Zusammenfassung:
Due to intensification of market competence and customers increasing socio-environmental concerns, sustainability has become a focal point for most organizations. Thus incorporation of customer sustainable expectations into the conventional supplier selection process has become necessary for manufacturers to sustain in the competitive market. To cope with this issue, it is necessary to model the customers' opinions and expectations into the strategic supplier selection and order allocation decision making, an area not delved upon much in the literature. Therefore, the aim of the study is to develop an analytical model for manufacturing firm for selection of suppliers based on customers' expectations which are reflected as retailers' expectation level and also for bringing in financial and socio-environmental stability to the whole SC. The research methodology adopted involves 1) use of fuzzy-TOPSIS technique using Z numbers for evaluation of suppliers based on traditional as well as social environmental attributes which reflect customer's social and green expectations, 2) development of a novel bi-objective mathematical model for selecting suppliers meeting the expectation levels, allocating orders proportionate to the performance scores of the suppliers, rewarding or penalizing the suppliers as per their sustainable performance, 3) Transformation of the bi-objective model into a weighted goal programming model for attaining a trade-off solution between the objectives of minimization of cost and maximization of sustainable value of purchase. To exemplify the efficacy of the model, case of electronics sector is presented. The findings of the result suggest that penalty and reward value incorporated in the cost objective system is effective in enhancing the sustainable performance of the suppliers and can prove to be an effective strategy for DMs. The study recommends that the proposed mathematical model can considerably aid the manufacturer in integrating supplier selection and order allocation decisions while optimizing the performance of the customer oriented supply chain.