Many real-world supply chains can be characterised as large and complex multi-echelon systems since they consist of several stages incorporating assembly and distribution processes. A challenge facing such systems is the efficient management of inventory when demand is uncertain, operating costs and customer service requirements are high. This requires specifying the inventory levels at different stages that minimise the total cost and meet target customer service levels. In order to address this problem, researchers proposed the Stochastic-Service Model and the Guaranteed-Service Model (GSM) approaches. These two approaches differ in terms of assumptions with regard to how to address demand variations and service times. This thesis develops several contributions to the GSM based multi-echelon inventory optimisation problem. First of all, we conduct a comprehensive literature review which gives a synthesis of the various GSM work developed so far. Then, we study the impact of some specific assumptions of the GSM such as bounded demand, guaranteed-service times and common review periods. Our numerical analysis shows that the bounded demand assumption may cause a deviation on customer service levels while the guaranteed-service times and common review periods assumptions may result in an increase on the total cost. In real-world supply chains the impact of these assumptions might be significant. Based on the findings presented while investigating the impact of the common review periods assumption, we develop an extension of the GSM that enables to simultaneously optimise the review periods (reorder intervals) and safety stock levels (order-up-to levels) in general acyclic multiechelon systems. We formulate this problem as a nonlinear integer programming model. Then, we propose a sequential optimisation procedure that enables to obtain near optimal solutions with reasonable computational time. Finally, we focus on the issue of customer service level deviation in the GSM and propose two approaches in order to mitigate this deviation. The numerical study shows that the first approach outperforms the second one in terms of computational time while the second approach provides more accurate solutions in terms of cost. We also present some related issues in decentralised supply chain settings.
|Qualification||Doctor of Philosophy|
|Award date||13 Feb 2014|
|Place of Publication||Paris|
|Publication status||Published - 2014|