Rapid technological developments and the growing desire of customers to acquire latest technology has led to a new environmental problem "<i>waste</i>", comprising of both end-of-life products and used products that are disposed prematurely. As a result, 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. Contrary to a traditional/forward supply chain, a closed-loop supply chain involves more variability. In this paper, we explore the use of Motorola's Six Sigma methodology to achieve better synchronization in a closed-loop supply chain network by tailoring the individual processes in a way that maximizes the overall delivery performance. A numerical example is considered to illustrate the 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.
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.
While the strategic planning of a supply chain, which is typically a long range planning, deals with the design aspect of the supply chain (what products should be processed/produced in what facilities etc.), tactical planning is typically a medium-range planning that involves the optimization of flow of goods and services across the supply chain. In this paper, we present a multi-criteria optimization model for the strategic and tactical planning of a closed-loop supply chain under uncertainty, where the aspiration levels for various goals are more likely to be in the "approximately more/less than" and/or "more/less is better" form. We use fuzzy goal programming technique to solve the problem. When solved, the model identifies simultaneously the most economical used-product to re-process in the supply chain, the efficient production facilities and the right mix and quantity of goods to be transported across the supply chain. A numerical example is considered to illustrate the methodology.
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 maker's level of confidence in the fuzzy assessments and his/her attitude towards risk. A numerical example is considered to illustrate the methodology.
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.