Today’s manufacturers face more significant challenges than ever, with increasing competition and decreasing natural resources. It’s a time of political uncertainty, raging pandemics, and escalating costs, with no end in sight.
That’s why manufacturing companies that want to survive and thrive need to embrace resources that give them a degree of control over any part of the manufacturing process possible. That’s where statistical process control or SPC for short, comes in.
This article explores statistical process control, what it is, where it comes from, why it’s needed, and available tools and resources that make the process easier to implement and run.
What is Statistical Process Control?
SPC is typically defined as a method of using statistical analysis to control and measure quality, thereby improving the manufacturing process. Manufacturers collect quality real-time data in the form of process or product measurements taken from different instrumentation and machines. The collected data is then used to monitor, evaluate, and control the manufacturing process.
By gathering this information and displaying it on graphs and charts, manufacturers can see if their processes are functioning at their highest potential. SPC highlights the areas that need improvement, enabling companies to eliminate waste, delays, and the likelihood of producing defective products.
The History of SPC
It may surprise you to know that statistical process control has been around for almost a century. In 1924, Doctor William Shewart of Bell Laboratories researched methods of improving manufacturing quality and lowering costs, which laid the groundwork for SPC. He created the control chart to determine whether a process was in or out of control. He published a book, “Statistical Method from the Viewpoint of Quality Control,” in 1939.
The US military relied heavily on SPC during the Second World War, to oversee product quality in weapons and munitions without sacrificing safety. Unfortunately, SPC fell out of practice when the war ended.
After the war, Dr. W. Edwards Deming refined and updated Shewart’s work and brought it to Japan, where Japanese businesses enthusiastically embraced the ideas. SPC helped Japan recover its manufacturing capability and even exceed it, giving the Japanese a world-wide reputation for industrial excellence.
Finally, in the 1970s, the United States, feeling the pressure of the competition stemming from Japan’s high-quality manufactured goods, began to adopt SPC. Today, statistical process control can be found all over the world.
Why Use SPC?
As previously mentioned, today’s industries face an array of obstacles that they cannot control. To make things worse, they are operating in a highly competitive environment. Statistical process control lets companies exercise control over at least one aspect of manufacturing, the processes. By taking control of the manufacturing process, businesses can improve quality and efficiency while managing costs.
SPC emphasizes prevention over detection. It enables operators to monitor process performance in real-time, spotting trends or unfavorable process changes before the company ends up making inferior goods.
Statistical Process Control’s chief objective is improving processes by reducing unwanted and unexpected variables. However, when an organization uses SPC to meet that goal, that helps achieve other related business goals like:
- Increasing customer satisfaction while decreasing customer complaints
- Reducing or eliminating the need for supply chain inspections
- Establishing a consistent and predictable level of quality
- Decreasing costs related to scrap materials, reworking, and inspections
- Increasing machine operator motivation and morale
- Increasing efficiency of data entry, analysis, and reporting
- Improving communication among all levels in the organization
- Increasing overall productivity
- Reducing investments in infrastructure because process improvements make existing infrastructure more efficient
SPC Tools Overview
There are fourteen quality control tools used in Statistical Process Control, broken down into seven Quality Control tools and seven Supplemental tools. They are:
Quality Control Tools
- Cause-and-effect Diagrams. Also called an Ishikawa diagram or fishbone diagram, cause-and-effect diagrams identify multiple causes of a problem. The diagrams use a fishbone structure, and each branch represents a problem category.
- Check Sheets. Check sheets are simple, prepared forms used to illustrate collected data and analyze it. These sheets work well with data that can be repeatedly observed and collected by either the same person or in the same location.
- Control Charts. Control charts are the oldest and most popular SPC tool. They aid in recording data and spotting unusual events, particularly when compared to typical process performances. The charts distinguish between two types of process variation: common cause and special cause.
- Histograms. Histograms are graphs that show frequency distributions and often resemble a bar chart. They work best when most of the data is in number form.
- Pareto Charts. Pareto charts are bar graphs that represent time or money, or alternately, frequency or cost. They are especially useful for measuring problem frequency.
- Scatter Diagrams. It is also known as an X-Y graph, scatter diagrams graph pairs of number data, and one variable assigned to each axis. Scatter diagrams work best with paired numerical data.
- Stratification. This tool separates data to make patterns easier to identify. Stratification is the process of sorting objects, people, and data into layers or distinct groups. It is ideal for data that comes from different sources.
- Data Stratification. A variation of the stratification tool mentioned above.
- Defect Maps. These maps help visualize and track a product’s defects, identifying the physical locations and types of flaws. Each found defect is identified on a map.
- Events Logs. Events logs are a centralized, standardized means of recording important software and hardware events.
- Process Flowcharts. Process flowcharts are pictures of a process’s steps, displayed in sequential order.
- Progress Centers. Progress centers are centralized locations that monitor progress, and decisions are made based on the data collected.
- Randomization. Randomization uses chance to assign the units in question across a collection of treatment groups.
- Sample Size Determination. This tool involves determining the number of individuals or events to include in creating a statistical analysis.
The right software makes it easy to implement SPC practices. There are many products to choose from, tailor-made to suit the requirements of any business, regardless of size, industry, or type.
Here are four examples.
- aACE. aACE provides business management software for small and midsize businesses. It includes modules for accounting, customer relationship management, enterprise resource planning, inventory management, order management, and production management.
- Katana. Katana is a popular manufacturing and inventory software application that’s ideal for scaling businesses. It provides seamless integration with QuickBooks, Shopify, WooCommerce, Xero, to name a few.
- GeniusERP. Genius ERP provides custom manufacturers tools needed to increase productivity, reduce costs, and improve production site performance. It works best with small to mid-sized custom manufacturers in the US and Canada. GeniusERP consists of a centralized system that includes valuable tools such as account management, CRM, inventory management, product engineering, job costing, scheduling, and production planning.
- Infor VISUAL. Infor VISUAL provides applications for managing MRP, MES, supply chains, reporting, and customer management. It works best for manufacturers in the automotive, aerospace, electronics, industrial equipment, and medical device industries.
More Control Resources
Simplilearn offers a wealth of process control-related resources. The following one provide the perfect way to increase your process knowledge at your own pace and convenience.
Check out the Six Sigma Green Belt Tutorial, which deals with Six Sigma’s Control Phase, which is relevant to what you’ve learned in this article. You can learn all about the history and evolution of Six Sigma here. And to round out your Six Sigma experience, this article poses the question Is Six Sigma a Zero Defects Standard?
Moving away from Six Sigma a little, though still on a related note, you can brush up on using control chart constants here. Finally, take a look into process and data distribution variables with Stability, Capability, or Normality – What comes first?
Learn the DMAIC methodology with real-world case studies and the skills you need to help your organization grow with our Lean Six Sigma Green Belt certification online program aligned with the IASSC exam.
How to Use Spc to Improve Your Organization and Your Career
Today’s corporate world needs Lean Six Sigma professionals to help them navigate through the countless variables and the problems they create. It’s a competitive world out there, and the right professional is a game-changer.
Simplilearn's Lean Six Sigma Green Belt certification training course will benefit your organization as well as sharpen your skills in quality control and business solutions. The course gives you an overview of Six Sigma and the DMAIC methodology and is aligned with the leading Green Belt certifications at ASQ and IASSC. You will learn how to measure current performances to better identify process issues, and formulate solutions to those challenges.
Whether you choose the self-paced learning, corporate training solution, or Simplilearn’s unique Blended Learning approach, you will enjoy 56 hours of high-quality education and access to 33 Professional Development Units. The course will assign your four simulation test papers and four real-life projects to help you apply your new-found knowledge in a training environment. Finally, you will receive a voucher to take the IASSC test and earn your certification.
The course is perfect for quality system managers or engineers, quality supervisors, quality analysts and managers, quality auditors, or anyone who wants to improve their organization’s quality and processes.
According to Payscale, quality managers can earn an annual average of USD 80,165, with an upper range of USD 115,000. Whether you’re already involved in a position involved with improving your company’s processes or considering a career change, let Simplilearn help. Check them out today!