In today’s competitive business environment, the use of Machine Learning (ML) is an essential tool to gain a competitive edge and maximize profits. ML enables computer systems to learn from data, identify patterns, and make decisions without any human programming. By leveraging ML algorithms, businesses can reduce expenses and increase profits all while providing better customer experiences. This article looks into the common uses of ML techniques in Supply Chain Management (SCM).
SCM is the set of activities from sourcing of the materials to the delivery of the final product or service to the customer. It is to ensure proper control and coordination of the materials, services and related information from the supplier to the customer. ML techniques are being used to automate certain supply chain task such as inventory management, demand forecasting, and order fulfillment. As a result, this automation of processes can aid corporations in reducing costs, minimizing delays, and obtaining customer satisfaction.
Using ML applications in supply chain can also help businesses to predict and forecast customer demands and optimize production planning. This can further help companies optimize performance, reduce costs, and increase profit margins. Additionally, ML algorithms are being used to identify and predict financial and supply chain risks. By utilizing ML algorithms to track customer queries, it can free up staff resources for more invaluable tasks such as marketing or product development.
Apart from the applications mentioned above, ML algorithms can also be used to optimize the entire supply chain process from start to end in order to save costs and improve efficiencies. Additionally, it can be used to optimize transport routes and schedules to reduce fuel costs, ensure final deliveries, and accurately predict lead times. Lastly, ML algorithms can be used for inventory management and to select the ideal vendors for the business.
Arindam Mukherjee is an IT supply chain architect and published author in leading supply chain publications. He is an advocate for the use of Machine Learning in supply chain and believes that it is essential for businesses to embrace AI & ML for greater efficiency in their operations and improved customer experience.
McKinsey’s report indicates a large potential for AI to become an important economic growth driver. Businesses around the world are gradually realizing the importance of ML to increase efficiency and profit margins in their supply chain operations. By utilizing ML algorithms to automate task, identify and predict risks, as well as optimize their supply chains, companies will be able to remain competitive and stay ahead of the game.
