What Is SPC in Manufacturing?

The definition of statistical process control and how it’s used hasn’t always been the same. Walter Shewhart developed the foundations of SPC at Bell Telephone Laboratories in the 1920s while working to improve the reliability of telephone systems.

During World War II, the U.S. military adopted quality statistical process control to improve its weapons and equipment. W. Edwards Deming later brought SPC principles to Japan in the 1950s, where companies like Toyota used them to build world-class quality management systems. The success of Japanese manufacturers in the automotive and electronics industries during the 1970s and 1980s led to American companies also embracing SPC.

SPC provides manufacturers with tools to consistently produce high-quality products while reducing costs and improving efficiency. Let's explore the specific statistical process control benefits that make it such a valuable investment for manufacturing operations.

Improving Product Quality

SPC helps you catch quality problems before they reach your customers. When you monitor your processes in real-time, you can make adjustments immediately rather than discovering problems during final inspection or, worse, after shipment. This proactive approach to quality control and management means fewer customer complaints, returns, or warranty claims, plus higher customer satisfaction and stronger relationships that lead to repeat orders and referrals.

Reducing Waste

Manufacturing waste eats into your profit margins through scrapped materials, rework, and inefficient use of labor and equipment. SPC identifies the root causes of variation in your processes, allowing you to address problems that create waste before they impact your production runs. One of the biggest SPC benefits is improvements in material utilization and labor productivity, which translate directly to better margins and higher profitability.

Optimizing Processes

SPC gives you the data you need to optimize your production processes for maximum throughput and minimum downtime. You'll be able to identify bottlenecks, understand capacity constraints, and make informed decisions about where to focus improvement efforts. This level of process visibility helps you meet production commitments more consistently while maximizing return on investment (ROI).

To really understand the definition of SPC, you need to know about the fundamental components. Understanding these core elements will help you collect the right information, analyze it effectively, and make informed decisions about your operations.

Data

You can't manage what you don't measure. That’s why data forms the foundation of every SPC system. Measurements can be individual values taken from single products or averaged values calculated from a set of readings taken over time, and you'll work with two main types of data:

• Continuous variable data: Precise measurements like temperature, pressure, dimensions, or weight that can take any value within a range and show exactly how much variation exists in your process
• Attribute data: Pass/fail, good/bad, or present/absent classifications that help you track defect rates and compliance with specifications.
  The key is collecting data consistently and frequently enough to detect meaningful changes in your process performance.

Analysis

Analysis transforms your raw data into actionable insights that help you understand what's happening in your processes and why. SPC uses several proven tools to help you visualize patterns, identify root causes, and give you a complete picture of your process performance. The most common examples of statistical process control tools include:

• Cause-and-effect diagrams: Map out all potential causes of quality problems to help you focus your investigation efforts.
• Check sheets: Provide structured forms for collecting data consistently and identifying patterns in defects or problems.
• Control charts: Display process data over time to show when your processes are stable and when they need attention.
• Histograms: Show the distribution of your data to help you understand process capability and identify improvement opportunities.
• Pareto charts: Rank problems by frequency or impact so you can focus on the issues that will give you the biggest return on improvement efforts.
• Scatter diagrams: Reveal relationships between different process variables to help you understand cause-and-effect relationships.
• Stratification: Break down your data into meaningful groups to identify hidden patterns and root causes.

Variability

So how does quality statistical process control actually help you make better decisions? It’s all about variability. SPC helps you distinguish between different types of variation so you know when to adjust your processes and when to leave them alone:

• Common cause variation: All manufacturing processes have natural variability. Some normal examples of SPC variation include fluctuations in materials, equipment, environment, and operators. These are predictable and stable fluctuations that create a consistent pattern of small, random changes.
• Special cause variation: On the other hand, special cause variations are unusual points, trends, or shifts that indicate something abnormal has happened in your process. They require attention to prevent quality problems.
  Understanding variability helps you avoid overreacting to normal process variations while responding quickly to real issues.

It’s always best to prevent quality problems, rather than having to react to them. Companies that delay SPC implementation often find themselves struggling to catch up with competitors who have already built quality advantages into their operations. But introducing statistical process control software and processes into your operations comes with challenges:

• Resistance to change: Employees may worry that SPC will create more work or threaten their job security by identifying issues in their area of work. Address these concerns through clear communication about SPC benefits and involve your team in the implementation process so they feel ownership of the changes.
• Lack of training: Your staff needs to understand both concepts and real-life applications, like how to use SPC software for decision-making. Invest in comprehensive training programs that include hands-on practice with real production data and ongoing support as your team develops their skills.
• Data collection difficulties: Data collection can be time-consuming and error-prone, and departmental data silos are common. Start with critical processes and gradually expand your data capabilities, focusing on areas where you'll see the biggest impact on quality and costs. Also evaluate areas and methods for more automated data collection to minimize time and errors in data collection.
• Cultural barriers: SPC requires a culture that values continuous improvement and data-driven decision-making over intuition and tradition. Leadership must model the behaviors they want to see and recognize employees who are taking charge.

We’ve talked about what SPC is today. But what about the future? Modern manufacturing is moving toward smart factories where SPC integrates seamlessly with advanced technologies like IoT sensors, machine learning, and cloud computing. Real-time data collection from connected machines provides immediate feedback on process performance, allowing you to respond to quality issues within minutes rather than hours or days.

Meanwhile, machine learning algorithms are making SPC even more powerful by identifying subtle patterns in your process data that traditional methods might miss. Something as simple as 4 data points trending in the same direction may indicate an underlying problem even before data points go outside of acceptable ranges. Machine learning can automate identification of these trends, improving responsiveness and avoiding quality issues. Predictive analytics can even forecast when processes are likely to drift out of control, so you can take preventive action before quality problems occur. These advanced SPC examples are the next level for manufacturers: predictive quality control that prevents problems rather than just detecting them.

Answering the question “What is SPC?” requires more than just understanding the concepts. You’ll need SPC software that integrates seamlessly with your existing operations and provides current insights into your processes.

DELMIAWorks offers SPC solutions designed specifically for manufacturers who want to streamline their operations while improving quality and profitability. Our integrated SPC modules work directly with our enterprise resource planning (ERP) solution, offering real-time control charts and capability analyses without requiring separate software systems or manual data transfer.