Spare part stock modeling and cost optimization

Abstract

Modeling and planning the spare part item stock is a quite complex, but utmost rewarding task regarding the operational excellence of a manufacturing industry company. The challenge with the design is derived from multi-variable optimization tasks, including e.g. consumption rate of an item, division of stocking costs, and potential shortage costs. This applies especially in case of new spare part item with no history data for defining the consumption rate. The extra reward from a systematic spare part stock design process with the simulation based method is the ability to test stocking scenarios (optimized own stock vs stock outsourcing) and to obtain summary reports of different stocking cost type distributions (e.g. net interests costs, stock upkeep costs, order costs and shortage costs). In practice, the design process often focuses on finding optimal stocking parameters for hundreds or thousands of stock items that belong to the same criticality class. The optimization is either done to fulfill service rate requirement or for direct cost optimization purposes. The whole process helps to refine gathered history data and expert knowledge to true understanding, and with the systematic method to identify key indicators for the optimal spare part stock (min. risks, max. availability, min. costs). A Finnish advanced analytics expert company, called Ramentor Oy, together with the research institute (Tampere University of Technology) and several industry partners, has developed a systematic methodology combined with a pragmatic software tool that guides through the stock design and optimization process. The tool, called StockOptim, also provides interface to operative IT systems (ERP, CMMS, EAM) and to modern data analytics tools (e.g. ELMAS) for managing the precedent process modeling, data collection, criticality and risk analyses tasks. StockOptim simulates spare part stock events, calculates stocking costs and stock items shortage data (e.g. mean shortage time or mean shortage costs). The software is also able to find approximation for the items optimal stocking parameters (reorder point and order amount). The simulation based approach is flexible, also for adding new details or extensions, and not as exposed to distorting and restricting assumptions as analytic-numeric methods.