Methodologies for performance enhancement in decentralized supply chains

Abstract

Tough business competition and globalization have meant that companies have to leverage heavily on proper supply chain management to gain/retain competitive advantage. Supply chains need to develop strong operation strategies, continually assess them and act proactively with reference to forecasted demand information. The performance of the supply chain must be quantified and improved to remain competitive in terms of cost and service. Inappropriate internal strategies and lack of coordination between decentralized supply chain nodes are the crucial bottlenecks limiting the performance of decentralized supply chains. The focus of this work is to enhance the performance of decentralized supply chains. In this study, we have taken the role of a third party supply chain consultant and devised diverse performance enhancement methodologies: (1) a bottleneck-troubleshooting and performance benchmarking methodology to improve overall network behavior, (2) a multi-objective performance enhancement methodology and (3) complexity management strategies to improve distribution network behavior with reference to the business goals/scenarios. Most of the work aims to identify the best tactical decision (internal strategy) for all distribution nodes in the decentralized network taking into account the forecasted demand and business objectives. The performance metrics employed in this work include supply chain cost (resources), customer service (output) and complexity (uncertainty). Current government regulations encourage supply chain practitioners to consider environmental impact as an important performance measure - closed-loop supply chains are the natural outcome of such regulations. Used product collection centers, reverse logistics, re-fabrication/refurbishment facilities become new players in this context. We show that it is possible to optimize a large-scale closed loop supply chain by decomposing it into various subsystems and intelligently optimizing all the subsystems in a cooperative manner avoiding local optima, constraint violations and convergence issues.