Managing Decentralized Supply Chain Systems

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Embargoed until 2024-02-08
Copyright: Haque, Marjia
Supply chain management has been a widely studied research topic over the past few decades due to its applicability for all local and global movements of products and materials. Appropriate planning is necessary to ensure the efficient flow of products and/or services through a chain. In the literature, most studies focused on centralized and integrated supply chain networks assuming complete information-sharing among their members. However, the most practical supply chains are decentralized with distributed structures where complete information-sharing is not common. Although substantial research has been conducted on centralized supply chain planning, studies of decentralized structures are scarce. The aim of this thesis is to study decentralized supply chain systems considering a multi-echelon network with multiple non-cooperative entities. The research considers commonly used supply chain structures, where individual business entities act independently to generate their operational plan with their own goals and strategies, without having access to full information of the whole chain. Thereby, these autonomous entities optimize their own objectives based on limited information obtained from other members in the chain. This research focuses on restricted information-sharing option as well as the independence and non-dominant behaviors of the individual entities committed to maximize their own benefits. To deal with such supply chain networks, a novel two-phase planning framework that incorporates a coordination action through a central independent body is introduced. It is assumed that each supply chain member will share only its demand and supply related information while keeping the rest private. In this thesis, the study is conducted in several steps. First, a simple serial decentralized network problem with a single objective for each disjointed entity under deterministic scenarios is considered. Then, it is extended to more complex network structures with additional considerations; for example, a supply chain consisting of multiple non-cooperative and competitive entities in each stage. Following that, multi-objectives for analyzing the trade-off decisions of supply chain members under decentralized network structures are examined. Finally, parameter uncertainties in a multi-echelon supply chain network are considered. In the proposed two-phase planning approach, different mathematical models are formulated for the first-phase coordination problem and each individual members’ planning problems developed for the second-phase. These models are solved using either an existing algorithm or the heuristics developed in this thesis. Also, heuristics are implemented to deal with the overall planning problem. A scenario-based stochastic model for analyzing a problem with demand uncertainties is developed. Finally, an optimization-based simulation approach for dealing with multiple uncertainties is developed. The models and solution approaches proposed in each study are validated using extensive numerical experimentation using different test problems. Their results are compared with those of a traditional centralized structure in which planning is performed centrally considering full information-sharing across the chain. The comparison uncovered many interesting insights for decentralized networks. It showed that the current centralized solutions system is inappropriate from a practical decision-making perspective. Also, sensitivity analyses are performed to show the effects of the important parameters on the outcomes. In summary, in the thesis, effective decision-making frameworks with mathematical models and extensive quantitative analyses that consider various aspects of decentralized supply chain systems are developed. Based on the numerical study, these frameworks are proven to be more beneficial and feasible than the usual supply chain networks and existing approaches. Also, the insights gained from the studies can provide important guidance for managers and practitioners in terms of their planning processes.
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PhD Doctorate
UNSW Faculty
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