Abstract
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 work, we have tried to solve this problem using various demand forecasting models to predict product demand for grocery items with machine learning techniques. A representative set ML-based forecasting techniques have been applied to the demand data and the accuracy of the methods was compared. As measurement metrics we have used R2 score, Mean Squared Error score and Mean Absolute Error score. Based on ranking, Random Forest classifier gives better performance result on this specific demand forecasting problem compared with the Artificial Neural Network falling behind in the tested category.
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References
Min, H.: Artificial intelligence in supply chain management: theory and applications. Int. J. Logist. Res. Appl. 26, 13–39 (2019). https://doi.org/10.1080/13675560902736537. ISSN: 0018-9162
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-supply-chain-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-that-thinks-for-itself/. Accessed Jan 2019
Breiman, L.: Random forests (2001). https://www.stat.berkeley.edu/~breiman/randomforest2001.pdf. Accessed Feb 2019
Keras: The Python deep learning library. https://keras.io/. Accessed Jan 2019
Brownlee, J.: 5 step life-cycle for neural network models in Keras (2016). https://machinelearningmastery.com/5-step-life-cycle-neural-network-models-keras/. Accessed Jan 2019
Navlani, A.: Understanding random forests classifiers in Python. https://www.datacamp.com/community/tutorials/random-forests-classifier-python. Accessed Jan 2019
Yue, L., Yafeng, Y., Junjun, G., Chongli, T.: Demand forecasting by using support vector machine. In: Third International Conference on Natural Computation, ICNC 2007, pp. 272–276 (2007)
Mupparaju, K., Soni, A., Gujela, P., Lanham, M.A.: A comparative study of machine learning frameworks for demand forecasting. Academia (2018). http://matthewalanham.com/Students/2018_MWDSI_A%20Comparative%20Study%20of%20Machine%20Learning%20Frameworks%20for%20Demand%20Forecasting.pdf. Accessed Feb 2019
Carbonneau, R., Vahidov, R., Laframboise, K.: Machine learning-based demand forecasting in supply chains. IJIIT, 3 (2007). https://doi.org/10.4018/jiit.2007100103. https://www.researchgate.net/publication/220208887_Machine_Learning-Based_Demand_Forecasting_in_Supply_Chains. Accessed Feb 2019
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Vairagade, N., Logofatu, D., Leon, F., Muharemi, F. (2019). Demand Forecasting Using Random Forest and Artificial Neural Network for Supply Chain Management. In: Nguyen, N., Chbeir, R., Exposito, E., Aniorté, P., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2019. Lecture Notes in Computer Science(), vol 11683. Springer, Cham. https://doi.org/10.1007/978-3-030-28377-3_27
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DOI: https://doi.org/10.1007/978-3-030-28377-3_27
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