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

• Published in: International journal of production research 2024-08, Vol.62 (15), p.5510-5534
• Main Authors: Jauhar, Sunil Kumar, Jani, Shashank Mayurkumar, Kamble, Sachin S., Pratap, Saurabh, Belhadi, Amine, Gupta, Shivam
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Distortion; Inventory; Inventory distortion; Machine learning; No-Code Artificial Intelligence (NCAI); out-of-stocks; overstocks; Retailing industry; supply chain resilience; Supply chains
• ISSN: 0020-7543; 1366-588X
• DOI: 10.1080/00207543.2023.2166139

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.