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Natwar Lal

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  1. Genchi Genbutsu - "Go and See" to investigate the issue and truly understand the customer situation. It basically refers to go and observe the process where the actual value is being added. As the question suggests, it makes perfect sense to use in in manufacturing however it is a myth that it is only used in manufacturing. As a concept Genchi Genbutsu is domain and industry agnostic. While preparing process maps, we usually tell the participants to create a map of "What the process is" and not "What it should be" or "what you think it is". One of the best means of understanding "What the process is" is to pick up a transaction and do a walkthrough of the process with it. This is Genchi Genbutsu for you as when you do a walkthrough of the process with the transaction you actually go to the process and see how it works. I am providing some examples below where the idea is same "Go and See". 1. Issue Resolution: when you raise an issue, the first thing that the agent / engineer will do is try to replicate the issue. They might do a screen share or take control of your computer and replicate the issue to understand where to attack and what to do 2. Software Testing: The first one happens when the code is compiled. The compiler does a walkthrough of the entire code and highlights the section of the codes that could not be compiled due to incorrect coding. Second happens during the multiple stages of testing - unit testing, integration testing and UAT. If a particular test case fails and the code is sent back to developer, the developer will first recreate the situation to see the failure (this is Genchi Genbutsu) 3. Medical conditions: Various invasive and non-invasive screening methods are used to first go to the specific location in the body and see the extent of the problem. E.g. X-ray, MRI, CT-scans, angiography etc. 4. Servicing of car: when you take your car for its regular service, the mechanic will first take a test drive of the car. What he is trying to do is to get a feel of how the car is driving so that he could pinpoint the issue which he will not be able to do unless he drives it himself.
  2. Looking at the above differences, it becomes clear as to why Test of Equivalence is considered as opposite of Hypothesis testing. Having laid down the differences, there are some similarities as well 1. Both work with samples and apply the concepts of Inferential Statistics (Significance Level, Confidence Intervals etc.) 2. Researcher is interested in Alternate Hypothesis in both (even though the alternate hypothesis are opposite in the two) Choice between hypothesis testing and equivalence will depend on the purpose of the study. Equivalence tests are most commonly used in pharma industry to check if a generic drug (lower cost option) has the same efficacy as the patented drug. To summarize, equivalence tests could be used wherever we want to use substitutes to an original item without significantly impacting the final outcome. Some e.g. that I could think off 1. Construction - Substituting building materials without impacting the compressive strength 2. Chemical / oil / pharma - Substituting chemicals without impacting the reaction time 3. Medical devices - substituting the type of laser without impacting the burning efficiency and precision 4. Tyre industry - substituting the rubber components without affecting the grip or the life of the tyre
  3. Hahaha!! Bench is a victim of not knowing Simpson's Paradox OR Both Bench and Mark are victims of lack of Operational Definition for winning criteria PS - I got to know about Simpson's Paradox from the 2 weekly questions that are asked. So thanks for it. For the uninitiated, go to Forum Dictionary and search for Simpson's Paradox!!
  4. Not sure if Mark is on the Mark this time. It is relatively easier for a BB and/or MBB to switch domains basis their knowledge in business improvement methodologies, however, companies tend to prefer BB / MBBs who have specific domain knowledge as well. Only if companies followed Mark's view.....what say?
  5. Sampling Errors are of two types (as already mentioned in the question) - Biased and Unbiased. Biased Sampling Error - is one which results in a bias in the sample. The effect of this bias is that the result of the sample will not reflect the true nature of the population. There are three sources of such bias 1. Survey Bias: where the survey questionnaire or the process of collecting data is biased 2. Researcher Bias: bias introduced by the researcher of the study 3. Respondent Bias: bias in the responses if the respondent chooses not to give the correct answer Unbiased Sampling Error - is one which is the resultant of chance. The sample will never reflect the population simply because the observations will vary from each other. Selecting a large sample size is one way in which both these biases could be avoided. However, since our analyst has decided to choose a smaller sample size, he should take care of the following things 1. Sampling method: choose the one which gives a random representative sample 2. If there is a questionnaire involved, then ensure that there are no leading questions or questions for which the respondents might have a tendency to not give the right response. Make the survey anonymous so that respondents could give correct responses 3. Determine which is more important - alpha or beta error? Since sample size is fixed, he could then determine either the significance level or Power of the Test that he is going to get and whether it is ok or not
  6. One of the reasons for project failure is 'Lack of Planning' and this not only includes planning for what one is going to do in the project but it also involves planning on how to check that the project is on track. Doing effective tollgates is an excellent mechanism to check the progress and ensure that project is still on the right path. For the tollgates to be effective, one basically has to seek answers to 5W and 1H (What, Why, Where, When, Who and How) Let us look at each element in slightly more detail 1. WHAT - Determine the requirements. What is the purpose of the tollgate? What is the information / artifacts that are required? What questions have to be asked? 2. WHY - Determine the objectives of the tollgate. Why are we doing tollgate? Why is it important to do the tollgate? Is the purpose only to review or also to approve? 3. WHERE - Determine the logistics of the tollgate. Where are we doing the tollgate? 4. WHEN - Determine the frequency, duration of the tollgates. When should the tollgates be set up during the project lifecycle? 5. WHO - Determine the participants in the tollgate. Who should be presenting the progress? Who should be audience during the tollgate? Who should be asking the questions? Who is going to take down the action items and meeting minutes? 6. HOW - Determine the decision criteria for acceptance / rejection of the tollgate. How are we going to judge the success of the tollgate? How many tollgates are required in the project lifecycle? If the team has thought through the above indicative questions, the chances of having an effective tollgate increases manifold. An effective tollgate will have following benefits 1. Keep the project team honest and true to the project objective 2. Ensure that scope, cost and schedule creep DO not happen 3. Effective communication across various levels in the organization (as the sponsor and/or other stakeholders may not be too close to the project) 4. Any issues / challenges are brought to notice at the right time and to the right people so that solutions could be identified
  7. This is so on the spot. This snapshot excellently captures the true essence of Lean Six Sigma!!
  8. Customer Satisfaction is a key metric for all organizations but more so for any marketing company. It is commonly known as CSAT score and is an average of ratings provided by customers who have used the organization's service / product. CSAT happens to be most common way of capturing Voice of Customer (VOC). Business to Customer (B2C) company is where the product or service is offered directly to customer. E.g. Uber, Fast Food Joints, Netflix, FMCG companies, Airtel DTH etc. Sometimes the CSAT captured for such companies could be misleading. Below are the reasons and how best to tackle it 1. Intermediaries between the company and customer: Following a typical value chain, you will have Manufacturer --> Distributor --> Wholesaler --> Retailer --> Customer. Ideally speaking the immediate customer for Manufacturer is the Distributor, however it is a very narrow view by the organization and is not a good idea. In this value chain, the customer experience is influenced by a lot of other factors as well which may not be in direct control of the the manufacturer. However, it is only the manufacturer that captures the CSAT. 2. Customer does not provide unbiased and specific CSAT score (even if there are not too many intermediaries between the provider and customer) 3. Average of scores is used to check for overall customer satisfaction. Even though average works in most cases, sometimes it ignores the extreme scores which might be of importance 4. Capturing of CSAT is outsourced to a third party which might not give it the due importance. Even if it is done inhouse, one should be vary of the below a. Customer segments or sample not selected appropriately: With B2C companies the customer base is very big and hence selecting the right sample sometimes becomes a challenge as the service offering could be different for say different regions b. Questions in CSAT survey not designed effectively: CSAT could be done for multiple reasons. One of the most common drawback in a CSAT survey is that it usually focusses on the overall satisfaction of the customer. At times, the company might want to know about the additional features that they want to provide and if the survey does not capture these correctly in questions, then the effectiveness of the survey becomes a challenge c. Inappropriate rating scale in the CSAT survey: Giving too many options to the customer might confuse them. Also it might possess a challenge in inferences d. Too small a sample to make any meaningful inferences e. Purpose of capturing CSAT is unclear f. Data cleansing not performed. There are methods in which you could actually check if the customer feedback is consistent or not as one would ideally want to filter out those customers who have just filled the survey without giving due attention to it. Certain questions may not be answered etc. and these have to factored in during reporting How to overcome the above: 1. Do not outsource the work that is core to the organization. E.g. You may purchase an Airtel DTH set from any electrical shop. However, it is only the Airtel servicemen who will come and install it 2. Ask the customer to be more specific when capturing feedback. E.g. If a less than 5 star rating is given in Uber, they ask customers to choose from multiple options for giving low rating. Some of the parameters are linked to driver while rest are for Uber 3. Increase the touch points with the customer i.e. the company should be easily accessible to the customers who want to give a feedback 4. If CSAT is a key performance indicator for the organization, do not outsource it and have competent team plan and execute on how CSAT has to be captured 5. Requesting customers to register the product. This could not be done for all B2C companies, however, it gives an opportunity to the organization to know about the customer 6. Value chain integration i.e. the manufacturer works with various partners in the value chain to inculcate the same values and attitude towards customer service. E.g. automobile companies 7. Keep the questionnaire relevant to the purpose of seeking the CSAT
  9. Outlier – is a data point that is significantly different from the rest of the data point. Another definition of an outlier is a data point that significantly varies from the distribution of the data set. It is a common phenomenon to get outliers in any data one collects. There can be many reasons for outliers in a data set. Some of the more common reasons are as below 1. Experimental error 2. Measurement system error (either the gauge gave an incorrect reading or it was noted down incorrectly by the operator) 3. Data collected from 2 or more distributions 4. Special Cause occurrences or anomalies 5. Attempts of fraud Outlier management may be a thesis topic in itself. However, I will try to keep it simple and practical here How to identify the outliers 1. Box Plot: It is the most common method to identify the outliers. An outlier will be marked in * in a box plot. These star marks are observations which are smaller or greater than 1.5 times IQR (Inter Quartile Range) 2. Control Charts: Any point outside the control limits is considered as an Outlier. Applying Nelson rules, one could also identify the unusual observations though these may not be significantly different from the rest of the data points 3. Modelling (like Regression Analysis, Probability distribution fitting etc.): whenever a model or a probability is fitted using historical data, it lets us know if there are any data points that do not fit the model. Usually such data points are outliers in the dataset which do not follow the fitted model There are other statistical methods also to identify the outliers in any data set. The ones listed above are the basic ones. What to do if you encounter an outlier? First do not panic And I write this because I have seen people panicking about outliers in their data sets. Getting outliers in a data set is not an outlier (it is very common) Second, take a structured approach to resolve the Outliers 1. Find out the reason for the outlier. Best and the easiest method is to do a 5 Why analysis 2. Determine if the root cause is a part of the nature of the business or process (say a seasonal or cyclical effect etc). One will need common sense and business knowledge for this determination. If one does not have either then it is advisable to work with an SME while doing this step. E.g. – the daily transactions during a month end show an unusual spike. However this spike is in the nature of banking business. Compare it with spike of transactions that happened due to demonetisation (Demonetisation is not the usual nature of business) 3. If the root cause is part of the nature of business, then the Outlier CANNOT be removed from the data set. Removing such an outlier would result in undesired data modification. If the outlier cannot be removed from the data set, then there are two possibilities a. Outlier is beneficial for the process – in which case the root cause for the outlier should be replicated b. Outlier is bad for the process – in which case the root cause for the outlier should be eliminated from the process (eliminating root cause is different from removing the data point. Elimination would ensure non recurrence of a bad outlier) 4. Alternatively, if the root cause if not part of the nature of business, then it may be EXCLUDED. It can be done in following two ways a. Trimming i.e. deleting the outlier from the analysis b. Winsorising i.e. replacing the outlier with the border case values (these border case values are not outliers) If there is outlier or any data point that is excluded or winsorised, it should be clearly called out in the reporting
  10. I couldn't agree more to the statement "not every customer is worth the time and effort". Some of the most common reasons for denying business with a potential customer are as follows 1. Business ideologies or work ethics do not match 2. Product is developed for a specific customer segment. E.g. Credit cards are issued basis the annual salary of an individual. Some of the high end credit cards might not be available to some customers 3. Financials do not work out between the service provider and the customer 4. Regulatory restrictions. E.g. tobacco products could not be sold to people below the age of 25 5. Customer does not follow the guidelines laid down by the service provider. E.g. in some fine dine restaurants, they require the customers to be dressed up in formals and if the customer is not dressed up accordingly, the restaurant may deny entry 6. Customer is too demanding and/or is finicky and/or is unruly. E.g. recently airlines have become intolerant to unruly customers and they might blacklist such customers from flying 7. Background of the customer. E.g. Services not being sold to people or companies with criminal background 8. Sanctions (economic or financial or military). E.g. Financial transactions are prohibited with companies in OFAC countries
  11. Kanabn Board is a tool that is used to depict the position of work in the process. As the question mentions itself, Kanban boards were primarily done for work allocation, monitoring the progress, decision making and reporting (at the end of the day). The most common usage of these boards were found in the daily huddles / daily team meetings / stand up meeting (what ever you might want to call it). It is mostly done on a white board where columns are created to track progress. These days there are multiple online versions of Kanban boards (but the joy of doing it is using post it notes or a marker pen on a white board - the good old way). The selection of manual or a systemic Kanban board is of lesser significance. What is more important is to track the progress. The simplest of Kanban board looks like Source: Google Images (smartsheet.com) Source: Google Images The best feature about the Kanban board is how it has evolved across various industries and domains and how it is being utilized these days. The underlying feature of allocating work, tracking progress and decision making remain the same. 1. Kanban Board in Agile Software Development / Project Management Source: Google Images search 2. Kanban Board in sales Source: Google Images search 3. Kanban Board in Hiring Source: Google Images search 4. Kanban Board in Incident Management Source: Google Images search 5. Kanban Board in aviation (flight progress strips) Source: Google Images search Automated version of flight progress strips Source: Google Images search 6. Kanban Board in Food Ordering Source: Google Images search You notice that there are multiple variations of Kanban board (manual or systemic) with all trying to help the business and/or customer know the progress of their product/service through the various process stages. A more advanced or recent variation of Kanban board is a Swimlane Kanban Board where additional characteristics could also be tracked. Source: Google Images search
  12. Imagine you got to choose a solution from a list of probable solutions with the following conditions 1. all solutions will be evaluated one after the other 2. a solution if evaluated and rejected cannot be selected again 3. each solution has a different reward or benefit associated with it which you are unaware of. You will be aware of the rewards for only those solutions that have been evaluated 4. probable solutions are in no particular order 5. If you reject all solutions, by default the last solution will be selected even though it may no give you the best result In such a scenario, the biggest challenge is to determine where to stop? Ideally you want the maximum reward or the best solution. However, you do not know if it is still to be evaluated or whether you have already rejecting it assuming that there is a better solution yet to be evaluated. Optimal Stopping Problem provides a solution in such situations. It says that if you have to choose from 'n' solutions, always reject the first 'n/e' (where e = 2.71) solutions. Let us call this number as 'x'. Then select the next solution which is better than the 'x' solutions already evaluated. Working with this rule, you will select the best solution in about 37% of the cases (which as per Wikipedia is a very good success rate - i have not gone into the validation part of it yet). 'x' is basically a sample that is drawn from the population 'n'. And 'n/e' ensures that we have a sufficient sample size to consider. E.g. picked from the classic 'Secretary Problem' associated with Optimal Stopping Problem (source: Wikipedia) You have 100 applicants for the position of Secretary. All the above rules (points 1 through 5) apply here and you have to select the best candidate. As per the Optimal Stopping Problem, one should reject the first 100/2.71 ~ 37 candidates and then select the next candidate who is the best fit from among the candidates interviewed so far. P.S. This will ideally not happen as the interviewer will always have the option to go back to any candidate. I do not have examples of any practical application of this this concept is new to me. Hoping someone shares practical examples here.
  13. What are existing process controls? Simply stated these are the measures taken in the process to ensure that defective items are not produced. What are the types of existing process controls? There are two types 1. Preventive 2. Detective Preventive process controls have an effect on Occurrence ratings as they prevent the occurrence of the failure modes. Detective controls have an effect on Detection ratings as they help detect that a failure mode has occurred. The same principle is applied when Mistake Proofing is done in the Control phase of a six sigma project. Let us take some examples 1. Production checklist that an agent uses is a preventive control as it ensures that processing is done correctly. If it is used, it will reduce the occurrence of defects. Audit checklist is a detective control as it checks if a defect has occurred or not 2. Using coding best practices is a preventive control as it reduces the number of bugs. Unit testing is a detective control as it checks for bugs present in the system 3. The spelling auto correction or highlighting of the word with red underline in MS word is a preventive control which reduces the number of spelling mistakes (while writing the article). However, a spell check in MS word is a detective control as it detects the incorrect spellings (after the article is written). By the way, in Excel there is only spell check and no auto correction 4. Preventive Maintenance is done in manufacturing. It reduces the occurrence of unplanned downtimes due to faults and hence impacts the occurrence rating 5. The "Caps Lock is on" warning is a preventive control in order to prevent instances of entering incorrect passwords and hence impacts the recurrence rating. The system not allowing to log in if an incorrect password is entered is a detective control which first checks for a valid password and hence impacts detection rating 6. In certain websites, one cannot enter alphabets if it is a numeric field. This is a preventive control which reduces the occurrence of incorrect entry in the field. The warning that some mandatory fields are left blank and system not allowed to go the next screen is the detective control where it checks for entries in all mandatory fields 7. Metal detectors and smoke detectors are examples of detective process controls. These will not impact the occurrence but will definitely have a bearing on the detection rating. 8. The seat belt not worn is a detective process control as it detects that the seat belt is not worn. In some advanced cars, the car will not start unless the seat belt is worn. This is a preventive control as it does not let the occurrence happen In all the above examples, preventive controls impacts the occurrence rating while detective controls impact the detection rating. To sum it up, while doing process FMEA, the below format is more useful where it clearly shows that preventive process controls impact Occurrence ratings and detective process controls impact Detection rating. Source: APQP FMEA format sourced from Google images
  14. Run Chart is a plot of the data points for a particular metric with respect to time. It is primarily used for following two purposes 1. Graphical representation of performance of the metric (without checking for any patterns in it). E.g. The scoring comparison in a cricket match. The runs are plotted on Y axis and X axis has overs (which is a substitute for time spent) Source: The Telegraph 2. To check if the data from the process is random or if there is a particular pattern in it. These patterns could be one or more of the following a. Clusters b. Mixtures c. Trends d. Oscillations Source: Minitab help section Run chart if used for point number 2, performs following tests for randomness - Test for number of runs about the median. This is used for checking Clusters and Mixtures. Clusters are present if the actual number of runs about the median is less than the expected runs. This implies that there are data points in one part of the chart Mixtures are present if the actual number of runs are more than expected runs. This implies that there are frequent crossings of the median line - Test for number of runs up or down. This is used for checking Trends and Oscillations. Trends are present if the actual number of runs is less than the expected runs. This implies that there is a sustainable drift in the process (either up or down) Oscillations are present if the actual number of runs is more than the expected runs. This implies that the process is not steady These are hypothetical cases with the below hypothesis Ho - Data is random Ha - Data is not random p values are calculated for all the 4 patterns. A p value of less than 0.05 indicates acceptance of Ha implying that the particular pattern is present in the data set. Absence of these patterns indicate that the process is random. Advantages of Run chart over Control chart Ideally control chart is a more advanced tool as compared to a run chart. However following situations warrant the use of run chart over a control chart 1. Run chart is preferred when we need a snapshot of the metric performance with time without taking into account the control limits or if the process is stable/unstable. E.g. like the scoring run rate comparison for cricket (refer the screenshot above) 2. One can start creating run chart without any prior data collection unlike in a control chart (where data is collected first to determine the control limits) 3. As a quick check to see if the process data is random or not. For doing such checks (clusters, mixtures, trends and oscillations) in a control chart, one would have to run all the Nelson tests (usually control charts are used with only one test i.e. any points outside 3 standard deviations and hence might not be able to detect such patterned data) 4. Apart from the above, it is easy to prepare and interpret a run chart in comparison to a control chart
  15. Verification and Validation are used interchangeably and often considered as same. However the two are different. Validation simply means – are you making the right thing? (Source: https://en.wikipedia.org/wiki/Verification_and_validation) Verification simply means – are you making it right? (Source: https://en.wikipedia.org/wiki/Verification_and_validation) The same difference is also highlighted in the definitions of the two as provided by PMBOK "Validation - The assurance that a product, service, or system meets the needs of the customer and other identified stakeholders. It often involves acceptance and suitability with external customers. Contrast with verification." (Source: https://en.wikipedia.org/wiki/Verification_and_validation) "Verification - The evaluation of whether or not a product, service, or system complies with a regulation, requirement, specification, or imposed condition. It is often an internal process. Contrast with validation." (Source: https://en.wikipedia.org/wiki/Verification_and_validation) Simple example to illustrate the difference between the two Voice of Customer: Customer wants to have a hot cup of coffee Validation: Customer would accept the coffee basis below specifications 1. It is hot (research suggests that temp. should be close to 96 degree Celsius) 2. Right amount of sugar (say 5 gm) 3. Right amount of milk (say 200 ml) 4. Right strength of coffee (say strong) These specifications are the needs of the customers and the coffee will only be accepted only if these specifications are met. These specifications only talk about what is the right product. It does not talk about how the coffee will be made to ensure that these specifications are met Verification: The process of brewing the coffee should be designed in a way which ensures that the above 4 specifications are met. Let us assume that we are using a coffee machine to make this coffee. If the machine keeps the temperature around 96 C, adds just the right amount of sugar, milk and coffee, then we would say that the machine is verified to provide the right coffee to the customer. The verification could either be done during development (of coffee machine) or during the production (like QC check). Other Examples 1. Maneuvering Characteristics Augmentation System (MCAS) is a software to control the plane from stalling and is the software which is argued to be the reason behind the 2 fatal crashes of Boeing 737 Max planes (one for Lion Air and the other for Ethiopian Airlines). Verification: When the software was developed, it was put through hundreds of hours of analysis, laboratory testing, verification in a simulator and two test flights, including an in-flight certification test with Federal Aviation Administration (FAA) representatives on board as observers (Source: https://www.boeing.com/commercial/737max/737-max-software-updates.page). This is where the software was verified as per the guidelines provided by Boeing. Validation: Even though verified, it is not acceptable or let me say validated by the airlines or by the aviation regulator as it fails to meet a key customer requirement of SAFETY (the most important requirement in aviation) 2. NASA has an independent verification and validation facility which was set up after the Challenger accident. This facility is set up as an independent verification and validation facility. Reason for challenge accident – failure of O-rings. These O-rings were designed to work at high temperatures and design required each hole (in the rocket motor) to have 2 O-rings. The manufacturing process verified that the O-rings are prepared as per the design specifications. The fabrication process verified that each hole has 2 O-rings in the motor. However, the validation i.e. customer acceptance failed which resulted in the accident. The reason for passed verification but failed validation what the incorrect design specifications for O-rings. 3. Software Development (Agile method). Let us a say a new web-page is developed which captures the demographic data of the user (details like name, age, address, pin code etc.) Verification: This is like unit testing. This would include the following things a. Code written as per the agreed upon standard b. Individual sprint testing to check that all fields are coded perfectly and the overall page is working fine Validation: This is when the new web-page is being integrated with the existing system and released to the customer or in a duplicate replica. This is more like User Acceptance Testing (UAT). This would include things like a. User navigation to page b. User experience on the page c. Overall system working fine 4. Satellite phone developed by Motorola. Motorola at one point of time was the front runner in the field of telecommunications devices. They had a vision of making the satellite phone a household thing. The phone was a verified product (isn't is obvious?). Motorola is the pioneer of Lean Six Sigma. However, even this verified product failed on validation as the customers did not make a beeline to buy the product (unlike what we see for Iphones) As per system engineering, a product or a system being developed will have following levels (similar to what APQP also prescribes) 1. System Level 2. Sub-system Level 3. Component Level Verification is done at all the 3 levels however validation is only done at the system level as the customer is going to use the overall system (with all its parts). From the above it is clear that verification is a more internal process while validation is a more external process (i.e. involves the customer). One could link verification to the process width or the control limits and validation to specification width or the specification limits. Process (manufacturing or services) could well be stable or verifiable, but still not be capable or validated. Best case for a process is to be both verifiable and validated.
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