Review on Trends in Machine Learning Applied to Demand & Sales Forecasting

Authors

  • Ravindra Singh Sengar M. Tech. Scholar, Department of M.E, Sagar Institute of Research & Technology(SIRT), Bhopal, India
  • Dr. Syed Faisal Ahmed Associate Professor, Department of M.E, Sagar Institute of Research & Technology(SIRT), Bhopal, India

DOI:

https://doi.org/10.24113/ijoscience.v5i6.244

Keywords:

Supply Chain Management, Demand forecasting, Warehouse, Sale Forecasting, Machine Learning

Abstract

Supply Chain Management (SCM) is one of the new concepts put into practice in the commercial sector. At the beginning, Multinational Companies (MNCs) incorporated the supply chain into their structures, then other private conglomerates and local people defended these concepts. From the beginning, the main functions of SCM were the management of purchases and purchases, but subsequently SCM took the integrated form i.e. consists of sourcing, materials management, production support and sales management. Given the highly competitive market scenario, supply chain management is becoming the most important functional area of the business. Demand forecasting is affecting the success of Supply Chain Management (SCM), and the organizations which support them and are in the early stage of a digital transformation. In a near future it could represent the most significant change in the integrated SCM era in today’s complex, dynamic, and uncertain environment. The ability to adequately predict demand by the customers in an SCM is vital to the survival of any business. In this paper a review is presented in which this problem is tried to solved by using various demand forecasting models to predict product demand for grocery items with machine learning techniques.

Downloads

Download data is not yet available.

References

Min, H.: Artificial intelligence in supply chain management: theory and applications. Int. J. Logist. Res. Appl. 26, 13–39 (2019).
Stank, T., Scott, S., Hazen, B.: A savy guide to the digital supply chain. Appendix of Safeware: System Safety and Computers (2018). https://haslam.utk.edu/sites/default/files/GSCI.WhitePaper.Savvy .FINALFORISSUU.pdf. Accessed Jan 2019.
Keunstler, B.: Guideline supply chain management in electronics manufacturing. ZVEI -German Electrical and Electronic Manufacturers Association (2014). https://www.zvei.org/fileadmin/user upload/Presse und Medien/Publikationen/2014/november/Guideline Supply Chain Management in Electronics Manufacturing/Guideline-Supply-Chain-Management.pdf. Accessed Jan 2019.
Perkins, B., Wailgum, T.: What is supply chain management (SCM)? Definitions and best practices. CIO from IDG Commun., August 2017. https://www.cio.com/article/2439493/supply-chain-management/supply-chain-management-supplychain-management-definition-and solutions.html. Accessed Jan 2019
Byrne, R.O.: How AI helps build the supply chain that thinks for itself. Logistics Bur. https://www.logisticsbureau.com/how-ai-helps-build-the-supply-chain-thatthinks-for-itself/. Accessed Jan 2019.
Irem Islek, Sule Gunduz Oguducu, “A Decision Support System for Demand Forecasting based on Classifier Ensemble”, Communication papers of the Federated Conference on Computer Science and Information Systems, Vol. 13, 2017, pp. 35–41.
HaixiaSang's “A dynamic modeling simulation for supply chain management inventory service: a case study on a rental housing unit manufacturing and logistics company” Conference: the 2018 International Conference August 2018
ChristophFlöthmann, Kai Hoberg “Competency requirements of supply chain planners and analysts and personal preferences of hiring managers” November 2018.
Yuchen Weng, Xiujuan Wang, Jing Hua, Haoyu Wang, Mengzhen Kang, Fei-Yue Wang, “Forecasting Horticultural Products Price Using ARIMA Model and Neural Network Based on a Large-Scale Data Set Collected by Web Crawler”, IEEE Transactions on Computational Social Systems, Volume: 6, Issue: 3, 2019.
Mandeep Mittal, Prabodh Ranjan Swain,, Hemant Rana, “A Nature Inspired Optimisation Method for Supply Chain Management Problem ”, Amity International Conference on Artificial Intelligence (AICAI), IEEE, 2019.

Downloads

Published

12/12/2019

How to Cite

Sengar, R. S., & Ahmed, D. S. F. (2019). Review on Trends in Machine Learning Applied to Demand & Sales Forecasting. SMART MOVES JOURNAL IJOSCIENCE, 5(12), 25–29. https://doi.org/10.24113/ijoscience.v5i6.244