Improving product quality is an essential objective for any manufacturer, for several important reasons:
In short, investing in improving product quality is a powerful lever for optimizing the overall performance of the company, reducing operational costs and strengthening its position on the market.
Several approaches make it possible to improve or better control the quality of industrial process, products.
Although primarily focused on efficiency, Lean Manufacturing also contributes to quality by eliminating waste and standardizing processes, which reduces the risk of errors. In particular, “good at first try” approaches make it possible to work on the various axes to achieve better control of processes and therefore of quality.
Various approaches and tools based on statistics aim at better quality control.
The Six Sigma method aims to reduce the variability of processes to reach a very low level of non-quality (hence the name 6 sigma). It uses advanced statistical tools to understand process variability and a structured approach to problem solving.
For its part, Statistical Process Control (SPC) uses statistical methods to monitor and control process parameters. It makes it possible to detect abnormal variations and to intervene before quality problems occur.
These approaches can be combined with an experimental approach: experimental designs (DOE). They allow, through tests on production lines or pilots, to determine which parameters generate variability, or even optimize the process.
Thanks to the solution's unique contextualization engine, establish the link between the various data in your process by integrating traceability and genealogy linked to the structure of your production processes. Thus, for each finished product, you benefit from a map showing all the transformations it has undergone, from the raw material to the finished product. This approach makes it possible to conduct a comprehensive analysis of the causes of variability and deviation.
Thanks to the solution's advanced analysis tools, quickly identify the influencing factors (recipe, process parameters, quality of raw materials, etc.) on the performance — including the quality — of your processes. You will also be able to determine the optimal operating conditions for these key parameters. By using robust and powerful Machine Learning algorithms, our solution simplifies the work of your teams in deploying Six Sigma approaches.
Variability is one thing, but when the situation gets out of hand, it may be appropriate to look at the problem from a different perspective. Let's ask ourselves these questions: what elements changed between the period when production was operating normally and when performance deteriorated? What do faulty or non-compliant productions have in common? Our advanced analysis tools allow you to answer these questions and accelerate the work of investigation and problem solving by your teams.
Once the standard control conditions have been determined, put your processes under control using advanced statistical control tools (control cards, “golden batch”, etc.). Generate relevant alerts to allow your teams to react as soon as possible.
In conclusion, the control and improvement of product quality are crucial issues for any industrial company, and the exploitation of data is a powerful lever for continuously improving product quality, strengthening the company's competitive position in the long term.