Abstract Spare parts are often associated with intermittent demand patterns that render their forecasting a challenging task. Forecasting of spare parts demand has been researched through both parametric and non-parametric approaches. However, little has been contributed in this area from a Bayesian perspective, and most of such research is built around the Poisson demand distributional assumption. However, the Poisson distribution is known to have certain limitations and, further, empirical evidence on the inventory performance of Bayesian methods is lacking. In this paper, we propose a new Bayesian method based on compound Poisson distributions. The proposed method is compared to the Poisson-based Bayesian method with a Gamma prior distribution as well as to a parametric frequentist method and to a non-parametric one. A numerical investigation (on 7400 theoretically generated series) is complemented by an empirical assessment on demand data from about 3000 stock keeping units in the automotive sector to analyse the performance of the four forecasting methods. We find that both Bayesian methods outperform the other methods with a higher inventory efficiency reported for the Poisson Bayesian method with a Gamma prior. This outperformance increases for higher demand variability. From a practical perspective, the outperformance of the proposed method is associated with some added complexity. We also find that the performance of the non-parametric method improves for longer lead-times and higher demand variability when compared to the parametric one.

中文翻译：

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备件库存预测的复合泊松贝叶斯方法

摘要 备件通常与间歇性需求模式相关联，这使得他们的预测成为一项具有挑战性的任务。已经通过参数和非参数方法研究了备件需求的预测。然而，从贝叶斯的角度来看，这方面的贡献很少，而且大多数此类研究都是围绕泊松需求分布假设建立的。然而，已知泊松分布有一定的局限性，此外，缺乏贝叶斯方法库存绩效的经验证据。在本文中，我们提出了一种基于复合泊松分布的新贝叶斯方法。将所提出的方法与具有 Gamma 先验分布的基于泊松的贝叶斯方法以及参数频率论方法和非参数方法进行比较。数值调查（对 7400 个理论上生成的系列）辅以对汽车行业约 3000 个库存单位的需求数据的实证评估，以分析四种预测方法的性能。我们发现这两种贝叶斯方法都优于其他具有更高库存效率的方法，这些方法报告了具有 Gamma 先验的 Poisson Bayesian 方法。对于更高的需求可变性，这种出色的表现会增加。从实践的角度来看，所提出方法的优异性能与一些额外的复杂性有关。我们还发现，与参数方法相比，非参数方法的性能提高了更长的提前期和更高的需求可变性。