Managing information and supplies inventory operations in a manufacturing environment. Part 2: An order-timing and sizing algorithm

• Published in: International journal of production research Vol. 50; no. 7; pp. 1767 - 1779
• Main Authors: Baker, Tim, Jayaraman, Vaidyanathan
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis Group 01.04.2012; Taylor & Francis; Taylor & Francis LLC
• Subjects: algorithm; Algorithms; Applications; Applied sciences; Computer simulation; Demand; Exact sciences and technology; Firm modelling; forecasting; Forecasting techniques; Inference from stochastic processes; time series analysis; Insurance, economics, finance; Inventories; Inventory control, production control. Distribution; Marketing; Mathematics; Nuclear fuels; Operational research and scientific management; Operational research. Management science; optimisation; Optimization algorithms; Order disorder; Order quantity; Probability and statistics; Repair & maintenance; Sciences and techniques of general use; Statistics; Stockpiling; Studies; Supply chains; Demand forecasting; forecasting; Time series; Nuclear fuel; Maintenance; Non stationary process; History; Flexibility; Integrated management; Dimensioning; optimisation; Replenishment; Economic order quantity; Inventory control; Firm management; Planning; Timing; Resource management; Repair; Logistics; algorithm
• ISSN: 0020-7543; 1366-588X
• DOI: 10.1080/00207543.2011.570421

We develop a new, flexible independent demand forecasting-optimisation algorithm, and apply it to nine difficult-to-manage maintenance and repair products at the AREVA nuclear fuel rod manufacturing facility. The algorithm results in a 27% reduction in inventory holding and ordering costs relative to AREVA's baseline ERP method. This is in addition to improving the line item fill rates from 96 to 98%. This new algorithm is more flexible than the baseline method in that (1) our forecast error distribution is not assumed to be normal-we automatically find the best-fitting distribution from a large family of distributions, (2) we jointly optimise the order quantity and reorder point by using an optimisation routine that is embedded in a simulation methodology. Our algorithm can therefore handle a non-stationary demand process during the planning horizon, and (3) we dynamically select the best time series forecaster for demand based on the most recent history. This flexibility drove the performance improvements. Our algorithm can be easily adapted to any independent demand situation across any industry's supply chain.