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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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%

 

 

How Non-Fatal Errors contributes to decrease in Quality?

Many customer contact centers report quality performance that they believe is acceptable.  However, high performance centers have found that in order to drive real business performance — customer satisfaction improvement and reduction in costly errors — they have to rethink how they measure and report Quality.

i have consulted  three customer contact centers on this topic.  A key finding: The best centers distinguish fatal from non-fatal errors — they know that one quality score doesn’t work!

However, most centers have just one quality score for a transaction (a call, an email, etc.) and they establish a threshold that they think is appropriate.  For example, one center’s quality form has 25 elements (many are weighted differently) with a passing grade of 80%.  This approach is typical, but it doesn’t work to drive high performance.

High performance centers create a distinct score for both fatal (or critical) and non-fatal (or non-critical) errors.  This enables them to (a) focus on fixing those errors that have the most impact on the business, and (b) drive performance to very high levels.

In my previous Blog about “Transactional Quality”, i have explained about Fatal and Non-Fatal Errors

What Is A Fatal Error?

We find that there are at least six types of fatal errors, which fall into two categories.  The first category includes those things that impact the customer.  Fatal errors in this category include:

1.  Giving the customer the wrong answer.  This can be further divided into two types:

• The customer will call back or otherwise re-contact the center.  This is the “classic” fatal error.

• The customer does not know they received the wrong answer (e.g., telling the customer they are not eligible for something that they are, in fact, eligible for).

2.  Something that costs the customer unnecessary expense.  An example would be telling the customer that they need to visit a retail store when they could have handled the inquiry over the phone.

3.  Anything highly correlated with customer satisfaction.  We find that first-call resolution is the single attribute most often correlated with customer satisfaction, although attribute correlations are different for different businesses (e.g., one center found that agent professionalism was the number-two driver of customer satisfaction—unusual given that professionalism is typically a non-fatal attribute).

The second category includes the next three fatal errors — those things that affect the business:

4.  Anything illegal.  The best example of this is breach of privacy (e.g., a HIPAA violation in a healthcare contact center, or an FDCPA violation in a collections center).

5.  Something that costs the company.  A good example is typing the wrong address into the system, which then results in undelivered mail.  This is another “classic” fatal error.

6.  Lost revenue opportunity.  This is primarily for a sales or collections center.

So… What is a Non-Fatal Error?

Non-fatal errors can be considered as annoyances.  These typically include misspellings on emails and what is often referred to as “soft skills” (using the customer’s name, politeness, etc.) on the phone.

If they are annoyances, then why spend time tracking them?  Because too many non-fatal errors can create a transaction that is fatally defective.  One misspelling or one bad word choice on an email probably won’t even elicit a response from a customer, but multiple misspellings, bad word choices, bad sentence structures, etc. will cause the customer to think that the substance of the email is likely incorrect.

What’s the Right Way to Score?

In a high performance center, one fatal error will make the entire transaction defective.  There is no middle ground.  So, the score for the center at the end of the month is simple—it’s the number of transactions (e.g., calls) without a fatal error divided by the number of transactions monitored.

So, what happens in a center that changes from the traditional scoring to the more accurate “one fatal error = defect” scoring.  This center thought that their quality performance was good.  However, when they re-scored, they found that the percentage of transactions with a fatal error ranged from 2%-15%, with the average at about 10%.  This was a real shock to the executives who had been used to hearing that their quality was around 97%.

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.

 Getting Started Six Sigma within an Organization

Is Six Sigma Right for you ?

The starting point in gearing up for a Six Sigma is to verify that you are ready to embrace a change that says “There is a better way to run your Organization”.

There are number of essential questions and facts you will have to consider in making a readiness assessment:

  • Is the strategic course clear for the company?
  • Is the business healthy enough to meet the expectations of analysts and investors?
  • Is there a strong theme or vision for the future of the organization that is well understood and consistently communicated?
  • If the organization good at responding effectively and efficiently to new circumstances?
  • Evaluating current overall business results.
  • Evaluating how effectively do we focus on and meet customers’ requirements?
  • Evaluating how effectively are we operating?
  • How effective are your current improvement and change management systems?
  • How well are your crosses functional processes managed?
  • What other change efforts or activities might conflict with or support Six Sigma initiative?
  • Six Sigma demands investments. If you cannot make a solid case for future or current return then it may be better to stay away.
  • If you already have in place a strong, effective, performance and process improvement offer then why do you need Six Sigma?

There could be many questions to be answered to have an extensive assessment before deciding if you should go for Six Sigma or not. This may need time and a thorough consultation with Six Sigma Experts to take a better decision.

The Cost of Six Sigma Implementation:

Some of the most important Six Sigma budget items can include the followings:

  • Direct Payroll for the individuals dedicated to the effort full time.
  • Indirect Payroll for the time devoted by executives, team members, process owners and others involved in activities like data gathering and measurement.
  • Training and Consultation fee to teach people Six Sigma Skills and getting advice on how to make effort successful.
  • Improvement Implementation Cost.

Six Sigma Start-up:

Now you have decided to go for Six Sigma. So what’s next?

Deploying a Six Sigma within an organization is a big step and involved many activities including define, measure, analyze, improve, and control phases. These phases are discussed in subsequent session. Here are some steps which are required for an organization at the time of starting Six Sigma implementation.

  • Plan your own route:There may be many paths to Six Sigma but the best is the one that works for your organization.
  • Define your objective:It’s important to decide what you want to achieve and priorities are important
  • Stick to what is feasible:Set up your plans so that they can match your influences, resources and scope.
  • Preparing Leaders:They are required to launch and guide the Six Sigma Effort.
  • Creating Six Sigma organization:This includes preparing Black Belts and other roles and assigning them their responsibilities.
  • Training the organization:Apart from having black belts it is required to have all employees Six Sigma skilled.
  • Piloting Six Sigma Efforts:Piloting can be applied to any aspect of Six Sigma including solutions derived from process improvement or design redesign projects.

Project Selection for Six Sigma:

One of the more difficult challenges in Six Sigma is the selection of the most appropriate problems to attack. There are generally two ways to generate projects:

  • Top-down:approach is generally tied to business strategy and is aligned with customer needs. The major weakness is they are too broad in scope to be completed in a timely manner (most six sigma projects are expected to be completed in 3-6 months).
  • Bottom-up:In this approach Black Belts choose the projects that are well-suited for the capabilities of teams. A major drawback of this approach is that projects may not be tied directly to strategic concerns of management thereby receiving little support and low recognition from the top.