یکپارچه سازی روش های QFD، AHP و LPP در مشکلات توسعه تامین کنندگان تحت عدم قطعیت

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

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, upstream to downstream activities (Gebennini et al. 2009). Supply chain management (SCM) integrates suppliers, manufacturers, distributors, and customers to meet final consumer needs and expectations efficiently and effectively (Cox 1999).

Quality function deployment (QFD) was developed by Yoji Akao in the 1960s. The basis of QFD is to obtain and translate customer requirements into engineering characteristics, and subsequently into part characteristics, process plans, and production requirements. This paper is concentrated on the HOQ, which translates customer requirements into the engineering characteristics. By better managing the SC, companies can increase their customers’ satisfaction and achieve sustainable business success. SC has different levels and each level can be considered as a customer of the previous level that should be maximized to the customer satisfaction in each level. QFD can be used as a useful method to translate the requirements of each level to the ECs of the previous level. AHP method can be used as a powerful multi-criteria tool to extract the relationships between the requirements of each level and ECs of the previous level. Humans are often uncertain in assigning the evaluation scores in crisp AHP. So FAHP can capture this difficulty. Although QFD implementation extended recently, only a few researchers focused in the supply chain (e.g., Zarei et al. 2011; Hassanzadeh Amin and Razmi 2009).

Satisfying customer requirement is multi-objective optimization problem. Different optimization methods have been applied in the field of QFD to maximize customer satisfaction. Mathematical programming is one of these optimization methods. Linear programming model is used to maximize the overall customer satisfaction (e.g., Chen and Ko 2009; Lai et al. 2007). Park and Kim (1998) used integer programming to optimize product design in the QFD. 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 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.

Bhattacharya et al. (2010) present a concurrent engineering approach integrating AHP with QFD in combination with cost factor measure (CFM), has been delineated to rank and subsequently select candidate suppliers under multiple, conflicting-in-nature criteria environment within a value-chain framework. Raissi et al. (2012) prioritize engineering characteristic in QFD using fuzzy common set of weight. Lai et al. (2006) used LPP as an effective multi-objective optimization method to optimize QFD. In this paper, we extended Lai et al. (2006) approach using fuzzy numbers instead of the crisp numbers to build HOQ. We used HOQ with triangular fuzzy numbers to extract mathematical model to deal with the fuzziness of the problem to achieve the optimal values of the ECs under different degrees of uncertainty.

Due to the high importance of the SCM, the aim of this paper is to develop a useful approach by integrating fuzzy AHP, fuzzy QFD (FQFD), and LPP to obtain the optimal values of the ECs of the suppliers. Supplier development is an important issue in the context of the SCM. Also, supplier development is a multi-criterion decision-making (MCDM) problem which includes both qualitative and quantitative factors (e.g., Xia and Wu 2007; Chan and Kumar 2007).

In this section literature review of QFD, fuzzy AHP, LPP methods, applying LPP with QFD and fuzzy linear programming are presented. In “Proposed methodology”, we present proposed methodology and illustrated it solving a numerical example in “Numerical example”. In “Discussion of results”, the obtained results are discussed and, finally in “Conclusions” the conclusion is presented.