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.

 

 

 

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%.

 What is Transactional Quality?????

Transactional Quality

 What are Transactions?

Interactions with end-users are called Transactions. Examples of calls, faxes -mails, web-based session’s etc. Monitoring of all types of end-user transactions is done to ensure that call-centre, client and end-user requirements and targets are met.

Why Transaction Monitoring? 

  • Lesser mistakes and satisfied customers.
  • Helps trainers identify training needs of CSRs.
  • Ensures the deliverability of the set targets, standards &parameters defined in S.L.A. (Service Level Agreement) with the client.
  • Positive impact on profitability & growth of business.
  • Positive impact on Personal growth, skill set improvement, confidence &motivational level of a CSR.

And Also for ….…

Process Control 

To maintain our own standard of quality of work.

Process Analysis

Calculate FA and NFA Scores Studying trends over a period of time and incorporate that accordingly.

Continual Improvement 

To be able to identify problem areas and take preventive actions.

How is it done?

 There are six basic levels of quality monitoring:

  • Walk-around observation
  • Side-by-side monitoring
  • Plug-in/double jack monitoring
  • Silent monitoring
  • Record and review
  • Voice and screen/multi-media monitoring

Monitoring Methods for Telephone Transactions

Remote Monitoring:

Auditing recorded calls.

Live Barge-in:

Auditing real time calls.

Screen Capture:

Auditing voice and screen component of recorded/ live calls.

Side by Side Monitoring:

Auditing a call sitting next to a CSR.

Terminologies in TM

CTQ:

Critical To Quality Characteristics. Customer performance requirements of a product or service.

Defect:

Any event that does not meet the specifications of a CTQ.

Defect Opportunity:

Any event that can be measured that provides a chance of not meeting a customer requirement. These are the number of parameters (on account of Non-Fatal Errors) which are monitored in any one call. In case of multiple calls, these are a product of number of calls by the number of parameters. (Note- This will exclude the compliance parameters or the Fatal Error parameters)


Fatal Error:

Any Defect in the transaction that has legal or financial implications or gross errors on customer handling such as rude or abusive language is termed as fatal error. Any fatal errors would result in the whole transaction being declared VOID.

There are 6 such categories:

  • Wrong Resolution
  • Misleading Information
  • Financial loss to the client (wrong address details)
  • Foul language
  • Case Note defects like incomplete details mentioned in the case notes, wrong customer profile.

Non-Fatal Error:

Any parameter, the occurrence of which is not desirable yet may not result in a VOID transaction. Defects which may lead to customer dissatisfaction are also included in this category.

Threshold Scores:

Any score above which a transaction is deemed pass and below which it is considered failed.

Defective Transaction:

Any transaction which is monitored, and is deemed VOID on account of any FATAL ERROR occurrence. Note – Any transaction, which may not have any fatal errors, yet may have multiple Non-Fatal errors, resulting in a Transaction score below 75% will also be considered as a defective transaction.

Sampling Methodology:

Calls are picked at random from the recording device based on Random Table to make the sample relevant, representative and remove bias. Some minimum length calls are always included in the sample to ensure review of all aspects.

How is it measured?

Metrics 

Following accuracy metrics are measured During TM:

  • Fatal Accuracy: COPC Threshold >98%
  • Non-Fatal Accuracy: COPC Threshold >98%
  • TM Score: SLA Threshold

 TM Calculations

  • FA – Number of pass calls / Total Calls
  • NFA – 100% – (Non-fatal defects/ Total Opp.)
  • Total Opp. – Total Calls x Number of parameters
  • TM Score – Absolute scores/ Total calls

Audit Sheets

An Audit sheet is used to mark the observations of Transaction Monitoring during a call audit session by a Monitor. It is the tool which has the following mentioned:

  • Parameters (Fatal and Non-fatal) based on Call Flow
  • Brief description
  • Weightages
  • Score methodology
  • Space for comments

Different audit sheets are generally used during monitoring of different type of transactions. Example: In call Audit sheet, Side-by-Side Audit sheet, Escalation audit sheet, Email Audit Sheet.