The Benefits of Statistical Process Control Software for Food Manufacturers

Clara Garviliuc
Contributing Writer

Manufacturers use statistical process control (SPC) to reduce variability in processes and increase compliance. Several SPC tools are commonly used, but the control chart is arguably the most popular. Introduced in the 1920s, control charts utilize recorded data over time to indicate when deviations in quality occur that may still be within specifications. Control charts can help manufacturers distinguish between common cause and special cause variation. However, managing manual control charts can be a complicated and time-consuming process. Many organizations are looking for more efficient and cost-effective ways to use SPC. In this blog, we’ll discuss:

  • How software can make SPC implementation more effective

  • What SPC can look like in your facility

  • The most commonly used SPC tools today

What Is Statistical Process Control Software, and Why Do People Use It?

Statistical process control software can help organizations acquire and implement process data in real-time, making control charts far more effective. SPC software can collect data and create actionable reports in a fraction of the time it takes to create them manually. Rather than spending hours each day tediously recording data, software users can either enter data into the software or capture data directly from the machines and equipment using connected sensors, scales, or thermostats. Because statistical process control software collects data efficiently, manufacturers can reallocate that time to more productive tasks while diminishing the margin of error and reducing scrap and rework.

What Is an Example of Statistical Process Control?

Maintaining certain temperatures is critical for food safety and quality in food manufacturing. Even incremental temperature changes can significantly affect product quality and appearance. While the product might still meet specifications, customers may still notice a difference in quality. SPC software can help organizations closely monitor temperatures and set customized alerts when the temperature fluctuates too much in either direction. Software allows a manufacturer to pinpoint an issue much faster, preventing excess waste and poor-quality products.

Which Is the Most Successful Tool Used for Statistical Process Control?

While SPC can include several tools, today’s most popular and successful tool is control charts. Control charts appear as graphs, with the top line representing the upper control limit, the center line representing the average, and the lower line, the lower control limit. Data points are plotted over time, creating a line indicating how much data deviates from the average and in which direction. Control charts are the most successful tools used for statistical process control today because they effectively monitor quality and reveal variations otherwise hidden from direct observation.

What Are the 4 Types of Control Charts? How Does Software Help?

There are now many types of control charts available to organizations depending on the type of processes and information, but the four most commonly used are:

  • Xbar and S charts allow manufacturers to calculate measurement averages at each point in time

  • P charts express classification data usually as a percentage

  • C and U charts organize data to look for the number of incidents

  • XiMR charts track individual measurements

Utilizing multiple control charts can help organizations get a holistic view of the effect, severity, and frequency of process variation. SPC software collects vital data in real-time and can populate multiple control charts at once. Users can create reports with these control charts and quickly zoom in on deviations. Statistical process control software like SafetyChain increases yield and reduces scrap, rework, and returns, driving continuous improvement in every targeted process.

Interested in learning more about how software can improve your operations? Check out the benefits of SPC software here