Theoretical economics Theoretical economics Теоретическая экономика 2221-3260 81582 НОВАЯ ИНДУСТРИАЛИЗАЦИЯ: ТЕОРЕТИКО-ЭКОНОМИЧЕСКИЙ АСПЕКТ NEW INDUSTRIALIZATION: THEORETICAL AND ECONOMIC ASPECT НОВАЯ ИНДУСТРИАЛИЗАЦИЯ: ТЕОРЕТИКО-ЭКОНОМИЧЕСКИЙ АСПЕКТ Using data analytics and machine learning techniques to forecast and plan demand, to optimize inventory levels, reduce stockouts, and improve customer satisfaction Использование методов анализа данных и машинного обучения для прогнозирования и планирования спроса при управлении цепочками поставок https://orcid.org/0000-0002-3235-6429 Рогулин Родион Сергеевич Rogulin Rodion Sergeevich rafassiaofusa@mail.ru

кандидат экономических наук;

candidate of economic sciences;

 Владивостокский государственный университет экономики и сервиса Vladivostok State University of Economics and Service 02 05 2024 02 05 2024 8 35 53 07 07 2023 13 07 2023 https://theoreticaleconomy.ru/en/nauka/article/81582/view

В работе обсуждаются возможные преимущества объединения методов анализа данных и машинного обучения для прогнозирования спроса и планирования в управлении цепями поставок. Работа включает в себя анализ тематических исследований и документов, в которых эти методы были успешно интегрированы для улучшения эффективности управления цепями поставок, и описывается их влияние на уровень запасов, дефицит и удовлетворенность клиентов. В работе также обсуждаются проблемы и ограничения использования этих методов, включая вопросы качества данных и потребность в квалифицированных сотрудниках, а также предлагаются стратегии для преодоления этих проблем. Исследование также рассматривает будущие направления исследований в области прогнозирования и планирования спроса, включая интеграцию данных в режиме реального времени и использование прогнозной аналитики. Результаты работы обобщаются и приводятся выводы для практики и будущих исследований. В целом, интеграция методов анализа данных и машинного обучения может значительно улучшить прогнозирование спроса и планирование в управлении цепями поставок, однако это требует тщательного анализа качества данных, обучения персонала и технологической инфраструктуры.

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.

 анализ данных машинное обучение прогнозирование спроса планирование цепочка поставок. data analysis machine learning demand forecasting planning supply chain.

Breiman L. Random forests // Machine Learning. 2001. 45(1). Pp. 5–32. DOI: 10.1023/A:1010933404324 Breiman L. Random forests // Machine Learning. 2001. 45(1). Pp. 5–32. DOI: 10.1023/A:1010933404324 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 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 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: 10.1109/ICMLC.2011.6016870 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: 10.1109/ICMLC.2011.6016870 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 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 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: 10.1007/s00521-006-0077-3 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: 10.1007/s00521-006-0077-3 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 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 James G., Witten D., Hastie T., Tibshirani R. An Introduction to Statistical Learning: With Applications in R. Springer Publishing Company, Incorporated 2014. James G., Witten D., Hastie T., Tibshirani R. An Introduction to Statistical Learning: With Applications in R. Springer Publishing Company, Incorporated 2014. 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. 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. 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 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 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 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 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 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 van der Maaten L., Hinton G. Visualizing data using t-SNE // Journal of Machine Learning Research. 2018. 9. Pp. 2579–2605 van der Maaten L., Hinton G. Visualizing data using t-SNE // Journal of Machine Learning Research. 2018. 9. Pp. 2579–2605 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 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 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. 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. 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 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 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 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 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 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 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 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 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. 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. 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 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 Zhang G. P. Time series forecasting using a hybrid ARIMA and neural network model // Neurocomputing. 2003. 50. Pp. 159–175. Zhang G. P. Time series forecasting using a hybrid ARIMA and neural network model // Neurocomputing. 2003. 50. Pp. 159–175. 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. 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. 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. 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. Moon M.A. Demand and Supply Integration: The Key to World-Class Demand Forecasting; Walter de Gruyter GmbH & Co KG: Berlin, Germany, 2018. Moon M.A. Demand and Supply Integration: The Key to World-Class Demand Forecasting; Walter de Gruyter GmbH & Co KG: Berlin, Germany, 2018. Bruzda J. Demand forecasting under fill rate constraints—The case of re-order points // International journal of Forecast. 2020, 36, 1342–1361. Bruzda J. Demand forecasting under fill rate constraints—The case of re-order points // International journal of Forecast. 2020, 36, 1342–1361. 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. 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. Nia A.R., Awasthi A., Bhuiyan N. Industry 4.0 and demand forecasting of the energy supply chain // Computers & Industrial Engineering. 2021, 154, 107128. Nia A.R., Awasthi A., Bhuiyan N. Industry 4.0 and demand forecasting of the energy supply chain // Computers & Industrial Engineering. 2021, 154, 107128. Hu M., Qiu R.T., Wu D.C., Song H. Hierarchical pattern recognition for tourism demand forecasting // Tourism Management. 2021, 84, 104263. Hu M., Qiu R.T., Wu D.C., Song H. Hierarchical pattern recognition for tourism demand forecasting // Tourism Management. 2021, 84, 104263. Kozik P., Sp J. Aircraft engine overhaul demand forecasting using ANN // Management and Production Engineering Review. 2012, 3, 21–26. Kozik P., Sp J. Aircraft engine overhaul demand forecasting using ANN // Management and Production Engineering Review. 2012, 3, 21–26. Gutierrez R.S., Solis A.O., Mukhopadhyay S. Lumpy demand forecasting using neural networks // International journal of production economy. 2008, 111, 409–420. Gutierrez R.S., Solis A.O., Mukhopadhyay S. Lumpy demand forecasting using neural networks // International journal of production economy. 2008, 111, 409–420. 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. 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. Dunn W.N. Poblicy Analysis: An Introduction, 2nd ed.; Prentice Hall Englewood Cliffs: Hoboken, NJ, USA, 1994. Dunn W.N. Poblicy Analysis: An Introduction, 2nd ed.; Prentice Hall Englewood Cliffs: Hoboken, NJ, USA, 1994. Rosienkiewicz M., Chlebus E., Detyna J. A hybrid spares demand forecasting method dedicated to mining industry // Applied mathematical modeling. 2017, 49, 87–107. Rosienkiewicz M., Chlebus E., Detyna J. A hybrid spares demand forecasting method dedicated to mining industry // Applied mathematical modeling. 2017, 49, 87–107. Box G.E.P., Jenkins G.M. Time Series Analysis: Forecasting and Control; Holden-Day: San Francisco, CA, USA, 1976. Box G.E.P., Jenkins G.M. Time Series Analysis: Forecasting and Control; Holden-Day: San Francisco, CA, USA, 1976. 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. 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. 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. 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. Salman A.G., Kanigoro B. Visibility forecasting using autoregressive integrated moving average (ARIMA) models // Procedia Computational Sciences. 2021, 179, 252–259. Salman A.G., Kanigoro B. Visibility forecasting using autoregressive integrated moving average (ARIMA) models // Procedia Computational Sciences. 2021, 179, 252–259. Roondiwala M., Patel H., Varma S. Predicting stock prices using LSTM // International Journal of Science and Research. 2017, 6, 1754–1756. Roondiwala M., Patel H., Varma S. Predicting stock prices using LSTM // International Journal of Science and Research. 2017, 6, 1754–1756. 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. 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. 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. 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. Abbasimehr H., Shabani M., Yousefi M. An optimized model using LSTM network for demand forecasting // Computational Industrial Engineering. 2020, 143, 106435. Abbasimehr H., Shabani M., Yousefi M. An optimized model using LSTM network for demand forecasting // Computational Industrial Engineering. 2020, 143, 106435. Priya C.B., Arulanand N. Univariate and multivariate models for Short-term wind speed forecasting // Materials Today: Proceedings. 2021. Priya C.B., Arulanand N. Univariate and multivariate models for Short-term wind speed forecasting // Materials Today: Proceedings. 2021. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Kourentzes N. Intermittent demand forecasts with neural networks // International Journal of Production Economics, 2013, 198-206, 2013. DOI: 10.1016/j.ijpe.2013.01.009. Kourentzes N. Intermittent demand forecasts with neural networks // International Journal of Production Economics, 2013, 198-206, 2013. DOI: 10.1016/j.ijpe.2013.01.009. 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°. 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°. 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 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 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 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 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 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 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: 10.7240/jeps.1127844 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: 10.7240/jeps.1127844 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 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 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 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