How SPC for Manufacturing Drives Quality and Efficiency Gains

In an increasingly competitive global market, controlling the quality of products is key to staying ahead. In this blog post, we look at how you can use SPC to improving your manufacturing processes, enabling you to deliver consistent quality.

What is SPC for manufacturing? 

Statistical Process Control (SPC) is a statistical method of measuring, controlling and improving quality during production. It allows manufacturers to use their real-time data to monitor production and ensure each product adheres to the required specifications . Unlike traditional mass-production methods, which rely on a thorough inspection of finished products to determine quality, SPC for manufacturing helps manufacturers identify potential quality issues early, so they can intervene and address issues early, before they lead to defective products, compromised lines, wasted resources and lost time and revenue.  

Essentially, SPC enables a shift from detection-based to prevention-based quality control. It is particularly useful in industries where compliance, precision and consistency are crucial, such as automotive, electronics and medical device manufacturing. By using SPC, manufacturers are better able to meet all their compliance obligations, as well as prevent problems, streamline operations and reduce waste. 

How does SPC improve manufacturing processes? 

In any production process, variation is inevitable. Whether it’s a change in the quality of raw materials or fluctuating environmental temperature, it’s impossible for manufacturers to control every tiny element of production. Where SPC for manufacturing can help, however, is in identifying and differentiating between the two main types of variation, so that any negative impact on production can be kept to an absolute minimum. 

These two types of variation are common cause and special cause. 

• Common cause variation refers to the fluctuations inherent in a process, such as minor changes in material quality or normal wear and tear of machinery. These are consistent and predictable. 

• Special cause variation indicates an unpredictable event that may affect production, such as equipment failure or a sudden change in the quality of raw materials. These are unpredictable and uncommon. 

By continuously monitoring these variations over time with SPC tools, manufacturers can address minor issues before they escalate, thereby improving product quality and consistency and reducing downtime. 

The benefits of SPC for manufacturing 

The advantages of using SPC for manufacturing go way beyond ensuring high quality is being achieved consistently. Some other key benefits include: 

• Reduced waste – Early detection of process issues prevents the production of defective products, thereby reducing waste and the need to rework product lines. 

• Increased efficiency – Automated data collection and real-time monitoring allow for faster adjustments, ensuring operations continue running smoothly. 

• Cost control – Being able to identify and address issues early helps keep production costs down and maximises profitability. 

• Enhanced customer satisfaction – Consistent high quality of products leads to fewer customer complaints and warranty claims, ensuring a good reputation is maintained. 

• Improved analytics – Detailed reporting enables manufacturers to track performance over time, making it easier to identify areas for ongoing improvement. 

The use of data in manufacturing 

Traditionally, collecting and analysing data in manufacturing environments has been a complex and time-consuming task requiring specialist knowledge and tools. Manual updating of multiple spreadsheets has often meant issues along the production line going unnoticed until the last minute, often when the damage has already been done and product lines have been affected. It has been almost impossible for floor managers to be proactive with emerging issues. 

But as manufacturers adopt more advanced technologies, the potential of their data for driving process improvements has grown exponentially. A new raft of intuitive software solutions is making it empowering manufacturers to automate the gathering, monitoring and analysis of data, and tools like BCN’s EasySPC enable organisations to unlock the true potential of SPC within their organisation. 

The role of SPC in modern manufacturing 

Unlike traditional tools that require manual data entry, tools like EasySPC automate the entire SPC process. With real-time insights, automated reporting and seamless integration with Microsoft Power BI, EasySPC means manufacturers can avoid the pitfalls of relying on spreadsheets and legacy systems and instead monitor process quality and consistency continuously. And with capability analysis embedded in the EasySPC tool, it’s easier than ever for manufacturers to monitor not only the stability of their processes, but their ability to meet performance standards. 

How to implement SPC for manufacturing 

Due to its reliance on the quality of data, implementing SPC for manufacturing can feel like a massive undertaking. It can be helpful to seek expert support in the first instance, and BCN’s SPC Consultancy service is a good place to start. Broadly speaking, however, the journey to implementing SPC for manufacturing follows seven key steps. 

1. Identify Critical-to-Quality (CTQ) characteristics. Determine the qualities of the product that are essential for meeting customer expectations. For example, a pie manufacturer might decide a high-quality crumbly pastry is a defining characteristic of their product.  

1. Choose key processes. Key or critical processes are those that would have a big impact on at least one of the CTQ characteristics if something were to go wrong with it. Let’s say, in our example, the manufacturer’s oven needs to operate at a consistent temperature to ensure the defining crumbly pastry texture. Any drops or fluctuations in temperature would ruin the bake CTQ. 

1. Assess data collection methods. Ensure machines and software are able to automatically gather and analyse all the relevant SPC data, and can issue alerts if things start to go wrong. Will your oven alarm sound if the temperature dips below what’s needed for the pastry to crust up? 

1. Identify process variables. Identify all the things that impact the output of your processes and that you need to control to ensure the quality of your product. In our pie factory, this might include things like ambient temperature and humidity, which we need to maintain below a certain level. 

1. Monitor variables. When variables have been chosen, you’ll need to monitor them by gathering data. Choose SPC tools like EasySPC that can automate monitoring and analysis of data to ensure a stable production process. 

1. Continuous improvement. Once all variables are visible in your SPC charts, you can regularly review the data and fine-tune processes to reduce variation and increase process efficiency. 

1. Set new goals. The idea is to embed SPC and make it a part of your long-term strategy for process and quality improvement. So as soon as you achieve one goal, set a new target for improvement. 

A future of smarter manufacturing 

As industries adopt more advanced technologies, the potential of SPC to drive improvements for manufacturers will only grow. With so many competitive factors outside of their control, manufacturers can harness real-time data through intuitive automation tools like EasySPC to stay ahead of the competition and differentiate themselves by consistently delivering higher quality products at lower costs. 

By investing in SPC today, manufacturers can transform their processes, save time and money, and build a strong, insights-based foundation for sustainable success.