Increasing On-Time Delivery and Transparency Along the Production Chain

The estimates and actual values for delivery times are rarely identical. Find out how machine learning (ML) optimizes production and process planning by improving the accuracy of forecasts for component lead times.

Use Case

Summary

• With the machine learning component of our production planning software, a system supplier optimized its production planning.
• The company identified a need for optimization in planning the lead times (LT) of components. The previously used static planning data in the ERP system, taken from the material master, was too inaccurate.
• By using our AI-supported production planning software, forecasts of lead times became 19 times more accurate. This reduced both delivery delays and unnecessary inventory.
• The effort required for order tracking and shop floor management decreased.
• Users can flexibly utilize the forecast data, always track it transparently, and make decisions more easily.

How It's Done

• Using special ML algorithms, it is possible to accurately predict how long the delivery of a needed component will actually take based on the available data.
• With AI-based technologies, it is possible to keep an eye on all orders and to achieve demand-synchronous procurement and scheduling as well as improved synchronization of complex order networks across all areas.

Did You Know?

• A mere one-week deviation in delivery times requires maintaining up to 5.5 times larger safety stock compared to when replenishment times are certain.
• Inconsistent delivery and replenishment times can lead to additional annual costs, often in the seven-figure range, due to either excessive inventory or disrupted downstream processes.