Process optimisation in an aluminium company
How an early warning model built on data measured before the production run started cut the repetition caused by quality defects by 23 percent.
Published results
23% less repetition
Process repetition caused by a lack of quality, down 23 percent.
Before the run, not after
The model works from input data measured before the production process starts, which is what makes an early warning possible in a flow process that cannot be stopped.
Time, waste and energy
Production time saved, wastage reduced and energy costs lower, alongside the 23 percent.
Starting situation
The manufacturer was experiencing high costs for quality defects that were only identified at the end of the production process of aluminium blocks. The production was a flow process that could not be interrupted once started. The finished product was inspected for defects at the end of the process, and any items that did not meet the quality standard had to be discarded and returned to the start of the manufacturing process.
Project
Prospero created an early warning model with DetectX® Business Monitoring on the basis of available input data, which are measured before the production process is started.
Results
Reduction of process repetition due to lack of quality by 23 percent, resulting in significant savings. Production time was saved and wastage reduced, which, combined with reduced energy costs, resulted in significant cost savings.
Where to look next
- Beyond financial services
Where DetectX® is applied outside banking and insurance.
- DetectX® platform
The predictive analytics layer an early warning model is built on.
- Early detection of delivery delays
The same module, reading delivery and customer data instead of production inputs.
- Optimisation of production planning
A sales forecast model feeding production planning, on the same platform.
Find the early warning before the production run starts
No scripted demo. A working session on your own data and your own questions.