An empirical Bayes approach to incorporating demand intermittency and irregularity into inventory control

Spare parts inventory management is complex due to the combined impact of intermittent and variable demand patterns. It becomes even more challenging if the spare parts demand distribution is highly complex due to strong interdependent demand intermittency and extremely irregular demand. The research literature proposes many analytical methods for forecasting spare parts demand. But, due to their limited flexibility in modeling complex demand patterns, existing forecasting methods may not produce satisfactory results for a spare parts portfolio displaying extremely complex demand patterns. This study proposes a novel nonparametric Bayesian forecasting approach with its roots in the empirical Bayes paradigm. The method is subject to few performance constraints and is highly flexible in dealing with a rich diversity of demand patterns, including extreme demand complexity. We assess the relative performance of this new approach with several prominent methods in the literature using an automotive parts distributor's empirical demand data for 46,272 stock-keeping units. This dataset is representative of typical spare parts portfolios that are characterized by a wide variety and extremely complex demand patterns. The experimental findings show the new Bayesian approach achieves the best overall performance in terms of inventory efficiency and minimal backorders for meeting specified target service levels. This favorable performance reflects the approach's flexibility to accommodate disparate and complex demand patterns, including interdependence of demand intermittency, irregular demand distribution, and even nonstationary demand distribution to some extent, and provide robust solutions.