Beyond Lean Six Sigma
The quest for quality continues
by Ronald D. Snee and Roger W. Hoerl
Quality continues to be critical to organizational success in business, government, nonprofits and all types of organizations. Organizations using Lean Six Sigma have made great strides forward in the last 25 years, enhancing organizational health and putting billions of dollars on the bottom line. What is next on the horizon, you ask?
There are three specific advances that can help us continue and accelerate this progress: holistic improvement, statistical engineering, and using big data to solve previously intractable problems. As we take a look at each of these advances, we will see the need for greater use of strategic thinking, using the fundamentals, and thinking about problem solving and organizational impact in new ways.
Today we are recognizing more and more that organizations are systems, and a systemwide view is needed to create significant and lasting improvements. This means taking a holistic view of all aspects of the business, at any location around the world, in any culture, in any business function. A focus on holistic improvement means looking beyond the factory floor, and realizing that no one technique or methodology is universally best for all problems.
Taking advantage of the new advances, building on the fundamentals, thinking strategically and aggressively, and pursuing the opportunities will improve quality and strengthen our organizations in the process.
Our research and organizational work has shown that holistic improvement has four critical building blocks: leadership, top talent, supporting infrastructure, and a holistic improvement methodology. These building blocks, along with integration of multiple methodologies, enable the organization to solve virtually any problem, in any function, manufacturing and service alike, across the organization.
Management owns the organization’s systems and processes and must be involved to lead improvement efforts. Top talent is required to change how the organization works – its culture. A supportive infrastructure – including planning, project selection and management processes, reward and recognition, and communication – is required to sustain improvement initiatives and the resulting improvements over time.
The holistic improvement methodology integrates lean principles with Six Sigma methods and other approaches – such as workout, TRIZ, and debottlenecking – as needed. The nature of the problem should guide identification of the approach, rather than assuming that one methodology is ideal for all problems. Project identification and selection becomes a critical step because it is then that the best approach becomes apparent. The organization’s improvement methodology must be robust enough to handle any problem the organization encounters in the course of its improvement work. Integration of multiple methodologies is clearly required.
Lean Six Sigma as currently practiced in many areas tends to miss large, complex problems that face the organization. These problems typically take a large amount of time and effort to identify and solve because they are large, complex and unstructured. They are too big to be solved by one Six Sigma project. This is exactly the type of problem statistical engineering was designed to handle, for example, development of a corporation’s product quality management system; a fill weight targeting system for hundreds of products; or NASA’s system for planetary entry, descent and landing.
Statistical engineering’s five building blocks for such problems are: problem identification, creation of structure, understanding the context of the problem, development of an overall strategy, and creation of tactics. Large, complex, unstructured problems typically require several projects to solve, which is a result of the strategy and tactics building blocks. This is where Lean Six Sigma concepts, methods and tools have a role to play.
Though data mining has been used for the last 20 years, the trend picked up steam around 2005 with the concept of “big data.” Because of the ubiquitous nature of the internet and computer hardware and software, we are now talking about terabytes and petabytes of data. And we now have software such as SAS®, R, and JMP that can help us make sense of it all.
Big data offers the opportunity for professionals to solve problems previously thought to be unsolvable. While much progress has been made in medical research and internet marketing, one area overlooked to date is the design and improvement of products, services and process quality. Customer surveys can help us better understand customer needs and experiences. Collection of manufacturing data and integrating it with customer data can help improve products and processes.
On the other hand, many now assume that big data plus fancy algorithms equals great results. If only it were so easy. First we are reminded that big data from studies is observational data at best, often collected without attention to study design and measurement accuracy. This assumption also ignores what has been learned over the years regarding fundamentals:
- Sequential nature of problem solving. Studies are not done with a single data set, but with the sequential analysis of several data sets over time.
- Strategic thinking is needed to identify how the project will be executed and how the data analysis will proceed.
- Data pedigree must be assessed to determine the value of the data for solving the problem, quality of the data and how the data will be analyzed.
- Subject-matter knowledge should be used to help define the problem, assess the data pedigree, guide analysis and interpret the results.
These fundamentals are all part of the statistical engineering philosophy and methodology. Indeed, big data is frequently associated with large, complex, unstructured problems. As a result, statistical engineering provides the concepts, methods and tools to deal with them.
The work continues. Quality is still important and will continue to be; consumers demand it. There are new and better ways to accelerate quality improvement. The internet, and IT in general, is a major ally. Taking advantage of the new advances, building on the fundamentals, thinking strategically and aggressively, and pursuing the opportunities will improve quality and strengthen our organizations in the process. The future for quality improvement is bright!