Realize the symmetry of lean manufacturing and Six Sigma

Lean manufacturing and Six Sigma used to be competing methodologies. Today many manufacturers have found how the two work even better together. Getty Images

The origins of lean manufacturing go back to the 1950s with the Toyota Production System and even further back to manufacturing innovations Henry Ford made at the River Rouge complex outside Detroit. The term lean came to life in the 1980s with the James Womack and Daniel T. Jones study of the American automotive industry, "The Machine That Changed the World." The lean body of knowledge we know today has a rich legacy and many prominent contributors.

Six Sigma emerged in the 1980s with work done by leading thinkers at Motorola. They recognized there was great value in understanding and controlling the manufacturing processes. Statistical analysis served as the foundation for this deep understanding and development of targeted countermeasures to reduce variation. As the Six Sigma body of knowledge emerged, other large companies like GE, Allied Signal, IBM, and Honeywell got on board.

For years the lean and Six Sigma bodies of knowledge were fierce competitors on the battlefield of continuous improvement. As silly as that sounds, it was the way improvement people conducted themselves on shop floors and in corporate offices. Thank goodness leading thinkers eventually recognized the synergy between the two and began to operate in terms of lean Six Sigma—without an "and" between them. Although this might appear subtle, it makes an enormous difference and shifts our continuous improvement behavior.

Keep in mind that certain overarching concepts—including respect for people and operating with humility—should apply across the entire lean-Six Sigma spectrum. These are very fundamental to any continuous improvement approach.

And to be clear, you should understand the different focuses of each improvement approach. As you explore this topic with your colleagues, consider the following definitions:

So simple but also immensely clear and powerful, these definitions serve as a starting point without the perceived noise and fear of acronyms and jargon. Regardless of whether you work on the shop floor or in the corporate office, you can adapt these definitions to the way you work.

Put lean and Six Sigma on a Venn diagram and you’d see significant areas of overlap. On the "pure lean" side, you’d find analysis tools and improvement techniques that require only modest data for effective implementation. For example, you can decide where and how to implement 5S and visual controls without performing in-depth quantitative analysis. Sometimes you simply pick a reasonable place and just get started. The same goes with takt-time and cycle-time analysis. You need to collect data to understand cycle times, volumes, and the resources required, but the effort generally does not include statistical analysis beyond basic mathematics.

At the intersection between lean and Six Sigma, where the two circles overlap, is where more advanced lean ideas and the basic Six Sigma ideas meet. You might consider this as the area where Six Sigma Green Belt practitioners and advanced lean thinkers might focus. Performing a line-balancing analysis, they could develop a deeper understanding of sources of variation. Using an F-test analysis, they could understand the probability of a certain throughput for a given improvement in flow. By applying basic Six Sigma statistical analysis to the aggressive lean flow application, they can end up with much richer solutions.

The pure Six Sigma area requires more sophisticated data collection, analysis, and improvement tools that tend to use statistical methods. Here, the Six Sigma Black Belt practitioner is a most valuable contributor. Examples of analysis might include studying two populations of production parts to determine if they are statistically similar and conducting a design of experiment (DOE) to determine which factors and at what level of settings are most likely to produce an expected outcome.

Lean and Six Sigma, previously fierce competitors, now can fit together to shift operational performance into high gear. To show how this can happen, consider a fabrication job we’ll call widget assembly. It has three formed parts welded to a machined casting.

Suppose the assembly has a performance issue. A customer is putting pressure on you for cost reduction and on-time shipments. Using a basic lean focus, you first might analyze the current state. As you go to the assembly gemba (where the work happens) to look for muda (waste), you see disorganization and find it difficult to see the flow. You use a spaghetti diagram to document the path the assembly's parts take. You gather cycle times, available times, and expected volumes to perform a takt-time/cycle-time analysis. You find that the cycle time exceeds takt time. Something has to give!

You might tackle the transportation and overprocessing types of waste. Maybe you find that the changeovers could be cut in half by modifying a couple of locating pins and moving certain internal steps (those that occur while the machine or process is idle) to external steps (those that occur while a machine or system is making good parts). This is pure lean.

As you develop a better understanding of the flow, you notice there is variation in flow times in the press brake and welding operations. Here, you can apply tools associated with the intersection of lean and Six Sigma. You know there is difference in performance from run to run, at least anecdotally, but you have not taken the time to assess the magnitude of the variation nor the impact on on-time shipments. So, you do some basic statistical analyses to understand the standard deviation around the mean for the cycle times and the sources of variation for these two operations. From here you can delve into the streamline-flow and continuous-flow portions of the lean body of knowledge.

Once you make these improvements, you could use the basic F-test analysis to determine the probability of meeting targeted cycle times based on a few pieces of data. This might be work performed by a more advanced lean thinker and a Six Sigma Green Belt practitioner.

Finally, you find problems with the flatness of one of the machined surfaces on a casting. This is a critical-to-quality feature, and scrapping the parts is expensive and disruptive. You perform a process-capability analysis and set up a control chart to monitor the operation's performance. You see on the control chart patterns that begin to provide insight into when the deviations occur.

You might conduct DOE to determine which variables make a difference and what the optimal settings might be. This improvement work resides in the pure Six Sigma area of the spectrum. You would likely count on a Six Sigma Black Belt practitioner to lead this analysis.

The sizes of the Venn diagram sections are not precise. The overlap may be larger or smaller depending on the lean and Six Sigma talent in your organization. The walk-through with the widget assembly is intended to illustrate how the various improvement approaches can work together to address various issues, from the simplest to the most complex.

Most organizations are slower or reluctant to adopt Six Sigma into their improvement portfolio. This is unfortunate because, though lean is powerful in its ability to make significant change in an organization, it has some limitations.

Instead of approaching lean and Six Sigma as distinct bodies of knowledge, consider pursuing lean Six Sigma. In a hyper-competitive marketplace, this can be the difference between winning and losing. Go harness the synergy!