Green supply chain performance measurement using fuzzy ANP-based balanced scorecard:a collaborative decision-making approach

The purpose of this paper is to delineate a green supply chain (GSC) performance measurement framework using an intra-organisational collaborative decision-making (CDM) approach. A fuzzy analytic network process (ANP)-based green-balanced scorecard (GrBSc) has been used within the CDM approach to assist in arriving at a consistent, accurate and timely data flow across all cross-functional areas of a business. A green causal relationship is established and linked to the fuzzy ANP approach. The causal relationship involves organisational commitment, eco-design, GSC process, social performance and sustainable performance constructs. Sub-constructs and sub-sub-constructs are also identified and linked to the causal relationship to form a network. The fuzzy ANP approach suitably handles the vagueness of the linguistics information of the CDM approach. The CDM approach is implemented in a UK-based carpet-manufacturing firm. The performance measurement approach, in addition to the traditional financial performance and accounting measures, aids in firms decision-making with regard to the overall organisational goals. The implemented approach assists the firm in identifying further requirements of the collaborative data across the supply-cain and information about customers and markets. Overall, the CDM-based GrBSc approach assists managers in deciding if the suppliers performances meet the industry and environment standards with effective human resource

A predictive integrated genetic-based model for supplier evaluation and selection

Supplier evaluation and selection is a complicated multiple criteria decision-making process which affects supply chain management (SCM) directly. Recent studies emphasize that artificial intelligence approaches obtain better performance than conventional methods in evaluating the suppliers’ performance and determining the best suppliers. Hence, this study proposes a new robust genetic-based intelligent approach, namely gene expression programming (GEP), to improve the supplier selection process for a supply chain and to cope with the drawback of the other intelligent approaches in this area. The applicability of this method was exhibited by a case study in the textile manufacturing industry. To show the performance of the mathematical-genetic model, comparisons with four intelligent techniques, namely multi-layer perceptron (MLP) neural network, radial basis function (RBF) neural network, adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM), were performed. The results derived from the intelligent approaches were compared by using a collected dataset from a textile factory. The obtained results demonstrated that first the GEP-based model provides a mathematical model for the suppliers’ performance based on the determined criteria, and the developed GEP model is more accurate than the four other intelligent models in terms of accuracy in performance estimation. In addition, to verify the validity of the developed model, different statistical tests were used and the results showed that the GEP model is statistically powerful

Strategy selection for sustainable manufacturing with integrated AHP-VIKOR method under interval-valued fuzzy environment

Selection of an appropriate sustainability strategy is a multi-criteria decision making (MCDM) problem for manufacturing organizations due to incommensurate and conflicting evaluation criteria. In addition, incomplete information and different opinions of decision makers lead to uncertainties such as interval data and fuzziness. This study proposes a hierarchal MCDM method by combining Analytical Hierarchal Process (AHP) and VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) methods under interval-valued fuzzy environment to deal with ranking of sustainable manufacturing strategies. Linguistic variables were used to assess the ratings of strategies and weights for selection criteria. These linguistic variables were expressed in the triangular interval-valued fuzzy sets. Using a case study of manufacturing small and medium enterprise, the final ranking of the strategies was elicited in accordance with this procedure. Subsequently, a sensitivity analysis was performed to validate the stability of the proposed final ranking. This method can be used as a decision making tool for alternative or strategy selection in other areas where uncertainties are inherent

Sustainable supply chain management in Malaysian SMEs: Perspectives from practitioners

This study was aimed at eliciting the perception of practitioners on sustainable supply chain management in small and medium enterprises (SMEs). A total of 15 companies from the SMEs in Malaysia were selected. 5 of these companies serve as suppliers to larger firms within and outside Malaysia, another 5 as manufacturers, and the rest as reverse logistics companies (collectors and recycling plant operating companies). All the companies were selected based on their involvement in sustainable supply chain management. A structured interview was conducted to uncover their perceptions and challenges in sustainable supply chain management. Feedback from the conducted interview revealed that most of the practitioners from these companies believe that sustainable supply chain management requires well-structured and efficient supply chain integration. In essence, this study enhanced the understanding of the requirements for an efficient sustainable supply chain management in the SMEs of a developing country and sustainable manufacturing in general