Effectively manage quality control processes

By Valentina Lupo

A perfectly planned product requires well managed production and supply processes to achieve the desired quality. This is an important statement to consider, especially today, where domains are removing their boundaries and the interaction between them is stronger than ever.

Quality management involves the planning, organization, and supervision of all quality assurance activities. Manufacturers have come to understand that quality must be integrated throughout the enterprise. So, the Quality Control department can no longer play a separate role. The Quality Control function is not only a supporting role to the manufacturing floor, but it has also become part of a bigger picture to ensure consistency, traceability of quality measurements and their deviations.

Considering the holistic picture below, it is important to start from the Design stage with Quality insights.

Chart illustrating the holistic nature of quality control functions.

Quality requirements are captured in the 2D drawings or 3D model, they can be used, for example, to create an aggregate tolerance analysis. The quality information, in form of PMIs – product manufacturing information – are relevant in downstream processes.

Figure showing technical information available on the 3D model or the 2D drawing with PMIs
Fig. Technical information available on the 3D model or the 2D drawing with PMIs

With Quality Planning, the project quality is monitored and the FMEA (Failure mode and effects analysis) provides the toolset to identify and mitigate risks in the virtual product and process structures. Residual risks are managed through the Control Plan with inspection plans for critical characteristics to be checked on the real product.

As described, the technical information is available directly on the 3D model or the 2D drawing with their PMIs. By leveraging on PMI’s, quality and manufacturing engineers can access this enhanced information in a way that provides required information for production and quality inspection that the design characteristics alone cannot provide.

Quality Control Plan screenshot showing 2D ballooning from a CAD drawing over PMI
Fig. 2D ballooning from a CAD drawing over PMI

Detect tolerance variances, identify root causes and correct defects

Quality data can be captured in several systems to execute defined inspections. Thus, the statistical process control (SPC) methodology helps manufacturers measure and control quality. Leveraging SPC, it is possible to detect tolerance variances, identify deviations in real time.

The Quality Control engineer can create control charts to graphically plot and distinguish between coincidental and systemic quality factors that can influence production. By evaluating the control charts, the quality specialist determines whether instability problems can be solved on the manufacturing shop floor or if measures need to be escalated for management review. If a deviation is detected, the Quality engineer needs to initiate a systematic problem-solving process to immediate solve the issue.

Screen shot showing control charts to aid aquality engineer in identifying problems.
Fig. Capture all data and deviations from quality execution system

Leveraging on effective quality planning, SPC tools and related evaluation systems, manufactures can reduce the margin of error and dramatically improve quality control process, while lowering the risks and costs associated with defective products.

Discover more about Quality Planning, Quality Assurance and Quality Control today.

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This article first appeared on the Siemens Digital Industries Software blog at