Early detection of delivery delays
How a predictive model over delivery, customer and production data identified which factors were actually causing late deliveries, and what targeted stock optimisation did about them.
Published results
27% fewer delays
Delivery delays down 27 percent after targeted stock optimisation.
Data already held
The model was built from the delivery and customer data the manufacturer already had, including production details and input material stock levels.
Stock, not schedule
The action taken on the insight was targeted stock optimisation.
The business challenge
The Germany based manufacturer with global reach was grappling with additional costs and customer dissatisfaction stemming from consistent delivery delays.
The solution
Using predictive analytics and the DetectX® Business Monitoring solution, Prospero were able to identify the relevant factors for delay in delivery on the basis of all the available delivery and customer data, for example production details and input material stock levels.
The results
The manufacturer was able to take direct action for improvement by acting on the insight the model provided, knowing which factors were most relevant in causing the delays. Targeted stock optimisation based on those insights resulted in a reduction of delivery delays by 27 percent. The manufacturer also reports cost savings and higher customer satisfaction.
Where to look next
- Beyond financial services
Where DetectX® is applied outside banking and insurance.
- DetectX® platform
The predictive analytics layer these models are built on.
- Optimisation of production planning
The same module, forecasting sales volume instead of finding delay factors.
- Process optimisation in aluminium company
An early warning model on input data measured before the production run.
Find the factors behind your delivery delays
No scripted demo. A working session on your own data and your own questions.