Almost all the current applications of AI in SCM are in the following three areas:
Supply Chain Automation:
after automation of the factories now it is the turn of the warehouses and eventually the trucks to give way to robotics. Although the current applications are still mostly in the experimental stages, the time is not far away when a large part of logistics execution task will be carried out by a combination of AI, IoT and automation.
Supply Chain Decision Support (DSS):
Gradually, over the period of last 2 decades the algorithms embedded into the DSS are getting more sophisticated and accurate. As the crunching power, bandwidth and connectivity expands, and as data collection and availability become ubiquitous, DSS have access to far more accurate and complete dataset than ever before in the past. Sophisticated AI is now built so deeply into the supply chain decision making process that we just take it for granted, and do not even call it AI in many cases.
Supply Chain Reporting:
Almost every supply chain dashboard today deploys some sort of minor AI to make value judgement on the supply chain performance. Many others are also making a judgment on what data to report on, and what to ignore. Many others are going a step ahead and making recommendations on actions emanating from the data analytics - going into the realm of decision support that was earlier out of the purview of dashboards.
Whether they are replacing the human intervention, or supplementing it AI applications in supply chain are only going to grow. Almost in every case the value is accruing more to the AI vendors, than to the end user for the simple reason that the end users are neither initiating the projects, not participating in the value building stage of the AI smile curve.