AI/ML for building Intelligent Supply Chains

An Intelligent Supply Chain integrates data, automation, and advanced analytical technologies to bring efficiencies and reduced costs to different parts of the supply chain. It can be a source of strategic advantage to an organization by enhancing customer experience. It is typically supported by a combination of optimization and AI/ML technologies. We explore five use cases mapping them to five elements of supply chain management: Plan → Source → Make → Deliver → Reverse Logistics.

1. Short-term demand sensing (Plan): Inventory management is a critical supply chain activity. Working capital and storage costs are tied to inventory levels, which in turn, are dependent on accurately forecasting demand among other factors. A high level of forecast accuracy helps to reduce inventory costs and stock outs by driving the manufacture of the right quantity of product at the right location at the right time. This helps optimize inventory in warehouses reducing safety stock levels, thereby reducing costs, while avoiding stock outs that may lead to long-term business and revenue losses. An AI enabled application can identify patterns in customer orders and correlate certain leading indicators to the short-term demand to be more accurate than from traditional methods. With real-time orders information, we can automate demand predictions for the current and next month that are refreshed regularly.

2. Supplier segmentation (Source): Large organizations may have hundreds or thousands of suppliers that form an integral part of their supply chain. Different raw materials in varying quantities are sourced from suppliers. To reduce cost of procurement while increasing reliability of supply, it would be helpful to apply different strategies to different suppliers. Clustering suppliers into a few key groups would be beneficial in this regard given large number of suppliers. This type of segmentation will enable targeted contract negotiations on raw material prices, volumes and supply timing to optimize spend. An AI enabled application can segment suppliers based on features such as volume, price, strategic relationship, reliability to enable sourcing specialists to adopt similar procurement strategies for suppliers within a cluster. This segmentation can be refreshed on a monthly or quarterly basis to account for supplier changes.

3. Equipment failure predictions (Make): Unplanned downtimes due to reliability failures are common in manufacturing operations. These tend to stress product inventory positions and can lead to delays in customer fulfilment. Predicting when such downtimes may occur can help a facility either take actions to mitigate the risk of equipment failure or prepare in advance by building up inventory to account for the downtime. These predictions and consequent mitigation actions can help avoid stock outs and lost revenues and as well as drive investment in preventative maintenance options to avoid costly repairs. An AI enabled application can correlate equipment failures to key measurements (such as throughput, pressure, temperature etc.) for days or weeks leading up to an unplanned equipment failure and provide insights on the timing of failures. Depending on the granularity of measurements, the application may be run every day or every few days to assess the risk of failures.

4. Delivery time prediction (Delivery): On-time delivery to customers is a key metric to gauge performance of supply chains that directly impacts customer experience. Organizations measure this metric and actively attempt to improve it based on historical delay trends. Being able to predict delivery times accurately can help get the product to the customer at the requested time, thereby avoiding poor customer experience and lost business consequently and avoid penalties and other charges due to late deliveries. An AI enabled application can be used to predict delivery times based on start day and time, season, carrier, source, and destination among other attributes. Such a prediction for each shipment will enable keeping the customer informed and enhance customer experience and retention. Furthermore, this presents an opportunity to optimize the routes and select the better performing carriers.

5. Customer returns forecast (Reverse Logistics): A percentage of sales is returned by customers for a variety of reasons including not meeting quality specifications or delays in shipping. To prepare for returns shipments, storage and processing, companies need to have an accurate estimate of volume of products coming back. This will help in reducing storage and processing costs. An AI enabled application can provide an aggregate volume of returns as a function of past several weeks of customer shipments. This prediction can be updated on a weekly basis.