Developing robust Statistical Process Control (SPC) can do wonders for your quality control. But where do you begin when outlining this strategy? The pilot, of course. And that happens to be the most critical step.
The foolish man built his house on sand and the wise man on rock. The same goes for Statistical Process Control (SPC) – the success is in its foundation, or rather, a successful pilot.
Every company is different and thus, results differ as well. However, the basic steps to achieve a strong control strategy are the same. Manufacturers must begin with a pilot project focused on developing and customising an SPC system that corresponds to a company's specific processes. Additionally, manufacturers would do well to prepare the entire organisation so the populace can embrace change by creating awareness and defining new roles and responsibilities.
The ability to react flexibly is crucial in the initial phases of preparing for Statistical Process Control implementation.
Successful SPC implementation requires more than just software installation. It is a journey founded on science and risk-based analysis, throughout which you identify critical aspects of the process, establish reliable process measures and indicators and determine the relationship between a healthy process and products.
Agility – the key ingredient
The ability to react flexibly is crucial in the initial phases of preparing for Statistical Process Control implementation. This is true not only in regards to the system, but also the involved organisation. Working with control charts requires a change in approach - from being reactive, to being proactive.
Therefore, it is important to have an engaged group that can adapt as the project evolves. A shift from iterative learning processes to fully standardised working procedures will occur as roles, requirements and action plans start to form.
SPC as a business strategy
The implementation of SPC could be a great business strategy to consider if your company is experiencing one or more of the following issues:
- Variation in yield or product quality
- Random component errors
- Verifying effect of error correction
- Anchoring optimisation projects
- Meeting recent regulatory requirements regarding process verification