Automate the transition between control standards for optimized quality
Quality management in industrial processes relies to a large extent on sampling, a method for evaluating production batches by analyzing a subset of products, based on international standards such as the ISO 2859-1 or the ISO 3951-1. Traditionally, sampling checks are carried out in different ways: normal, reduced, reinforced, 100% or skip-lot (skip-lot) based on product variability or supplier compliance history. However, manually switching from one level of control to another can become a cumbersome, reactive and inefficient process, especially if it has to be done per characteristic and per supplier. Dynamizing sampling controls involves making these transitions in order to respond rapidly to quality variations and optimize resources. This avoids frequent situations on the shop floor, where an operator wonders: "Why keep checking this diameter, which for years has never shown any non-conformity? And, despite regular non-conformities on peelability strength, why do we always take the same sample size?
What is dynamic sampling?
Dynamization in sampling refers to an automated process enabling instant and adaptive switching between different types of sampling control: normal, reduced, reinforced, 100% or sometimes even skip-lot. This flexibility enables an optimum balance to be maintained between the rigor of quality controls and the efficiency of production processes, by adapting controls according to product performance or detected trends.
In ISO 2859-1 or 3951-1 the term revitalization is not used, it refers to the Control modification rules paragraph 9.3 of these standards.
The different types of sampling control :
1. Normal control The standard level of control used when production is stable and quality meets expectations.
2. Reduced Control This level is used when production quality is stable and regularly compliant, and allows you to reduce the frequency of inspections to save time and cut costs.
3. Reinforced control This level is applied when quality problems or significant variations are detected. It is used to increase the frequency of checks to quickly identify defects and take corrective action.
4. Control 100% This level consists of checking each unit in a batch. It is generally applied in critical cases, when quality is imperative and no defect can be tolerated. Although more costly and slower, this level of control ensures total absence of non-conformity in the batch.
5. Skip-Lot : Used only in the case of unprecedented absence of defects over a very long history, it allows certain batches to be skipped in inspections, thus reducing costs and speeding up the production flow.
Why boost these transitions automatically?
Dynamic sampling controls offer a number of advantages:
- Increased reactivity Automation enables you to switch from one level of control to another in real time, responding immediately to changes in batch quality or stability.
- Resource optimization (just-in-time control) By dynamically adapting the frequency of checks, companies can reduce the resources and costs associated with enhanced checks, reserving them for when they are really needed.
- Better drift detection By rapidly switching to reinforced control as soon as a variation in quality is detected, it is possible to intervene quickly to prevent the spread of defects in the production chain.
- Production Acceleration By reserving tighter controls for less efficient features, and using reduced or skip-lot types when quality is stable, production can advance more rapidly, with minimal interruptions.
How automated control dynamization works
Sampling ?
Introduce more dynamic sampling controls, i.e. a system for
automated, adaptive switching between different types of controls
(normal, reduced, reinforced, 100 %, or skip-lot), presents several challenges. This
method, which aims to optimize controls by automatically adjusting the level
results and production trends, it is necessary to identify the most appropriate
following difficulties :
1. Technical and software complexity
Dynamization requires software capable of monitoring the performance data of the
and automatically trigger the transition from one type of control to another, according to
pre-established criteria. This requires a tool for collecting, processing and analyzing data.
production data.
2. Setting the rules for switching between sampling control types :
Define rules for switching between sampling types (normal, reduced),
is important for the smooth running of the dynamization process. These rules must
be set up to accurately reflect production quality without inducing
excessive or unjustified mode changes
3. Alignment with ISO Standards
In ISO 2859-1 and 3951-1, the notion of "dynamization" as such does not exist.
not. However, they do provide rules for changing control (section 9.3) which
describe when and how to switch from one type of control to another. Adapting these guidelines
to an automated dynamization process requires careful interpretation for
remain compliant while integrating flexible, automated rules. It's a balance
between regulatory compliance and innovative control methods.
4. Integrity and reliability of historical data
The integrity and reliability of historical data are fundamental to the success of the
more dynamic sampling controls. This process is based on a
accurate analysis of production performance, which requires solid, reliable data.
to dynamically adjust control levels.
Conclusion
The revitalization of sampling controls represents a significant step forward for
quality management. By automating the transition between different types of control, it
enables companies to respond flexibly to quality fluctuations, optimize
resources and maintain efficient production. For modern companies,
this approach offers a means of strengthening their competitiveness while guaranteeing
products that meet customer expectations and regulatory standards.
With our Ellistat IQC solution, dynamic sampling controls overcome
these challenges to offer a more flexible, adaptive and efficient approach. Ellistat IQC enables
to maintain an optimal balance between the rigour of quality control and the imperatives of the
productivity, by adjusting control levels in real time according to
production and the trends detected.
