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This article presents and compares two techniques for making multiobjective product design decisions: quality function deployment (QFD) and decision analysis (DA). A research and development (R&D) case study describes the application of these techniques to the selection of the design for a new cargo/passenger aircraft. QFD provides a process for maintaining the voice of the customer, structuring the information in a concise format, developing teamwork, and improving communication. DA improves the quality of decisions by providing the decision maker with a better understanding of his values, insights into value trade‐offs, an understanding of major uncertainties, and the value of additional information. In this study, we found many similarities between the two techniques; however, for multiobjective decisions, decision analysis provides some significant advantages for generating alternatives and performing analysis. We recommend combining the best features of QFD and DA for R&D decision making.

This paper presents an interactive fuzzy satisfying method by assuming that the decision maker (DM) has fuzzy goals for each of the objective functions in multiobjective nonlinear programming problems. The fuzzy goals of the DM are quantified by eliciting the corresponding membership functions through the interaction with the DM. After determining the membership functions for each of the objective functions, in order to generate a candidate for the satisficing solution which is also a Pareto optimal, the DM selects an appropriate standing membership function and specifies his/her aspiration levels of achievement of the other membership functions, called constraint membership values. For the DM's constraint membership values, the corresponding constraint problem is solved and the DM is supplied with the Pareto optima] solution together with the trade‐off rates between a standing membership function and each of the other membership functions. Then by considering the current values of the membership functions as well as the trade‐off rates, the DM acts on this solution by updating his/her constraint membership values. In this way, the satisficing solution for the DM can be derived efficiently from among a Pareto optimal solution set by updating his/her constraint membership values. On the basis of the proposed method, a time‐sharing computer program is written and an application to regional planning is demonstrated along with the corresponding computer outputs.

This paper is motivated by the need to assess the risk profiles associated with the substantial number of items within military supply chains. The scale of supply chain management processes creates difficulties in both the complexity of the analysis and in performing risk assessments that are based on the manual (human analyst) assessment methods. Thus, analysts require methods that can be automated and that can incorporate on-going operational data on a regular basis.

The approach taken to address the identification of supply chain risk within an operational setting is based on aspects of multiobjective decision analysis (MODA). The approach constructs a risk and importance index for supply chain elements based on operational data. These indices are commensurate in value, leading to interpretable measures for decision-making.

Risk and importance indices were developed for the analysis of items within an example supply chain. Using the data on items, individual MODA models were formed and demonstrated using a prototype tool.

To better prepare risk mitigation strategies, analysts require the ability to identify potential sources of risk, especially in times of disruption such as natural disasters.

Emergency situations may differ in many ways but they share some common characteristics, such as the sudden onset and the need to transparently evaluate various usually conflicting objectives. In nuclear power generation, however, emergency situations constitute a special challenge. The focus of this paper is to highlight the role of multi‐criteria decision analysis (MCDA) in nuclear emergency and recovery management on the basis of a hypothetical case study.

Multi‐attribute value theory as one field of research within MCDA is introduced. Special emphasis is placed on the modelling of the decision makers' preferences which is a crucial part in any multi‐criteria analysis. A central aim is to facilitate the preference elicitation in group decision processes.

The management of emergency situations in nuclear power generation necessitates the consideration of technical, economic, environmental, socio‐psychological and political aspects. Furthermore, various stakeholder and expert groups with diverse background knowledge and different views, responsibilities and interests are involved in such a decision‐making process. MCDA can help to take into account various incommensurable aspects and the subjective preferences of the decision makers and thus contribute to transparency and traceability of decision‐making processes. Since the preference parameters are inherently afflicted with uncertainties, thorough sensitivity analyses are important to visualise the impact of the uncertainties in an understandable way.

A new approach to sensitivity analysis is proposed, allowing one to comprehensibly visualise and communicate the impact of the uncertainties associated with the subjective preference parameters on the results of the decision analysis.

The president of a well-established manufacturer of mattresses has asked his assistant to recommend whether to consolidate plants of three different locations. He must decide not only which criteria are most useful in making such a decision, but also how to weigh the different criteria in coming up with a single decision.

The purpose of this article is to address the measure selection problem and to propose the use of a multi‐criteria approach to address the problem more effectively. The main objective of this research is to propose a methodology (not a new performance measurement framework) that will support existing measurement framework(s) during the process of performance measurement systems' design, implementation and use, and to advance the decision‐making process.

Conforming to the most favoured approach, the balanced scorecard is adopted to illustrate the proposed methodology. This paper briefly illustrates the application of the proposed methodology. This illustration is based on a real case study from a Greek financial institution, which has considered the proposed methodology in order to select appropriate measures. The paper begins with a brief literature review on the balanced scorecard, the theory of MCDM and smart technique. In section three the proposed methodology is presented and each of the stages involved. The paper then illustrates the proposed methodology.

The greatest significance of the methodology suggested here is that it provides a structure to guide decision makers through the process of measure selection. Criteria must be identified and considered systematically, as must alternatives (i.e. measures).

While the smart was chosen to select appropriate measures for the balanced scorecard, the basic approach used in formulating the problem serves also as a framework for the application of other multi‐criteria approaches to this problem as well as to other performance measurement frameworks. Ultimately, better quality decisions will result; both as a consequence of the support provided by the multi‐criteria tools and as a result of a structure that will help the decision makers to better understand the issues associated with the problem involved.

This article examines the approach in incorporating the six sigma quality concepts into the day‐to‐day business operations. The primary emphasis is not on the positive financial results that many companies were able to achieve with the implementation of this quality concept, but to illustrate how it can be linked into the overall company strategy.

One key aspect of commercial‐off‐the‐shelf (COTS) based systems pertains to operations and maintenance training. Therein lies the focus of this paper. Factors necessary in the development of a training program to support rapid COTS insertion at the system and platforms levels are initially identified. Thereafter, this paper summarizes the results of best training practices and metrics survey and presents a rational methodology to support the development and assessment of training programs for this type of environment. The results of a survey focused on assessing and identifying training delivery methods, trainee learning, and training results are also presented. Finally, a framework for evaluating candidate training program alternatives is presented.

The paper focuses on how knowledge visualization supports the development of a particular multiobjective decision-making problem as a portfolio optimization problem in the context of interorganizational collaboration between universities and a large automotive company. This paper fits with the emergent knowledge visualization literature because it helps to explain decision-making related to the development of a multiobjective optimization model in Lean Product Development settings. We investigate how using ad hoc visual tools supports knowledge translation and knowledge sharing, enhancing managerial judgment and decision-making.

The empirical case in this study concerns the setting up of a multiobjective decision-making model as a portfolio optimization problem to analyze and select alternatives for upgrading the lean production process quality at an FCA plant.

The study shows how knowledge visualization and the associated tools work to enable knowledge translation and knowledge sharing, supporting decision-making. The empirical findings show why and how knowledge visualization can be used to foster knowledge translation and sharing among individuals and from individuals to groups. Knowledge visualization is understood as both a collective and interactional process and a systematic approach where different players translate their expertise, share a framework and develop common ground to support decision-making.

From a theoretical perspective, the paper expands the understanding of knowledge visualization as a system of practices that support the development of a multiobjective decision-making method. From an empirical point of view, our results may be useful to other firms in the automotive industry and for academics wishing to develop applied research on portfolio optimization.

The multiple objective decision making problem arises when two or more non‐comparable objective functions are to be simultaneously optimised. There is a definite trend towards utilising interactive techniques for solving the multiple objective decision making problem. Interactive techniques allow the involvement of the DM throughout the decision process. In this paper we first provide a brief overview of multiple objective decision making, and then give a survey of literature dealing with interactive multiple objective decision making from 1965 to 1988.