Integration of QFD, AHP, and LPP methods in supplier development problems under uncertainty

Zahra Shad,Fatemeh Mojibian,Emad Roghanian

doi:10.1186/2251-712x-10-2

Abstract

Abstract Quality function deployment (QFD) is a customer-driven approach, widely used to develop or process new product in order to maximize customer satisfaction. Last researches used linear physical programming (LPP) procedure to optimize QFD; however, QFD issue involved uncertainties, or fuzziness, which requires taking them into account for more realistic study. In this paper a set of fuzzy data is used to address linguistic values parameterized by triangular fuzzy numbers. The proposed integrated approach includes analytic hierarchy process (AHP), QFD, and LPP to maximize overall customer satisfaction under uncertain conditions and apply them in the supplier development problem. The fuzzy AHP (FAHP) approach is adopted as a powerful method to obtain the relationship between the customer requirements (CRs) and engineering characteristics (ECs) to construct the house of quality (HOQ) in QFD method. LPP is used to obtain the optimal achievement level of the ECs and subsequently the customer satisfaction level under different degrees of uncertainty. The effectiveness of proposed method will be illustrated by an example.

Highlights

The increasing global competition and cooperation and the vertical disintegration of production activities have created the logistical challenge of coordinating the entire supply chain (SC) effectively, in upstream to downstream activities (Gebennini et al 2009)
Due to the high importance of the Supply chain management (SCM), the aim of this paper is to develop a useful approach by integrating fuzzy analytic hierarchy process (AHP), fuzzy Quality function deployment (QFD) (FQFD), and linear physical programming (LPP) to obtain the optimal values of the engineering characteristics (ECs) of the suppliers
Geometric mean technique proposed by Buckley (1985) was used to define the fuzzy geometric mean and fuzzy weights of each criterion (e.g., Chen et al 2co0m08p; aGriüsonngömr aettricael.s,2À0D0~9Á).aAccfoterrdicnogntsotrugecotimngetpriacirm-weiasne technique by using Equations 5 and 6, we can define the fuzzy weights of each criterion as following: 2 1

Summary

Introduction

The increasing global competition and cooperation and the vertical disintegration of production activities have created the logistical challenge of coordinating the entire supply chain (SC) effectively, in upstream to downstream activities (Gebennini et al 2009). By better managing the SC, companies can increase their customers’ satisfaction and Satisfying customer requirement is a multi-objective optimization problem. Different optimization methods have been applied in the field of QFD to maximize customer satisfaction. The linear programming model is used to maximize the overall customer satisfaction (e.g., Chen and Ko 2009; Lai et al 2007). Chen and Weng (2006) used goal programming to determine the fulfillment levels of the design requirements in the QFD. Delice and Güngör (2009) applied mixed integer linear programming (MILP) to acquire the optimized solution of alternative customer requirements (CRs). Chen and Ko (2010) consider the close link between the four phases using the means-end chain (MEC) concept to build up a set of fuzzy linear programming models to determine the contribution levels of each ‘how’ for customer satisfaction

Objectives

Methods

Discussion

Conclusion