Quality function deployment (QFD) is a customer-oriented design tool for developing new or improved products to achieve higher customer satisfaction by integrating various functions of an organization. The engineering characteristics (ECs) affecting the product performances are designed to match the customer attributes (CAs). However, from the viewpoint of the QFD team, product design processes are performed in imprecise environments, and more than one factor must be taken into account in determining the target levels of ECs, especially the limited resources and increased market competition. This paper presents an imprecise goal programming (GP) approach to determine the optimum target levels of ECs in QFD for maximizing customer satisfaction under resource limitation and considerations of market competition. Based on benchmarking data of CAs, the concept of satisfaction functions is utilized to formulate explicitly the customer's preferences and to integrate the competitive analysis of target market into the modelling and solution process. In addition, the relationships linking CAs and ECs and the ECs to each other are integrated by functional relationships. The proposed approach will be illustrated through a car door design example.
The Quality Function Deployment (QFD) is an approach that facilitates designing product by analyzing and projecting the Customer's Needs (CNs) in the Engineering Characteristics (ECs) of a product. The aim of QFD planning process is to determine the target levels for ECs of a product that achieve high level of overall customers' satisfaction. However, integrating design team's preferences in this preliminary stage of product design could make the design more realistic and could also avoid unfeasibility in posterior phases of the product development processes. Moreover, this process is performed within an imprecise environment, and more than one factor must be taken into account in determining targets levels of ECs; especially, the limited resources and increased market competition. This paper presents an imprecise goal programming approach to determine the best aspiration levels of ECs in QFD planning process. Based on benchmarking data of ECs, the concept of satisfaction functions will be utilized to integrate explicitly the design team's preferences and incorporate the competitive analysis of target market into the modelling and solution processes. In addition, the relationships linking CNs and ECs and the ECs to each other are integrated by functional relationships. The proposed approach will be illustrated through an example of product development of an emulsification dynamite packing machine.
Among many applications, several studies using Data Envelopment Analysis (DEA) have examined and studied the efficiency of supply chains. However, the majority of existing approaches dealing with this research area have ignored the important factor of decision makers’ preferences. The main objective of this article is to provide consistent DEA models that allow for efficiency analysis in order to determine the optimal allocation of resources according to these preferences. We propose three cases that are inspired from the geometric decomposition of preference attributions: (1) horizontal attribution, which is when decision makers treat each supply chain as a single non-detachable entity; (2) vertical attribution, which is when decision makers consider supply chains detachable and (3) combined attribution, which is when decision makers concurrently assign weights to the supply chain and to its members. Based on this suggested decomposition, new DEA models are developed, and an illustrative example is applied. The obtained results are relevant and show that DEA is capable of easily incorporating the preferences of decision-makers without resorting to weight restrictions on inputs or outputs.
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