Is the lower scrap rate really an improvement?
A new machine is running, the first parts are being produced and the scrap rate is falling. At first glance, this looks like a clear success. But one important question remains: Is the improvement statistically reliable, or simply a temporary fluctuation?
In manufacturing and quality management, a positive first impression is not enough. Process improvements should be validated with data before further decisions or investments are made.
What matters when evaluating improvement
- Comparable conditions: Before-and-after data should come from similar process conditions
- Sufficient data: Too few observations can give a misleading picture of actual process performance
- Statistical significance: Appropriate statistical methods help determine whether a change is genuinely measurable
- Sustainable improvement: The goal is not a short-term effect, but a consistently more stable process
Reliable data analysis separates random variation from real improvement. It provides a stronger basis for decisions and shows whether actions genuinely reduce scrap, improve quality and increase process stability
