A Bayesian approach to demand forecasting
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Demand forecasting is a fundamental aspect of inventory management. Forecasts are crucial in determining inventory stock levels, and accurately estimating future demand for spare part's has been an ongoing challenge, especially in the aerospace industry. If spare parts are not readily available, aircraft availability can be compromised leading to excessive downtime costs. As a result, inventory investment for spare parts can be significant to ensure down time is minimized. Additionally, most aircraft spare parts are considered "slow-moving" and experience intermittent demand making the use of traditional forecasting methods difficult in this industry. In this research, a forecasting method is developed using Bayes' rule to improve the demand forecasting of spare parts. The proposed Bayesian method is especially targeted to support new aircraft programs and is not intended to change how inventory is currently optimized. A case study based on a real aircraft program's data is performed in order to validate the use of the proposed Bayesian method. In the case study, three forecasting methods are compared: judgmental forecasting, a traditional statistical forecasting approach, and the proposed Bayesian method. The methods' impact on forecast accuracy, inventory costs, and fill rate performance (evaluated using simulation) are analyzed. The results conclude that the proposed Bayesian approach outperforms the other methods in terms of fill rate performance. Hence, the Bayesian method improves demand prediction and thus, more accurately estimates inventory needs allowing managers to make better inventory investment decisions.