Satish Nukala

Strategic Planning Models for Reverse and Closed-Loop Supply Chains

Introduction. Strategic Planning of Reverse and Closed-Loop Supply Chains. Literature Review. Quantitative Modeling Techniques. Selection of Economical Used Products. Evaluation of Collection Centers. Evaluation of Recovery Facilities. Optimal Transportation of Goods. Evaluation of Marketing Strategies. Evaluation of Production Facilities. Evaluation of Futurity of Used Products. Selection of Economical New Products. Selection of Potential Secondhand Markets. Conclusions and Future Research.

Eco-procurement strategies for environmentally conscious manufacturers

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?

A FUZZY AHP BASED APPROACH FOR SELECTING POTENTIAL RECOVERY FACILITIES IN A CLOSED-LOOP SUPPLY CHAIN

In this paper, we employ fuzzy AHP methodology for selecting potential recovery facilities in a closed-loop supply chain. This methodology utilizes triangular fuzzy numbers for pair-wise comparisons and the extent analysis method for the synthetic extent value of the fuzzy pair-wise comparisons and principle of comparison of fuzzy numbers to derive the weight vectors to address the criticism traditional AHP often faces due to its unbalanced scale of judgments and inability to handle inherent uncertainty in carrying out pair-wise comparisons. A numerical example is considered to illustrate the methodology.

Supplier selection in a closed-loop supply chain network: an ANP-goal programming based methodology

Traditionally, in supply chain literature, the supplier selection problem is treated as an optimization problem that requires formulating a single objective function. However, not all supplier selection criteria can be quantified, as a result of which, only a few quantitative criteria are included in the problem formulation. To this end, in this paper, we develop an integrated analytic network process (ANP) and preemptive goal programming (PGP) based multi-criteria decision making methodology to address the qualitative and quantitative criteria that influence the supplier selection problem in a closed-loop supply chain network (CLSC). While the ANP methodology aids in determining qualitatively the supply chain strategy by evaluating the suppliers with respect to several criteria, the PGP methodology uses the ANP ratings as inputs and aids in mathematically determining the optimal quantities to be ordered from the suppliers.

Effective marketing of a closed-loop supply chain network: a fuzzy QFD approach

Both consumer and government concerns for the environment are driving many original equipment manufacturers (OEM) to engage in additional series of activities stemming from the reverse supply chain. The combination of forward/traditional supply chain and reverse supply chain forms the closed-loop supply chain. Apart from its efficient design, the success of a closed-loop supply chain network depends on its marketing strategy as well. Hence, it is important that the planned marketing strategy be evaluated with respect to the drivers of public participation in the network. To this end, we identify the important drivers of public participation and propose a fuzzy Quality Function Deployment based methodology and method of total preferences to evaluate the marketing strategy of a closed-loop supply chain with respect to those drivers. A numerical example is considered to illustrate the methodology.

Planning an efficient closed-loop supply chain network: a unified single-phase approach

Economic incentives, government regulations and customer perspective on environmental consciousness (EC) are driving more and more companies into the product recovery business, which forms a reverse supply chain. The combination of traditional/forward supply chain and reverse supply chain is called a closed-loop supply chain (CLSC). A supply chain involves three stages of planning, viz., strategic, tactical and operational. Strategic planning primarily deals with the design (what products should be processed/produced in what facilities etc) of the supply chain that is typically a long-range planning performed every few years when a supply chain needs to expand its capabilities (Pochampally and Gupta, 2005). Tactical planning involves the optimization of flow of goods and services across the supply chain and is typically a medium-range planning performed on a monthly basis. Finally, Operational planning is a short-range planning that deals with the day-to-day production planning and inventory issues on the factory floor. In this paper, we formulate a single-phase linear physical programming model in designing a closed-loop supply chain. This model when solved addresses simultaneously the critical issues, mentioned above, in the strategic and tactical planning of a CLSC

Multi-criteria analysis of potential recovery facilities in a reverse supply chain

Analytic Hierarchy Process (AHP) has been employed by researchers for solving multi-criteria analysis problems. However, AHP is often criticized for its unbalanced scale of judgments and failure to precisely handle the inherent uncertainty and vagueness in carrying out the pair-wise comparisons. With an objective to address these drawbacks, in this paper, we employ a fuzzy approach in selecting potential recovery facilities in the strategic planning of a reverse supply chain network that addresses the decision makers level of confidence in the fuzzy assessments and his/her attitude towards risk. A numerical example is considered to illustrate the methodology.