A Predictive Approach to Define the Best Forecasting Method for Spare Parts: A Case Study in Business Aircrafts’ Industry

Reza Babajanivalashedi, A. Baboli, M. Shahzad, R. Tonadre
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引用次数: 3

Abstract

The cost-effective management of spare parts is an important objective for all manufacturing and service companies. One of the most difficult challenges, for this objective, is accurate demand forecasting and optimized supply planning decisions to achieve best availability level for the spare parts. The main objective of this paper is to propose a predictive approach to identify the best forecasting method with least error cost. Moreover, in business aircraft industry the best forecasting method for a part can change due to the high-level uncertainty in demand. To this purpose, a methodology to select the best forecasting method based on binary classifier machine learning is developed. Proposed methodology is applied in a real case for a well-known business aircraft. The results indicate that neural network is the best machine learning method for 98% of demand and random forest is the best machine learning method for only 2% of parts.
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一种确定备件最佳预测方法的预测方法——以公务机行业为例
备件的成本效益管理是所有制造和服务公司的重要目标。为了实现这一目标,最困难的挑战之一是准确的需求预测和优化的供应计划决策,以达到备件的最佳可用性水平。本文的主要目的是提出一种预测方法,以确定误差代价最小的最佳预测方法。此外,在公务机行业中,由于需求的高度不确定性,零件的最佳预测方法可能会发生变化。为此,提出了一种基于二元分类器机器学习的最佳预测方法选择方法。并将所提出的方法应用于某知名公务机的实际案例。结果表明,神经网络对98%的需求是最好的机器学习方法,而随机森林仅对2%的零件是最好的机器学习方法。
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