The Lean Six Sigma doctrine proposes a set of data-oriented management techniques to eliminate defects and raise quality in process-driven environments. It has become standard in manufacturing. In essence, it describes a process to solve a problem, comprising five basic phases of resolution: to define, measure, analyse, improve, and control a process. This technique also goes by its acronym, DMAIC.
The methodology is alternatively declined under five easy-to-remember S-words, where the objective is to sort, straighten (or set in order), shine (or scrub, sweep), standardise, and sustain. A recent modification includes ‘safety’ as a sixth technique. The 5S model comes originally from a Japanese manufacturing method, which labels five steps in Japanese for process improvement (as seiri, seiton, seiso, seiketsu, and shitsuke).
The Lean Six Sigma methodology is accredited by an independent third-party certification body, the IASSC, which provides neither training or coaching. Certification takes up to three months to complete, depending on the institution and the training, and requires either two completed projects, or three years’ experience and a single one project. Six Sigma management has four levels of certification: champion, green belt, black belt, and master black belt, in ascending order.
The advent of cloud computing, the internet of things (IoT) and artificial intelligence (AI) has made Lean Six Sigma techniques, arguably, more relevant. After all, they enable dynamic and efficient analysis of complex processes. The volume of data has changed, and continues to spiral upwards, and machine intelligence, including learning, simplifies the sorting process. But humans are required, still, to impose order, and orchestrate operations, particularly in industrial settings.
Proper implementation of Six Sigma practices can turn data into insights to refine and improve industrial processes and products. This has always been the way. Black Belts have traditionally been engaged to turn raw data into actionable information; it is just they have the digital tools at their disposal now to guide decision making around much larger volumes of data.
Greg Kinsey, vice president of industrial solutions at Hitachi Vantara, explains: “It used to be this stuff was done by trial and error, and then with very limited algorithms, written by the Six Sigma Black Belts using factorial analysis in Minitab. But it was a small data set, with 10 variables, perhaps. Modern analytics tools allow us to process much bigger data sets, and to apply machine learning to make the process smarter.”
But the most crucial role of Lean Six Sigma Black Belts, however, is to guide the digital transformation of industry. Beyond the initial boardroom discussion to establish the scope of the transformation project, Kinsey’s team will seek first to engage the ‘wizards’ within each manufacturing operation.
“If they have Black-Belts, we’ll engage them — because they understand the processes and the numbers, and have been running stats on the shop floor for some time. They are likely to be engaged in analysing process capabilities and process controls already. When upgrading to a digital operation, it’s better to build on what the work that has already started.”
The effect of cloud computing and artificial intelligence has been to “turbo charge” the efficiency drive in every process-driven market, informed by data in the first place, and guided ultimately by Lean Six Sigma practitioners.
“What we used to figure out on paper and whiteboards can now be done rapidly through machine learning and advanced analytics. What a Six-Sigma Black Belt used to do on his laptop using mini-tab and four or five variables can now been done in data lakes with 500 variables,” explains Kinsey.
“But the principles of how you run a production line, and how you find defects and bottlenecks, and how you increase capacity, are underlined in physics. We’re turbo-charging everything we’ve learned about manufacturing in the last 25 years. Using advanced analytics to solve those same problems just accelerates your ability to reach manufacturing excellence.”
Hitachi Vantara vice president Greg Kinsey will join Enterprise IoT Insights, and leading industry executives and analysts, for a webinar on smart manufacturing on August 22. The session, entitled IoT for manufacturing: predictive quality, predictive maintenance and other key use cases, will consider primary use cases for internet-of-things based technologies in the manufacturing sector, and provides practical guidance for enterprises seeking to advance with technology. You can register to join the webinar here.
