SBIR Phase I: Optimal Replisnishment Algorithms for Service Parts Logistics Systems

这个小企业创新研究（SBIR）第一阶段项目将开发一种新的方法来管理用于提供关键任务产品售后支持的服务部件库存。特别是这项研究将开发计算效率和最优算法的补充和分配库存的服务零件物流网络。随后，这些算法将被纳入商业软件产品平台，用于服务供应链优化。服务部件优化需要专门的模型，因为需求（由于机器故障和计划外维护）很少，而且很难预测。零件的移动必须在许多相互连接的库存地点之间进行协调，以促进准时交货，通常在几小时甚至几分钟内。此外，这些零件有多种供应来源，如内部制造、外部供应商、维修供应商和去制造。当前的商业服务供应链优化系统没有纳入服务供应链的这些复杂性。因此，他们在售后服务环境中表现不佳，导致维修零件库存大量，每年只周转1到2次。这项研究可以导致商业技术，可以大大减少这一费用。

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.