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.

 

 

 

  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.

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.