A hybrid approach to configure eco-efficient supply chains under consideration of performance and risk aspects

Formal models that support multi-criteria decision making represent a strongly growing area in sustainable supply chain management research. However, uncertainties and risks are seldom considered in quantitative models for green supply chain (SC) design. The paper at hand suggests a hybrid approach to configure an eco-efficient SC for a new product under consideration of economic and environmental risks. Discrete-event simulation is applied to assess the financial, operational and environmental performance of different SC configuration options while the value-at-risk concept is adapted to evaluate related SC risks. The analytic hierarchy process is employed to solve the resulting multi-criteria decision problem of choosing the best option. The approach is illustrated at a case example of a fast moving consumer goods manufacturer.     

Coordination in supply chains: an evaluation using fuzzy logic

Supply chain coordination (SCC) is an effective approach to improve supply chain (SC) performance. The coordination can be achieved when interdependent entities work together by sharing resources and information to achieve common objectives aligned to maximise customer value for the entire SC. There are a number of mechanisms by which the SC members can coordinate, e.g. contracts, information sharing, information technology and collaborative initiatives. To communicate frequently and effectively, the partners are required to have good information systems and capability to share information. To coordinate with each other the SC members are required to have capabilities to implement coordination mechanisms effectively. In this paper, a model is proposed to measure the effect of these mechanisms on the extent of coordination. Currently, there is no scale of measurement reported in the literature to measure the SCC. A fuzzy logic approach combined with the analytic hierarchy process (AHP) is proposed to evaluate the extent of coordination. The proposed methodology is demonstrated through a case study of an automotive parts manufacturer.     

Evaluation of the green supply chain management practices: a fuzzy ANP approach

Environmentally responsible manufacturing, green supply chain management (GSCM), and related principles have become important strategies for companies to achieve profit and gain market share by lowering their environmental impacts and increasing their efficiency. As environment has become a key strategic consideration in supply chains, this study examines the components and elements of GSCM and suggests a novel GSCM evaluation framework. It also provides a real-case study of Ford Otosan, one of the pioneering companies about environmental subjects in Turkey, to illustrate the industrial application of our theoretical assessment model. The identified components are integrated into a strategic assessment and evaluation tool using analytical network process (ANP). The dynamic characteristics and complexity of the GSCM analysis environment make the ANP technique a suitable tool for this study. Moreover, to cope with ambiguity and vagueness of the decision maker's evaluations, the fuzzy extension of the ANP method is preferred.     

An integrated model of supply chain performance evaluation and decision-making using structural equation modelling and fuzzy AHP

This paper aims to propose a comprehensive evaluation tool and decision model, which can help the practitioners to gauge their supply chain performance and guide them in decision-making for further improvements. The model provides a salient notion of integrated supply chain performance evaluation approach distilled from the literature. The five important performance evaluation criteria (supply chain planning performance, supply chain partnership performance, production performance, delivery and logistic performance and customer service and satisfaction performance) and corresponding 19 sub-criteria have been identified. An integrated methodology of structural equation modelling (SEM) and fuzzy analytic hierarchy process (FAHP) have been applied to the proposed model to a real case study of Indian textile–apparel–retail supply chain network.     

Prioritising indicators in improving supply chain performance using fuzzy AHP: insights from the case example of four Indian manufacturing companies

Abstract

In this contribution, we aim to prioritise the indicators to enhance the organisational supply chain’s (SC) effectiveness from an industrial perspective. It will help industries to develop strategies for managing the SC effectively and ensuring improvement in performance continuously. To achieve this, this work proposes to use a two-phase research methodology. First, 36 SC performance improvement indicators are recognised from a literature survey and from field and industrial expert’s inputs. Secondly, a structural model is proposed using the fuzzy analytical hierarchy process (AHP) to prioritise the indicators strategically to improve the SC performance. The fuzzy AHP method helps determine the priority of concerns of the identified indicators under fuzzy surroundings. Inputs in this research are taken from four ancillary Indian plastic manufacturing firms. Research findings indicate that collaborations and information exchange dimension indicators obtained the highest priority in improving SC performance. The model proposed is considered very useful for the SC managers/practitioners/decision-makers to understand better and distinguish the essential SC performance improvement indicators and to take systematic decisions specifically to improve the performance of business in a SC context. Sensitivity analysis was conducted to examine the priority ranking of the indicators.     

Fuzzy LINMAP approach to heterogeneous MADM considering comparisons of alternatives with hesitation degrees

Multiattribute decision making (MADM) with multiple formats of information, which is called heterogeneous MADM for short, is very complex and interesting in applications. The purpose of this paper is to extend the Linear Programming Technique for Multidimensional Analysis of Preference (LINMAP) for solving heterogeneous MADM problems which involve intuitionistic fuzzy (IF) sets (IFSs), trapezoidal fuzzy numbers (TrFNs), intervals and real numbers. In this method, DM's preference is given through pair-wise comparisons of alternatives with hesitation degrees which are represented as IFSs. The IF consistency and inconsistency indices are defined on the basis of pair-wise comparisons of alternatives. Each alternative is assessed on the basis of its distance to a fuzzy ideal solution (FIS) unknown a priori. Based on the defined IF consistency and inconsistency indices, we construct a new fuzzy mathematical programming model, which is solved by the developed method of fuzzy mathematical programming with IFSs. Once the FIS and the attribute weights are obtained, we can calculate the distances of all alternatives to the FIS, which are used to determine the ranking order of the alternatives. A supplier selection example is presented to demonstrate the validity and applicability of the proposed method.     

An integrated multi-objective decision-making process for multi-period lot-sizing with supplier selection

Supplier selection is a multi-criteria problem which includes both tangible and intangible factors. In these problems if suppliers have capacity or other different constraints two problems will exist: which suppliers are the best and how much should be purchased from each selected supplier? In this paper an integrated approach of analytic network process (ANP) and multi-objective mixed integer linear programming (MOMILP) is proposed. This integrated approach considers both tangible and intangible factors in choosing the best suppliers and defines the optimum quantities among selected suppliers to maximize the total value of purchasing (TVP), and to minimize the total cost and total defect rate and to balance the total cost among periods. The priorities are calculated for each supplier by using ANP. Four different plastic molding firms working with a refrigerator plant are evaluated according to 14 criteria that are involved in the four clusters: benefits, opportunities, costs and risks (BOCR). The priorities of suppliers will also be used as the parameters of the first objective function. This multi-objective and multi-period real-life problem is solved by using previous techniques and a reservation level driven Tchebycheff procedure (RLTP). Finally the most preferred nondominated solutions are determined by considering the decision maker's (DM's) preferences and the results obtained by these techniques are compared.