Study on Customer Demand Forecasting Models, Stock Management, Classification and Policies for Automobile Parts Manufacturing Company N.A.C.C. (An Advance on Classical Models)

Sory Ibrahima Cisse (School of Management, Northwestern Polytechnical University, Xi’an, Shaanxi, 710072, China)
Jianwu Xue (School of Management, Northwestern Polytechnical University, Xi’an, Shaanxi, 710072, China)
Samuel Akwasi Agyemang (School of Computer Science, Northwestern Polytechnical University, Xi’an, Shaanxi, 710072, China)

Article ID: 4436



The primary intent of the current research is to provide insights regarding the management of spare parts within the supply chain, in conjunction with offering some methods for enhancing forecasting and inventory management. In particular, to use classical forecasting methods, the use of weak and unstable demand is not recommended. Furthermore, statistical performance measures are not involved in this particular context. Furthermore, it is expected that maintenance contracts will be aligned with different levels. In addition to the examination of some literature reviews, some tools will guide us through this process. The article proposes new performance analysis methods that will help integrate inventory management and statistical performance while considering decision maker priorities through the use of different methodologies and parts age segmentation. The study will also identify critical level policies by comparing different types of spenders according to the inventory management model, also with separate and common inventory policies. Each process of the study is combined with a comparative analysis of different forecasting methods and inventory management models based on N.A.C.C. parts supply chain data, allowing us to identify a set of methodologies and parameter recommendations based on parts segmentation and supply chain prioritization.


Forecast; Hybrid approach; Stock management; Classification; Policies

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