Data Science and Machine Learning: Application of Machine Learning Models to Improve Supply Chain Management of Organization: Inyange Industries, Rwanda

For the modern industrial sector, data created by machine learning and devices, product lifecycle management (PLM) tools, production planning tools, or quality and inventory control tools has reached a volume of more than a thousand Exabyte yearly and is anticipated to rise in the next years. To store, manage, analyze, interpret, and visualize such a large volume of data, Data technologies are now required.Supply Chains (SC) are a network of locations that connect a variety of enterprises. To reduce the overall cost of the supply chain, these organizations should cooperate. This necessitates that these entitiescooperate, integrate, and share information. However, there is still a disconnect between the supply chain network's ideal and actual states. the digital transformation of the supply chain is needed today more than ever. The Digital Transformation has emerged as an important preoccupation and a key strategic matter for all kinds of organizations. One of the causes could be that producers increased output in expectation of increased demand despite not knowing the consumers' actual need. The goal of this study is to identify several business applications of machine learning (ML) in supply chain management. The study examines instances of supply chain optimization that make use ofmachine learning