E. Ertugrul Karsak

Product planning in quality function deployment using a combined analytic network process and goal programming approach

Quality function deployment (QFD) is a customer-oriented design tool with cross-functional team members reaching a consensus in developing a new or improved product to increase customer satisfaction. QFD starts with the house of quality (HOQ), which is a planning matrix translating the customer needs into measurable product technical requirements (PTRs). A robust evaluation method should consider the interrelationships among customer needs and PTRs while determining the importance levels of PTRs in the HOQ. This paper employs the analytic network process (ANP) to fulfill this requirement. Furthermore, the proposed analytic procedure should take into account the multi-objective nature of the problem, and thus, incorporate other goals such as cost, extendibility and manufacturability of PTRs. This paper presents a zero-one goal programming methodology that includes importance levels of PTRs derived using the ANP, cost budget, extendibility level and manufacturability level goals to determine the PTRs to be considered in designing the product. A numerical example is presented to illustrate the application of the decision approach.

Fuzzy multi-criteria decision-making procedure for evaluating advanced manufacturing system investments

Abstract In this paper, a fuzzy decision algorithm is proposed to select the most suitable advanced manufacturing system (AMS) alternative from a set of mutually exclusive alternatives. Both economic evaluation criterion and strategic criteria such as flexibility, quality improvement, which are not quantitative in nature, are considered for selection. The economic aspects of the AMS selection process are addressed using the fuzzy discounted cash flow analysis. The decision algorithm aggregates the experts’ preference ratings for the economic and strategic criteria weights, and the suitability of AMS investment alternatives versus the selection criteria to calculate fuzzy suitability indices. The fuzzy indices are then used to rank the AMS investment alternatives. Triangular fuzzy numbers are used throughout the analysis to quantify the vagueness inherent in the financial estimates such as periodic cash flows, interest rate and inflation rates, experts’ linguistic assessments for strategic justification criteria, and importance weight of each criterion. A comprehensive numerical example is provided to illustrate the results of the analysis.

An integrated decision making approach for ERP system selection

Enterprise resource planning (ERP) systems have gained major prominence by enabling companies to streamline their operations, leverage and integrate business data process. In order to implement an ERP project successfully, it is necessary to select an ERP system which can be aligned with the needs of the company. Thus, a robust decision making approach for ERP software selection requires both company needs and characteristics of the ERP system and their interactions to be taken into account. This paper develops a novel decision framework for ERP software selection based on quality function deployment (QFD), fuzzy linear regression and zero-one goal programming. The proposed framework enables both company demands and ERP system characteristics to be considered, and provides the means for incorporating not only the relationships between company demands and ERP system characteristics but also the interactions between ERP system characteristics through adopting the QFD principles. The presented methodology appears as a sound investment decision making tool for ERP systems as well as other information systems. The potential use of the proposed decision framework is illustrated through an application.

A fuzzy MCDM approach for personnel selection

Many individual attributes considered for personnel selection such as organizing ability, creativity, personality, and leadership exhibit vagueness and imprecision. The fuzzy set theory appears as an essential tool to provide a decision framework that incorporates imprecise judgments inherent in the personnel selection process. In this paper, a fuzzy multi-criteria decision making (MCDM) algorithm using the principles of fusion of fuzzy information, 2-tuple linguistic representation model, and technique for order preference by similarity to ideal solution (TOPSIS) is developed. The proposed method is apt to manage information assessed using both linguistic and numerical scales in a decision making problem with multiple information sources. Furthermore, it enables managers to deal with heterogeneous information. The decision making framework presented in this paper employs ordered weighted averaging (OWA) operator that encompasses several operators as the aggregation operator since it can implement different aggregation rules by changing the order weights. The aggregation process is based on the unification of information by means of fuzzy sets on a basic linguistic term set (BLTS). Then, the unified information is transformed into linguistic 2-tuples in a way to rectify the problem of loss information of other fuzzy linguistic approaches. The computational procedure of the proposed framework is illustrated through a personnel selection problem reported in an earlier study.

Fuzzy multiple objective programming framework to prioritize design requirements in quality function deployment

Quality Function Deployment (QFD) is a customer-oriented design tool for developmg new or improved products to increase customer satisfaction by integrating marketing, design engineering, manufacturing, and other related functions of an organization. QFD aims to maximize customer satisfaction; however, considerations such as cost budget, technical difficulty, etc. limit the number and the extent of the possible design requirements that can be incorporated into a product. This paper presents a fuzzy multiple objective programming approach that incorporates imprecise and subjective information inherent in the QFD planning process to determine the level of fulfillment of design requirements. Linguistic variables are employed to represent the imprecise design information and the importance degree of each design objective. A real-world application illustrates the proposed fuzzy multiple objective decision analysis.

Practical common weight multi-criteria decision-making approach with an improved discriminating power for technology selection

A practical common weight multi-criteria decision-making (MCDM) methodology with an improved discriminating power for technology selection is introduced. The proposed MCDM methodology enables the evaluation of the relative efficiency of decision-making units (DMUs) with respect to multiple outputs and a single exact input. Its robustness and discriminating power are illustrated via a previously reported robot evaluation problem by comparing the ranking obtained by the proposed MCDM framework with that obtained by the cross-efficiency analysis, which is a well-known data envelopment analysis-based methodology. The results indicate that the proposed methodology enables further ranking of data envelopment analysis-efficient DMUs with a notable saving in computations compared with cross-efficiency analysis. Finally, the proposed MCDM framework is extended to incorporate ordinal as well as exact outputs, and an application is presented to illustrate the methodology.

A fuzzy multiple objective programming approach for the selection of a flexible manufacturing system

Abstract Global competition in manufacturing environment has forced the firms to consider increasing the quality and responsiveness to customization, while decreasing costs. The evolution of flexible manufacturing systems offers great potential for increasing flexibility and changing the basis of competition by ensuring both cost effective and customized manufacturing at the same time. This paper presents a fuzzy multiple objective programming approach to facilitate decision making in the selection of a flexible manufacturing system (FMS). Fuzzy set theory is introduced in the model to incorporate the vague nature of future investments and the uncertainty of the production environment. Linguistic variables and triangular fuzzy numbers are used to quantify the vagueness inherent in decision parameters, e.g., increase in market response, improvement in quality, reduction in setup cost, and so forth. The model proposed in this paper determines the most appropriate FMS alternative through maximization of objectives such as reduction in labor cost, reduction in setup cost, reduction in work-in-process (WIP), increase in market response and improvement in quality, and minimization of capital and maintenance cost and floor space used. These objectives are assigned priorities indicating their importance levels using linguistic variables. A numerical example is presented to illustrate the application of the model developed in this paper.

Fuzzy multiple objective decision making approach to prioritize design requirements in quality function deployment

Quality function deployment (QFD) is a customer-oriented design tool for developing new or improved products to increase customer satisfaction by integrating marketing, design engineering, manufacturing, and other related functions of an organization. QFD focuses on delivering value by taking into account customer needs and then deploying this information throughout the development process. Although QFD aims to maximize customer satisfaction, technology and cost considerations limit the number and the extent of the possible design requirements that can be incorporated into a product. This paper presents a fuzzy multiple objective programming approach that incorporates imprecise and subjective information inherent in the QFD planning process to determine the level of fulfilment of design requirements. Linguistic variables are employed to represent the imprecise design information and the importance degree of each design objective. The fuzzy Delphi method is utilized to achieve the consensus of customers in...Quality function deployment (QFD) is a customer-oriented design tool for developing new or improved products to increase customer satisfaction by integrating marketing, design engineering, manufacturing, and other related functions of an organization. QFD focuses on delivering value by taking into account customer needs and then deploying this information throughout the development process. Although QFD aims to maximize customer satisfaction, technology and cost considerations limit the number and the extent of the possible design requirements that can be incorporated into a product. This paper presents a fuzzy multiple objective programming approach that incorporates imprecise and subjective information inherent in the QFD planning process to determine the level of fulfilment of design requirements. Linguistic variables are employed to represent the imprecise design information and the importance degree of each design objective. The fuzzy Delphi method is utilized to achieve the consensus of customers in determining the importance of customer needs. A pencil design example illustrates the application of the multiple objective decision analysis.

An integrated fuzzy MCDM approach for supplier evaluation and selection

A methodology that uses QFD, fusion of fuzzy information, and 2-tuple linguistic representation is proposed.It manages non-homogeneous information in supplier selection with multiple information sources.It computes the weights of criteria and ratings of suppliers using interrelated HOQ matrices.It quantifies vagueness and pertinent relationships in supplier selection while tackling multi-granularity.The developed approach is illustrated through a case study. A fuzzy multi-criteria group decision making approach that makes use of quality function deployment (QFD), fusion of fuzzy information and 2-tuple linguistic representation model is developed for supplier selection. The proposed methodology seeks to establish the relevant supplier assessment criteria while also considering the impacts of inner dependence among them. Two interrelated house of quality matrices are constructed, and fusion of fuzzy information and 2-tuple linguistic representation model are employed to compute the weights of supplier selection criteria and subsequently the ratings of suppliers. The proposed method is apt to manage non-homogeneous information in a decision setting with multiple information sources. The decision framework presented in this paper employs ordered weighted averaging (OWA) operator, and the aggregation process is based on combining information by means of fuzzy sets on a basic linguistic term set. The proposed framework is illustrated through a case study conducted in a private hospital in Istanbul.

Robot selection using an integrated approach based on quality function deployment and fuzzy regression

The decision-makers have been experiencing difficulties in determining the most suitable robot alternative due to the increase in number of robots and the diversity in their application areas. A robust decision framework for robot selection should consider multiple and conflicting criteria and the dependencies among them. This paper introduces a decision model for robot selection based on quality function deployment (QFD) and fuzzy linear regression. The proposed approach benefits from the fact that QFD focuses on delivering value by taking into account the customer requirements and then by deploying this information throughout the development process, and applies this perspective to robot selection. Fuzzy linear regression is considered as an alternative decision aid for robot selection problems where imprecise relationships among system parameters exist. An example robot selection problem is presented to illustrate the proposed decision-making approach.