Featured Product
This Week in Quality Digest Live
Innovation Features
Kurt Kleiner
Bend it. Stretch it. Use it to conduct electricity.
Merilee Kern
Radicle Science brings AI-driven clinical trials to cannabinoid and wellness research
Duxin Sun
Working at such small scale becomes the next big thing
Oak Ridge National Laboratory
Training based on data from 11,000 quantum chemistry calculations
Dario Lirio
Modernization is critical to enhance patient experience and boost clinical trial productivity

More Features

Innovation News
Industrial Scan&Sand solution wins RBR50 Innovation Award
SynthAI service solves the challenge of training machine vision systems
Appointments are the first for recently established committee to advise the President
For the correlative analysis of Raman, AFM, AFM-Raman, cathodoluminescence, and fluorescence data and microscopy images
Xcelerator enables Saildrone to easily integrate mechanical and electronic design information
Vibroseis trucks better equipped to tell what’s shaking
Twice as powerful, more accurate, and more user-friendly than ever
Apex Skating raises the bar in athletic performance coaching

More News

Anthony Tarantino

Innovation

Using Smart Technologies to Improve Quality and Reliability

Smart technologies provide a single source of truth for rapid response and accurate decision-making

Published: Thursday, April 14, 2022 - 12:03

In 2007, Nassim Taleb described black swans as highly improbable events that had dramatic or even catastrophic effects on markets and economies. Until recently, it seemed that such events were indeed rare.1 There’s now a major rethinking with the world entering the third year of the Covid-19 pandemic, reshoring and near-shoring of supply chains, and the first major land war in Europe since World War II.

The disruptions to fragile global supply chains are becoming more the norm than the rare exception. Such dramatic and sudden changes call for faster and more-accurate decision-making, i.e., a consensus around one data-driven source of truth.

The impact of ongoing supply-chain shocks on quality assurance is dramatic. Well-established sources of supply are disrupted or cut off completely. Deciding which new suppliers to select, and what processes should be automated, must be based on more than supposition or educated guesses. Anyone who has been a member of a tiger team, war room, or kaizen blitz should be able to relate to the problems faced in agreeing on a course of action with competing sources of truth. There’s usually pressure to act quickly and decisively, regardless of the quality of the data, that drives decision-making. Often, the loudest voices in the room prevail because there’s little data to back alternative courses of action.

The problem also exists in more measured quality and process improvement programs such as kaizen, lean, and Six Sigma. It’s not uncommon to struggle in obtaining accurate data in a timely manner without labor-intensive efforts. In many cases, the various data sources provide competing sources of the truth.

I saw this firsthand and was surprised that the problems were worse in large organizations with their silos of information. In the most extreme examples, senior directors and vice presidents involved couldn’t even agree on what problem we were trying to solve.

Achieving a single source of truth can be challenging in any environment, large or small. Defining a single source of truth means an organization must reach a consensus around what data to aggregate so all stakeholders are comfortable using one source of information that is available to all. The goal is that decision-makers have the right data at the right time.

Smart technologies offer the means to provide a single source of the truth without the time-consuming and labor-intensive efforts of the past. These technologies can work individually but are most effective in combination. For example, my focus during the last five years has been in using computer vision with deep-learning algorithms to watch the interaction of operators with the equipment they are operating and the materials they are handling.

But the smart camera pictures are only the first step. The images must be evaluated with edge computers (those closest to the action). To determine trends over time, the images need to be sent to the cloud for analysis using big-data analytics. The process may also involve mobile computing, where cameras are mounted on vehicles.

In this example, computer vision, edge computing, mobile computing, and big data analytics are all needed to detect defects, improve safety, and reduce cycle times.

Smart technologies have the ability to provide a single source of truth to facilitate more timely decision-making. But quality data are only the beginning. In most situations, the data will generate more questions than answers and, therefore, the need for a process improvement framework. Three of the most popular frameworks are kaizen, kaizen blitz, and lean Six Sigma.

Continuous improvement using kaizen

Kaizen is the Japanese term for change for the better, or for improvement. It was developed in Japan after World War II, based on the work of Walter Shewhart and W. Edwards Deming that Toyota applied to its lean philosophy of continuous improvement.2 As envisioned by Toyota, kaizen’s philosophy can be combined with other process improvement frameworks to drive continuous improvement. The kaizen way is to take smaller, commonsense steps forward in a never-ending process of saving money, improving quality, reducing accidents, or increasing customer satisfaction.

Kaizen isn’t designed to drive major process improvement projects or other radical changes. This is a major difference between Japanese and Western thinking. In the West, major and abrupt change is often the preferred method to drive big operational improvements. The problem comes in making it stick, avoiding backsliding, or reverting back to the old ways of doing things, a syndrome known as the Hawthorne effect.

Kaizen and lean thinking is also built into the Agile philosophy and framework to take on smaller pieces of work, known as sprints, that can be achieved in days versus weeks or months.

There’s a good argument for combining Six Sigma with kaizen. Six Sigma will teach kaizen leaders and participants the disciplines and tools to define problems and solutions, the best metrics to measure improvements, and the basics of root cause analysis. Participating in a kaizen event is a great way for new Six Sigma practitioners to start implementing process improvement before they take on major lean Six Sigma projects.

Solving bigger problems with kaizen blitz and lean Six Sigma

Although kaizen strives for gradual but continuous improvement, there are times when more immediate, intensive, and powerful change is needed. That’s when some organizations turn to the kaizen blitz. The general idea is to throw overwhelming and dedicated resources at a major problem. This necessitates cross-functional teams with members from all stakeholder disciplines, e.g., manufacturing, engineering, supply chain, finance, quality, product management. The key here is that the resources assigned are truly dedicated, removed from all other duties for the duration of the kaizen blitz, typically one week.3

A kaizen blitz follows Six Sigma’s DMAIC methodology (define, measure, analyze, improve, and control) but in a more simplified form of preparation, event, and follow-up. The simpler framework is to fit the shorter time frame.

For more complex, mission-critical problems, Six Sigma is a tried and proven framework for data-driven problem solving in which the solution isn’t known, but the customer (voice of the customer) is well known. Six Sigma became popular during the 1990s, first at Motorola and then at General Electric with Jack Welch, GE’s CEO, as its most vocal advocate.

Although DMAIC is the most well-known framework for improving an existing process, it has its limitations in designing a new process or fixing a process that is badly broken. In these situations, design for Six Sigma or design, measure, analyze, design, verify (DMADV) are good options. DMADV uses many of the same tools of DMAIC but adds such design tools as Pugh matrix and quality function deployment, commonly known as House of Quality.

The table below summarizes the major features of the three problem-solving frameworks. These are only general estimates, and wide variations are common. For example, I’ve led successful Six Sigma projects that took a year to complete.

Smart technologies, the equalizer to reach a single source of truth

I witnessed the power of smart technologies in providing a single source of truth on one of my earliest computer-vision deployments more than five years ago. It was at a luxury mattress manufacturer that was pricing its products higher than its competitors, resulting in a loss of market share. Their cost of goods sold was based on labor standards using their manufacturing execution system (MES) labor reporting.

Our computer-vision smart cameras observed actual labor times on a 24-hour-day basis and consistently showed actual labor rates lower than the standards. Further investigation revealed lax reporting practices by employees. Using the data from computer vision, the company was able to lower the cost of goods sold and reduce prices to a more competitive level, increasing sales.

Computer vision also helped reinforce labor reporting by flagging discrepancies between actual and reported labor. Regardless of how disciplined the labor reporting, smart technologies such as computer vision provide an irrefutable single source of truth.

We’ve come to expect a continuation of shocks to our fragile global supply chains, whether they be a national disaster, armed conflict, pandemic, or financial crisis. These situations require a rapid response and accurate decision-making. Smart technologies will provide the single source of truth essential in making this happen while fostering continuous improvement initiatives regardless of the framework selected.

References
1. Tarantino, Anthony. The Essentials of Risk Management. Wiley & Sons, 2010.
2. Tarantino, Anthony. Smart Manufacturing, The Lean Six Sigma Way. Wiley & Sons, 2022.
3. iSixSigma. Definition of kaizen blitz.

Discuss

About The Author

Anthony Tarantino’s picture

Anthony Tarantino

Anthony G. Tarantino, Ph.D., CPIM, CPM, began his career with 25 years in manufacturing supply chain operations. Over the next 15 years he led dozens of supply chain, lean/continuous improvement, and risk management projects for KPMG Consulting/BearingPoint, IBM, and Cisco. While at Cisco, he became a Lean Six Sigma Master Black Belt. Over the last five years, he has supported multiple smart manufacturing startups focused on applying computer vision to help companies improve efficiencies, quality, and safety. He has been an adjunct faculty member at Santa Clara University for 12 years, developing lean/continuous improvement training and certification programs for students and corporate clients. The author of more than 20 articles, along with five books for Wiley and Sons, his latest book, Smart Manufacturing, the Lean Six Sigma Way, will be released in May 2022.

Comments

The fallacy of equating data with truth

Someone with your obvious knowledge of manufacturing environments should know there is a long distance between data and truth.