Lean Versus Six Sigma

Six Sigma is problem focused with a view that process variation is waste. Lean Thinking, on the other hand, is focused on process flow and views any activity that does not add value as waste. Six sigma uses statistics to understand variation. Lean uses visuals: process mapping, flowcharting, and value stream mapping, to understand the process flow.

Taiichi Ohno is credited with creating the Toyota Production System (TPS), which is one of the better known implementations of Lean Thinking anywhere in the world. The concepts of lean were born out of the severe resource constraints in postwar Japan, which leads us to next week’s process improvement program - Theory of Constraints.

Lean Thinking is ideal for mature (energy), slow growth (automotive), low transaction industries (small business) or an organization where mathematical tools are not common. Lean begins to use systems thinking and considers all of the process interactions.

But lean is still a reductionist approach focused on eliminating waste (cutting costs). What is needed is to balance the resources released through Lean or Six Sigma improvement programs with an increase in throughput and need for resources. Otherwise you enter a cost cutting, job losing cycle and your process improvement program will grind to a halt.

If you are in a mature, slow growth, low transaction, or non-math business then Lean Thinking will work real well for your organization.

So what’s left? Six Sigma and Lean use two different approaches to get the same end result – process improvement. The Theory of Constraints (throughput improvement) takes the concepts of Lean Thinking to another level of systems thinking.

Four Qualities That Identify a Successful Six Sigma Belt Candidate

Six Sigma is a process that needs the human element to exercise. To bridge the gap between mediocrity and excellence there are four critical characteristics to look for in potential candidates who will take the process and execute to a level of performance that will drive dramatic, sustainable results. These are learning ability, leadership, desire and determination. Let us break each of these characteristics down and provide some factors to consider.

Learning Ability

This particular characteristic has been the primary focus, and at times the only one considered for many organizations choosing their Six Sigma Belt candidates. To compound this issue, the focus is generally geared to only those having prior experience with statistics. While this quality is important, it is not the determining factor. A candidate must be able to understand instructions and underlying principles with the ability to reason and make sound decisions possessing an analytical mindset. The ability to gather, analyze and summarize data in order to draw rational, fact-based conclusions is needed. A Six Sigma Belt candidate will be placed in a learning environment where mathematical science is discussed and instructed. Statistical reasoning is needed for proper understanding and execution of the Six Sigma DMAIC method.

Leadership

A candidate must have demonstrated leadership in a team environment with the ability to mentor team members to achieve a level of knowledge that fosters cultural growth and individual project success. The candidate usually knows what must be done and how to get it done, but genuinely involves others and listens to their concerns and ideas. He or she is willing to change course as a result in order to achieve greater results. This person can convince others to follow his/her lead despite skepticism, and is generally able to win the hearts and minds of others to achieve breakthrough objectives. The candidate works collaboratively with fellow employees at all levels of the organization and is able to maintain a blameless environment by focusing on issues and facts rather than seeking who is at fault. This quality to lead and inspire others to achieve a common goal is paramount for Six Sigma success.

Desire

Strong intention or aim for excellence is another characteristic to be considered. A candidate must possess the trait of motivation that has been demonstrated in past performance. The ability to be proactive with foresight to undertake strategic actions aligned with organizational goals is important. The ability to plan and execute work that is accurate, complete, timely and reliable while never compromising integrity shows that the candidate takes pride in all that he or she does. This personal desire for excellence is a good indicator that the candidate will apply the same for anything he or she undertakes. As a company searching for the right Six Sigma Belt candidate it is necessary to communicate the positive education in this continual improvement methodology and the personal growth experience that the candidate will receive. This understanding will help to stimulate the already existing desire and help facilitate the continuing aim for excellence.

Determination

Once a candidate has been selected and embarking on this Six Sigma journey it is crucial that they are devoting full strength and concentrated attention to achieving results in the short and long term. This focused results orientation will keep the candidate moving toward breakthrough objectives while potentially undergoing immense pressure from outside influences to forego the Six Sigma process and fall back into the traditional methods of operation. The ability to adapt effectively in an environment of change, uncertainty and ambiguity is required. The candidate will implement creative solutions that fall outside of the current paradigm, thus challenging the current standard. This will prompt a strong reaction within the status quo and steadfast determination from the Six Sigma Belt candidate is needed to ensure successful implementation and sustainable results.

In Conclusion: Six Sigma Belt Candidate Must Possess All Four Characteristics

Each of these characteristics discussed are crucial for choosing a candidate that will be successful in completing the rigorous Six Sigma training and certification process. The more important aspect is the continued return that a company receives from this individual as a change agent within their continual improvement journey. To ensure that this takes place the Six Sigma Belt candidate must possess all four described characteristics. Six Sigma is a commitment to a management philosophy that drives for total customer value creation. This commitment is not guaranteed by focusing only on the attribute of possessing an analytical mind. The combined characteristics of learning ability, leadership, desire, and determination are paramount for selecting a successful Six Sigma Belt candidate.

Implementing Six Sigma for Software

In our experience with implementing Six Sigma for software we found the following three principles most useful:
Principle No 1: Measure customer related metrics only
- Use Combinatory Metrics to cover all topics
Principle No 2: Adjust to moving targets
- Your goals may need change; accept change and manage it accordingly
Principle No 3: Enforce measurement
- Do not enforce meeting targets

The last principle is the most difficult one, because this is against the project manager’s carnet of duties.Indeed, this is what most often goes wrong in software projects: People want to enforce targets and consequently miss the goals. This is because setting targets that really lead to the goal is so difficult in software projects.
Just consider the ever-lurking dilemma: Should we make the milestones, or do it right the first time such that we can later use the knowledge gained to proceed faster? Writing or implementing software is not just an engineering discipline but in essence it is knowledge transfer.

Does Six Sigma work for software?

Nevertheless, there were only occasionally attempts to implement Six Sigma for software.Applying Six Sigma to software development makes product development and otherprojects transparent to both management and customers. However, transparency requires animportant cultural change.

We know from experience that bad communication is a major reason why projects fail, and software projects in particular. We expect from better transparency that meeting both deadlines and customer requirements becomes easier.

The major problem with early Six Sigma attempts was that there was no connection of
software metrics to economic success.Counting mistakes and defects is not a clear indication if the software project is going tobe successfulOther metrics like time-to-market and user friendliness are muchmore important in many application areas. Sometimes, reliability is of essence, but notalways.

There is no software metrics that serves all. It depends what kind of software we are
developing or installing.Finding good metrics for software development or deployment is a major task in itself.

Why do we need Six Sigma For Software?

Software today is responsible for most of the added value in products, and must be blamed for many of its failures. Even if the iron hook breaks, it may be the software embedded in the measurement instrument to blame for not having detected it in time. When in Germany the high speed intercity express train crashed into an overpass, it was software that didn’t detect
the broken wheel ring well before the accident.

Mobile networks are suffering from not being able to provide interconnection to the Internet and interoperability between their own services. It is the software that fails.In e-Commerce and for making Web Services to work, security, reliability and fault tolerance are of essence. Software and business processes are not cooperating, as they should to make it profitable.

Software is so ubiquitous that we must solve the software development problem to address a lot of other problems the society has.

The Six Sigma approach is:
Set the goal – Define
Define the metrics – Measure
Measure where you go – Analyse
Improve your processes while you go – Improve
Act immediately if going the wrong path – Control

We talk about “software development”, however we not only intend writing new software, but also software integration, deployment, and maintenance, as long as it has the character of a project. This means that there is a goal that can be reached or missed. Using Six Sigma, we want to measure how much we are going to miss that goal.

Six Sigma Tools

Variety of tools can be used to drive quality improvements within the DMAIC model. Many of these tools have been incorporated into Six Sigma software so that the computer carries out the underlying calculations.

Most can be classified into two categories:
a) Process optimization tools - which enable teams to design more efficient workflows,
b) Statistical analysis tools - which enable teams to analyze data more effectively.

Some of the most important tools:

1) Quality Function Deployment (QFD): The QFD is used to understand customer requirements. The "deployment" part comes from the fact that quality engineers used to be deployed to customer locations to fully understand a customer's needs. Today, a physical deployment might not take place, but the idea behind the tool is still valid. Basically, the QFD identifies customer requirements and rates them on a numerical scale, with higher numbers corresponding to pressing "must-haves" and lower numbers to "nice-to-haves." Then, various design options are listed and rated on their ability to address the customer's needs. Each design option earns a score, and those with high scores become the preferred solutions to pursue.

2) Fishbone Diagrams: In Six Sigma, all outcomes are the result of specific inputs. This cause-and-effect relationship can be clarified using either a fishbone diagram or a cause-and-effect matrix (see below). The fishbone diagram helps identify which input variables should be studied further. The finished diagram looks like a fish skeleton, which is how it earned its name. To create a fishbone diagram, you start with the problem of interest -- the head of the fish. Then you draw in the spine and, coming off the spine, six bones on which to list input variables that affect the problem. Each bone is reserved for a specific category of input variable, as shown below. After listing all input variables in their respective categories, a team of experts analyzes the diagram and identifies two or three input variables that are likely to be the source of the problem.


3) Cause-and-Effect (C&E) Matrix: The C&E matrix is an extension of the fishbone diagram. It helps Six Sigma teams identify, explore and graphically display all the possible causes related to a problem and search for the root cause. The C&E Matrix is typically used in the Measure phase of the DMAIC methodology.
­Failure Modes and Effects Analysis (FMEA): FMEA combats Murphy's Law by identifying ways a new product, process or service might fail. FMEA isn't worried just about issues with the Six Sigma project itself, but with other activities and processes that are related to the project. It's similar to the QFD in how it is set up. First, a list of possible failure scenarios is listed and rated by importance. Then a list of solutions is presented and ranked by how well they address the concerns. This generates scores that enable the team to prioritize things that could go wrong and develop preventative measures targeted at the failure scenarios.

4) T-Test: In Six­ Sigma, you need to be able to establish a confidence level about your measurements. Generally, a larger sample size is desirable when running any test, but sometimes it's not possible. The T-Test helps Six Sigma teams validate test results using sample sizes that range from two to 30 data points.
Control Charts: Statistical process control, or SPC, relies on statistical techniques to monitor and control the variation in processes. The control chart is the primary tool of SPC. Six Sigma teams use control charts to plot the performance of a process on one axis versus time on the other axis. The result is a visual representation of the process with three key components: a center line, an upper control limit and a lower control limit. Control charts are used to monitor variation in a process and determine if the variation falls within normal limits or is variation resulting from a problem or fundamental change in the process.

5) Design of Experiments: When a process is optimized, all inputs are set to deliver the best and most stable output. The trick, of course, is determining what those input settings should be. A design of experiments, or DOE, can help identify the optimum input settings. Performing a DOE can be time-consuming, but the payoffs can be significant. The biggest reward is the insight gained into the process.

WHAT WILL SIX SIGMA LOOK LIKE IN FUTURE

Six Sigma is a disciplined data-oriented approach to quality improvement that will survive and flourish. However, as it continues to evolve, some of its concepts—such as 3.4 defects per million opportunities—will be modified. Other characteristics of Six Sigma, such as its hierarchal organizational structure, will be adapted to changes in the environment. We will also continue to benefit from lessons learned1. Overall, Six Sigma will cast a continuingly broader net, but, will likely, be less in the headlines.

Key Concepts that Will Survive and Flourish

Six Sigma calls for a disciplined customer-focused approach to quality improvement, resting on the principle “In God we trust, all else bring data.” These basic concepts will be as important in the future as they are today. Here’s why.

Global competition and customer demands will continue to call for continuous quality improvement, coupled with cost reduction. Thus the pressures that lead to Six Sigma in the first place are unlikely to abate, and may even increase.

A disciplined and proactive approach to quality improvement that puts the customer first continues to make great sense and helps ensure that we move forward in an organized manner. The specific approach may vary from one situation to the next. DMAIC (define, measure, analyze, improve, control) was appropriate for the typical manufacturing applications for which Six Sigma was originally proposed. DMADOV (define, measure, analyze, design, optimize and verify) was subsequently proposed for Design for Six Sigma. Other approaches may be appropriate for other application areas. Necip Doganaksoy and I have, for example, recently proposed DEUPM (define, evaluate, understand, plan and monitor) as a disciplined approach for data acquisition2.

The need to base decisions on data, rather than on hunches, has always been there. What has changed in recent years—and has helped make Six Sigma a reality—is our ability to collect, harness and rapidly bring to bear such data in the decision-making process. We, no longer, need to rely on statisticians to do the work. Instead, Six Sigma practitioners are empowered to proceed on their own—calling on statisticians for guidance on the more complex issues. This trend is likely to accelerate. As technology continues to develop, so will our capabilities to use data to make the right decisions to improve quality. We can expect, for example, greater emphasis on the all-important task of getting the right data and more focus on meaningful and simple ways of presenting results graphically.

A Concept that May be Modified: The Goal of 3.4 Defects per Million Opportunities

Six Sigma has been presented as a drive to achieve “no more than 3.4 defects per million opportunities.” That, of course, is the basis for the terminology (with a little juggling to take into account the claimed mean shift of 1.5 sigma in moving from short-term to long-term variability). As quality practitioners become savvier, they recognize that this objective has its limitations.

As Deming pointed out repeatedly3, setting numerical goals can often lead to a search for ways to manipulate the measurement process, rather than improve the system. This is especially so when one is dealing with often vaguely defined terms, such as “defects” and “opportunities.” There is, indeed, frequently much subjectivity in deciding exactly what is a defect and, even more so, an opportunity.

All products, moreover, are not created equal. There are many more defect opportunities for a jet engine than for, say, a toaster. But, yet, the need for high reliability for a jet engine is generally greater. For a toaster, aiming for 3.4 defects per million opportunities might be overkill, or just too expensive. This level, on the other hand, may be fully appropriate for a jet engine.

Despite Deming’s warnings, we will continue to need numerical goals. But these will be tailored to the product at hand and closely tied to customer needs. For example, for a telephone-servicing center, a clear goal might be that of keeping the time that a customer has to wait for service to less than one minute 95% of the time and to less than three minutes 99% of the time.

The prediction that we will move away from the concept of 3.4 defects per million opportunities defies classical Six Sigma thinking and is, surely, controversial. I welcome other viewpoints.

Simplification of the Hierarchal Structure

Six Sigma came with a formal organizational structure of champions, master black belts, black belts, and green belts—each with designated responsibilities. As Six Sigma approaches its goal of becoming “the way we work,” this formal structure will be modified. Thus, as the value of Six Sigma is recognized universally, all managers will, ideally, be Six Sigma advocates, reducing the need for special champions. Master black belts will continue to serve as trainers, hands-on advocates and gurus—but their role may be merged with that of black belts. Also, master black belt status will increasingly be a stepping-stone to management. As Six Sigma training becomes commonplace both within companies and in universities, all professional employees will be green belts (even if not always identified as such). This, in fact, has been the situation in GE for some time. The Six Sigma organizational structure, like other parts of the organization, will, in addition, not be immune from cost-cutting pressures.

Casting a Continuingly Broader Net

Six Sigma will be enriched by the continued emergence of useful tools, such as simulation, that may not have been part of the original toolkit, but whose value has been demonstrated. It will also gain from integration with other approaches, such as lean thinking.

The areas of application for Six Sigma will continue to multiply. Early uses of Six Sigma tended to be mainly in product manufacturing. This provided relatively speedy and easy-to-quantify paybacks. It was soon recognized, however, that only so much can be achieved in manufacturing, and that product performance and reliability are principally determined at product design. This led to Design for Six Sigma and Design for Reliability. Next, Six Sigma was applied to business processes and customer service. As we move away from manufacturing applications, our ability to quantify the benefits of Six Sigma becomes more complicated. It is harder to measure the impact of avoiding a field failure or of having a delighted customer, than, say, that of a 90% reduction in scrap. This trend will continue as we seek new applications. We need to recognize and address the associated challenges.
Most of the applications of Six Sigma to date have been in large companies. In the future, Six Sigma will be adapted for use in small business with limited resources. Also, we are beginning to witness its application in areas other than manufacturing businesses, especially in the medical arena. Similar opportunities exist and will be leveraged in future years in such diverse fields as banking, schools, and government.