Procurement Glossary
Cpk/Process Capability: Process capability index for quality assurance
November 19, 2025
Process capability (Cpk) is a statistical parameter that measures the ability of a manufacturing process to produce products within specified limits. In Procurement , Cpk Procurement as a decisive criterion for evaluating supplier quality and minimizing risk in purchasing. Read on to find out what process capability means, how it is calculated, and what strategic role it plays in modern quality management.
Key Facts
- Cpk values above 1.33 are considered sufficiently processable for most applications
- The index takes into account both process dispersion and centering of the mean value.
- The automotive industry often requires Cpk ≥ 1.67 for critical characteristics.
- Process capability studies are part of the PPAP approval process for suppliers.
- Cpk analyses reduce scrap costs and improve supplier evaluation
Contents
What is Cpk/Process Capability?
Process capability describes the statistical ability of a production process to consistently manufacture products within defined tolerance limits.
Basics and calculation
The Cpk index is calculated from the ratio between the tolerance range and six times the standard deviation. Both the process dispersion and the centering of the process are taken into account. An SPC system supports the continuous monitoring of these parameters.
- Cpk = min[(USG - μ)/(3σ), (μ - OSG)/(3σ)]
- LSD = Lower Specification Limit
- Upper specification limit
- μ = process mean, σ = standard deviation
Cpk vs. Cp value
While Cp only considers process dispersion, Cpk also takes into account the position of the process. A centered process shows identical Cp and Cpk values; in the event of deviations, Cpk is always less than or equal to Cp.
Importance in Procurement
Purchasers use Cpk values for objective supplier evaluation and risk assessment. The quality assurance agreement defines minimum Cpk values for various product characteristics.
Procedure: How it works
The systematic determination of process capability takes place in several structured phases, which enable a reliable assessment of supplier quality.
Data collection and sampling design
At least 100 consecutive measurements are required for meaningful Cpk studies. The sample testing must be representative of the normal production process. Particular attention must be paid to the calibration of the measuring instruments through MSA studies.
Statistical analysis
After data collection, the mean, standard deviation, and Cpk index are calculated. Normal distribution tests validate the applicability of the statistical methods. A control plan documents the monitoring parameters for series production.
Evaluation and derivation of measures
Cpk values below 1.33 require process improvements at the supplier. The analysis identifies causes using systematic problem-solving methods and defines specific improvement measures with timetables.
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Important KPIs and targets
Process capability indicators form the basis for data-driven quality decisions and supplier management in Procurement.
Primary Cpk indicators
The Cpk index itself is the focus of the evaluation, supplemented by Cp values for assessing pure process dispersion. Quality costs per PPM (parts per million) defective parts quantify the economic impact of insufficient process capability.
- Cpk ≥ 1.33 for standard processes
- Cpk ≥ 1.67 for critical automotive components
- PPM-Rate < 233 bei Cpk = 1,33
Secondary performance indicators
Process stability is measured using control chart indicators such as Cp/Cpk trends over time. The number of process improvement measures per quarter shows the continuous development of supplier performance.
Strategic performance indicators
The proportion of suppliers with sufficient process capability directly influences procurement risks. Quality gates define approval criteria for new suppliers based on Cpk evidence and reduce start-up risks in series production.
Risks, dependencies and countermeasures
Inadequate process capability analyses can lead to significant quality and cost risks in the supply chain.
Statistical misinterpretations
Incorrect sample sizes or non-normally distributed data significantly distort Cpk calculations. Untrained employees misinterpret key figures and thus make suboptimal supplier decisions. Regular training and standardized evaluation procedures minimize these risks.
Process drift and instability
Process parameters that fluctuate over time lead to seemingly good Cpk values in unstable processes. Layered process audits reveal systematic deviations. Continuous monitoring using control charts prevents undetected quality deterioration.
Supplier dependencies
A one-sided focus on Cpk values neglects other critical supplier factors such as delivery reliability and innovative capability. A balanced supplier evaluation takes multiple performance indicators into account and reduces dependency risks through diversification strategies.
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Practical example
An automotive supplier of brake components must demonstrate a Cpk value of at least 1.67 for critical bore diameters. The process capability study comprises 125 consecutive measurements during normal production. The calculated Cpk value of 1.45 is below the requirement, prompting the supplier to optimize the process. After adjusting the machine tool and improving temperature control, the process achieves a Cpk of 1.72 and receives series approval.
- Identification of the main causes using an Ishikawa diagram
- Implementation of temperature monitoring
- Validation through renewed Cpk study
Current developments and effects
Digitalization and artificial intelligence are revolutionizing the application of process capability analysis in modern procurement.
AI-supported process monitoring
Machine learning algorithms enable real-time analysis of process data and predict Cpk deterioration before quality problems occur. Predictive analytics optimizes preventive maintenance cycles and reduces unplanned production downtime.
Industry 4.0 integration
Networked production systems automatically transfer Cpk data to purchasing systems and enable dynamic supplier evaluations. IoT sensors continuously record process parameters and update capability indices in real time.
Enhanced quality standards
New industries such as electromobility and medical technology demand stricter Cpk requirements. Six Sigma methods are establishing themselves as the standard for critical processes with Cpk targets of 2.0 and higher.
Conclusion
Process capability analyses are indispensable tools for objectively evaluating supplier quality and minimizing risk in procurement. Cpk metrics enable data-driven decisions in supplier selection and development. The integration of digital technologies significantly expands the range of possible applications and creates new potential for preventive quality assurance. Successful companies use process capability analyses as a strategic tool to ensure their competitiveness.
FAQ
What does a Cpk value of 1.33 mean?
A Cpk of 1.33 means that the process statistically produces 233 defective parts per million. This corresponds to a sigma level of 4.0 and is considered the minimum requirement for most industrial applications. The process utilizes approximately 75% of the available tolerance.
How does Cpk differ from other quality metrics?
Cpk takes into account both process dispersion and centering, while Cp only measures dispersion. Unlike simple rejection rates, Cpk is based on statistical methods and enables predictions about future process performance. SPC methods use Cpk for preventive quality assurance.
What sample size is required for Cpk studies?
At least 100 consecutive measured values are necessary for meaningful Cpk calculations. For critical processes, 125-150 data points are often collected. The measurements must be taken under normal production conditions and must not contain any special influences.
How should one respond to inadequate Cpk values?
If Cpk values fall below requirements, systematic process improvements are necessary. First, a root cause analysis is performed using statistical methods, followed by targeted optimization measures. A new Cpk study validates the effectiveness of the improvements before series production is approved.
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