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This question is amongst one of the many misunderstood concepts in Lean Six Sigma. The question particularly asked for tests to compare variances for more than 2 populations, however, there were many responses that described ANOVA and other similar methods. These methods are used to compare the central tendencies (mean or median) of the multiple populations. They do not compare the variances (spread of the data). From all the approved responses, the best response is from Afzal. Well done! P.S. - responses that detailed ANOVA and other methods have not been approved as they are incorrect responses.

There are multiple statistical test are available to compare the variance of different populations. Below are some of the test: F test: This is suitable in case the populations are normally distributed or near to that. This is a parametric test and used for comparing 2 populations variance. Bartlett's test : Like F test , this is also used if the populations are normally distributed or near to that. This is also a parametric test and can be used for comparing more than 2 populations variance. Levene’s Test: This is again a test for variance comparison and can be used for both normal and non-normal data. This can be used for two or more than two population samples. These test are often used as part of mean comparison test wherever we have assumed equal variance for the population samples. For multiple comparisons in these test , we have individual and Simultaneous Confidence interval. Individual confidence level will show percentage of confidence that the true population parameter for that population will lie in the Confidence interval. Simultaneous Confidence interval is derived from the individual confidence intervals of multiple populations. It can be interpreted as % of confidence that the entire set of confidence intervals includes the true population standard deviations for all groups.

There 2 tests available to compare variances for more than 2 populations, namely: Bartlett's test of variance – You can use this test If your sample comes from normally distributed data. In other words, It is used to test the variances are equal for all the samples drawn. It checks that the assumption of equal variances is true before using certain statistical tests like the One-Way ANOVA etc.,. It’s used only when you’re fairly certain that your data comes from a normal distribution. Levene's test – This is an alternate to Bartlett’s test when the sample comes from non-normal data Concept of confidence intervals (CI) : The population variance gives an indication of how the data set is spread out and it is typically impossible to know exact population parameter(variance).for this reason, we use a topic in statistics called confidence intervals.. It refers that the probability that a population parameter will fall between a set of values for a certain proportion of times. For a variance from a normal distribution with unknown mean, a two-sided, 100(1 - α)% confidence interval is calculated and for two-sided intervals, the distance from the variance to each of the limits is different. Thus, instead of stating the distance to the limits we state the width of the interval

Q 438. What is a Yamazumi chart? How can it help improve the process efficiency? Explain using an example Note for website visitors - Two questions are asked every week on this platform. One on Tuesday and the other on Friday. All questions so far can be seen here - https://www.benchmarksixsigma.com/forum/lean-six-sigma-business-excellence-questions/ Please visit the forum home page at https://www.benchmarksixsigma.com/forum/ to respond to the latest question open till the next Tuesday/ Friday evening 5 PM as per Indian Standard Time. Questions launched on Tuesdays are open till Friday and questions launched on Friday are open till Tuesday. When you respond to this question, your answer will not be visible till it is reviewed. Only non-plagiarised (plagiarism below 5-10%) responses will be approved. If you have doubts about plagiarism, please check your answer with a plagiarism checker tool like https://smallseotools.com/plagiarism-checker/ before submitting. The best answer is always shown at the top among responses and the author finds honorable mention in our Business Excellence dictionary at https://www.benchmarksixsigma.com/forum/business-excellence-dictionary-glossary/ along with the related term

To compare 1-1 variance, we use F-Test or Levene’s Test. To compare variances for 2 or more populations, we use Levene's test or Bartlett's test. If we are sure data are normally distributed, Bartlett's test might have greater statistical power. The hypothesis testing is performed to assess variance of at least 1 group is significantly different from the rest or if all have equal variance. The individual confidence level indicates how confident we can be that an individual confidence interval contains the population variance of that specific group. However, because there are multiple confidence intervals in the set, you can be only 95% confident that all the intervals contain the true values. Each confidence interval may be a range of likely values for Standard deviation of the corresponding population. To maintain the simultaneous confidence level, the Bonferroni confidence intervals are adjusted. Controlling the simultaneous confidence level is particularly important when multiple confidence intervals are assessed. If the simultaneous confidence level is not controlled, the chance that at least one confidence interval does not contain the true standard deviation increases with the number of confidence intervals.

It was easy to identify the winner - Johanan Collins. Response from Manish Manjhi is a must read.

Outliers are the data points which looks different and far from the rest of the data. Outliers can influence estimates such as mean , variance ,etc. and reduces the power of statistical test. It is important to handle the outliers carefully before working on any estimates. Outliers can be detected though tools like boxplot, scatterplot, Z score, etc. Below are some of the the approaches to handle the outliers: Identify the outliers in the data using some of tools mentioned above. Check whether outliers are the results of measurement or data entry error. We can discuss the same with the data provider and correct the entry if possible. In case , we know that it is a data entry error but we don’t know the actual data value we can simply replace the value using various imputation techniques (Example: replacing it with mean or median values). We can also use some filters to cap the outliers values. For example , any value above the 95th percentile can be replaced with 95th percentile value. Similar approach can be taken for low outliers. If we think that outlier is because of mixing of another population then we can simply remove them so that our sample becomes more representative. In this case, we should document the removed data values along with the reasons for the data removal. Finally deciding on the best way to handle outliers requires detailed evaluation on the problem under study, data distribution, research methodology, etc.

Q 437. What are the various tests to compare variances for more than 2 populations? How do we use the concept of confidence intervals in these test? Note for website visitors - Two questions are asked every week on this platform. One on Tuesday and the other on Friday. All questions so far can be seen here - https://www.benchmarksixsigma.com/forum/lean-six-sigma-business-excellence-questions/ Please visit the forum home page at https://www.benchmarksixsigma.com/forum/ to respond to the latest question open till the next Tuesday/ Friday evening 5 PM as per Indian Standard Time. Questions launched on Tuesdays are open till Friday and questions launched on Friday are open till Tuesday. When you respond to this question, your answer will not be visible till it is reviewed. Only non-plagiarised (plagiarism below 5-10%) responses will be approved. If you have doubts about plagiarism, please check your answer with a plagiarism checker tool like https://smallseotools.com/plagiarism-checker/ before submitting. The best answer is always shown at the top among responses and the author finds honorable mention in our Business Excellence dictionary at https://www.benchmarksixsigma.com/forum/business-excellence-dictionary-glossary/ along with the related term

An unusual or abnormal distance of an observation from the other values taken from a random sample of a population is called an outlier. The degrees of this these outliers could be mild or extreme and it is up to the model or an analyst to define what is abnormal or an outlier. These outliers may contain valuable information or could be a meaningless deviation resulting from measuring or recording errors. The outliers can be detected using the Box Plot, Z-Score, or the Inter Quartile Range (IQR) techniques. Once the outliers are detected we can use the below method to handle them. Removing or trimming the outliers – Remove the abnormal data from the data set, its not a good practice though. Flooring and capping based on quantile – capping the value at a certain percentile (ex 90th percentile) or flooring at a factor below the 10th percentile Imputation of Mean/Median – take the Median value instead of the Mean which will be influenced by the outliers.

This response is based on typical scenarios experienced in the off shoring/BPS processes. Outliers are those data points which are different from the rest of the data points and can be distinguished using graphical analysis. Visually they stand out when we view a scatter plot or box plot. In a control chart, the data points beyond the LCL & LCL are the outliers. They impact the overall central tendency if the values are significantly high or low. Example of AHT metric of a process, wherein 2-3 transactions or calls may have a significantly high AHT or low AHT. Let’s say AHT is 15 mins where few odd data points are 72 mins or 1 min. These impact the overall calculation of central tendency due to extreme values. From the below, Section B has extreme values hence the Mean is influenced. So is the Std deviation. In comparison with section A data set, the values in B range from 2 to 80 while A has more closer values. Mean 15.09 18.60 Median 15.00 14.00 Mode 15.00 14.50 Std Dev 2.39 20.70 A B 15.00 14.00 14.00 2.00 15.00 14.50 15.00 11.50 12.00 12.50 17.00 80.00 12.00 12.00 16.00 16.00 13.00 14.50 17.00 13.00 20.00 14.60 These can be because of: Ø Erroneous data entry: A manual input in data tracking of AHT Ø Erroneous measurement system: the system or tool which tracks AHT had a glitch that incorrectly tracked few data points, or a processor had to manually stop the timer which wasn’t stopped at the right time causing the timer to run longer Ø Genuine scenarios: A truly lengthy call or case where it took much longer. o A complicated case o A long call wherein a complaint has been resolved o A long call wherein a person with disability is being assisted where its makes sense to remain customer centric and try to provide a better customer experience. Options to deal with Outliers: · Investigate and eliminate: if we know the data points found as outliers are inaccurate reflection of process, it can be eliminated due to obvious reasons. Example if there is a known issue in AHT data tracking which spikes up the AHT to >50 mins we can safely eliminate it. · Investigate and retain: As per research, if we realize the outlier data point is valid, can acknowledge and retain it, we may have to treat it differently however need not be excluded from the entire study. · Investigate and modify: As per research, if we conclude there is a known reason for a spike in AHT, we can modify the data point by capping it. Example, if we know there is manual error in capture AHT; many be timer ran a bit too long and associate didn’t click on stop button, we can cap the AHT tracked to a known value basis the nature of the case handled · Use different method: for assessing central tendency we can use median instead using mean which doesn’t get heavily impacted by outliers or use of equivalent nonparametric tests etc.

Outliers are part of the real world and need to be investigated before analyzing and interpreting the data. This is, even more, the case with small sample sizes, as the outliers have a greater impact on the results. Some models such as Principal Component Analysis, Hierarchical Models, K-Means, Linear, and Logistic Regression are very sensitive to outliers. Detection of unusual transactions may be the aim of the operations. This unusual transaction is generally in the form of outliers, such as fraud detection, stock forecasting, etc. Hence understanding outliers is critical because outliers are most likely to bias the entire interpretation or the outliers maybe what we are looking for. Reason for Outliers Error The error may be due to Data Entry, Recording, Measurement in Gage, Measurement due Operator, Measurement error due to calibration, Sampling Errors, Data Processing Errors. Part of Normal Process Outliers may be present in the data due to Bulk orders, Resellers or Extra Loyal Customers, etc. How to Detect? Data Visualization Outliers can be detected through Data Visualization such as Box plots, Scatter Plots, Histograms, Run Charts, Lag Plots, Line Charts. Statistical Methods Outliers can be detected through Statistical Methods such as the Standard Deviation Method, Tukey's Method. Etc. What is the strategy to deal with outliers? Keep the outlier and carry out the test with the outliers. Segment the data and carry out a deeper analysis. Imputing outliers and treating them separately. Set up a filter to do the test without the outliers. Since significant effects are hidden by outliers, it may be appropriate to set up a filter to examine the results without the outliers. Delete the outlier - The outliers may be deleted if there was an error in data or the reason for the outlier is not likely to happen again Delete the outlier after post-test analysis Change the value of the outlier. This may be done by replacing it with a more appropriate value such as the mean or the median. Consider the underlying distribution. An Anderson Darlings or Shapiro Wilk Test may be done to check the normality of the data. Carry out a Non-Parametric Test in case the underlying distribution is not Normal. Transform the Data. Data can be transformed using the Box-Cox Transformation, Johnson Transformation, log transformations, scaling, cube root normalization, etc. Methods and Tests that can be done for data having Outliers Winsorizing or Winsorization It is named after Charles P Winsor, who was an Engineer and Biostatistician. In this process the effect of the outliers is reduced by limiting the extreme values. It sets the value of all the outliers to a specific percentile of the sample. Data estimated through the Winsorization method is generally more robust to outliers. Example. A 95% Winsorization would set the bottom 2.5 percentile of the data to the 2.5 percentile value and the top 2.5 percentile of the data to the 97.5 percentile value. Trimming/ Truncation This is a method of censoring data. All data above/below a certain percentile is removed. Example. A 95% truncated data would eliminate the bottom 2.5% of the data and the top 2.5% of the data above the 97.5 percentile. TRIMMEAN function in Excel may be used from trimming the data. Winsorized mean and truncated mean are not the same. Non-Parametric Tests such as 1 Sample Sign Test, 1 Sample Wilcoxon Test, Mann Whitney, Kruskal Wallis, Moods Median, Friedman, Runs can be done in case of the underlying distributions being not normal. Transformation - Transform Data and carry out Parametric tests. Univariate Methods Box Plot - The box plot is the easiest method for identifying outliers. It uses the median and the Q1 and Q3 to determine the outliers. Tukey Method - This method identifies the extreme outliers as being greater than three 3 times the Inter Quartile Range below/above first/third quartile, Mild Outliers as between 1.5 to 3 times IQR. Multivariate Methods At times the univariate method may not detect the outliers. Multivariate methods such as multiple linear regression may be used. Minkowski Error. This method can be used to minimize the impact of the outliers on the model. It is a loss index and more insensitive to outliers than the mean square error since in the mean square error the contribution of the outliers increases exponentially. References https://en.wikipedia.org/wiki/Winsorizing https://www.sigmamagic.com/blogs/how-to-handle-outliers/ https://cxl.com/blog/outliers/ https://aichapters.com/how-do-you-handle-outliers-in-data/ https://aichapters.com/how-do-you-handle-outliers-in-data/ https://www.aquare.la/en/what-are-outliers-and-how-to-treat-them-in-data-analytics/

One of our Partners in my firm, always used to quote a saying of W. Edwards Deming, which goes by ”In God we trust. All others must bring data.”, whenever he wanted to explain the importance of the data for an exercise. And in my limited experience, I was able to see the power of data in quite a few industries. And as another saying goes “with great power comes great responsibility”, with data as well you have to use data very responsibly. One such responsibility is to identify the Outliers. As per the definition “Outliers is a data point or an observation that is located far from the rest of the data points and maybe an outcome of variability in measurement or due to an experimental error.” But whenever I hear the term outlier I start visualizing a scene where my mother using Supa to clean rice, where all the foreign particles are outliers, which has to be separated to make the delicious rice, else it can lead to poor taste or sometimes small stones comes with rice and we all know how much it hurt once you chew on that. Hence similarly in the case of data, if outliers are not removed it may lead to wrong or skewed analysis and ultimately lead to failure in achieving the desired results. Origin Before going into different approaches to deal with the outliers, let me first define the possible generation of outliers: Data entry errors: These are human errors where errors can occur during data collection and data entry. For eg. On one day you accidentally wrote production as 100 units instead of average production of 10 and with an available capacity of 12, then that week average production will be 23 instead of 10. Instrument errors or Measurement errors: This error occurs when we are using a faulty instrument or measurement system. For eg. in one of my exercises client asked me to understand the reason for high truck freight variation, but after understanding the data we were able to see since they are not capturing the truck type and truck utilization data, they are not comparing freight of similar scenarios and by defining above two parameters, it got very much clear that variation was not high and the team is doing a good job in keeping it in control. Similar example one can also see on the manufacturing side, where we use faulty or uncalibrated devices to capture the control parameters. Sampling errors — Best analogy one can think of this type of error is comparing apples with oranges, where we collect and mix data from wrong or different types or characteristics and then try to analyse assuming characteristics are the same. For eg. In a work content estimation exercise in a manufacturing setup, one has to analyse the work content of blue and white-collar separately, since in the case of blue-collar one should see a high percentage of active work and in the case of white-collar, one must see a high percentage of supervisory type of work. Data processing errors — Outliers can be generated when we extract data from multiple sources and see some unknown manipulation or when we have some gaps in our data analysis model or formula that is leading to the generation of outliers for the scenario which is not considered in the model. For eg. If you try to get the cycle time between two activities and you don’t put the logic for calculating cycle time when the start time is 23:00 Hrs 1 Jan 2022 and end time is 5:00 Hrs 2 Jan 2022, it will lead to the generation of outliers. Natural novelties in data: Data points that are not generated due to some errors but are generated naturally and are unusual in nature. For eg. To do any cost optimization exercise (manpower, operational, logistics, etc) when we have to take 2021 data, we remove data points of the lockdown period (Mar 2021 to Jul 2021) to remove the unusual situation that occurred due to Covid. Identification The above points highlight possible generation points of outliers, now let us understand how to effectively identify them using statistical tools: Plot the data in the box plot and identify the data points outside the minimum and maximum whiskers. (To know more, check out: Box Plot ) Plot the data in the scatter plot and identify the data points going away from the pattern (To know more, check out: Scatter Plot) Use Z score, where you distribute the data in different frequency ranges and create a histogram out of it and identify the ranges in both x-axis extremes where the occurrence of the data is very low or data points lie between +/- 3 standard deviation. (To know more, check out: Z - Score) Resolution Now we have clarity on the outliers origin and way of measurement, let us now talk about the cure. To deal with outliers I generally take either one or a combination of the activities explained below: Deleting the values: I delete the outlier if I am confident that the identified outlier are wrongly entered or wrongly calculated from the model due to missing information or the outlier occurred due to one of the cases which never going to happen in the future. As stated in the above examples, where we deleted the data of the lockdown period to calculate the actual cost and example of wrong production data entered despite knowing capacity is low but actual production is high. Changing the values: I go ahead and change the values in the cases where I know the reason for the outliers. Consider the above example of lockdown data removal, but when I am checking year on year cost variation then I take the average of remaining months data or average of last year and populate data points of the lockdown period. Using different analysis methods: One can use different statistical tests which will not create an impact on the final output with the presence of the outliers. For example, In the production data example, if one would have taken median instead of average, the value we would have got will be ~10 and hence will not be impacted by the outlier. Valuing the outliers: Those outliers which caused naturally and have a valid reason to exist should be analyzed further to understand the root cause of the outlier. This type of outliers may be hiding precious information to improve your process and performance. This has to be classified as special causes and separately analyzed to get that precious information if any. For eg. when observing data of employee wise product reports if we found one employee out of 100 is ensuring more than 90% performance across the month the rest are maintaining performance at ~80%, then the work practice of that employee has to be analyzed and if found something tangible which can be implemented across the organization can be captured. Apart from the above points, I also believe one should focus on working towards reducing the generation of unnatural outliers instead of spending time on identifying one for analysis. To do that one can take the help of tools such as robotic process automation, digitalization of systems to gather data, etc. to reduce the possibility of generating unnatural outliers And now we can conclude our understanding of the Outliers where we have seen how outliers can impact the data, how it can be generated, how it can be tracked or measured, how to resolve it and how to control it from generation. Since the identification of outliers and taking appropriate action is an important activity or task everyone should follow to extract the right power of the available data.

This was a tough choice as there are answers which explain multi-voting well but without an example and vice versa. Considering that the question explicitly asked for an example, the winning answer has been selected from Chaitanya Shankar Nemani. Plus there is another common limitation in multi-voting - Bias. Voting can be influenced. Finally, while there is a school of thought that believes multi-voting and NGT are same (and some of your answers did reflect that), there is another that feels otherwise. While both the tools appear to be similar, they are different. I guess it is obvious as to which school I belong to P.S. The 2 different thought schools were not a part of decision criteria.

Multi voting is a decision making method, to gain consensus in a group having a large list of choices to select and priorities. It helps to select the choice which is favored by most in the individuals in a group. It involves following steps: Brainstorm the list of ideas Discuss the idea with the group participants so that everyone understand it. All participants then vote for the ideas. Generally they rank the ideas by assigning a number for each idea. The votes are then counted (or rank are summed) and ideas having highest votes (or rank sum) are selected. Its benefits include team engagement and consensus and is especially useful for large teams and when we have a long list of ideas. However it is possible that no consensus is reached in the voting. We can then assign weights and repeat the voting process if required. For example. If we want to increase the sales of the company product, the possible ideas can be: giving more discounts, reduce the prices, better services , etc. To select the top 2 ideas, we can ask the group members to rank the ideas which are then summed. The ideas having highest sum will be picked to improve the sales.

Q 436. Outliers are unusual observations in the data set and whenever we work with real world data, we will find outliers. What are the different approaches to deal with outliers? Answer with the most number of unique approaches and examples will be the winner. Note for website visitors - Two questions are asked every week on this platform. One on Tuesday and the other on Friday. All questions so far can be seen here - https://www.benchmarksixsigma.com/forum/lean-six-sigma-business-excellence-questions/ Please visit the forum home page at https://www.benchmarksixsigma.com/forum/ to respond to the latest question open till the next Tuesday/ Friday evening 5 PM as per Indian Standard Time. Questions launched on Tuesdays are open till Friday and questions launched on Friday are open till Tuesday. When you respond to this question, your answer will not be visible till it is reviewed. Only non-plagiarised (plagiarism below 5-10%) responses will be approved. If you have doubts about plagiarism, please check your answer with a plagiarism checker tool like https://smallseotools.com/plagiarism-checker/ before submitting. The best answer is always shown at the top among responses and the author finds honorable mention in our Business Excellence dictionary at https://www.benchmarksixsigma.com/forum/business-excellence-dictionary-glossary/ along with the related term

Multi-Voting or Nominal Group Technique (NGT) voting is a decision making method that is used to narrow a large list of possibilities to a final selection of top priorities. Multivoting is preferable to straight voting because it allows an item that is chosen by all while not allowing the first choice of any to be the topmost option. Use of Multi-Voting Multi-volting is typically used right after brainstorming or other techniques of idea generation when the exhaustive list of ideas has to be narrowed down. This technique is always used for a decision made by a group judgement. Procedure 1. List all the ideas generated by brainstorming in a numbered list 2. Apply affinity diagram or other methods to combine or group similar ideas 3. Decide on number of votes each person in the group has, usually five per person 4. Each member is given a voting sheet on which they individually rank five options in order of preference. Highest preference gets score of 5 , next one gets 4 and so on 5. Tally the votes and record them on a flipchart/whiteboard 6. If a decision is clear, stop, else briefly discuss on the votes if there are many items with same score in the final tally 7. If necessary, repeat the voting process after discussion 8. Prepare the final list of narrowed down options for further work Benefits · - Simplicity of the technique, Less time consuming · - Streamlines the process of finding the highest priority or most supported ideas · - Allows full participation of all team members irrespective of their experience or expertise · - Allows for silent contemplation after a supercharged brainstorming session · - Allows for the idea with the broadest support to win Limitations - - Some very useful or creative choices could be totally ignored while applying multivoting - - Effectiveness of the brainstorming and voting strongly depends on the composition of the group Example Let's explore how Multi-voting can be used in a real life case in the below example 1. Problem Statement: How to improve the process of recruitment/hiring at ABC Corp 2. Brainstorming: Team brainstorms on the problem and generated. Clarify each idea and use affinity diagram or other grouping methods to group ideas or eliminating any duplicates 3. List of Ideas: Prepare a final list of ideas and distribute list to participants as a voting sheet 1. 4. Voting : Allow 10 minutes for voting where each participant ranks top five ideas. If the list of ideas is larger you can also use the formula of N/2 +1 to decide on the total number of votes each participant gets. Top idea gets a 5, next 4 and so on… 5. Final Score: Collect the voting sheets and combine the rankings. Idea with the maximum score is shortlisted for further action and development Hence, we have a list of 3 ideas for further action 1. Process Digitization - Off the Shelf Solution 2. Streamline Application and Interview Process 3. Rework Job Descriptions Remarks In the above example we have been successful in shortlisting 3 ideas out of a possible 12 for further action. There seems to be a broad consensus on the topmost priority options. Limitation of the process is that some brilliant idea which could be a game changer could be left out of further exploration due to its low/no score. However, after the project is underway one could always revisit other possible ideas to improve the process. References: https://lucidspark.com/blog/techniques-for-group-decision-making https://accendoreliability.com/multi-voting-one-vote-better/ https://asq.org/quality-resources/multivoting https://www.health.state.mn.us/communities/practice/resources/phqitoolbox/nominalgroup.html https://topworkplaces.com/how-to-improve-employee-recruitment-process/

Multi voting is one of the effective methods to refine and select the best amongst many or during the situation of multiple available choices/items/ideas/proposals etc. There are 3 benefits of multi voting :- 1) Sequencing/Ranking the most important amongst all a. Choosing the first & best top items b. Selecting what goes first and followed by the next (preferably action items) 2) Elimination of least priorities 3) Categorization or structuring of items to various buckets/units Multi voting follows 3 steps (AVR) :- Agenda – Clearly defining the purpose of voting, selecting the voters, orienting the procedure of voting Voting – Choice Selection, Sequencing, Categorization (based on Voter’s knowledge & Judgment) Result – Evaluation Structuring & Execution The motto of this technique is to make good decisions to breed satisfactory result/outcome or to prioritize the sequential best actions. Though multi voting technique has greater significance, it pushes some limitations :- - If the constraints of voting methods not addressed appropriately - If the Voters Judgement & Analysis failed to select/rank the choices - If the committee don’t re-evaluate the outcomes which have some un important/unfavorable choices Example :- The General Manager of a Business Unit was very happy with the Team’s performance and Business growth. GM want to appreciate for the Teams’ contribution and wanted to gift the team. He used multi voting technique to rank the choices amongst them so that team can feel they are recognized and sense satisfactory rewards. GM has good amount of budget to gift more than 1 item as per their choice of reward and has a plan to satisfy the team within the approved limit. GM opened the voting for all the team members (30 members) Apparel Vouchers – priority choice of 8 votes Electronic Gift Items – priority choice of 2 votes Team Outing in a resort – priority choice of 7 votes Taxable Bonus – priority choice of 10 votes 5 days’ vacation (Compensated PTO) – priority choice of 3 votes In the above voting, team members gave their priority of reward in the below sequence :- 1st = Taxable Bonus 2nd = Apparel Vouchers 3rd = Team Outing in a resort 4th = 5 days’ vacation (Compensated PTO) 5th = Electronic Gift Items Thus, GM found a way to reward the team members with rankings. Other perspectives in the above example :- - The complexity of voting or number of voting round increases if there is, a) Only 1 item must be selected amongst the extensive or a greater number of choices b) Very less budget. (Requires more refinement through elimination of bottom/least choices) c) Reward choice should be highly satisfied by the team members or missing of their desired choice d) Alternate choices allowed - Teams feedback must be taken even after declaring the choice to do well next time while rewarding them.

Multivoting is a technique used for making decision in a group that focus on reducing the long list of ideas to manageable numbers through a structured series of votes. This technique helps identify and prioritize ideas that are worthy and needs immediate attention. Though this technique is simple, easy to use and not time consuming. There is no assurance that a consensus would be reached which is the disadvantage.

Multi-voting is a structured method for group brainstorming that encourages contribution from everyone and helps with decision making without getting into arguments. Encourages consensus and agreement on relative importance of issues, problems, or solutions. When to use: When teams have a long list of possibilities and teams must narrow their options into a manageable size that can be discussed When teams must decide on which options should be given priority When some members are much more vocal than others or some members think better in silence When group does not generate quantities of ideas When all or some group members are new to the team and/or some members not participating Steps: We have recently used this technique for the process deep dive where teams across the organization had their individual long list of irritants which was leading to delays in prioritizing actions. It was also leading to conflict. We followed following steps to create a nominal based prioritization. Preparation – A good preparation that explains the purpose and expected outcome. Ensure participation from all relevant stakeholders and most important, schedule well ahead of time. Idea generation - Generate list of options by every participant. Several ways of achieving this, we can take offline feedback, round robin collection, via individual posts etc. Discussion of the ideas - Create clarity for each option amongst the participant, allow questions and queries to create deep understanding of each idea Voting – Number each idea and allow each member to Vote and rank in priority. We used for our brainstorming 5 votes per participant. As we are in virtual times with Covid, ensure everyone understands how to vote digitally, and has used their voting rights successfully. Discussion on initial voting - Tally results and review top choices. This is an important step as it helps realize the top problems. As well it might happen that a particular top issue is not perceived as important as it felts during discussions. Final voting – This steps is required if there is high number of choices and need further narrowing down. Benefits: Promotes participation and team engagement Eliminates arguments, gives fair chance to all Allows teams to select most important items Allows groups to narrow options into manageable size Saves group time by focusing on greatest potential Prioritizes a list of options following of brainstorming session Pitfalls/limitations: Voting gets skewed if the team is unequally represented, sometimes one division may have subdivisions and more representation than others Risk of voting being perception and feeling based instead of it being fact driven If the team members are not well informed or have the knowledge of the subject During the brainstorming, some members may not get time to share all their ideas End goal: Multi-voting is a great visual tool as everyone can see what everyone else in the group considers as important. Result is to get a list of options, not necessarily to make an actual decision or have one specific option that everyone agrees on, but really is to get a list of options that are more specific and more narrowed down. And, also to create a discussion on options with highest priority.

Multi Voting is used for decision-making to narrow down a very large list of ideas to a smaller one. It is generally done after brainstorming, as brainstorming generates a large number of ideas. In Multi Voting all members of the team have an equal vote thus weak members cannot be overpowered by strong members. Multi Voting allows for an item that is the choice of many, but not the top choice of any of the members to get selected. It is also called Nominal Group Technique (NGT) Steps · Generate a list of ideas and display them (maybe after Brainstorming) · Combine similar ideas into groups (may use Affinity mapping) · Give an Id to each item such as alphabets or numbers. · Decide how many items each person is to vote for (generally 1/3rd) · Tally votes · Eliminate items with few votes. (Generally, depends upon the group size) · Have a brief discussion. · Repeat process till decision arrived at. Benefits Helps to narrow down a large number of ideas/problems in a systematic and democratic manner All members have an equal vote. Powerful and outspoken members cannot overpower weak and introverted members. Popular It is popular because it is a very easy and democratic process. Pitfalls or Limitations Members with lesser experience or/and professional knowledge have an equal vote as compared to members who have more experience and greater professional knowledge. If team members are from various domains, various experience levels, multi-voting can lead to a sub-optimal decision. Example There are 15 Green/Black Belt Certified employees in ABC Enterprises. The Departmental Heads consisting of Rashmi, Ted, Prakash, Murthy, and Anita have to select three employees who have to attend a two-week Black Belt Six Sigma Training Workshop. After having a discussion on the experience, skills, etc of each candidate, they carry out Multi-Voting to decide on the three candidates. Multi Voting is done in two stages since Stage 1 had a tie between 4 candidates. The results are given below: References https://asq.org/quality-resources/multivoting https://sixsigmastudyguide.com/multivoting/

Company wanted to decide what should be the high competency, qualifying criteria for lateral hires selection. For that they want to decide what should be weight-age for these elements. The competency elements are more than 10 numbers. There are many stakeholders in the company like HR, Direct Line function Managers, Quality, EHS, Finance, L&D etc. Company decided to conduct Multi Voting, which is also called Nominal Group Technique in prioritizing the competencies. Rationale of the company is that - as there are many options, it is important to narrow down the options so that during the selection process, decision making would be easier. They do not want to be biased identifying the weight-ages to competency elements by just few people’s opinion. For better cultural fit of candidates and also help in organization having less employee turnover and have long term career, they decided to take majority stake holders opinion. Each Head has been asked to select top-5 competency elements with rating on scale of 1 ,2,3,4,5. They have organized flip charts, post-it sticks, or ease also decided to make table of options so that one can give their preference based on the scale. After the end of session, their goal is to develop below table with desirable outcome for clear prioritization. Competencies or qualifying criteria HR Production Head Quality Head EHS Head Finance Head L&D Head Engg Head Opex Head Total Education back ground 1 2 3 Total no. years of experience 1 5 6 No.of years of exact experience 1 5 1 4 11 Communication ( in chosen Language) 2 2 4 Functional knowledge 3 4 4 3 14 Result orientation 5 4 4 2 3 18 People orientation 4 3 1 8 Innovation 1 5 5 4 15 Problem solving 3 3 5 11 Receiving feedback 2 2 Collaboration and teamwork 5 2 3 10 Disadvantages Nominal group technique or Multi voting should not be used for options prioritization where there are definite quantitative assessments, guidelines. NGT should also not used when the leaders/ managers involved in process do not have adequate knowledge, experience and also how the results are going to help.

Multivoting is a method used to narrow down or minimize the output from ideation workshops or sessions when there is huge list of ideas to be narrowed down and prioritized. It allows team to quickly come to a consensus on issues, problems, or solutions by accounting for individual importance rankings in a team's final priorities. This method • Helps build commitment with in the team through equal participation in the process • Team can rank the ideas/ issues without being pressured by others • Brings up non vocal team mates also to contribute • Helps discuss difference in opinion or reach consensus Steps for Multivoting 1. Create the list of problems, issues, or solutions to be prioritized. 2. Pen statements on a flipchart or board. 3. Clarify issues by eliminating duplicate and/or clarify meanings of any of the statements. 4. Record the final list of statements on a flipchart or board. 5. Rank issues Types of Voting Dots. Show of Hands or Ballots Advantage of Multivoting 1. The major advantage of the technique is producing a large number of ideas and providing a means of closure which is often not found in other less-structured group methods. 2. This techniques is particularly useful in narrowing down when here is a huge list of items generated part of ideation . 3. The technique is very simple process and structured to execute. 4. The techniques also ensures participation across the group which is he major advantage of the technique, especially with people who are reluctant to speak up in a crowd either shy or worries about being criticized or may be don’t want to get into a conflict. 5. It hence ensures relatively equal participation across the team. Disadvantages of Multivoting 1. Major limitations of the technique would be the huge preparation time for this activity and also the fact that it lacks the flexibility of dealing with only one problem at a time. 2. The team should be comfortable and should have some about of mutual flexibility in reaching consensus 3. The process unlike other methods lack spontaneity and needs to be facilitated and planned in advance and appears too mechanical. 4. Opinions may not be discussed in detail or covered in the voting process in interest of time and restricted to only vote. 5. The technique also is not data driven except from opinions on relevant group of people are collated and voted for prioritization, hence is more group perception driven 6. Since Opinions may not converge in the voting process, cross-fertilization of ideas may be constrained in this process Conclusion : Hence Multi-voting techniques is a great decision making or prioritization method extremely useful in scenarios with large list of options needs to be pared down to to those that are the most popular among the group or most important to be addressed with out hurting anybody.

Excellent explanation by Johanan and hence his answer is selected as the best answer.

Q 435. What is Multi-voting method for idea evaluation? What are its benefits that makes it so popular? What are its pitfalls or limitations? Explain using an example. Note for website visitors - Two questions are asked every week on this platform. One on Tuesday and the other on Friday. All questions so far can be seen here - https://www.benchmarksixsigma.com/forum/lean-six-sigma-business-excellence-questions/ Please visit the forum home page at https://www.benchmarksixsigma.com/forum/ to respond to the latest question open till the next Tuesday/ Friday evening 5 PM as per Indian Standard Time. Questions launched on Tuesdays are open till Friday and questions launched on Friday are open till Tuesday. When you respond to this question, your answer will not be visible till it is reviewed. Only non-plagiarised (plagiarism below 5-10%) responses will be approved. If you have doubts about plagiarism, please check your answer with a plagiarism checker tool like https://smallseotools.com/plagiarism-checker/ before submitting. The best answer is always shown at the top among responses and the author finds honorable mention in our Business Excellence dictionary at https://www.benchmarksixsigma.com/forum/business-excellence-dictionary-glossary/ along with the related term

A two-proportion test is a hypothesis test to check if the differences between two population proportions are statistically significant. For example, are the proportion of girls to boys in a school significantly different. The null hypothesis is that there is no difference between the population proportions. It can be tested against an alternate hypothesis that can be two-tailed or left/right-tailed. The output of a 2 proportions test has two p-values, one is the normal approximation based on the Z statistic and the other is the Fisher’s Exact Test. As their names indicate, the normal approximation test is an approximation and has greater error for smaller sample sizes and becomes more accurate as the sample size increases whereas the Fisher’s Exact test is always exact irrespective of the sample size however is more difficult to calculate as the sample size increases. The Fisher’s Exact Test is calculated using the hypergeometric distribution. The factorials in the formula make it more and more difficult to calculate the p-value as the sample size increases since it runs every possible combination from the sample, and calculates the total number of successes and failures at that given sample size. It then calculates the p-value from the total successes and failures. Thus, for larger samples, it is not only easier to calculate the p-value using the Normal Approximation Test, but the results are closer to Fisher’s Exact test results. Since we no longer do manual calculations and statistical software have the ability to quickly calculate the p-value from the Fisher’s Exact Test, it makes more sense to use the Fisher’s Exact Test irrespective of the sample size. For a small number of expected values, when compared to the Chi-Square or G-Test of independence, the Fisher’s exact test is more accurate. The Normal Approximation Test (Z-test), is not accurate when the number of events/non-events are < 5. This is based on the rule that N*P or N(1-P) should be >5 (where N is the No. of trials and P is the proportion of successes. In other words, the normal distribution can be used in place of the binomial distribution when the sample size is large. If N is small and P is small, the binomial distribution will be skewed and the normal distribution cannot be taken to represent it. This is evident from Table 1 where N is increased and Table 2 where P is increased. It can be seen from these 2 tables, that as N and P are increased the Normal Approximation approaches the Fisher’s Exact Test. It is also evident from Table 2 that for small samples/P, the Normal Approximation test may indicate that a difference between the population exist when no difference exists. References https://stats.stackexchange.com/questions/234010/2-sample-proportions-z-test-vs-fishers-exact-test https://blog.minitab.com/en/quality-data-analysis-and-statistics/two-p-values-for-a-2-proportions-test-am-i-seeing-double http://www.biostathandbook.com/fishers.html