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How to use no-code artificial intelligence to predict and minimize the inventory distortions for resilient supply chains

Author

Listed:
  • Sunil Kumar Jauhar
  • Shashank Mayurkumar Jani
  • Sachin S. Kamble
  • Saurabh Pratap
  • Amine Belhadi
  • Shivam Gupta

Abstract

Consumers’ dramatic demand has a pernicious effect throughout the supply chain. It exacerbates inventory distortion because of significant revenue loss caused by stock-level issues. Despite the availability of several forecasting techniques, large organisations, manufacturing firms, and e-commerce websites collectively lose around $1.8 trillion annually to inventory distortion. If this problem is solved, sales may increase by 10.3 percent. The businesses are concerned about mitigating this loss. Artificial intelligence (AI) can play a significant role in building resilient supply chains. However, developing AI models consumes time and cost. In this paper, we propose a No Code Artificial Intelligence (NCAI) enabling non-technical companies to build machine learning models based on production quantity and inventory replenishment. The development of the NCAI model is fast and inexpensive. However, little research deals with applying NCAI to operations and supply chain problems. Addressing the existing gap, we show the application of NCAI in the retail industry.

Suggested Citation

  • Sunil Kumar Jauhar & Shashank Mayurkumar Jani & Sachin S. Kamble & Saurabh Pratap & Amine Belhadi & Shivam Gupta, 2024. "How to use no-code artificial intelligence to predict and minimize the inventory distortions for resilient supply chains," International Journal of Production Research, Taylor & Francis Journals, vol. 62(15), pages 5510-5534, August.
    • Handle: RePEc:taf:tprsxx:v:62:y:2024:i:15:p:5510-5534
    DOI: 10.1080/00207543.2023.2166139
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