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When Worlds Collide: Lean and Six Sigma
by Ronald D. Snee
acing unprecedented pressure to improve performance across the board, organizations cannot afford to forego the benefits of either Six Sigma or lean.
Organizations are expected to improve their performance as measured by, among other things, yield, capacity and productivity, defects, downtime, scrap and waste, on-time delivery, cost of goods, cycle time and customer satisfaction. And they must translate those improvements into increased market share, profitability and shareholder value.
But proponents of differing performance improvement methodologies sometimes look like members of warring religions.
On one side, the partisans of Six Sigma fiercely maintain the key to better performance is the reduction of process variation.
On the other side, the adherents of lean question the wisdom of reducing the variation in a process that is inherently wasteful. When these two powerful improvement methodologies collide in an organization, they both suffer.
What is required to meet the nearly universal imperative to improve performance is not the coexistence of Six Sigma and lean or even their integration, but rather a holistic performance improvement methodology that applies to improvement opportunities of all kinds.
To build such a framework, we must reorient our thinking about Six Sigma and lean. Each is often regarded as a methodology and as a set of tools. However, conceptually as well as practically, it’s difficult to say where the methodology stops and the tools begin.
Is the methodology for Six Sigma its define, measure, analyze, improve, control (DMAIC) process improvement framework? Are its tools such techniques as design of experiments (DOE) and failure modes and effects analysis (FMEA)? Or are those tools a methodology—the hallmark of which is statistical control? Similar questions can be asked of design for Six Sigma’s (DFSS’s) define, measure, analyze, design, verify (DMADV) approach to designing new processes.
One thing is certain: DMAIC and DMADV are proven frameworks for improving existing processes and designing new ones, respectively.1, 2 By sharply distinguishing them from the Six Sigma tools with which they are associated, you can generalize them to a higher level as overall frameworks for process improvement and process design.
Then the Six Sigma or lean tools that are appropriate to a particular problem can be applied at the correct point in a highly structured and sequenced approach.
Stages of Process Improvement
Process improvement goes through different stages. In the early stages, a project team harvests the low hanging fruit by correcting obvious problems with a process, fixing broken measurement systems and ensuring the consistency of process inputs, whether raw materials in manufacturing or data in nonmanufacturing processes. Work should also be streamlined through the reduction of complexity, waste and nonvalue added work.
The later stages of improvement focus on optimizing and controlling processes by improving value added work steps, shifting the process average and reducing variation around it, improving process flow and reducing cycle time. At this point, the team is basically finding the operating sweet spot.
These different stages require different tools, which an improvement methodology must encompass if it is to work at all stages. Six Sigma provides powerful tools for shifting the process average and reducing variation around it, lean helps improve process flow, and both bodies of knowledge include tools for reducing waste, nonvalue added work and cycle time.
DMAIC, as a generic problem solving methodology, brings both disciplines together in a holistic framework that meets the need for diverse improvements, provides sustainable gains and offers a language that is widely understood.
The broad use of DMAIC as an overall framework for improving existing processes adds predictability, discipline and repeatability to improvement projects. Along with DMADV for new processes, it can constitute the improvement infrastructure of the organization, linking and sequencing the required tools regardless of their source.
Define: Setting Up for Success
The define phase sets up a project for success by identifying the problem clearly, determining its financial impact and selecting and organizing the right people to lead it. A typical starting point might be the recognition of a problem and its value-destroying consequences. Once the problem has been identified, Six Sigma and lean approaches and tools can be used to home in on what the project should entail.
The key tools in this phase include:
• Value mapping:
Defining a project begins with an understanding of the customer’s needs, whether
the customer is an external end user of the product or service or an internal user
of the output of a process. Six Sigma calls it listening to the voice of the customer,
and lean calls it value stream mapping.
The goal is to help a project
team understand how a process
or sequence of processes creates value and to begin to focus on a
particular process to improve by eliminating nonvalue.
• Project charter: This concise document precisely defines the project and forms a contract among all parties about what is to be accomplished.
Measure: Getting To Know
In the measure phase, the project team comes to better understand the process through various techniques of measurement. Key tools at this stage include:
• Process mapping: This technique, common to Six Sigma and lean, details the precise sequence of actions involved in a process and vividly foregrounds opportunities for improvement. Value stream mapping includes cycle time measurement and waste identification at each process step.
• Cause and effect matrix: Once the project team has a clear picture of the major steps in a process and its input and output variables, the cause and effect matrix guides the team in determining which input variables and steps have the greatest effect on the process output variables.
• Videotaping: For observable processes, such as the actions of operators in production, videotaping can document the physical flow of work.
• Measurement system analysis (MSA): Study of the measurement system capability allows a team to quantify measurement repeatability and reproducibility. An MSA sometimes includes the design and redesign of measurement systems.
• Capability analysis: Once the team knows it can accurately measure key variables, it analyzes the process’s capability—the total range of inherent variation in a stable process using data reflecting current process performance.
Analyze: Identifying Root Causes
In the analyze phase, the data gathered in the measure phase are subjected to a variety of analytical techniques to get at the root causes of the variation or observed waste in the process.
Traditionally, process variation has been the primary concern of Six Sigma. Lean focuses on waste, which can be broken down into seven types: overproduction, time on hand (waiting), transportation, process inefficiency, stock on hand (inventory), motion and defective product. These forms of waste are interrelated and often have process variation and poor process design as common root causes.
The particular tools that dominate at this stage depend on the type of process under analysis. Some of the most common tools include:
• Videotape analysis: For observable processes, the videotape collected in the measure phase is analyzed to uncover waste in the form of waiting, process inefficiency and motion.
• FMEA: An FMEA helps the team create a risk analysis to determine what can go wrong in the process.
• Multi-vari study: The team undertakes a multi-vari study by sampling the process as it operates and identifying the key process variables causing the variation in the process outputs. Particular attention is paid to identifying important uncontrolled variables (noise).
Improve: Implementing Solutions
With the understanding of root causes gained in the analyze phase, the project team can identify, test and implement a solution. The specific solution it chooses will depend on the nature of the root causes of the variation or waste. Once the solution has been implemented, it is tested with a confirmatory study to ensure the predicted improvements actually materialize.
Remember, the tools don’t make improvements—people do. Some of the tools that can help include:
• DOE: If ambiguities about root causes remain, DOE provides an efficient method of experimentation to resolve them and quantify cause-effect relationships.
• Production smoothing: By distributing the flow and mix of work more evenly over time, production smoothing helps eliminate the waste of waiting, long set-up times and overproduction. Pro-duction smoothing can also be used as a process design tool.
• Kaizen events: These two- to three-day events bring together groups of workers to generate ideas for improving processes. Kaizen events can be even more powerful within the DMAIC framework because the root causes of the problem will already be known when the workers come together.
Control: Holding the Gains
The control phase of DMAIC is concerned with sustaining the gains achieved by the improvements implemented in the improve phase. Key tools include:
• Control plan: When properly applied and sequenced, the tools used in a project provide outputs that become inputs for the control plan. The control plan identifies the variables process owners must monitor and details the actions to be taken when out of control signals are identified.
• Statistical process control (SPC): Often used in conjunction with the control plan, SPC continually plots data against variation limits to provide early warning of any occurrence that might warrant attention, enabling process owners to detect when process improvements have occurred and take corrective action before output variations affect customers.
• Standard work: Detailed, standard work procedures for a process are provided for personnel to perform the work, helping eliminate one of the most frequent causes of variation and waste—human error.
• 5S (sift, sort, shine, straighten, sustain): These rules help keep the workplace clean and organized for maximum efficiency and minimal waste.
• Poka-yoke or mistake proofing: This tool adds methods and procedures to the process to prevent mistakes from happening and clearly identify when a mistake has occurred.
Interestingly, most of the lean tools fall under the improve and control phases. This suggests that, although lean provides a useful set of improvement principles, it lacks a structured improvement methodology such as DMAIC. Hence the need to bring lean and Six Sigma tools into a holistic improvement framework.
Short of creating this comprehensive framework, you can still strengthen your improvement meth-odology. If you started from a Six Sigma perspective, you can add lean tools, with their power to reduce waste. If you began with lean, you can add the DMAIC framework and Six Sigma tools designed to reduce process variation and find the operating sweet spot.
Because all processes vary and process variation increases if the process is left alone, statistical tools are needed to identify, characterize, quantify and reduce variation, which must be addressed if the process is to perform at the desired level. For example, one company using lean based approaches found it needed to integrate Six Sigma methods into its improvement process because it had been unsuccessful over a three-year period in reducing process variation for key products.
Other companies have seen the value of bringing together Six Sigma and lean from the outset. Honeywell International, for example, optimized a multistep chemical process at one of the company’s plants in Europe through detailed process mapping that enabled it to divide the process into value added and nonvalue added steps and discover bottlenecks at every step of the process.3
The bottlenecks were classified according to whether they were quality issues or process flow issues. Six Sigma tools were used to resolve the quality issues, and lean tools were used to resolve the flow issues. By combining the toolsets, the team doubled the production capacity of the plant and cut manufacturing costs in half.
DMADV for Designing
Just as the broad use of DMAIC can bring together tools for improving existing processes, DMADV can bring together tools for designing new processes. It requires only a sharp distinction between DMADV as a general framework for new process design and the DFSS tools with which it has been associated.
Like DMAIC, DMADV provides
a highly structured, sequenced ap-proach for using tools such as value stream mapping, work cells or computer simulation.
The detailed DMAIC framework provides some idea of how to create an analogous DMADV framework. But because the DMADV framework embodies key elements of DMAIC—the use of metrics, analytical tools and data based decision making—it results in a consistent approach and language for the organization’s improvement infrastructure.
Once a new process has been designed and deployed, process owners can easily employ the DMAIC framework to improve the process going forward. The result is a seamless, holistic improvement infrastructure that can improve existing processes and design new ones. It can also transcend sectarian battles over competing methodologies.
RONALD D. SNEE is principal of process and organizational excellence at Tunnell Consult-ing in King of Prussia, PA. He earned a doctorate in applied and mathematical statistics from Rutgers University, New Brunswick, NJ. Snee has also received ASQ’s Shewhart and Grant Medals and is an ASQ Fellow.
1. R.D. Snee and R.W. Hoerl, Leading Six Sigma—A Step by Step Guide Based on the Experience With General Electric and Other Six Sigma Companies, FT Prentice Hall, 2003.
2. R.D. Snee and R.W. Hoerl, Six Sigma Beyond the Factory Floor: Deployment Strategies for Financial Services, Healthcare and the Rest of the Real Economy, Pearson Prentice Hall, 2005.
3. William J. Hill and Willie Kearney, “The Honeywell Experience,” Six Sigma Forum Magazine, February 2003, pp. 34-37.
© 2005 Ronald D. Snee.
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