Tag Archives: Machine Learning

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: separating data into subcategories by unique characteristics to clarify the origins of an existing problem.
• Histogram: A bar graph that displays the frequency of variability and the most common offenders.
• Check sheet: A document with tabular or metric format that tracks the number of special cause variations.
• Cause-and-effect diagram: A chart that shows all of the factors that lead to special cause variations and draws potential correlations between them.
• Scatter diagram: A dotted diagram that displays the overlap between dependent variables on the y-axis and independent variables on the x-axis.
• Control chart: A line-based graph that shows processes’ stability levels and pinpoints the likely variation within produced items.
• Pareto chart: This chart applies the 80/20 principle — 20% of variables cause 80% of problems — to display the most common causes of manufacturing failures.

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:

• Flowchart: A straightforward diagram that outlines the step-by-step process of a manufacturing sequence.
• Defect mapping: A chart that shows the different types of known product flaws within a business’s manufacturing operations.
• Events logs: A variable summary showing the chain of events that resulted from an undesired occurrence.
• Progress centers: Centralized locations dedicated to tracking improvements and supporting informed decision making.
• Randomization: The deployment of random manual and automated input variables to eliminate human bias.
• Sample size determination: Choosing the number of subjects to include in a representative group when tracking manufacturing trends.

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:

• Reduces manufacturing costs
• Monitors employee productivity
• Improves resource utilization
• Optimizes manual inspections
• Reduces rework and warranty claims

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 2010s are now a memory. Every decade has its fair share of ups and downs, but by most measures, this past decade was a good one for much of the country.

Granted, due to the Great Recession, the U.S. economy started out in a bit of a rough patch. However, thanks in part to regulatory changes and good old fashioned entrepreneurialism, the unemployment rate has reached record lows nationwide and extreme poverty globally is now in the single digits (8.6% from 18.2%, according to World Bank data).

There were many notable strides in the 2010s aside from sheer job growth. Such improvements were largely due to the role of operational excellence. Whether in terms of productivity, creativity or ingenuity, operational excellence is the unyielding pursuit of greatness, the constant and consistent refining of current processes in order to achieve a better outcome. According to polling conducted by the Institute for Operational Excellence, more than 70% of businesses professionals say OpEx is instilled in the very fabric of their company’s culture. Whether it’s business transformation, lean six sigma, process improvements or business process management, these methods are all designed to help businesses reach a little farther and dig a little deeper in terms of becoming better than they were yesterday, a month, or a year ago.

There are many ways to examine the OpEx lifecycle from 2010 to today, but perhaps the most salient examples are technological development, process management and ideologies, meaning the beliefs that help inform businesses’ strategy and understanding of what is the most important aspect of their operations. Here are a few examples from each category that show how the role of operational excellence has evolved over time.

Technology: Automated intelligence

Automation has changed the world in an extraordinary number of ways. From ubiquitous handheld technology, fast-food kiosks in restaurants and robotic installations in factory settings, automation today is everywhere. In the early 2000s, the share of new robot installations in hi-tech manufacturing rose 21% to a total of 21,000 worldwide, according to Oxford Economics. But by the mid-2010s, they grew an additional 31% to 91,000 in 2016.

What accounts for the surge? For starters, automation-related processes are not only better by today, but cheaper. As a result, more employees are working alongside robotics in order to manufacture and deliver products quicker and more efficiently. Much of this is attributable to growth and development in technological improvements in things like machine learning.

Karen Hao of MIT Technology Review wrote in 2018 that were it not for machine learning, many of the artificial intelligence advancements — such as viewing recommendations on Netflix or “fill in the blank” search suggestions on Google — would have stalled.

“Machine learning has enabled near-human and even superhuman abilities in transcribing speech from voice, recognizing emotions from audio or video recordings, as well as forging handwriting or video,” Hao explained, as quoted by Popular Mechanics.

While some presidential hopefuls and economists warn of significant job losses posed by automation, only 27% of respondents are worried about such a scenario affecting them, according to polling conducted by CNBC and Survey Monkey. This may be a function of  employers retraining employees and repositioning them in roles where their skills can be better leveraged and in a better position for the company to achieve operational excellence.

Processes: Change management

In order to achieve results and get to a better place, change may be necessary. By its very nature, change is difficult, but in order to move forward, develop and learn from previous mistakes, structural or process-related changes may be required.

The roots of change management trace back to the early-to-mid 20th century from thought pioneers like Arnold van Gennep and  Kurt Lewin. The last 10 years or so has resulted in change management taking on a life of its own, as not only have most businesses heard of the term, they’ve refined the process so whole-scale changes are less drastic.

As noted by Oracle Technical Program Manager Burhan Syed, this has come from a greater focus on implementing evolutionary changes rather than revolutionary, using a more methodical, incremental approach versus those that are all at once. Today, change management is a process-related strategy as well as a profession, as companies hire individuals or operations management consultants to lead these sweeping efforts. Regardless of who pilots them, leadership is key.

“Leaders need to understand that their management styles must be able to adapt to the nuances of championing organizational change,” Syed wrote.

Ideology: Customer experience

While many would argue that the customer experience is every bit as important today as it was in 2010, few can deny the extent to which its become a singular focus. This is largely due to a greater number of companies vying over a smaller pool of consumers, so they must distinguish themselves to earn their loyalty. When it comes to measuring the success of improvements efforts, the third most common response among business owners point to is customer satisfaction, the Institute for Operational Excellence found.

Connie Moore of the Digital Clarity Group points to organizational change management, innovation, “outside the box” thinking and analytics as some of the key drivers to improving and refining the customer experience on an ongoing basis.

What will be the key takeaways in the 2020s and beyond? Time will tell, but you can make the decade a successful one by working with USC Consulting Group. From asset utilization to productivity improvements, sales effectiveness to cycle time reduction, we can help you achieve operational excellence so your greatest challenges in 2019 become your biggest strengths in the days ahead. Contact us to learn more.

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