Application of Artificial Intelligence in Forecasting the Demand for Supply Chains Considering Industry 4.0
Alireza Goli
Department of Industrial Engineering and Future Studies, Faculty of Engineering, University of Isfahan, Isfahan, Iran
Search for more papers by this authorAmir-Mohammad Golmohammadi
Department of Industrial Engineering, Arak University, Arak, Iran
Search for more papers by this authorS. A. Edalatpanah
Department of Applied Mathematics, Ayandegan Institute of Higher Education, Tonekabon, Iran
Search for more papers by this authorAlireza Goli
Department of Industrial Engineering and Future Studies, Faculty of Engineering, University of Isfahan, Isfahan, Iran
Search for more papers by this authorAmir-Mohammad Golmohammadi
Department of Industrial Engineering, Arak University, Arak, Iran
Search for more papers by this authorS. A. Edalatpanah
Department of Applied Mathematics, Ayandegan Institute of Higher Education, Tonekabon, Iran
Search for more papers by this authorJyotir Moy Chatterjee
Search for more papers by this authorHarish Garg
Search for more papers by this authorR. N. Thakur
Search for more papers by this authorSummary
Nowadays, the speed of implementation of technological advances is increasing. Moreover, the economic challenges posed by technological and social developments have led industrial companies to increase their agility and responsiveness in order to be able to manage the entire value chain. On the other hand, one of the most interesting and at the same time one of the most important challenges facing supply chains is Industry 4.0, which is based on digital technology and varies greatly in scale and complexity. It is more than what humanity has experienced through previous industrial revolutions, and decisions need to be made faster and more accurately. Accordingly, in this paper, a comprehensive framework for accelerating supply chain decisions with respect to Industry 4.0 is provided. In this regard, first, Industry 4.0 is described in detail. Next, a framework for demand forecasting in the 4.0 industry-based supply chain is provided using artificial intelligence tools. In this context, the simultaneous use of time series methods and machine learning methods is emphasized. The analysis of the proposed framework demonstrates the suitable benefits of using it in different supply chains.
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