Vladivostok, Vladivostok, Russian Federation
The paper discusses the potential benefits of integrating data analysis and machine learning methods for demand forecasting and planning in supply chain management. It includes an analysis of thematic studies and documents in which these methods have been successfully integrated to improve the effectiveness of supply chain management, and describes their impact on inventory levels, shortages, and customer satisfaction. The paper also discusses the problems and limitations of using these methods, including data quality issues and the need for qualified personnel, and offers strategies to overcome these problems. The study also considers future research directions in demand forecasting and planning, including real-time data integration and the use of predictive analytics. The results of the paper are summarized and conclusions are drawn for practice and future research. Overall, the integration of data analysis and machine learning methods can significantly improve demand forecasting and planning in supply chain management, but it requires careful analysis of data quality, personnel training, and technological infrastructure.
data analysis, machine learning, demand forecasting, planning, supply chain.
1. Breiman L. Random forests // Machine Learning. 2001. 45(1). Pp. 5–32. DOI:https://doi.org/10.1023/A:1010933404324
2. Carbonneau R., Laframboise K., Vahidov R. Application of machine learning techniques for supply chain demand forecasting // European Journal of Operational Research. 2008. 184(3). Pp. 1140 – 1154. DOI: https://doi.org/10.1016/j.ejor.2006.12.004
3. Choi T., Hui C., Yu Y. Intelligent time series fast forecasting for fashion sales: A research agenda. In: International Conference on Machine Learning and Cybernetics, ICMLC 2011, Guilin, China, July 10-13, 2011, Proceedings, pp 1010–1014. DOI:https://doi.org/10.1109/ICMLC.2011.6016870
4. Choi T. M., Hui C. L., Liu N., Ng S. F., Yu Y. Fast fashion sales forecasting with limited data and time // Decision Support Systems. 2014. 59. Pp. 84 – 92. https://doi.org/10.1016/j.dss.2013.10.008
5. Das P., Chaudhury S. Prediction of retail sales of footwear using feedforward and recurrent neural networks // Neural Computing and Applications. 2007. 16(4). Pp. 491–502. DOI:https://doi.org/10.1007/s00521-006-0077-3
6. Hui P., Choi T. M. 5 - using artificial neural networks to improve decision making in apparel supply chain systems. In: Choi TM (ed) Information Systems for the Fashion and Apparel Industry, Woodhead Publishing Series in Textiles, Woodhead Publishing, 2016, pp 97 – 107, DOI https://doi.org/10.1016/B978-0-08- 100571-2.00005-1
7. James G., Witten D., Hastie T., Tibshirani R. An Introduction to Statistical Learning: With Applications in R. Springer Publishing Company, Incorporated 2014.
8. Kaya M., Yesil E., Dodurka M. F., Sıradag S. Fuzzy Forecast Combining for Apparel Demand Forecasting, Springer Berlin Heidelberg, Berlin, Heidelberg, 2014, pp 123–146.
9. Kogan K., Herbon A. Production under periodic demand update prior to a single selling season: A decomposition approach // European Journal of Operational Research. 2008. 184(1). Pp. 133 – 146, DOI https://doi.org/10.1016/j.ejor.2006.11.009
10. Loureiro A., Miguéis V., da Silva L. F. Exploring the use of deep neural networks for sales forecasting in fashion retail // Decision Support Systems. 2018. 114. Pp. 81 – 93. https://doi.org/10.1016/j.dss.2018.08.010
11. Lu C.J. Sales forecasting of computer products based on variable selection scheme and support vector regression // Neurocomputing. 2014. 128. Pp. 491 – 499. DOI https://doi.org/10.1016/j.neucom.2013.08.012
12. van der Maaten L., Hinton G. Visualizing data using t-SNE // Journal of Machine Learning Research. 2018. 9. Pp. 2579–2605
13. Mostard J., Teunter R., de Koster R. Forecasting demand for single-period products: A case study in the apparel industry // European Journal of Operational Research. 2011. 211(1). Pp. 139 – 147. DOI https://doi.org/10.1016/j.ejor.2010.11.001
14. Pillo G. D., Latorre V., Lucidi S., Procacci E. An application of support vector machines to sales forecasting under promotions // 4OR. 2016. 14. Pp. 309–325.
15. Rousseeuw P. J. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis // Journal of Computational and Applied Mathematics. 1987. 20. Pp. 53 – 65. DOI https://doi.org/10.1016/0377-0427(87)90125-7
16. Sun Z.L., Choi T.M., Au K.F., Yu Y. Sales forecasting using extreme learning machine with applications in fashion retailing // Decision Support Systems. 2008. 46(1). Pp. 411 – 419. DOI https://doi.org/10.1016/j.dss.2008.07.009
17. Thomassey S. Sales forecasts in clothing industry: The key success factor of the supply chain management // International Journal of Production Economics. 2010. 128(2). Pp. 470 – 483. DOI https://doi.org/10.1016/j.ijpe.2010.07.018
18. Thomassey S., Happiette M., Castelain J. M. A short and mean-term automatic forecasting system––application to textile logistics // European Journal of Operational Research. 2005. 161(1). Pp. 275 – 284. DOI https://doi.org/10.1016/j.ejor.2002.09.001
19. Wong W., Guo Z. A hybrid intelligent model for medium-term sales forecasting in fashion retail supply chains using extreme learning machine and harmony search algorithm // International Journal of Production Economics. 2010. 128(2). Pp. 614 – 624. DOI https://doi.org/10.1016/j.ijpe.2010.07.008.
20. Xia M., Zhang Y., Weng L., Ye X. Fashion retailing forecasting based on extreme learning machine with adaptive metrics of inputs // Knowledge-Based Systems. 2012. 36. Pp. 253 – 259. DOI https://doi.org/10.1016/j.knosys.2012.07.002
21. Zhang G. P. Time series forecasting using a hybrid ARIMA and neural network model // Neurocomputing. 2003. 50. Pp. 159–175.
22. Nian S.C., Fang Y.C., Huang M.S. In-mold and machine sensing and feature extraction for optimized IC-tray manufacturing // Polymers. 2019, 11, 1348.
23. Karimnezhad A., Moradi F. Bayes, E-Bayes and robust Bayes prediction of a future observation under precautionary prediction loss functions with applications. Applied mathematical modeling. 2016, 40, 7051–7061.
24. Moon M.A. Demand and Supply Integration: The Key to World-Class Demand Forecasting; Walter de Gruyter GmbH & Co KG: Berlin, Germany, 2018.
25. Bruzda J. Demand forecasting under fill rate constraints—The case of re-order points // International journal of Forecast. 2020, 36, 1342–1361.
26. Abadi S.N.R., Kouhikamali R. CFD-aided mathematical modeling of thermal vapor compressors in multiple effects distillation units // Applied mathematical modeling. 2016, 40, 6850–6868.
27. Nia A.R., Awasthi A., Bhuiyan N. Industry 4.0 and demand forecasting of the energy supply chain // Computers & Industrial Engineering. 2021, 154, 107128.
28. Hu M., Qiu R.T., Wu D.C., Song H. Hierarchical pattern recognition for tourism demand forecasting // Tourism Management. 2021, 84, 104263.
29. Kozik P., Sp J. Aircraft engine overhaul demand forecasting using ANN // Management and Production Engineering Review. 2012, 3, 21–26.
30. Gutierrez R.S., Solis A.O., Mukhopadhyay S. Lumpy demand forecasting using neural networks // International journal of production economy. 2008, 111, 409–420.
31. Willemain T.R., Smart C.N., Schwarz H.F. A new approach to forecasting intermittent demand for service parts inventories // International journal of Forecast. 2004, 20, 375–387.
32. Dunn W.N. Poblicy Analysis: An Introduction, 2nd ed.; Prentice Hall Englewood Cliffs: Hoboken, NJ, USA, 1994.
33. Rosienkiewicz M., Chlebus E., Detyna J. A hybrid spares demand forecasting method dedicated to mining industry // Applied mathematical modeling. 2017, 49, 87–107.
34. Box G.E.P., Jenkins G.M. Time Series Analysis: Forecasting and Control; Holden-Day: San Francisco, CA, USA, 1976.
35. Siami-Namini S., Tavakoli N., Namin A.S. A comparison of ARIMA and LSTM in forecasting time series. In Proceedings of the 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, FL, USA, 17–20 December 2018; pp. 1394–1401.
36. Fattah J., Ezzine L., Aman Z., El Moussami H., Lachhab A. Forecasting of demand using ARIMA model // International Journal of Engineering in Business management. 2018, 10, 1847979018808673.
37. Salman A.G., Kanigoro B. Visibility forecasting using autoregressive integrated moving average (ARIMA) models // Procedia Computational Sciences. 2021, 179, 252–259.
38. Roondiwala M., Patel H., Varma S. Predicting stock prices using LSTM // International Journal of Science and Research. 2017, 6, 1754–1756.
39. Pacella M., Papadia G. Evaluation of deep learning with long short-term memory networks for time series forecasting in supply chain management // Procedia CIRP 2021, 99, 604–609.
40. Chien C.F., Hong T.Y., Guo H.Z. An empirical study for smart production for TFT-LCD to empower Industry 3.5 // Journal of Chinese Institute of Engineering. 2017, 40, 552–561.
41. Abbasimehr H., Shabani M., Yousefi M. An optimized model using LSTM network for demand forecasting // Computational Industrial Engineering. 2020, 143, 106435.
42. Priya C.B., Arulanand N. Univariate and multivariate models for Short-term wind speed forecasting // Materials Today: Proceedings. 2021.
43. Shi H., Xu M., Li R. Deep learning for household load forecasting—A novel pooling deep RNN // IEEE Transactions on Smart Grid. 2017, 9, 5271–5280.
44. Kong W., Dong Z.Y., Jia Y., Hill D.J., Xu Y., Zhang Y. Short-term residential load forecasting based on LSTM recurrent neural network// IEEE Transactions on Smart Grid. 2017, 10, 841–851.
45. Weng B., Martinez W., Tsai Y.T., Li C., Lu L., Barth J.R., Megahed F.M. Macroeconomic indicators alone can predict the monthly closing price of major US indices: Insights from artificial intelligence, time-series analysis and hybrid models // Applied Soft Computing. 2018, 71, 685–697.
46. Qiao W., Wang Y., Zhang J., Tian W., Tian Y., Yang Q. An innovative coupled model in view of wavelet transform for predicting short-term PM10 concentration. Journal of Environmental Management. 2021, 289, 112438.
47. Kang Y., Hyndman R.J., Smith-Miles K. Visualising forecasting algorithm performance using time series instance spaces // International Journal of Forecasting, 2017, vol. 33, no. 2, pp. 345–358.
48. Kourentzes N. Intermittent demand forecasts with neural networks // International Journal of Production Economics, 2013, 198-206, 2013. DOI:https://doi.org/10.1016/j.ijpe.2013.01.009.
49. Lasek A., Cercone N., Saunders J. Restaurant Sales and Customer Demand Forecasting: Literature Survey and Categorization of Methods. In: Leon-Garcia A. et al. (eds) Smart City 360°.
50. Kilimci Z. H., Akyuz A. O., Uysal M., Akyokus A., Uysal M. O., Bulbul B. A., Ekmis M. A. An Improved Demand Forecasting Model Using Deep Learning Approach and Proposed Decision Integration Strategy for Supply Chain // Complexity. 2019. Vol. 1. 9067367 | https://doi.org/10.1155/2019/9067367
51. Alfian G., Syafrudin M., Fitriyani N. L., Alam S., Pratomo D. N., Subekti L., Octava M. Q. H., Yulianingsih N. D., Atmaji F. T. D., Benes F. Utilizing Random Forest with iForest-Based Outlier Detection and SMOTE to Detect Movement and Direction of RFID Tags // Future Internet. 2023; 15(3):103. https://doi.org/10.3390/fi15030103
52. Rathipriya R., Abdul Rahman A.A., Dhamodharavadhani S. Demand forecasting model for time-series pharmaceutical data using shallow and deep neural network model // Neural Computations & Applications. 2023. 35, 1945–1957. https://doi.org/10.1007/s00521-022-07889-9
53. İmece S., Beyca Ö. F. Demand Forecasting with Integration of Time Series and Regression Models in Pharmaceutical Industry // International Journal of Advances in Engineering and Pure Sciences. 2022, 34(3): 415-425 DOI:https://doi.org/10.7240/jeps.1127844
54. Mitra A., Jain A., Kishore A. A Comparative Study of Demand Forecasting Models for a Multi-Channel Retail Company: A Novel Hybrid Machine Learning Approach // Operations Research Forum. 2022. 3, 58. https://doi.org/10.1007/s43069-022-00166-4
55. Kim J.-D., Kim T.-H., Han S. W. Demand Forecasting of Spare Parts Using Artificial Intelligence: A Case Study of K-X Tanks // Mathematics. 2023. 11(3). 501. https://doi.org/10.3390/math11030501
