For a manufacturing company, a supply chain is responsible for controlling the movement of different goods and materials as well as for keeping a 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.
What if you could have the best possible scenario for supplier selection and risk management, during every single supplier interaction? 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. With the application of AI into supply chain processes, manufacturers can not only keep the 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.
In this digitized age, tracing the products and goods with RFID and QR codes is not enough. We use machine learning algorithms to discover patterns in supply chain which can pinpoint the most influential factors to optimize the supply networks’ success. 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 analytics and machine learning. In all, combining the strengths of unsupervised learning, supervised learning, and reinforcement learning, machine learning provides such insights which are not available from previous technologies – to improve supply chain management performance.