Predictive analytics uses a mix of statistical and machine learning techniques to analyze current and historical data to make predictions.
Machine learning can analyse customers' historical data in real-time so that it can respond to demand fluctuations faster. Predictive models can account for a complex web of factors including consumer buying habits, raw material availability, trade war impacts, weather-related shipping conditions, supplier issues, and unseen disruptions.
Manufacturers can better optimize the number of dispatched vehicles to local warehouses and reduce operational costs since they improve their manpower planning, warehouses can reduce the holding costs, and customers are less likely to experience stockouts.
Defect detection & predictive maintenance
Being able to predict defects and failures using AI can reduce unplanned downtime on the shop floor, and significantly improve product quality, throughput, and yield.
Sensors in machines create a continuous stream of data on their use and state of maintenance. Predictive maintenance uses data analysis and algorithms to predict the need of maintenance, helping prevent unnecessary downtime.
AI can help businesses analyse demand in real-time so that organizations update their supply planning parameters dynamically to optimize supply chain flow.
With dynamic supply planning, businesses use fewer resources since dynamic planning minimizes waste.
AI can help businesses analyse and optimize routes.
Businesses can reduce shipping costs and speed up the shipping process.