Capacity planning and inventory decisions in supply chains

In recent years, the paradigm of competition has evolved from single firms competing against each other to a competition between supply chains. In this context, supply chains are under unprecedented pressure to deliver products at a lower cost and higher customer service. In fact, a recent survey conducted by Aberdeen group identifies improving service levels and reducing inventory costs as the biggest business pressures faced by supply chains (Aberdeen Group, 2016). Multi-echelon inventory optimization (MEIO) has thus become a central focus of many practitioners and researchers. MEIO can help companies optimally set inventory targets at different stages of the supply chain in order to adequately handle demand and supply uncertainties while achieving desired service levels at minimum costs. One important framework to address the issues related to MEIO is the guaranteed-service approach (GSA). Under GSA, a supply chain stage uses a combination of inventory and extraordinary measures to fulfill all customer demand within a guaranteed service time. GSA offers great computational efficiency and can be used to optimize inventory decisions in complex real-world networks. In addition, industry managers seem to be comfortable with the notion of guaranteed service. Despite its incredible success in recent years, the applicability of GSA to a wide range of industrial cases remains limited due to the assumptions it makes. Most GSA models assume exogenous production lead times, while in real-world problems, they are dependent on production capacity. Moreover, GSA models have largely ignored inventory budgets while in reality, firms tend to limit the amount of capital tied-up in inventories, and there is usually a limited amount of storage space at a warehouse or a retailer. In this dissertation, we contribute to the GSA literature by considering general production lead times that depend on capacity and by incorporating finite inventory budgets. In addition, we study the problem of coordinating production and order acceptance decisions in capacitated manufacturing systems and show the importance of optimizing inventory decisions to handle uncertainty in these systems