Leveraging data holds the key to driving greater efficiency and transparency. From plant facilities to entire supply chains, costly nonconformances can eat away at profit margins at a time when competition is high. As the global markets grow more complex, it’s crucial for manufacturing organizations to hone processes that access and interpret data in real-time to control variances and target quality.

As competition in the manufacturing industries ramps up on a global scale, organizations are seeking ways to drive sales while also remaining in compliance. Manufacturers may also want to capitalize on a breakthrough in their industry and compel continued momentum after the introduction of a new product or process. Whatever the reason, manufacturers are exploring tools like statistical process control (SPC) software in an effort to create higher-quality products without compromising productivity.

Many organizations understand the “improvement” part of Continuous Improvement but struggle with the “continuous” aspect. A company might improve a process once but then assume they have solved the problem and there is no further need for improvement. Other companies know that continuous improvement can help but are unsure which strategy is appropriate. Learning about key continuous improvement methods can ensure companies know which strategies will yield the results they seek. Many companies find themselves considering many different options, and lose sight of the most important thing which is to start somewhere.

Statistical Process Control (SPC) is an industry-standard procedure that utilizes statistical techniques during the manufacturing process. Managers using SPC can access quality data during manufacturing in real-time and plot data on a graph with predetermined control limits. The capacity of the process determines control limits, and the client’s needs determine specification limits. By implementing SPC, manufacturers use quality data to record and predict deviations in the production environment. Data are plotted on a graph, incorporating factors like control limits (natural process limits) and specification limits (requirements determined by the corporate). When recorded data falls within control limits, it indicates everything is operating correctly.

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

Specifications are the standards or the minimally accepted requirements for important features (or characteristics) of a product. Many manufacturers also set their own specifications. The confusion between specification compliance and quality can lead to financial loss, wasted time, and so on. For example, a product can fall within specifications but still prove unsatisfactory for clients. Additionally, manufacturers that rely solely on meeting specifications can miss out on opportunities to create more cost-effective processes. Thus, applying several statistical principles can immensely help a company identify ways to positively reform a process and product. By moving beyond the gauge parameters of specifications, manufacturers can boost quality with an efficient, optimized, and cost-effective process that performs better and satisfies the customer base. 

At its core, manufacturing success is all about quality. Consistently adhering to quality standards ultimately delights your customers and takes you far beyond the benefits of brand loyalty. Determining the cost of quality (COQ) is a complex but essential endeavor; there’s the cost of poor quality to consider and the cost of good quality or preventing issues from happening in the first place. In plants producing hundreds of separate items, tracking the many variables that influence quality can feel like a massive undertaking. And of course, it’s not just tracking this information that facilitates change; the goal is to derive meaningful insights from the data to inform future decisions.

Both SPC and SQC help plant management to drive smooth operations to promote efficient output and optimal results. They both play a role in overall success in operations, but those two roles are different. Here’s what to know about SPC and SQC to determine which is right for your Food & Beverage or CPG manufacturing facility.

What Is Statistical Process Control?

Simply put, SPC uses statistical methods and sampling programs to help plant and operations managers understand and control variability in their manufacturing processes. Charts highlight process variations in real-time, triggering alerts as processes begin to trend out of expected, standard limits. The main objective of statistical process control (SPC) is to manage operations better and reduce waste and rework. Plant and Ops managers benefit from SPC because it gives them the concrete information and clear markers needed to stop a line and fix problems before things spin out of control.