https://findresearcher.sdu.dk:8443/ws/files/116118561/reverse_logistics.pdf

Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future

Environmentally conscious manufacturing and product recovery (ECMPRO): A review of the state of the art

The main objective of this exploratory paper is to investigate the relationships between green practices of supply chain management and supply chain performance. This relationship is investigated in the context of the automotive industry. Five research propositions are suggested and tested with empirical data derived from five case studies taken from the Portuguese automotive supply chain. The data analysis identifies the most important green practices considered by managers, as well as the performance measures that are most appropriate and most widely used as means to evaluate the influence of green practices on supply chain performance. A conceptual model was derived from the data analysis and it can be used to assess the influence of green practices on supply chain performance. This model provides evidence as to which green practices have positive effects on quality, customer satisfaction and efficiency. It also identifies the practices which have negative effects on supply chain performance.

The influence of green practices on supply chain performance: a case study approach

Green supplier development: analytical evaluation using rough set theory

Green supply chain management: An investigation of pressures, practices, and performance within the Brazilian automotive supply chain

Many a firm today continually evaluates the performance of its supply chain in order to check whether the supply chain needs to be re-designed with respect to the customers' needs and requirements. Due to the inherent differences in a traditional supply chain and a reverse/closed-loop supply chain with respect to movement of goods as well as information, it is inappropriate to adopt the performance measures of a traditional supply chain for a reverse/closed-loop supply chain. To this end, this paper addresses the metrics that help evaluate the performance of a reverse/closed-loop supply chain. These metrics are incorporated in a mathematical model that uses quality function deployment (QFD) and linear physical programming (LPP) effectively to measure the 'satisfaction level' of the supply chain with respect to each of the metrics. The approach is illustrated through a numerical example. Brief introductions to the techniques used (QFD and LPP) and a brief review of the research in the field of strategic planning of a reverse/closed-loop supply chain are also given.

Metrics for performance measurement of a reverse/closed-loop supply chain

Reverse logistics aims at capturing the remaining value in end-of-use products. This also means saving natural resources, energy, clean air and water, landfill space, and money. Strategic planning (also called designing) of a reverse supply chain is a challenging problem due to various crucial issues, such as what end-of-use products to collect, where to collect them, how to reprocess them, where to reprocess them, etc. To this end, this paper addresses the following two crucial issues, and proposes a quantitative decision-making model for each of them: (i) how to select efficient collection centres? and (ii) how to evaluate whether repairing an end-of-use product is more sensible than remanufacturing/recycling the same? For the first problem, we propose a Linear Physical Programming model, and for the second problem, we employ Fuzzy Logic and Bayesian Updating. The models are demonstrated via numerical examples.

Use of linear physical programming and Bayesian updating for design issues in reverse logistics

Eco-procurement is about integrating environmental considerations into purchasing decisions. The advantages of this practice include cost savings, conservation of natural resources and energy, and compliance with environmental laws and regulations. While eco-procurement traditionally refers to customers (including companies) buying products containing recycled content, production of those products requires involvement of environmentally conscious manufacturers that must procure and reprocess used products containing recyclable content. In other words, eco-procurement in a supply chain refers as much to manufacturers as it does to customers. To this end, this paper addresses the following two crucial issues, and proposes a quantitative decision-making strategy for each of them: (1) which products must be chosen from a set of candidate-used products containing recyclable content? and (2) which suppliers, viz. companies that collect and sell the chosen used products, must be selected for eco-procurement?

Eco-procurement strategies for environmentally conscious manufacturers

The success of a reverse supply chain heavily relies on the efficiency of the collection facilities and recovery facilities chosen while designing that reverse supply chain. In this chapter, we propose a neural network approach to evaluate the efficiency of a facility (collection or recovery) of interest, which is being considered for inclusion in a reverse supply chain, using the available linguistic data of facilities that already exist in the reverse supply chain. The approach is carried out in four phases, as follows: In phase I, we identify criteria for evaluation of the facility of interest, for each group participating in the reverse supply chain. Then, in phase II, we use fuzzy ratings of already existing facilities to construct a neural network that gives impacts (importance values) of criteria identified for each group in phase I. Then, in phase III, using the impacts obtained in phase II, we employ a fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) approach to obtain the overall rating of the facility of interest, as calculated by each group. Finally, in phase IV, we employ Borda’s choice rule to calculate the maximized consensus (among the groups considered) rating of the facility of interest. Kishore K. Pochampally Southern New Hampshire University, USA

Reverse Supply Chain Design: A Neural Network Approach

What happens when one of the most widely used quality improvement methodologies meets the world's leading statistical software for quality improvement? Packed with case studies in a variety of sectors, including health care, manufacturing, airlines, and fast food restaurants, Six Sigma Case Studies with Minitab shows you how to maximize the quality

Kishore Pochampally

Papers

Metrics for performance measurement of a reverse/closed-loop supply chain

Use of linear physical programming and Bayesian updating for design issues in reverse logistics

Eco-procurement strategies for environmentally conscious manufacturers

Reverse Supply Chain Design: A Neural Network Approach

Six Sigma Case Studies with Minitab