A global manufacturer learned how much one part can matter. When a single component ran short, the company couldn't build the entire product. Production stalled, and its market share was at risk.
The shortage was a symptom of a larger problem. The client builds thousands of products from millions of components, and each one carries some supply risk. Assessing that risk in the early supply chain stages took hours, and high-risk parts still made their way into production, adding cost and delay. The client wanted to manage the product lifecycle better, shorten manufacturing time, and cut the cost of using high-risk parts.
What SoftServe built
SoftServe's AI team built, trained, and deployed a set of machine learning (ML) models that assess risk across the client's products and components. The solution scores incoming bills of materials in near real time, so teams see the risk while decisions are still easy to change.
To train the models, the team collected complex data from multiple sources. The solution runs on AWS infrastructure and other tools.
What changed:
- Faster decisions: Risk assessment in the early supply chain stages dropped from hours to minutes.
- Predictability: The client now plans its supply chain using forecasts that span a decade, not just the next quarter.
- Stability: A "what-if" simulation engine shows how the supply chain holds up in unexpected events, before they happen.
The client no longer waits for a shortage to find out which part is the problem. Teams see the risk when a bill of materials arrives, plan against decade-long forecasts, and test how the supply chain holds up before the next disruption.
Let's talk about finding supply chain risk before it reaches your production line.
