For a manufacturing company, a supply chain is responsible for controlling the movement of different goods and materials as well as for keeping track of the production processes. An ideal supply chain management system plans, controls, and executes daily supply chain activities that are meant to improve the operations, to minimize the wastage of raw materials, and to ultimately enhance the customer satisfaction.
How do we achieve this ideal supply chain? With the help of data analytics and machine learning techniques, manufacturers can accurately forecast the risk points and win points on the supply chain. Our machine learning platform – SIA (Softweb’s Intelligence and Analytics) – can deliver insights that are derived from various operations across the supply chain. Through these insights, manufacturers can not only keep track of all the materials and goods across the manufacturing processes, but also predict the probabilities of delay in production or in delivery of raw materials.
Traceability of products and goods is a crucial factor when it comes to supply chain management. On top of that, we can make use of machine learning to make accurate prediction to take better decisions and thus improve the overall productivity of the organization. Once manufacturers have the data to work on, using analytics and machine learning techniques, they can monitor the production, manage stock levels, forecast demand, and plan inventory for the entire plant.
Forecasting market trends and customer demands is one of the pain points that can be resolved using big data and machine learning. Once these factors are known, organizations can then plan their inventory, decide production schedules, and assign or hire workforce according to the demand.