allocation problems. Recent modeling approaches, however, have failed to consider that resource allocation planning is not a well-structured decision process. Additionally, many decision makers are necessarily involved in the academic planning process and may assume dissimilar perspectives on the importance of achieving different goals and objectives. Furthermore, satisfactory allocation solutions can be expected to vary considerably from decision maker to decision maker as the individual's cognitive processes, perceptions, and evaluations are taken into consideration. This paper describes a decision support system (DSS) approach that attempts to adapt to a variety of academic decision makers with differing planning views in an environment of multiple conflicting objectives. This DSS, which was successfully tested on four academic decision makers in a large midwestern university, shows considerable promise for providing decision support to decision makers with varied problem-solving styles.     

Collective Intuition: Implications for Improved Decision Making and Organizational Learning

This article establishes the foundation for research on collective intuition through a study of decision making and organizational learning processes in police senior management teams. We conceptualize collective intuition as independently formed judgement based on domain‐specific knowledge, experience and cognitive ability, shared and interpreted collectively. We contribute to intuition research, which has tended to focus its attention at the individual level, by studying intuition collectively in team settings. From a dual‐process perspective, we investigate how expert intuition and deliberation affect decision making and learning at various levels of the organization. Furthermore, we contribute to organizational learning research by offering an empirically derived elaboration of the foundational 4I framework, identifying additional ‘feed‐forward’ and ‘feedback’ loop processes, and thereby providing a more complete account of this organizational learning model. Bridging a variety of relevant but previously unconnected literatures via our focal concept of collective intuition, our research provides a foundation for future studies of this vitally important but under‐researched organizational phenomenon. We offer theoretical and practical implications whereby expert intuitions can be developed and leveraged collectively as valuable sources of organizational knowledge and learning, and contribute to improved decision making in organizations.     

Budgets for integrating decentralized investment and production planning

Planning for an economic enterprise can be dichotomized into short-run production planning and longer-run investment planning. Usually these problems are treated as if they were separate, if not independent. This paper briefly reviews the separate approaches to optimal production decision making and investment planning, ‘fusing’ these models in order to consider the two issues simultaneously. The resulting ‘fused’ model is used to illustrate several difficulties which result from an intuitive synthesis of the independent solutions of the production problem and the investment problem. An integrated model is presented representing a centralized simultaneous solution for decision variables from the two functional fields. The paper compares and contrasts the synthesis of separate functional models to the decomposition of a simultaneous model of those functional areas. A result of this comparison is a theoretical justification for operating budgets and revenue targets as organizational mechanisms for achieving coordinated plans among decentralized planning units. Further, the set of conditions are identified under which the two approaches to simultaneous decision making are equivalent.     

Integrated planning for health care organizations, especially hospitals

Planning in health care organizations is of considerable current interest in this country. Furthermore, effective planning processes require, as a necessary condition for that effectiveness, development within a framework which considers the organizational, managerial and service delivery environment.This paper examines the concept of planning in relation to health care organizations. Through an examination of the effects which proper planning can have upon industrial organizations the authors analyze the possible impacts of planning procedures upon health care institutions. Their analysis leads to certain tentative conclusions as to appropriate organizational structure which could support effective planning. Whilst the authors have yet to test their hypothesis the conclusion they draw from their initial analysis are worthy of further investigation.     

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.     

Generalized symmetric weight assignment technique: Incorporating managerial preferences in data envelopment analysis using a penalty function

In this paper, we generalize the Symmetric Weight Assignment Technique to incorporate all managerial preferences in Data Envelopment Analysis (DEA). This is a method that promotes managerial preferences, while not changing the feasibility region of the nominal DEA model, unlike standard techniques such as cone ratio and assurance region that potentially yield infeasible problems. We discuss how this generalization is motivated by a real word problem where the decision maker's preference is not precisely known. We also discuss how we are using our generalization in solving a workforce allocation problem for the United States Navy.