The Application of Machine Learning in Supporting Supply Chain Management Operations in the Manufacturing Sector Based in England

Machine learning has played a significant role in the growth and survival of businesses around the world. Supply chain operations within businesses have benefitted from the adoption of machine learning applications by personnel in a business. This is especially the case for manufacturing businesses looking to enhance their supply chain operations. Despite the highlighted benefits of machine learning in supply chain operations, especially for manufacturing businesses in England, a paucity of research was identified.

A dearth of studies investigating machine learning adoption for supply chain operations by businesses in England was highlighted. It should be noted that England has encountered struggles with the national economy and employability. Therefore, the purpose of this research was to investigate the role of machine learning in the manufacturing industry based in England in ensuring improved efficient supply chain management.

A questionnaire-based survey method involving 50 participants was implemented during the period 16th May to 30th May 2023. The frequency analysis’s conclusions show how important machine learning is to UK manufacturing companies’ supply chain management. It helps with demand forecasting, offers real-time insights, and lowers the possibility of overstocking or having too little inventory.

For machine learning to work as well as it can, though, precise data is essential. Moreover, supply chain management in UK manufacturing companies will be improved by integrating the Internet of Things (IoT) with machine learning and choosing the best suppliers, warehouses, and other factors. The findings of this research from this research also aligned with several related studies from around the world. However, a contribution to knowledge is made as no prior research has been conducted in England. Policy and practical implications related to the findings of this research as well as recommendations are outlined in the conclusion section of this paper.