Quality and manufacturing practitioners are most familiar with the effect of variation on product quality, and this is still the focus of the quality management and Six Sigma bodies of knowledge. Other forms of variation are, however, equally important—in terms of their ability to cause what Prussian general Carl von Clausewitz called friction, a form of muda, or waste—in production control and also many service-related activities. This article shows how variation affects the latter as well as the former applications.

W. Edwards Deming’s Red Bead experiment was an innovative, hands-on exercise that demonstrated conclusively the drawbacks of blaming or rewarding workers for unfavorable and favorable variation, respectively, in a manufacturing process. The exercise consisted of using a sampling paddle to withdraw a certain number of beads from a container. White beads represented good parts, and red beads nonconforming parts. Results for five workers might, for example, be as follows for 200 parts, of which 3 percent are nonconforming. (You can simulate this yourself with Excel by means of the Data Analysis menu and Random Number Generation. Use a binomial distribution with 200 as the number of trials, and nonconforming fraction p = 0.03.)

As companies seek to gain efficiencies in the workplace, provide choice for employees, and attract and retain talent, strategies involving agile working and free-address have gained traction. When our Gensler La Crosse office relocated last year, we leveraged the opportunity to support an agile workplace strategy. Although there are arguments for and against agile working, here’s what I’ve experienced firsthand.

“Going agile” is an optional program for my office. Twenty percent of my colleagues and I elected to be agile workers. This means that we no longer have an assigned seat in the office. Every day we choose between a selection of stations, and they are first come, first served.

Each station is configured with the same dual monitor setup that plugs into our laptops. The stations are intermixed within all of the departments. Although similar, each station has slightly different characteristics: Some are adjacent to windows, aisles, collaboration spaces, etc. Per company policy, one cannot sit in the same station twice per week (in other words, no squatting).

Although automation has been successful in replacing repetitive, simple tasks, the human workforce still plays a critical role in manufacturing. Even the most sophisticated and automated manufacturing operations rely on human operators to configure, run, and properly maintain production equipment.

However, with low unemployment, talent retention in the manufacturing industry is particularly difficult, meaning manufacturers often spend a lot of time and money training employees, only to have them quit. According to the Bureau of Labor Statistics, in 2017, 38 million people quit their jobs voluntarily. About 77 percent of those individuals quit for preventable reasons—like more structure around career development, better work/life balance, and manager behavior.

When a new employee gets hired at a manufacturing plant they often get little to no onboarding, or are treated poorly by a manager. Perhaps worst of all, more than half of all manufacturing employees report not having proper safety mechanisms in place to prevent an accident. With so many job openings currently unfilled, it is easy for an employee to find a new job where she can get a better experience from day one.

Does your organization benefit from lessons learned? Does it learn from previous quality issues? A vast amount of learning takes place every day in every manufacturing facility. Do global manufacturing companies share experiences gained from resolving quality issues between overseas plants? And what will they gain if they do?

What is lessons learned?

In project management nomenclature: Lessons learned is the learning gained from the process of performing the project. We learn from our own project experiences as well as the experiences of others. Sharing lessons learned among project team members prevents an organization from repeating the same mistakes and also allows it to take advantage of organizational best practices. Learning should be deliberate. Organizations should be prepared to take advantage of the key learning opportunities that projects provide. Unfortunately, capturing lessons learned too often is seen as optional… if time permits.

Cogeco’s technical distribution center in Burlington, Ontario, is one of Canada’s drop-off points for internet modem and cable device repair. In 2011, the company’s management carried out a kaizen blitz to improve the efficiency of its device repair process. The process was indeed challenging but did have the desired outcome. Read on to find out how Cogeco integrated a lean solution into its operations, and how it dealt with resistance to change.

Kaizen blitz: before and after

A consulting firm hired by Cogeco was tasked with analyzing just how efficient the Ontario warehouse was with regards to best practices. Once all was said and done, and the results were in, they were dismal. The production line was in no way efficient. “At that time nobody knew on the floor if we were productive or not because we had no way to measure, and we can’t improve something that we don’t measure,” explains Bill Jeffries, Cogeco’s warehouse manager.

Business partnerships are nothing new. Partnerships that result in leaner manufacturing processes, more consistent quality, and lower manufacturing costs—that is worth talking about.

With global competition so fierce, manufacturers must always be keen to spot areas of muda (waste). Even seemingly minor waste can have repercussions to overall customer satisfaction down the line. In the case of Star Rapid, a global rapid prototyping, rapid tooling, and low-volume manufacturer, issues with milling tools were recognized as a potential area to reduce muda and improve customer satisfaction.

Star Rapid had been buying a number of cutters from reputable international brands through agents. But reports started to come in from the shop floor, to the effect that cutters were wearing out quickly, breaking, and not performing well. Star Rapid sent some of these branded cutters back to Europe and the United States to confirm their authenticity, and found out that they were, in fact, fake. Not the fault of the brands themselves, but an unscrupulous stockist.

Boeing is demanding its suppliers reduce their prices by 10 percent, according to a February 2018 article published in Bloomberg Businessweek. It’s a hard pill for many to swallow, given that that these cuts are on top of the roughly 15-percent cuts demanded in 2012, when the company launched its Partnering for Success program.

Boeing programs like the Dreamliner ran into massive cost overruns due to delayed delivery and poor-quality components produced by its suppliers. This led the aerospace giant to compensate by demanding price decreases and improved quality from its suppliers.

The pressure has many aerospace suppliers wondering how they can remain profitable, especially as original equipment manufacturers (OEMs) also look to expand their own aftermarket maintenance and repair services.

A couple of weeks ago I posed the question, “Are you overproducing improvements?” and compared a typical improvement “blitz” with a large monument machine that produces in large batches.

I’d like to dive a little deeper into some of the paradoxes and implications of 1:1 flow of anything, improvements included.

What is overproduction, really?

In the classic seven wastes context, overproduction is making something faster than your customer needs it. In practical terms, this means that the cycle time of the producing process is faster than the cycle time of the consuming process, and the producing process keeps making output after a queue has built up above a predetermined “stop point.”

If the cycle times are matched, then as an item is completed by the upstream process, it is consumed by the downstream process.

If the upstream process is cycling faster, then there must be an accumulation of work in progress (WIP) in the middle, and that accumulation must be dealt with. Further, those accumulated items are not yet verified as fit for use by the downstream process that uses them.

Becoming a process-focused organization requires a sustained effort, and for most industrial and service organizations that is a difficult task. Failure to improve the performance of your processes leads to a failure to improve the organization and results in improperly managing the business.

All major business initiatives—quality, lean, Six Sigma, innovation—must focus on improving those processes that have the greatest impact on the organization’s critical success factors. These factors are the essentials, and the key processes that affect them should be the primary focus of management.

Focused process improvement is a fundamental requirement to sustain initiatives like quality or lean, and to generate positive results. Organizations succeed or fail based on what happens within specific key business processes. Many organizations don’t sustain their quality or lean efforts because they are not focused on improving critical business processes. Instead employee improvement teams are left adrift and end up working on trivial, inconsequential projects that matter least to improving the business.

Imagine a factory with a large monument machine. It takes several days to set up. When it does run, it runs very fast, much faster than you can actually use its output. Therefore, you take the excess output and store it to use later. Actually, you don’t know how many items you need to make, so you make as many as you can while the machine is available to you.

Some of that excess output may prove to be less useful than you thought, but there is pressure to use it all anyway since it was so expensive to produce.

After a run making items for one process, you change it over to make items for a different process, and build up a queue of output there.

When all of the output is used, it all may or may not work the way you expected it to.

Most of us would see this as a classic case of “overproduction”—overwhelming the system with excess output that hasn’t been checked for quality, that isn’t needed right now, and might or might not be useful in the future. But it seemed like good efficiency to make it while we could.