Procurement Glossary
Process capability: Key figure for evaluating supplier quality
November 19, 2025
Process capability is a statistical indicator that measures how well a manufacturing process meets the required specifications. In Procurement , it serves as a decisive criterion for supplier evaluation and quality assurance. Find out below what process capability means, how it is calculated and what strategic role it plays in procurement.
Key Facts
- Cpk values above 1.33 are considered sufficiently processable for most applications
- The key figure takes into account both scattering and centering of the process
- Process capability studies require at least 100 consecutive measured values
- Differentiation between short-term (Cp/Cpk) and long-term (Pp/Ppk) process capability
- Essential component of supplier qualification and series release
Contents
Definition and meaning of process capability
Process capability quantifies the ability of a manufacturing process to produce products within the specified tolerance limits.
Basic key figures and interpretation
The most important process capability indices are Cp, Cpk, Pp and Ppk. While Cp only considers the dispersion, Cpk also takes process centering into account. Pp and Ppk evaluate long-term performance over longer periods of time.
Process capability vs. machine capability
Machine capability measures short-term variability under ideal conditions, while process capability takes into account all influences of the production environment. This distinction is crucial for realistic supplier evaluations.
Importance of process capability in Procurement
In procurement management, process capability serves as an objective criterion for supplier selection and evaluation. It enables the prediction of quality problems and supports the definition of quality agreements with suppliers.
Measurement, database and calculation
Determining process capability requires systematic data collection and statistical evaluation according to defined procedures.
Data collection and sample size
At least 100 consecutive measured values are required for meaningful process capability studies. The data must be collected under normal production conditions in order to achieve realistic results. Measuring system analyses ensure that the measurements themselves are sufficiently accurate.
Calculation formulas and limit values
Cpk is calculated as the minimum of (OSG mean)/(3×standard deviation) and (mean-USG)/(3×standard deviation). Values above 1.33 are considered processable, above 1.67 as very good. The calculation assumes normally distributed data.
Software tools and automation
Modern SPC systems automatically calculate process capability indices and visualize trends. Integration into ERP systems enables continuous monitoring of supplier performance.
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Interpretation and target values for process capability
The correct interpretation of process capability KPIs is crucial for well-founded supplier decisions.
Industry-specific requirements
The automotive industry typically requires Cpk ≥ 1.33, aviation often Cpk ≥ 1.67. Medical technology and pharmaceuticals have even stricter requirements. The determination of appropriate target values depends on safety risks and cost-benefit ratios.
Continuous monitoring and trends
Process capability values must be updated regularly, as processes change over time. Trend analyses reveal deteriorations at an early stage. Quality gates define minimum requirements for different project phases.
Benchmarking and supplier comparison
Process capability data enables objective supplier comparisons and benchmarking. Relative assessments take into account product complexity and manufacturing processes. Scorecards integrate process capability into comprehensive supplier evaluation systems.
Measurement risks and bias in process capability
Misinterpretation of process capability data can lead to incorrect supplier evaluations and quality problems.
Statistical requirements and limits
Process capability indices require normally distributed and stable processes. In the case of non-normally distributed data or unstable processes, standard calculations lead to incorrect results. Control charts must confirm process stability prior to process capability studies.
Measuring system errors and distortions
Unsuitable measuring systems can falsify process capability values. Systematic measurement errors, inadequate resolution or drift of the measuring devices have a significant impact on the results. Regular Gage R&R studies are therefore essential.
Misinterpretation and management errors
Short-term process capability values cannot predict long-term performance. One-sided focus on Cpk values without consideration of process stability leads to false conclusions. Insufficient sample sizes or selective data selection distort the evaluation.
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Practical example
An automotive supplier produces brake disks with a critical thickness tolerance of 25.0 ± 0.1 mm. The process capability study of 125 measured values results in a mean value of 25.02 mm and a standard deviation of 0.025 mm. The calculated Cpk value of 1.07 is below the minimum requirement of 1.33. Procurement initiates improvement measures together with the supplier to center the process and reduce scattering.
- Root cause analysis using the Ishikawa diagram
- Implementation of statistical process control
- Post-qualification after process improvement
Current developments and effects
Digitalization and artificial intelligence are fundamentally changing the application of process capability analyses.
AI-supported process optimization
Artificial intelligence enables the real-time analysis of process data and the prediction of quality problems. Machine learning algorithms recognize patterns in complex data sets and automatically optimize process parameters. This leads to greater process stability and reduced reject rates.
Industry 4.0 and networking
Networked production systems provide continuous data streams for process capability analyses. IoT sensors record process parameters in real time and enable preventive quality measures. Traceability is improved by digital twins and blockchain technology.
Advanced analysis methods
Multivariate process capability analyses take into account interactions between different quality characteristics. New statistical methods such as Six Sigma and Design of Experiments extend the classic approaches to include systematic process improvement.
Conclusion
Process capability is an indispensable key figure for the objective evaluation of supplier quality and process stability. It enables data-based decisions in procurement and contributes to risk minimization. The continuous monitoring and correct interpretation of process capability data are crucial for sustainable procurement success. Modern digital tools expand the application options and significantly improve forecasting capability.
FAQ
What is the difference between Cp and Cpk?
Cp only measures the process variation in relation to the tolerance, while Cpk also takes the centering of the process into account. Cpk is always less than or equal to Cp and reflects the actual process capability more realistically, as the position of the process mean value is also included.
What minimum Cpk values are common in practice?
In the automotive industry, Cpk values of 1.33 are the minimum requirement and 1.67 is considered good. Critical safety parts often require Cpk ≥ 2.0. The requirements vary depending on the industry, component criticality and customer specifications.
How often should process capability studies be repeated?
Initially at the start of series production, then at least once a year or in the event of significant process changes. Quarterly checks are advisable for critical processes. Continuous SPC monitoring supplements periodic studies and identifies trends at an early stage.
What to do if process capability is insufficient?
First carry out a root cause analysis, then implement targeted improvement measures. Possible approaches include process optimization, tolerance expansion or alternative manufacturing processes. Development plans and requalifications are required for suppliers.
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