Tag Archives: Manufacturing Process


Statistical process control (SPC) is a commonly used machine learning software in manufacturing that measures the consistency of a product’s performance based on its design specifications. Minimizing variability is a crucial part of avoiding defects and maintaining resilient manufacturing operations.

This guide outlines the different ways that businesses can effectively utilize SPC and reap all of the benefits this technology has to offer.

How Statistical Process Control Works

SPC is a tried and true technology that businesses have been using for more than 100 years to improve their manufacturing operations. It conducts ongoing statistical analyses, taking into account factors such as the materials, design, employees who handled the product and the machinery used to create the product.

SPC’s constant vigilance enables businesses to make swift and accurate resolutions to quality control problems. However, it’s not fully autonomous like other manufacturing software that can identify statistical correlations without human help. Instead, it relies on large amounts of training datasets that another source must manually input to achieve the desired results.

This form of machine learning is known as supervised learning. Businesses can input human-labeled datasets by themselves, or they can recruit another algorithm to automatically input statistics in a process called “machine annotation.” In either case, SPC needs to absorb as much raw data as possible to maximize its efficiency.

SPC displays its findings in easy-to-read control charts, and it’s the business’s responsibility to set the parameters for each chart by providing the software with enough information. This process includes six basic steps:

  1. Define the manufacturing process you want to monitor and control by establishing the input variables, output variables, equipment, materials and any other external factors that might affect the process.
  2. Collect the data that the software extrapolated from the variables you provided, then organize it into a digestible format — usually a chart or spreadsheet.
  3. Select and construct the control charts based on the type of data you’re using, such as weight, length, temperature and any defects that might have occurred.
  4. Look for patterns in the control charts that indicate special cause variations in performance due to underlying defects. You can calculate process variability through a capability index, such as C, Cpk, Pp and PPk.
  5. Investigate the root causes of the variations and make the necessary equipment, material or operational adjustments to correct them.
  6. Continue to collect and organize data to identify more variations, updating the control specifications as needed.

This process sounds awfully similar to Statistical Quality Control (SQC), but there are some key differences. Statistical Process Control measures independent variables, while SQC strictly focuses on dependent process outputs. SQC also carries out acceptance tests by screening individual product samples, while SPC relies on large datasets and doesn’t have an acceptance testing feature.

Types of SPC Tools

Many types of analysis tools have developed during SPC’s century-long evolution. These tools are split into two main categories — basic tools of quality (7-QC tools) and supplemental tools (7-SUPP tools). Here’s a quick rundown of how businesses can use the 7-QC tools:

Stratification also often appears in the 7-SUPP tools category because of its versatility and importance to statistical analysis. Breaking up large datasets into smaller digestible chunks makes SPC software more accurate at identifying problems and reducing variability. Here are the other six 7-SUPP tools:

Today’s SPC software modules include all of these tools, allowing businesses to access dashboards that display the various charts and diagrams in one place. These insights can lead to identification of quantifiable improvement opportunities that maximize operational efficiency.

Benefits of Using SPC

SPC is one of the most effective machine learning resources for achieving consistent performance in manufacturing operations. Eliminating process errors allows businesses to simultaneously address the three biggest challenges in material handling — workplace hazards, equipment damage and carbon emissions — in many ways:

When these benefits combine, the final result is a more satisfied client base and a more profitable business. While SPC software can’t do all of the inspection work on its own, the tools and insights it provides are invaluable in a manufacturing environment.

Use Statistical Process Control to Its Full Potential

Business leaders who are willing to put in the necessary effort to provide SPC software with large datasets can use this technology to its full potential. They will gain access to numerous eye-opening statistics about operational inefficiencies and have all the knowledge they need to make accurate adjustments.

*This article is written by Jack Shaw. Jack is a seasoned automotive industry writer with over six years of experience. As the senior writer for Modded, he combines his passion for vehicles, manufacturing and technology with his expertise to deliver engaging content that resonates with enthusiasts worldwide.

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The U.S. economy is experiencing a period of job growth. The unemployment rate is at a low last seen more than 50 years ago, according to government data, and millions of jobs have been created since 2016, approximately 2.6 million of which were added to payrolls in 2018 alone.

Manufacturing has helped lead the way, with the industry contributing $2.2 trillion to the nation’s gross domestic product in 2016 and over 85% of small-business manufacturers confident that the good times will continue for the foreseeable future, according to the National Association of Manufacturers’ most recently updated outlook survey. Were America’s manufacturing sector its own country, it would be in the world’s top 10 economies, ahead of Spain, Brazil, and Canada, based on estimates from the Manufacturing Institute.

Largely fueling these improvements is the rise of modern manufacturing. Technology is in a constant state of enhancement and advancement. In order to drive process improvements on the shop floor, manufacturers — and companies that use the products made by them — are successfully adopting, installing, and allocating innovative technologies through the advent of modern machine manufacturing techniques, which in turn optimizes the supply chain.

What makes manufacturing techniques advanced?

From machine learning and artificial intelligence to nanotechnology and 3D printing, advanced manufacturing techniques and capabilities usually have a few characteristics in common: They’re founded in state-of-the-art functionalities, improve upon processes that already exist and serve as a workaround to existing workflow problems — without creating new complications.

For example, plastics are a major environmental hazard due largely to their ubiquity. In fact, food packaging manufacturers account for 40% of these materials. Recognizing the potential and existing problems emerging for plant life and the natural habitat, 75% of consumers want businesses to adopt sustainability initiatives.

Numerous small-business owners, franchises and multinational corporations have partnered with chemical manufacturing companies to make sustainability a reality through cleaner development of industrial plastics. In fact, dozens of chemical firms are working collaboratively to leverage process improvements to reduce output of new plastics, reuse what’s already been produced and re-engineer packaging so that it breaks down more quickly and naturally. Advancements and investments in cutting-edge manufacturing technology and adaptability have helped to make this possible.

Here are a few other technologies that stand to further transform modern manufacturing techniques:

1. Augmented reality

Augmented reality melds the real world with the imaginary by superimposing images, sounds, or places so they can be more authentically experienced. As noted by the Huffington Post, it has many applications in the manufacturing sphere, including data retrieval, real-time monitoring, communicating safety warnings, and enhancing the effectiveness of training methods.

2. Enhanced industrial sensors

From optical rotary encoders to inductive proximity sensors, industrial sensors are advancing in their capabilities and practical uses, with more businesses taking advantage of them. Prices are expected to decline for these tools over the course of 2019, which may encourage more manufacturers to invest if they haven’t already, according to ZDNet.

3. Collaborative robotics

Collaborative robots, or cobots, represent the fastest-growing category in industrial automation. This technology pairs robots with humans so they work in a more cohesive manner, as opposed to one replacing the other. Initially only utilized by large corporations, cobots are increasingly affordable and adoptable, which is why their valuation is expected to top $4.3 billion come 2023, according to a report from Markets and Markets.

Modernization is a must in the manufacturing space and at USC Consulting Group, we have the industry expertise to recognize and recommend the cutting-edge manufacturing tools and techniques that can help you achieve supply chain optimization. We have helped companies achieve operational excellence for more than 50 years — if you have a business problem, contact us to help solve it.


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