Control Phase in Six Sigma……

What is Control Phase in Lean Six Sigma and How it differs from Pre Control?

THE SIGMA ANALYTICS

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…

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Pre Control in Lean Six Sigma ?

The Pre-control Technique

Pre-control is a control charting methodology that uses specification limits instead of statistically-derived control limits to determine process capability over time. Pre-control charting is useful in initial process setup to get a rough idea of process capability. Pre-control charting does not use continuous data found upstream in the process which is more in alignment with prevention thinking.

An easy method of controlling the process average is known as “pre-control.” Pre-control was developed in 1954 by a group of consultants (including Dorin Shainin) in an attempt to replace the control chart. Pre-control is most successful with processes which are inherently stable and not subject to rapid process drifts once they are set up. Pre-control can act both as a guide in setting process aim and monitoring the continuing process.

The idea behind pre-control is to divide the total tolerance into zones. The two boundaries within the tolerance are called pre-control lines. The location of these lines is halfway between the center of the specification and specification limits. It can be shown that 86%of the parts will be inside the P-C lines with 7% in each of the outer sections, if the process is normally distributed and Cpk= 1. Usually the process will occupy much less of the tolerance range, so this extreme case will not apply.

The chance that two parts in a row will fall outside either P-C line is 1/7 times 1/7, or 1/49. This means that only once in every 49 pieces can we expect to get two pieces in a row outside the P-C lines just due to chance. There is a much greater chance (48/49) that the process has shifted. It is advisable, therefore, to reset the process to the center. It is equally unlikely that one piece will be outside one P-C line and the next outside the other P-C line. This is a definite indication that a special factor has widened the variation and action must be taken to find that special cause before continuing.

Pre-control rules:

. Set-up: The job is OK to run if five pieces in a row are inside the target .

. Running: Sample two consecutive pieces

. If the first piece is within target, run (don’t measure the second piece)

. If the first piece is not within target, check the second piece

. If the second piece is within target, continue to run

. If both pieces are out of target, adjust the process, go back to set up

. Any time a reading is out-of-specification, stop and adjust

The ideal frequency of sampling is 25 checks until a reset is required. Sampling can be relaxed if the process does not need adjustment in greater than 25 checks. Sampling must be increased if the opposite is true. To make pre-control even easier to use, gauges for the target area may be painted green. Yellow is used for the outer zones and red for out-of-specification.

The advantages of pre-control include:

. Shifts in process centering or increases in process spread can be detected

. The percentage of non-conforming product will not exceed a pre-determined level

. No recording, calculating or plotting is required

. Attribute or visual characteristics can be used

. Can serve as a set-up plan for short production runs, often found in job shops

. The specification tolerance is used directly

. Very simple instructions are needed for operators

The disadvantages of pre-control include:

. There is no permanent paper record of adjustments

. Subtle changes in process capability cannot be calculated

. It will not work for an unstable process

. It will not work effectively if the process spread is greater than the tolerance

Risk Management Framework

 

RMF

How to Calculate Asset value (AV)

The asset value (AV) is calculated on the basis of range value.

Range value is the product of the values of “C”, “I” and “A”.

 Range Value = C * I * A

 In case all three parameters of (C,I,A) are not applicable for an asset and only one or two out of the 3 parameters are applicable then the range value is calculated as the product of the applicable parameters.  Once the range value is calculated for an asset, the asset value (AV) is obtained as per the defined table which maps the range value with the AV depending on the number of applicable parameters.

How to Calculate “C”

Conf_Parameters

How to Calculate “A”

avail_para

How to Calculate “I”

intig_para

Net Promoter Score (NPS) Calculation and concept……

Have you ever liked a company so much that you’ve told your friends about it?

The Net Promoter Score system uses one basic question to measure customer loyalty:

“How likely is it that you would recommend our organisation to a friend or colleague?”

There are many formulae to understand customer’s opinions, such as the Customer Satisfaction Score (CSAT) system, but the NPS system is intended to go beyond testing how satisfied a customer is with a company: it’s designed to test whether someone likes a brand enough to recommend it to others.

In other words, the person isn’t merely “satisfied” with the company – by telling others about the brand, the person is effectively marketing the company’s services.

Although there are pros and cons to NPS, numerous research studies have shown that the NPS system also correlates with business growth.

Studies by the Harvard Business Review have found that companies ranging from banking to car-rental companies show higher income when they improve their Net Promoter Scores.

So, if you’re looking for a more scientific way than just relying on online reviews to understand your brand’s strength, the NPS is a straightforward system to use, and one of its big benefits is that it allows you to benchmark your company’s results against others in your industry.

The Way NPS formula works

Just as the main question of the Net Promoter Score sample survey is fairly simple, the Net Promoter Score calculation system is too. At first glance, it may seem rather complicated, but we’ll show you how to break it down and make figuring out your Net Promoter Score an easy process.

The Net Promoter Score Scale

To get started, customers are asked to rate their likelihood of recommending a company to a friend or colleague by using a 0-10 point scale:

The number on the scale that a customer chooses is then classified into one of the categories: “Detractors,” “Passives,” and “Promoters.”

Score breakdowns:

0 – 6: Detractors

7 – 8: Passives

9-10: Promoters

You can think of the NPS system as similar to a four-star system on an online review, but the NPS scale gives you a broader way (and a more accurate method) to measure customer’s opinions.

How to calculate Net Promoter Score ?

Let’s suppose you’ve sent out an online poll with the NPS question and the 0-10 scale and you’ve received 100 responses from customers. What do you do with the results? Is it as simple as averaging the responses? Well, not quite. But it’s almost that easy.

The NPS system gives you a percentage, based on the classification that respondents fall into – from Detractors to Promoters. So to calculate the percentage, follow these steps:

·  – Enter all of the survey responses into an Excel spreadsheet.

·  – Now break down the responses by Detractors, Passives and Promoters.

·  – Add up the total responses from each group.

·  – To get the percentage, take the group total and divide it by the total number of survey responses.

·  – Now subtract the percentage total of Detractors from the percentage total of Promoters – this is your NPS score.

Let’s break it down:

(Number of Promoters – Number of Detractors) / (Number of Respondents) x 100

Example: If you received 100 responses to your survey:

10 responses were in the 0-6 range (Detractors)

20 responses were in the 7-8 range (Passives)

70 responses were in the 9-10 range (Promoters)

When you calculate the percentages for each group, you get 10%, 20% and 70% respectively.

To finish off, subtract 10% (Detractors) from 70% (Promoters), which equals 60%. Since an example Net Promoter Score is always shown as just an integer and not a percentage, your NPS is simply 60. (And yes, you can have a negative NPS, as your score can range from -100 to +100.)

Once You’ve finished your Net Promoter Score Calculation. Now what?

So you’ve sent out the NPS survey sample to your customers. You’ve compiled the results and run the numbers. You now have your Net Promoter Score number – maybe it’s a 52. Is that good or bad?

Well, like many things in life, it’s really all relative. If your competitors have NPS numbers in the high 60s, you’re probably going to try to work out where your brand could improve. On the other hand, if your competitors all have scores in the low 40s, you’re doing just fine.

 

 

 

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

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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.