Statistical Process Control (SPC)

SPC is a statistical method used to analyze and control production processes, reduce variations, and ensure high product quality.

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About WEDEAQ

WEDEAQ Scandinavia AB is a Swedish consulting company specializing in quality management and quality development in the automotive business according to requirements in IATF 16949 and based on methods from VDA QMC and AIAG. We have more than a decade of experience in the field and are the official licensed partner of VDA QMC in Sweden, Norway, Denmark, and Finland.

We provide expert-led training, internal audits, consulting services, and translated VDA publications to support manufacturers in meeting international automotive quality standards. Our services are designed for companies across Scandinavia and the Baltic region – including Estonia – that aim to improve their quality systems, prepare for certification, and enhance process performance.
We operate in:
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Sweden

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Finland

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Norway

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Denmark

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Estonia

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confirmation letter for official licensed partner of VDA QMC

The only official VDA QMC partner in the Nordics.

What is SPC?

Statistical Process Control (SPC) is a systematic method for monitoring and controlling production processes by analyzing data from manufacturing. The method is used to identify and minimize variations in production, leading to increased quality and efficiency.

 

SPC was introduced by Walter A. Shewhart in the 1920s and is now used in many industries, especially in the automotive industry, pharmaceutical industry, and electronics manufacturing.

 

The method is an important part of quality work in the automotive industry and is used together with other tools such as FMEA, MSA, and PPAP, which are included in the Automotive Core Tools.

How does Statistical Process Control work?

SPC is based on data collection and analysis of production parameters to identify deviations and trends. The process involves:

  • Measurement of production parameters – Collecting data from machines and operators.

  • Statistical analysis of variations – Identifying systematic and random variations.

  • Visualization in control charts – Charts are used to monitor trends in real-time.

  • Actions in case of deviations – Adjustments in the process to ensure stable quality.

By using SPC, companies can prevent defects before they occur instead of detecting them through final inspections.

Tools within SPC

There are several statistical tools within SPC used to analyze and control production processes. Some of the most common include:

  1. Control Charts
    Graphical tools that show whether a process is stable or needs adjustment.
    Examples: X-bar & R-chart, P-chart, C-chart.

  2. Process Capability Analysis
    Measures how well a process can produce within specified tolerances.
    Examples: Cp, Cpk, Pp, Ppk.

  3. Quality Tools

  • Histogram – Shows the distribution of data.

  • Pareto Diagram – Identifies the most common problems in the process.

  • Ishikawa Diagram (Fishbone Diagram) – Used for root cause analysis.

Benefits of SPC

Implementing Statistical Process Control offers several business benefits:

 

✔ Reduces process variations and increases production stability.
✔ Identifies problems early and reduces the need for manual inspection.
✔ Improves product quality and reduces the number of defective units.
✔ Lowers production costs through reduced waste and rework.
✔ Increases customer satisfaction through consistent quality.

SPC within IATF 16949 & the Automotive Industry

Statistical Process Control is a fundamental part of quality management systems in the automotive industry and is a requirement under IATF 16949.

  • The automotive industry requires suppliers to use SPC to ensure that their processes are stable and that production is repeatable.
  • SPC is used to monitor critical production parameters and prevent quality issues.

Many car manufacturers and suppliers use SPC together with other quality tools such as FMEA, MSA, and PPAP to ensure robust processes.

 

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How to use SPC

Before implementing SPC, it is important to identify problem areas in production – for example, recurring rework, material waste, or long inspection times.

 

This is often where SPC can be most beneficial. A cross-functional team typically conducts this analysis, often using tools such as DFMEA to identify critical characteristics or process steps that require extra monitoring.

 

How to collect and monitor data

Once the key parameters have been identified, the next step is to collect data – both from product measurements and from sensors or equipment in the process. This data is then monitored using control charts adapted to the data type.

 

For variable data, X-bar and R or S charts are used, depending on sample size. For attribute data, P or U charts are often used. By following these values, early trends or changes in the process can be detected.

 

Detecting and addressing variations

Control charts show whether a process is stable or affected by unexpected changes. Common variations may be due to material properties or equipment wear.

 

However, special causes – such as machine failure or operator errors – lead to outlying values and require immediate action.

 

By identifying these in real-time, SPC enables teams to act quickly, reduce the number of defects, and ensure consistent quality.