Six Sigma DMAIC Process

LEAN SIX SIGMA PART…. 19 MARATHON STUDY

‘’In my last article, we learned Six Sigma Bar Diagram

Please read along as we attain another height in PART..19..

We progress by understanding Six Sigma DMAIC Process

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Six Sigma DMAIC Process - Analyze Phase - Hypothesis Testing

In a process, we may face Problem with Centering and/ or Problem with Spread. Below diagram will allow us to understand these two problems in detail.

Hypothesis testing tells us whether there exists statistically significant difference between the data sets for us to consider that they represent different distributions.

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Practical Six Sigma Problems that require Hypothesis Testing

What is the difference that can be detected using Hypothesis Testing?

For Continuous data, hypothesis testing can detect Difference in Average and Difference in Variance. For Discrete data, hypothesis testing can detect Difference in Proportion Defective.

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Steps in Hypothesis Testing:

???????? Step 1: Determine appropriate Hypothesis test

???????? Step 2: State the Null Hypothesis Ho and Alternate Hypothesis Ha

???????? Step 3: Calculate Test Statistics / P-value against table value of test statistic

???????? Step 4: Interpret results – Accept or reject Ho

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Mechanism:

???????? Ho???? =??????? Null?? Hypothesis????????? – There is No statistically significant difference between the two groups

???????? Ha = Alternate Hypothesis – There is statistically significant difference between the two groups

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Hypothesis Testing Errors:

Type I Error – P (Reject Ho when Ho is true) = α

In type I Error, we reject the Null Hypothesis when it is true. It is also called as Alpha error or Producer’s Risk.

Type II Error - P (Accept Ho when Ho is false) = β

Similarly, in type II Error, we accept Null Hypothesis when it is false. It is also called as Beta error or Consumer’s Risk.

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Six Sigma Hypothesis Testing Errors

P Value – Also known as Probability value, it is a statistical measure which indicates the probability of making an α error. The value ranges between 0 and 1. We normally work with 5% alpha risk, a p value lower

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than 0.05 means that we reject the Null hypothesis and accept alternate hypothesis.

Types of Hypothesis Testing:

We use the following grid to select the appropriate hypothesis test depending on the data types:

Normal Continuous Y and Discrete X

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Non-Normal Continuous Y and Discrete X

Types of Six Sigma Hypothesis Testing

Continuous Y and Continuous X

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Discrete Y and Discrete X

Six Sigma Hypothesis Test – Null and Alternate Summar

Six Sigma DMAIC Process - Analyze Phase - Analysis Examples

1-????? sample t-Test Example:

Definition: A hypothesis test for comparing a population mean against a given standard for any significant differences.

Situation: You have 50 data points on the TAT for your process and you want to check if the mean of your TAT is worse than your competitors TAT of 4.5 minutes.

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Null: Mean TAT <= 4.5 minutes

Alternate: Mean TAT > 4.5 minutes

Turn Around Time (in Mins)

Analysis Result

Result: A One-Sample t-test helps us compare the mean of the sample to that of one value. In our example, we wanted to identify if our TAT is better or worse than 4.5. Using the results of the session window described above, we observe that the P-value is less than 0.05 which indicates that we reject the null hypothesis. Thus, our performance is worse than our competitors performance.

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2-????? sample t-Test Example:

Definition: A hypothesis test for comparing means of two different populations for any significant differences. Situation: You have two shifts in your process, morning shift & evening shift and you want to find out if there is a significant difference in the average in TAT of morning shift and night shift.

Null: Mean of (Morning shift = Evening shift)

Alternate: Mean of (Morning shift != Evening shift

Morning Shift and Night Shift Data

Analysis Result

Result: A Two-Sample t-test helps us compare the mean of two samples. In our example, we wanted to identify if morning shift performance is equal to evening shift performance. Using the results of the session window described above, we observe that the P-value is less than 0.05 which indicates that we reject the null hypothesis. Thus, performance of morning shift is not equal to evening shift.

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One-Way ANOVA Example:

Definition: A hypothesis test for comparing the means of more than two populations for any significant differences.

Situation: You want to see if there is a significant difference in the average TAT of your staff A, B, C, D.

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Null: Mean of (Staff A = Staff B = Staff C = Staff D) Alternate: Mean of (Staff A != Staff B != Staff C != Staff D)

Staff 1, Staff 2, Staff 3 and Staff 4 Data

Staff 3 != Staff 4.

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Mood's Median Test Example:

Definition: A hypothesis test for comparing the medians of two or more than two populations for any significant differences.

Situation: You want to see if there is a significant difference in the TAT of your staff A, B, C, D.

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Analysis Result

Result: A One-Way ANOVA helps us compare the mean of more than two samples. In our example, we wanted to check if the performance of the four staff is same or different. Using the results of the session window described above, we observe that the P-value is less than 0.05 which indicates that we reject the null hypothesis. Thus, performance of Staff 1 != Staff 2 !=

Null: Median of (Staff A = Staff B = Staff C = Staff D) Alternate: Median of (Staff A != Staff B != Staff C != Staff D)

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Analysis Result

Staff 1, Staff 2, Staff 3 and Staff 4 Data

Result: A Mood’s Median Test helps us compare the medians of more than two samples. In our example, we wanted to check if the median performance of the four staff is same or different. Using the results of the session window described above, we observe that the P- value is less than 0.05 which indicates that we reject the null hypothesis. Thus, median performance of Staff 1 != Staff 2 != Staff 3 != Staff 4.

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Chi-SquareTest Example:

Definition: A hypothesis test for comparing output counts from two or more sub-groups for any significant differences.

Situation: You want to see if one set of defectives data is significantly different from another set of defectives data.

Null: No difference in the two sub-groups

Alternate: Difference exists in the two sub-groups

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Data for Chi-Square Test

Analysis Result

Result: A Chi-Square Test helps us compare the count data. In our example, we wanted to check if one set of defectives data is significantly different from another set of defectives data. Using the results of the session window described above, we observe that the P-value is greater than 0.05 which indicates that we fail to reject the null hypothesis. Thus, No difference in the two sub- groups.

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Six Sigma DMAIC Process - Improve Phase - Solution Parameter

To define and come up with solution parameters for statistically validated X’s we need to:

???????? Develop Decision Statement

???????? Develop Solution Criteria

???????? Classify Solution Criteria

???????? Refine Solution Criteria

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Develop Decision Statement:

Solutions should be generated to clarify the purpose of the decision to be made. Parameters to be considered

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while drafting a decision statement are:

1.????? How will you manage expectations of customer?

2.????? How will you establish boundaries on alternatives to be considered to resolve the problem?

3.????? How will you reflect on prior decisions taken – “Level of Decision”?

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The next thing to do is to Develop a Decision Statement and generate a list of six to twelve criteria to solve the problem. Consider desired results, restrictions being faced and availability of resources.

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Classify Solution Criteria:

Once criteria have been listed, we have a clearer statement of the objectives against which to judge the various alternatives. In most situations, criteria vary in their degree of importance. We need to classify these criteria to reflect their relative influence on the solution choice. We divide criteria into two basic categories:

1.????? Absolute Requirements or “Musts” – Mandatory,

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realistic and measurable requirements which help the project team to screen out unacceptable alternatives.

2.????? Comparison Criteria or “ Wants” – Desirable characteristics which provide a basis for comparison for criteria.

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Thanks For Learning With Maxwell Stay close for part ,,,,18,,,,,

Next we shall study Six Sigma Refine Solution Criteria.

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LEAN SIX SIGMA PART…. 16 MARATHON STUDY

‘’In my last article, we learned Six Sigma DMAIC Process Please read along as we attain another height in PART..16..

We progress by understanding Refine Solution Criteria

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Refine Solution Criteria

We need to refine the wants and musts criteria before generating possible solutions. The solution criteria can be refined by:

Clarifying Everyone’s Understanding - Clarify everyone’s understanding about each criteria by discussing and restating each criteria. Make use of the SCAMPER tool to refine or synthesize the solution criteria.

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SCAMPER is a checklist of idea-spurring questions and stands for:

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???????? S – Substitute

???????? C – Combine

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???????? A – Adapt

???????? M – Modify Or Magnify

???????? P – Put to other Uses

???????? E – Eliminate Or Minify

???????? R – Reverse Or Rearrange

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Weighing the “Wants” – Weigh the ‘wants’ to reflect upon the relative importance of each want criteria by using a Likert scale of 1-10.

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Six Sigma DMAIC Process - Improve Phase - Generate Possible Solution

Involve those who will be Affected: We need to make sure we solicit the upfront involvement of People affected by the problem or its solution and People with expertise in the subject matter. We should then Focus on the Root Causes i.e. Make the affected parties revisit the significant root causes to get to a solution. Then, pick on Idea Generation Technique.

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Five key techniques used for idea generation and synthesis are:

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1.????? Brain-writing

2.????? Benchmarking

3.????? Assumption Busting

4.????? Creative Brainstorming

5.????? Modified Brainstorming

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Idea Generation Technique - Brain-writing

Brain-writing is a technique used to generate many ideas in a short period of time. Two key modified brainstorming techniques used are Brain-writing 6-3-5 and Constrained Brain-writing.

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Brain –writing 6-3-5 - The name brain-writing 6-3-5 comes from the process of having 6 people write 3 ideas in 5 minutes on a pre-defined parameter.

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Constrained Brain-writing: The name constrained brain- writing comes the fact that on certain occasions the team may want to have a set of constrained ideas around a pre-determined focus, rather than ranging freely.

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Idea Generation Technique - Benchmarking

Process benchmarking is a technique of continually searching for the best methods, practices and processes, and either adopting or adapting their good features and implementing them to become the “best of the best”.

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Idea Generation Techniques – Assumption Busting Assumption busting as a technique is used to trace back from the current performance problems to identify rules and then surface underlying assumptions.

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The key steps involved in assumption busting are:

???????? Revisit the current problem at hand.

???????? Identify the rule(s) responsible for the problem.

???????? Trace the rule(s) back to an assumption in the process.

???????? Test to break the assumption – Is it wrong from the start? Or, Can it be made untrue?

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For example: In a personal loan approval process the problem is lengthy cycle time for approvals which is leading to dissatisfaction. Investigation of the process reveals that there is a rule existing in the process which makes every vital task in processing the loan pass through a specialist (legal expert, financial expert, credit expert) after it has been processed by an agent. The reason for rule existing in the process is an assumption which says that all loan deals are complex. On investigation and data-collection it is found that only 5% of deals are complex and thus the process assumption is wrong.

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Idea Generation Technique – Creative Brainstorming Nominal Group Technique: The nominal group technique is a structured method to narrow down & prioritize on a list of choices. It is called “nominal” because during the session the group doesn’t engage in the usual amount of interaction typical of a team. Because of this relatively low level of interaction, nominal group technique is an

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effective tool when some group members are new to each other, relatively low level of interaction is required, issues are of highly controversial nature and a team is stuck in disagreement.

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Idea Generation Technique – Modified Brainstorming Modified brainstorming technique makes some basic and/or simple amendments to the “regular” creative brainstorming in order to help expand the number and quality of ideas. Three key modified brainstorming techniques used are: Analogy Technique, Channeling Technique and Anti-Solution Technique.

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Analogy Technique - The ideas generated on the “analogy” then get translated to the real situation (the problem at hand). Channeling Technique - We begin by listing “categories” of ideas for the issue at hand. Then, as the team brainstorms, over a period of time it can “change channels” when new ideas slow down. The objective is to capture a broad range of ideas (several

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channels), as well as of quantity (as many ideas as possible in each channel).

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Anti-Solution Technique - We begin by brainstorming around the opposite of the issue at hand. This is probably the easiest of modified brainstorming methods. For example, rather than brainstorming on ways to ensure complete information on a personal loan form we brainstorm on how to ensure we get no/ incomplete information on the personal loan form.

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Thanks For Learning With Maxwell Stay close for part ,,,,20,,,,,

Next we shall study Six Sigma DMAIC Process - Introduction to Define Phase.