Process control in the food industry

Why Statistical Process Control (SPC)?

Extensive logging of data and monitoring of production processes doesn’t automatically lead to good process control. It requires the most important parameters to be monitored in a way that gives an early indication of process performance and deviant situations. The right information has to be provided at the right moment so that operators can intervene on time, as quickly as possible preventing disruptions to production without downtime or loss of quality, and against minimal costs. The strength of SPC is precisely that you can spot and rectify deviant situations and disruptions to production as quickly as possible. Making it the cornerstone of good process control.

 

Step-by-step plan for the introduction of Statistical Process Control (SPC)

To implement SPC, you need to take the following steps:

  1. Process analysis: process characteristics and the risks of process disruptions are mapped by teams of operators and process technologists. Where necessary, other functions, such as Maintenance or Quality, may be involved.
  2. Establishing the types of readings: for the most important risks, determine which process parameters are suitable to signal the process disruptions.
  3. Analysis of readings: on the basis of the data collected, determine what the appropriate frequency and sample size should be to achieve accurate and timely warnings.
  4. Determine SPC limits and OCAP: the process control system is made operational for application by the operators by designing Control Charts (when to intervene) and Out of Control Action Plans (OCAPs: how to intervene).

 

Observations from using Statistical Process Control (SPC) in the field

The following observations from the field can help you introduce SPC successfully and create extra added value while doing so:

  • Joint development of the control methodology by those employees directly involved leads to a good understanding of the concept, and ensures the available knowledge of operators and process technologists is well utilized.
  • To get good insight into the most important sources of variation, and the relationships between process parameters and the end-result, it’s vital to select suitable statistical analysis methods. Wrong choices will lead to a process that’s insufficiently understood and thus badly managed.
  • OCAPs seem to play a remarkably useful role in harmonizing the work practices of operators and facilitating discussions about improving how operators deal with process disruptions.
  • Based on the results of the process control, process technologists and Quality employees should work closely with Production, so they can quickly take steps to achieve process improvement with operators. In this sense, SPC fits perfectly with the lean approach to production.
  • The approach to process control should be tested for a limited time within the existing production process, and can then be fully included within operational management and the process review. When a new production line is introduced, SPC can be used to detect any deviations from the normal process; and test the OCAPs and, if necessary, adapt them to the new installation and circumstances.

 

CQM can help you optimize your process control

Our SPC methodology for process control has proven itself over decades now as a solid foundation for quality assurance and process improvement on the work floor. In many situations it’s proved possible to improve working methods or make them more efficient. Involving operators and process technologists, as well as a clear approach based on a good understanding of the process, are all essential to achieve this improvement. Because our approach delivers good results and high levels of acceptance, it can form a solid basis for managing your processes.

Want to optimize your control methods based on the possibilities offered by our Statistical Process Control (SPC) methodology? We’d be delighted to explain it to you further. Simply contact one of our experts for an informal discussion.

 

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Ing. Jean Claassens

Ing. Jean Claassens

Principal Consultant