Optimal Replisnishment Algorithms for Service Parts Logistics Systems

This Small Business Innovation Research (SBIR) Phase I project will develop a new methodology to manage the inventory of service parts used to provide after-sales support of mission-critical products. In particular this research will develop computationally efficient and optimal algorithms for replenishment and allocation of inventory in service parts logistics networks. Subsequently, the algorithms will be incorporated into a commercial software product platform for service supply chain optimization. Service part optimization requires specialized models, since demand (due to machine failures and unscheduled maintenance) is infrequent and difficult to predict. Movement of parts must be coordinated across many inter-connected stocking locations in order to facilitate on- time delivery, often within hours or even minutes. In addition, there are multiple sources of supply for these parts such as internal manufacturing, external suppliers, repair vendors, and de-manufacturing. Current commercial service supply chain optimization systems do not incorporate these complexities of the service supply chains. As a consequence, they perform poorly in after-sales service environments resulting in extensive in service parts inventory that turns only 1 to 2 times per year. This research can lead to commercial technology that can reduce this expense substantially.