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

DOI: https://doi.org/10.30564/jmser.v5i1.4436

Abstract


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.


Keywords


Forecast; Hybrid approach; Stock management; Classification; Policies

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References


[1] Boylan, J.E., Syntetos, A.A., 2007. The accuracy of a modified Croston procedure. International Journal of Production Economics. 107, 511-517.

[2] Teunter, R.H., Duncan, L., 2009. Forecasting intermittent demand: a comparative study. Journal of Operational Research Society. 60, 321-329. DOI: https://doi.org/10.1057/Palgrave.Jors.2602569

[3] Chiou, H.K., Tzeng, G.H., Cheng, C.K., et al., 2004. Grey prediction model for forecasting the planning material of equipment auto parts in the navy of Taiwan. Proceedings World Automation Congress. 17, 315-320.

[4] Zhang, H., 2019. Improved transudative support vector machine for a small labelled set-in motor imagery-based brain-computer interface. pp. 2-3.

[5] Gutierrez, R.S., Solis, A.O., Mukhopadhyay, S., 2008. Lumpy demand forecasting using neural networks. International Journal of Production Economics. 111(2), 409-420.

[6] Tang, Zh.J., Long, Y.L., 2012. Research on method of acquiring individual demand based on kano model. pp. 127-131.

[7] Chen, X., Cai, D., 2015. Large scale spectral clustering with landmark-based representation. IEEE Trans Cybern. pp. 313-318.

[8] Levi, et al., 2005. https: //www. ResearchGate. Net/ publication/260162679 approximation algorithms for the stochastic lot-sizing problem with order lead times. pp. 14-24.

[9] Valero, M., Averkin, R.G., Fernandez-Lamo, I., et al., 2017. Mechanisms for selective single-cell reactivation during offline sharp-wave ripples and their distortion by fast ripples. Neuron. pp. 1234-1247.

[10] Canyakmaz, et al., 2019. https: //www. ResearchGate. Net/publication/330894006_an_inventory_ model_where_customer_demand_is_dependent_on_ astochastic_price_process. pp. 27-38.

[11] van Nguyen, T., Zhou, L., Chong, A.Y., et al., 2020. Predicting customer demand for remanufactured products: a data-mining approach. European Journal of Operational Research. 281(3), 543-558.

[12] Zhang, W.X, Xiao, R.B., Lin, W.G., 2020. Review data-driven customer need model research based on product performance lexicon. pp. 34-43.

[13] Bontempi, G., Borgne, Y.A.L., Destefani, J., 2017. Factor-based framework for multivariate and multistep-ahead forecasting of large-scale time series. pp. 14-27.


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