-
Subscribe to Blog:
SEARCH THE BLOG
CATEGORIES
- Aerospace
- Asset Maintenance
- Automotive
- Blog
- Building Products
- Case Studies
- Chemical Processing
- Consulting
- Food & Beverage
- Forestry Products
- Hospitals & Healthcare
- Knowledge Transfer
- Lean Manufacturing
- Life Sciences
- Logistics
- Manufacturing
- Material Utilization
- Metals
- Mining
- News
- Office Politics
- Oil & Gas
- Plastics
- Private Equity
- Process Improvement
- Project Management
- Spend Management
- Supply Chain
- Uncategorized
- Utilities
- Whitepapers
BLOG ARCHIVES
- December 2023 (1)
- November 2023 (1)
- October 2023 (6)
- September 2023 (3)
- August 2023 (4)
- July 2023 (2)
- June 2023 (3)
- May 2023 (7)
- April 2023 (3)
- March 2023 (3)
- February 2023 (5)
- January 2023 (6)
- December 2022 (2)
- November 2022 (5)
- October 2022 (5)
- September 2022 (5)
- August 2022 (6)
- July 2022 (3)
- June 2022 (4)
- May 2022 (5)
- April 2022 (3)
- March 2022 (5)
- February 2022 (4)
- January 2022 (7)
- December 2021 (3)
- November 2021 (5)
- October 2021 (3)
- September 2021 (2)
- August 2021 (6)
- July 2021 (2)
- June 2021 (10)
- May 2021 (4)
- April 2021 (5)
- March 2021 (5)
- February 2021 (3)
- January 2021 (4)
- December 2020 (3)
- November 2020 (3)
- October 2020 (3)
- September 2020 (3)
- August 2020 (4)
- July 2020 (3)
- June 2020 (5)
- May 2020 (3)
- April 2020 (3)
- March 2020 (4)
- February 2020 (4)
- January 2020 (4)
- December 2019 (3)
- November 2019 (2)
- October 2019 (4)
- September 2019 (2)
- August 2019 (4)
- July 2019 (3)
- June 2019 (4)
- May 2019 (2)
- April 2019 (4)
- March 2019 (4)
- February 2019 (5)
- January 2019 (5)
- December 2018 (2)
- November 2018 (2)
- October 2018 (5)
- September 2018 (4)
- August 2018 (3)
- July 2018 (2)
- June 2018 (4)
- May 2018 (3)
- April 2018 (3)
- March 2018 (2)
- February 2018 (2)
- January 2018 (1)
- December 2017 (1)
- November 2017 (2)
- October 2017 (2)
- September 2017 (1)
- August 2017 (2)
- July 2017 (2)
- June 2017 (1)
- April 2017 (3)
- March 2017 (3)
- February 2017 (2)
- January 2017 (2)
- December 2016 (2)
- November 2016 (4)
- October 2016 (4)
- September 2016 (3)
- August 2016 (6)
- July 2016 (4)
- June 2016 (4)
- May 2016 (1)
- April 2016 (3)
- March 2016 (4)
- February 2016 (2)
- January 2016 (4)
- December 2015 (3)
- November 2015 (3)
- October 2015 (1)
- September 2015 (1)
- August 2015 (4)
- July 2015 (6)
- June 2015 (4)
- May 2015 (7)
- April 2015 (6)
- March 2015 (6)
- February 2015 (4)
- January 2015 (3)
CONNECT WITH US
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:
- 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.
- Collect the data that the software extrapolated from the variables you provided, then organize it into a digestible format — usually a chart or spreadsheet.
- 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.
- 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.
- Investigate the root causes of the variations and make the necessary equipment, material or operational adjustments to correct them.
- 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.
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).
“Operational excellence is the unyielding pursuit of greatness.”
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.
“Change management is best accomplished through evolutionary changes.”
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.