Статьи

FORECASTING AND FORECASTING DEMAND: THE CASE OF ARTIFICIAL INTELLIGENCE IN SUPPLY CHAIN MANAGEMENT

Received: 16.06.2023 Accepted: 26.06.2023 Published: 15.01.2023

DOI: 10.22394/1997-4469-2023-60-1-172-180

Full text

Abstract

Relevance of the topic. The need for expertise in data analysis and machine demand forecasting and forecasting in the management of probability chains. Target. analysis of variance, analysis, data analysis and machine forecasting, demand forecasting and planning, probability chain management. Methodology. Scoping case studies and research papers to explore the use of this method in finding chain-of-reference efficiency, stock utilization, shortage reductions, and customer characteristics. Results and samples. Several problems and limitations were identified, including quality issues and the need for qualified personnel. Suggestions have been made to overcome the problems, including improving the quality of data and investing in staff training and maintenance. The study also explores future research directions in data science research in the areas of time impact and predictive analytics. The results of this study have important implications for probability and observation chain managers in terms of the use cases, challenges, and expected outcomes of research on data analysis and machine learning methods in demand forecasting and planning. Application area. The sphere of chain management at the mesolevel.

Keywords

Received: 16 June 2023 · Accepted: 26 June 2023

How to cite

Для цитирования:

Рогулин Р.С., Санаков И.К. ПРОГНОЗИРОВАНИЕ И ПЛАНИРОВАНИЕ СПРОСА: КЕЙС ИСКУССТВЕННОГО ИНТЕЛЛЕКТА ПРИ УПРАВЛЕНИИ ЦЕПОЧКАМИ ПОСТАВОК // Регион: системы, экономика, управление. 2023. Т. 1, № 1 (60). С. 172–180. https://doi.org/10.22394/1997-4469-2023-60-1-172-180

For citation:

Rogulin R.S., Rogulin R.S. FORECASTING AND FORECASTING DEMAND: THE CASE OF ARTIFICIAL INTELLIGENCE IN SUPPLY CHAIN MANAGEMENT // Region: Systems, Economics, Management. 2023. Vol. 1, no. 1 (60). P. 172–180. https://doi.org/10.22394/1997-4469-2023-60-1-172-180

How to Cite

Rogulin R.S., Rogulin R.S. FORECASTING AND FORECASTING DEMAND: THE CASE OF ARTIFICIAL INTELLIGENCE IN SUPPLY CHAIN MANAGEMENT // Region: Systems, Economics, Management. 2023. Vol. 1, no. 1 (60). P. 172–180. https://doi.org/10.22394/1997-4469-2023-60-1-172-180

References

  1. Abolghasemi, M., Hyndman, R. J., Tarr, G., & Bergmeir, C. (2019). Machine Learning Applications in Time Series Hierarchical Forecasting. Journal arXiv (Computer Science, Mathematics) 1912.00370, 1—12.
  2. Aggarwal, S., Srivastava, M. K., & Bharadwaj, S. S. (2020). Towards a Definition and Concept of Collaborative Resilience in Supply Chain: A Study of 5 Indian Supply Chain Cases. International Journal of Information Systems and Supply Chain Management, 13 (1), 98—117. doi:10.4018/IJISSCM.2020010105
  3. Alaoui, A., & Elberrichi, Z. (2018). Feature Subset Selection Using Ant Colony Optimization for a Decision Trees Classification of Medical Data. International Journal of Information Retrieval Research, 8(4), 39—43. doi:10.4018/IJIRR.2018100103
  4. Almuiet, M. Z., & Zawaideh, F. (2019). Intelligent Agent Framework For Knowledge Acquisition In Supply Chain Management. International Journal of Scientific & Technology Research, 8 (9), 1984—1994.
  5. Annor-Antwi, A., Al-Dherasi, A. A. M., & Chunting, Y. (2019). Application of Artificial Intelligence in Forecasting: A Systematic Review. American Journal of Computer Sciences and Applications, 2 (22), 1—10.
  6. Attia, A., & Salama, I. (2018). Knowledge Management Capability and Supply Chain Management Practices in the Saudi Food Industry. Business Process Management Journal, 24 (2), 459—477. doi:10.1108/BPMJ-01-2017-0001
  7. Baryannis, G., Dani, S., & Antoniou, G. (2019). Predicting Supply Chain Risks using Machine Learning: The Trade-off Between Performance and Interpretability. Future Generation Computer Systems, 101, 993—1004. doi:10.1016/j.future.2019.07.059
  8. Bateh, D. (2019). Machine Impact in Supply Chain Management. The International Journal of Business Management and Technology, 3 (3), 13—18.
  9. Bousqaoui, H., Achchab, S., & Tikito, K. (2019). Machine Learning Applications in Supply Chains: Long Short-Term Memory for Demand Forecasting. In Cloud Computing and Big Data: Technologies, Applications and Security. CloudTech 2017. Lecture Notes in Networks and Systems, 49. Springer. doi:10.1007/978-3-319-97719-5_19
  10. Brahami, M., Adjaine, M., Semaoune, K., & Matta, N. (2020). The Roles of Knowledge Management and Customer Relationship Management on Improved Hotel Performance. Information Resources Management Journal, 33 (4), 74—93. doi:10.4018/IRMJ.2020100105
  11. Brahami, M., Atmani, B., & Matta, N. (2015). An Approach to Dynamic Fusion of the Knowledge Maps of an Activities Process: Application on Healthcare. International Journal of Information Systems in the Service Sector, 7 (4), 1—26. doi:10.4018/IJISSS.2015100101
  12. Brahami, M., & Matta, N. (2018). A model to reduce the Risk of Projects guided by the Knowledge Management Process — Application on Industrial Services. International Journal of Information Systems in the Service Sector, 10 (2), 2—18. doi:10.4018/IJISSS.2018040103
  13. Brahami, M., & Matta, N. (2019). The Relationship Between Knowledge Mapping and the Open Innovation Process: The Case of Education System. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 33 (4), 1—13.