Blogs

In today's challenging business environment, many direct procurement supply chains are reactive by nature, always chasing suppliers, deliveries, parts, and line shutdowns. Reactive supply chains lack visibility tools and are responding to events and changes after they occur, rather than anticipating and proactively planning for them. The focus is on addressing issues as they arise rather than using advanced analytical tools to prevent or mitigate potential problems. This approach is often characterized by a lack of real-time visibility into supplier's manufacturing and PO status, limited collaboration with suppliers, a reliance on manual processes, and the lack of a centralized supplier data management hub.

By contrast, a proactive risk management approach involves actively engaging and working closely with suppliers to enhance mutual benefits and achieve shared goals. This approach goes beyond traditional buyer-supplier relationships and focuses on building strategic partnerships that foster innovation, efficiency, and long-term success and help companies mitigate risk in the end-to-end Po life cycle.

So how can a supply chain team change from being responsive to being proactive, able to look ahead and predict which PO's will be late and re-organized in accordance? One way is by creating an algorithm that predicts which suppliers and what parts will be problematic. Today there is a whole world of ERP and MRP tools for scheduling and managing materials, and they do what is necessary, but ultimately they are all reactive - allow for a response only when the crisis is in its full swing.

We have created a solution that combines the algorithm and the service that accompanies it to be proactive, controlling the situation in advance by early identification and handling of the problematic components before the status turns from green or yellow to red. The traffic lights are not just an image. The statuses of different parts are illustrated with these colors, allowing the user to easily identify an item that is in danger of being delayed. The algorithm analyzes information from a large number of sources and draws conclusions based on that. The algorithm weighs the data and draws a forecast, of whether or not a part will arrive on time over the next three months. It relies on the supplier's supply history, the number of known parts he has in stock and production, his relevant purchase order confirmations, and finally his service index.