Analyze Phase in Six Sigma

Purpose

To pinpoint and verify causes affecting the key input and output variables tied to project goals. (“Finding the critical Xs”)

Deliverables

  • Documentation of potential causes considered in your analysis
  • Data charts and other analyses that show the link between the targeted input and process (Xs) variables and critical output (Y)
  • Identification of value-add and non-value-add work
  • Calculation of process cycle efficiency

Key steps in Analyze

  1. Conduct value analysis. Identify value-add, non-value-add and business non-value-add steps
  2. Calculate Process Cycle Efficiency (PCE). Compare to world-class benchmarks to help determine how much improvement is needed.
  3. Analyze the process flow. Identify bottleneck points and constraints in a process, fallout and rework points, and assess their impact on the process throughput and its ability to meet customer demands and CTQs.
  4. Analyze data collected in Measure.
  5. Generate theories to explain potential causes. Use brainstorming, FMEA, C&E diagrams or matrices, and other tools to come up with potential causes of the observed effects.
  6. Narrow the search. Use brainstorming, selection, and prioritization techniques (Pareto charts, hypothesis testing, etc.) to narrow the search for root causes and significant cause-and-effect relationships.
  7. Collect additional data to verify root causes. Use scatter plots or more sophisticated statistical tools (such as hypothesis testing, ANOVA, or regression) to verify significant relationships.
  8. Prepare for Analyze gate review.

Gate review checklist for Analyze

  1. Process Analysis
    • Calculations of Process Cycle Efficiency
    • Where process flow problems exist
  2. Root Cause Analysis
    • Documentation of the range of potential Key Process Input Variables (KPIVs) that were considered (such as cause-and-effect diagrams; FMEA)
    • Documentation of how the list of potential causes was narrowed (stratification, multivoting, Pareto analysis, etc.)
    • Statistical analyses and/or data charts that confirm or refute a cause-and-effect relationship and indicate the strength of the relationship (scatter plot, design of experiment results, regression calculations, ANOVA, component of variation, lead time calculations showing how much improvement is possible by elimination of NVA activities, etc.)
    • Documentation of which root causes will be targeted for action in Improve (include criteria used for selection)
  3. Updated charter and project plans
    • Team recommendations on potential changes in team membership considering what may happen in Improve (expertise and skills needed, work areas affected, etc.)
    • Revisions/updates to project plans for Improve, such as time and resource commitments needed to complete the project
    • Team analysis of project status (still on track? still appropriate to focus on original goals?)
    • Team analysis of current risks and potential for acceleration
    • Plans for the Improve phase

Tips for Analyze 

  • If you identify a quick-hit improvement opportunity, implement using a Kaizen approach. Get partial benefits now, then continue with project.
  • Be critical about your own data collection—the data must help you understand the causes of the problem you’re investigating. Avoid “paralysis by analysis”: wasting valuable project time by collecting data that don’t move the project forward.
  • This is a good time in a project to celebrate team success for finding the critical Xs and implementing some quick hits!

  MSA(Measurement System Analysis)

Measurement System Analysis: Hidden Factory Evaluation 

What Comprises the Hidden Factory in a Process/Production Area?

  • Reprocessed and Scrap materials — First time out of spec, not reworkable
  • Over-processed materials — Run higher than target with higher
    than needed utilities or reagents
  • Over-analyzed materials — High Capability, but multiple in-process
    samples are run, improper SPC leading to over-control

What Comprises the Hidden Factory in a Laboratory Setting?

  • Incapable Measurement Systems — purchased, but are unusable
    due to high repeatability variation and poor discrimination
  • Repetitive Analysis — Test that runs with repeats to improve known
    variation or to unsuccessfully deal with overwhelming sampling issues
  • Laboratory “Noise” Issues — Lab Tech to Lab Tech Variation, Shift to
    Shift Variation, Machine to Machine Variation, Lab to Lab Variation

Hidden factory Linkage –

  • Production Environments generally rely upon in-process sampling for adjustment
  • As Processes attain Six Sigma performance they begin to rely less on sampling and more upon leveraging the few influential X variables
  • The few influential X variables are determined largely through multi-vari studies and Design of Experimentation (DOE)
  • Good multi-vari and DOE results are based upon acceptable measurement analysis

Picture1

Picture2

Picture3

Measurement System Terminology

Discrimination Smallest detectable increment between two measured values

Accuracy related terms

True value – Theoretically correct value

Bias – Difference between the average value of all measurements of a sample and the true value for that sample

Precision related terms

Repeatability – Variability inherent in the measurement system under constant conditions

Reproducibility – Variability among measurements made under different conditions (e.g. different operators, measuring devices, etc

Stability distribution of measurements that remains constant and predictable over time for both the mean and standard deviation

Linearity A measure of any change in accuracy or precision over the range of instrument capability

Measurement System Capability Index – Precision to Tolerance Ratio:

  •  P/T = [5.15* Sigma (MS)]/Tolerence
  • Addresses what percent of the tolerance is taken up by measurement error
  • Includes both repeatability and reproducibility:  Operator * Unit * Trial experiment
  • Best case: 10%  Acceptable:  30%

Note: 5.15 standard deviations accounts for 99% of Measurement System (MS) variation.  The use of 5.15 is an industry standard.

Measurement System Capability Index – %Gage R & R:

  • % R & R =[Sigma (MS)/Sigma(Observed Process Variation)]*100
  • Addresses what percent of the Observed Process Variation is taken up by measurement error
  • %R&R is the best estimate of the effect of measurement systems on the validity of process improvement studies (DOE)
  • Includes both repeatability and reproducibility
  • As a target, look for %R&R < 30%

 

 

Why MSA and How it is different from Calibration??????

Measurement System Analysis:

Statistical Process Control has taught us to look at and evaluate the variation in processes. More the complexity of the processes more is the potential variation. What we get at the output end is the stacked up variation that is a resultant of variation at every step.

Measurement is a process of evaluating an unknown quantity and expressing it into numbers. The Measurement Process too is subject to all the laws of variation and Statistical Process Control.

Measurement Systems Analysis is the scientific and statistical Analysis of Variation that is induced into the process of measurement.

Why MSA?  

A measurement system tells us in numerical terms, an important information about the entity that we measure. How sure can we be about the data that the measurement system delivers? Is it the real value of the measure that we obtain out of the measurement process, or is it the measurement system error that we see? Indeed, measurement systems errors can be expensive, and can cost our capability to obtain the true value of what we measure. So, we can say that we can be confident about our reading of a parameter only to the extent that our measurement system can allow.

How does MSA differ from calibration?  

It is a standard practice to periodically calibrate all gages and measuring instruments used in measurement on the shop floor.

In simple terms, Calibration is a process of matching up the measuring instrument scale against standards of known value, and correcting the difference, if any. Calibration is done under controlled environment and by specially trained personnel.

Where Did Six Sigma Come From?

As with Lean, we can trace the roots of Six Sigma to the nineteenth-century craftsman, whose challenges as an individual a long time ago mirror the challenges of organizations today. The craftsman had to minimize wasted time, actions, and materials; he also had to make every product or service to a high standard of quality the first time, each time, every time.

Quality Beginning

The roots of what would later become Six Sigma were planted in 1908, when W. S. Gosset developed statistical tests to help analyze quality data obtained at Guinness Brewery. About the same time, A. K. Erlang studied telephone traffic problems for the Copenhagen Telephone Company in an effort to increase the reliability of service in an industry known for its inherent randomness. It’s likely that Erlang was the first mathematician to apply probability theory in an industrial setting, an effort that led to modern queuing and reliability theory. With these underpinnings, Walter Shewhart worked with Western Electric (a forerunner of AT& T) in the 1930s to develop the theoretical concepts of quality control. Lean-like industrial engineering techniques did not solve quality and variation-related problems; more statistical intelligence was needed to get to their root causes. Shewhart is also known as the originator of the Plan-Do-Check-Act cycle, which is sometimes ascribed to Dr. Edwards Deming, Shewhart’s understudy. As the story goes, Deming made the connection between quality and cost. If you find a way to prevent defects, and do everything right the first time, you won’t have any need to perform rework. Therefore, as quality goes up, the cost of doing business goes down. Deming’s words were echoed in the late 1970s by a guy named Philip Crosby, who popularized the notion that “quality is free.”

Quality Crazy

War and devastation bring us to Japan, where Deming did most of his initial quality proselytizing with another American, Dr. Joseph Juran. Both helped Japan rebuild its economy after World War II, consulting with numerous Japanese companies in the development of statistical quality control techniques, which later spread into the system known as Total Quality Control (TQC).

As the global economy grew, organizations grew in size and complexity. Many administrative, management, and enabling functions grew around the core function of a company to make this or that product. The thinking of efficiency and quality, therefore, began to spread from the manufacturing function to virtually all functions— procurement, billing, customer service, shipping, and so on. Quality is not just one person’s or one department’s job. Rather, quality is everyone’s job! This is when quality circles and suggestion programs abounded in Japanese companies: no mind should be wasted, and everyone’s ideas are necessary. Furthermore, everyone should continuously engage in finding better ways to create value and improve performance. By necessity, quality became everyone’s job, not just the job of a few … especially in Japan, at a time when there was precious little money to invest in new equipment and technology.

The rest of the story might be familiar if you’re old enough to remember. By the late 1970s, America had lost its quality edge in cars, TVs, and other electronics— and they were suffering significant market share losses. Japanese plants were far more productive and superior to American plants, according to a 1980 NBC television program, If Japan Can Why Can’t We? In response to all this, American companies took up the quality cause. They made Deming and Juran heroes, and institutionalized the Japanese-flavored TQC into its American counterpart, Total Quality Management (TQM). They developed a special government award, the Baldrige Award, to give companies that best embodied the ideal practice of TQM. They organized all the many elements and tools of quality improvement into a teachable, learnable, and doable system— and a booming field of quality professionals was born.

Quality Business

The co-founder of Six Sigma, Dr. Mikel Harry, has often said that Six Sigma shifts the focus from the business of quality to the quality of business. What he means is that for many years the practices of quality improvement floated loosely around a company, driven by the quality department. And as much as the experts said that quality improvement has to be driven and supported by top executives, it generally wasn’t. Enter Jack Welch, the iconic CEO who led General Electric through 2 decades of incredible growth and consistent returns for shareholders. In the late 1980s, Welch had a discussion with former AlliedSignal CEO Larry Bossidy, who said that Six Sigma could transform not only a process or product, but a company. In other words, GE could use Six Sigma as AlliedSignal was already doing: to improve the financial health and viability of the corporation through real and lasting operational improvements. Welch took note and hired Mikel Harry to train hundreds of his managers and specialists to become Six Sigma Black Belts, Master Black Belts, and Champions. Welch installed a deployment infrastructure so he could fan the Six Sigma methodology out as widely as possible across GE’s many departments and functions. In short, Welch elevated the idea and practice of quality from the engineering hallways of the corporation into the boardroom. Lest we not be clear, the first practical application of Six Sigma on a pervasive basis occurred at Motorola, where Dr. Harry and the co-inventor of Six Sigma, Bill Smith, worked as engineers. Bob Galvin, then CEO of Motorola, paved the way for Bossidy and Welch in that he proved how powerful Six Sigma was in solving difficult performance problems. He also used Six Sigma at Motorola to achieve unprecedented quality levels for key products. One such product was the Motorola Bandit pager, which failed so rarely that Motorola simply replaced rather than repaired them when they did fail.

Control Phase in Six Sigma……

Purpose

To complete project work and hand off improved process to process owner, with procedures for maintaining the gains

Deliverables

  • Documented plan to transition improved process back to process owner, participants and sponsor
  • Before and after data on process metrics
  • Operational, training, feedback, and control documents (updated process maps and instructions, control charts and plans, training documentation, visual process controls)
  • A system for monitoring the implemented solution (Process Control Plan), along with specific metrics to be used for regular process auditing
  • Completed project documentation, including lessons learned, and recommendations for further actions or opportunities

Key steps in Control

  1. Develop supporting methods and documentation to sustain full-scale implementation.
  2. Launch implementation.
  3. Lock in performance gains. Use mistake-proofing or other measures to prevent people from performing work in old ways.
  4. Monitor implementation. Use observation, interaction, and data collection and charting; make additional improvements as appropriate.
  5. Develop Process Control Plans and hand off control to process owner.
  6. Audit the results. Confirm measures of improvements and assign dollar figures where appropriate. Give audit plan to company’s auditing group.
  7. Finalize project:
    • Document ideas about where your company could apply the methods and lessons learned from this project
    • Hold the Control Gate Review
    • Communicate project methods and results to others in the organization
    • Celebrate project completion
  8. Validate performance and financial results several months after project completion.

Gate review checklist for Control

  1. Full-scale Implementation results
    • Data charts and other before/after documentation showing that the realized gains are in line with the project charter
    • Process Control Plan
  2. Documentation and measures prepared for sustainability
    • Essential documentation of the improved process, including key procedures and process maps
    • Procedures to be used to monitor process performance and continued effectiveness of the solution
    • Control charts, capability analysis, and other data displays showing current performance and verifying gains
    • Documentation of procedures (mistake-proofing, automated process controls) used to lock in gains
  3. Evidence of buy-in, sharing and celebrating
    • Testimonials or documentation showing that:
      • The appropriate people have evaluated and signed off on the changes
      • The process owner has taken over responsibility for managing continuing operations
      • The project work has been shared with the work area and company at large (using a project database, bulletin boards, etc.)
    • Summary of lessons learned throughout the project
    • List of issues/opportunities that were not addressed in this project (to be considered as candidates for future projects)
    • Identification of opportunities to use the methods from this project in other projects
    • Plans for celebrating the hard work and successful efforts

Tips for Control Phase

  • Set up a realistic transition plan that will occur over a series of meetings, training events, and progress checks scheduled between the team and the process participants (avoid blind hand-offs of implementation plans).
  • Schedule a validation check 6 to 12 months after the control gate review. Be sure the project sponsor and local controller/finance representative is present to validate that the results are in place and stable!
  • Never anticipate perfection! Something always goes wrong. Develop a rapid response plan to address unanticipated failures via FMEA (p. 270). Identify who will be part of the “rapid response team” when a problem arises. Get permission from sponsor to use personnel should the need arise.
  • Develop tools that are easy for process participants to reference and use. It’s hard to keep paying attention to how a process operates, so you need to make it as easy as possible for people to monitor the work automatically.
  • Work out the kinds before transferring responsibility for managing the new process. Handing off (to the sponsor or process owner) a process that is still being worked on will compromise success.