Optimization and improvement are what supply chain managers are constantly seeking out. AI is the perfect tool to help realize and facilitate this.
Uses of AI in SCM
Predictive analytics are at the forefront of current AI applications today. Companies are able to better analyze past and future data to predict demand of products. This will reduce demand disruptions, fluctuations in inventory, minimize stockouts, and improve customer experience. Previously, statistical forecasts were created by managers analyzing their data to develop the best prediction. These forecasts can only include so much data and the planners must choose which metrics are most important. AI will improve this by sifting through the data and coming up with better metrics by which to predict demand outcomes.
Logistics is another sector which has already seen a high adoption rate of AI into its technology interfaces. Many third-party logistics companies can implant chips into trucks to optimize driver routes. Amazon is the best example, creating an AI flywheel which has been incorporated into many parts of the company. This can interact with its logistics software to create a better customer-centric approach. It uses real-time data to inform on lead times, optimize operations, and inform customers where the deliveries are. This creates a feedback loop on which to improve satisfaction and lower costs as the AI is further integrated.
There are many other uses for AI inside SCM. A few more are warehouse tracking software, automated trucks, and enhanced risk management detection/mitigation. All of these and more are currently being used to help optimize many businesses' operations.
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