P Balakumaaran

First we need to understand the difference between Value added and Non-Value added activities.
 

These Non-Value added activities (NVA) are called as “Wastes” in the Lean world.
There are 7 classical wastes, which in short was termed as “TIMWOOD”. Of late, an eight waste was added to this list (Non utilization of skill (N)), with which the acronym was termed as “DOWNTIME”.
 
D – Defect / Scrap
O – Over processing
W – Waiting Time
N – Non utilization of skill
T – unwanted Transportation
I – Inventory
M – unwanted Motion / Movement
E – Excess Production
 
These wastes increase the Process Time, thus Increasing Cycle Time, in turn increasing the Lead Time.
Process Time – Time taken to complete an individual activity or process
Cycle Time – Net production time / Number of products produced
Lead Time – Time between the Order received from Customer to Order delivered back to the customer.
 
From Lean perspective, the ultimate goal is to eliminate all these wastes from the process. This will help to reduce the Process Time, and the saved time can be used to produce more products thus reducing the Cycle time. This will in turn help to reduce the Lead time, thus improving the On Time Delivery to the customers. This will provide the competitive edge in the market.
 
In the practical scenario, we can not always categorize the activities as only Value added (VA) & Non Value Added (NVA).
For example:
a)       Quality Inspection is considered as the Non Value added activity from the Lean Perspective. But, we cannot eliminate Quality Inspection and deliver good quality products to the customers. Hence Quality Inspection is a Business Value Added, which in turn is defined in Lean as Necessary Non Value Added (NNVA).
b)      Warehousing is considered as the NVA (from Inventory logic), however, we cannot eliminate the storage of Finished Goods in WH, which will have a direct impact on the Business. Hence this can be categorized as Necessary Non Value Added (NNVA)
c)       Equipment set-up or changeovers are supposed to be a Non Value Added activity (NVA). However, from the business perspective, it is not always possible to eliminate the Changeovers, as this limits the flexibility of the production line. Hence Changeovers are termed as Necessary Non Value Added (NNVA).
 
Unlike the NVA,  we cannot eliminate the NNVA, but we have to reduce them, thus reducing the impact of these NNVA’s in the process time, cycle time & Lead time and at the same time not impacting the Customer Satisfaction.
 
Business Value-Added Activities:
These are the activities for which the customer is not willing to pay for but they are needed for  running the processes and the business. These business value-added activities could include work done on audits, control, reduce risk, for regulation or to support value added work.
 
Taiichi Ohno called all these NVA as Muda ("waste" in Japanese). Business value-added activities are called Type-1 Muda while non-value-added activities are called Type-2 Muda.
 
Some of the critical questions that can help us to demarcate the VA & NVA are:
·         Does the activity transform the form, feature, feeling and function that the customer is willing to pay for?
·         Is it being done right the first time?
·         Is this something the customer expects to pay for?
 
A positive answer to all of these questions indicates that it is a VA. Even a single negative response indicates that it is either a NVA or a business value-added activity (NNVA)
 
Note : When we stop doing the value-added activity, the customers will complain, while eliminating a business value-added activity would lead to internal customers or regulators complaining.
 
Some of the approaches to manage the NNVA or Business Value Added activities are:
 
Approach 1:
 

 
Approach 2:
ElCoMoRe : Eliminate – Combine – Modify – Reduce
This approach talks about Eliminating all the Non Value Added activities, as much as possible.
For NNVA – we can Combine them with other VA so that they can be done in parallel, or modify the way in which it was done (like, automation / outsourcing), or Reduce the time taken to complete this activity.
This approach is also termed as ECRS : Eliminate – Combine – Reduce – Simplify.
 
Approach 3:
Theory of Constraints – this talks about identifying the limiting factor in the process, so that we can focus all the resources to eliminate the NVA  and boost the VA  and reduce the NNVA.
This approach helps to identify the bottleneck, optimized usage of resources and enable quick & effective way to improve the customer satisfaction.
 
Approach 4:
Waste Hunting – this is an approach to hunt down the wastes in the processes, down the value-stream. This a ruthless approach to cut down all the NVA’s from the process. Though this approach is effective, many times we end with conflicts among the different process owners ,as the demarcation between the NVA & NNVA is very thin. Blind implementation of this approach may cut down and create a lasting impact on the business.
 
Approach 5:
Value Stream Mapping & Line Balancing – This helps to visualize the current state Value flow and enables to identify the bottleneck process – from capacity perspective, manpower perspective and Lead time perspective. This method throws light onto the Takt Time, to assess the Customer Satisfaction. This approach is vastly helpful in those process which involves a lot of Changeovers and SMED application is determined from the VSM.
 
One of the effective metric for measuring the non-value-added content in a process is Process Efficiency.

Process Efficiency (PE) = (Value Added Time X 100) / (Value Added Time + Non Value Added Time + Business Value Added Time)

Process Maturity Model involves 6 stages :
Level 0 – Person-Dependent Practices
Level 1 – Documented Process
Level 2 – Partial Deployment
Level 3 – Full Deployment
Level 4 – Measured and Automated
Level 5 – Continuously Improving
 

 
We cannot compare between CMMI and Six Sigma. Simultaneous implementation of these concepts in an organization produces a synergy that helps in the successful accomplishment of company goals in a faster, better, and cheaper way[4]. Some  companies  use  integrated  process  architecture  to  integrate  CMMI  and Six  Sigma.  Such integration could take four approaches: 
·        Implementation of  CMMI to the  highest  level to establish  systems and processes  based on established  best  practices.  Then,  these  practices  are  followed  by  implementation  of Six Sigma to  fine-tune  such  processes  based  on  customer  expectations  and  to  continue  with needed improvements.
·        Implementation of Six Sigma as the governance model first, followed by using CMMI to close problematic gaps in process infrastructure.
·        Simultaneous  implementation  of  CMMI  and Six  Sigma by  using  CMMI  to identify critical process factors and deploying Six Sigma frameworks in such critical factors, or using Six Sigma to identify areas where CMMI is required.
·        Implementation  of  CMMI Level  3 to  establish defined  processes and then  establishing Six Sigma to  seek  higher  CMMI  maturity  levels. Six  Sigma enables  the  successful implementation of CMMI, or accelerate the transition from one CMMI level to another. The best approach is chosen based on the organization’s circumstances. The overall relationship between Six Sigma and CMMI
 
The relationship between CMMI process areas and the DMAIC framework can be summarized as
follows;
Relation 1: CMMI Process Areas, DMAIC Steps, and Generic Practices  
Several CMMI process areas and generic practices align with DMAIC roadmap steps. The diagram
in Fig 4 shows a flowchart of an organization’s overall measurement process, overlaid with DMAIC
steps and selected process areas. While this organization’s process was designed with model
compliance in mind, it represents an integrated approach to the overall use of measurement instead
of a replication of the specific practices of each process area. Similarly, this organizational process
leverages ideas of DMAIC, but is not a replication of the DMAIC steps
The relationship between CMMI process areas and the DMAIC framework can be summarized as follows;
Relation 1: CMMI Process Areas, DMAIC Steps, and Generic Practices. Many CMMI process areas and generic practices align with DMAIC roadmap steps. The diagram shows a flowchart of an organization’s overall measurement process, overlaid with DMAIC steps and selected process areas. While this organization’s process was designed with model compliance in mind, it represents an integrated approach to the overall use of measurement instead of a replication. Similarly, this organizational process takes the  ideas of DMAIC, but is not a replication of the DMAIC steps

 
Relation 2: CMMI Project Management Process Areas and Six Sigma Project Management  The CMMI process areas involving project management can be leveraged in the management of Six Sigma projects. This enables Six Sigma project teams to rely on the organizational norms for things like project launches, resource commitments, and schedule tracking.  The process areas that can be useful in this context are  • Project Planning (PP) 
• Project Monitoring and Control (PMC) 
• Integrated Project Management (IPM) 
• Organizational Process Performance (OPP) (for organization-level execution, management, and oversight of the aggregate set of Six Sigma projects) 
 
Relation 3: Incorporating DMAIC Steps within CMMI-Based Processes  As alluded to , aspects of DMAIC can be incorporated into the fabric of an organization’s process. As such, it  would become part of  the  organizational approach and  should  be documented within Organizational Process Focus (OPF) and Organizational Process Deployment (OPD). 
Relation 4: DMAIC-Based Improvement of Process Areas  All CMMI process areas are eligible for DMAIC-based improvement. For instance, the measurement process was created based on CMMI but also contained aspects of DMAIC. The defined process for measurement in that example, and for other processes defined based on each of the other process areas, could also be improved by applying multiple iterations of DMAIC.
 
DMAIC & CMMI Maturity Level:
When considering the implementation of DMAIC alongside a staged implementation of CMMI,
You may wonder what a DMAIC methodology might look like for an organization at a lower
maturity level.
When at a lower maturity level, this selective improvement likely means conducting
improvement projects in subsets of the organization, for instance in a specific development
project, group, or product line. These projects can be managed as pilots for potential
institutionalization across the organization.
 

 
Case 1: A process with higher maturity provides lesser opportunities for DMAIC
Case 2: A mature process open doors for many rapid DMAIC sequences
I agree with Case 2.
A process with higher maturity level / mature process, exhibits a well-defined system being followed and this gives a lot of opportunities to work on DMAIC in a stable environment. Both Process Maturity and DMAIC are a Continuous Improvement & never ending methodologies. Process maturity improvement happens continuously and this can be done as a DMAIC project. Not only DMAIC, it also open up the doors for DFSS / DMADV, TRIZ, and other detailed and focused improvement techniques.

The reporting metrics from many of the organizations are not descriptive in nature. We may have noticed that the most of those metrics are based on averages or sometimes median value. The average may be misleading because of uneven spread in the results or uncertainty about whether patients had an important improvement.
Some of the problems with averages are as below:
1. The mean does not show the spread of data
For example, if we want to look at the height of the students in a class, the average height is the same in classes A, B and C below but the individual students height are different.

2. The spread of data may not be even
In the above example, the data are spread evenly above and below the average. However, this is not always the case. One person’s data can have a large effect on the average of the whole group.
 
For example, we ask patients to rate their pain on a 0-100 scale and the results are:
Group A: For 99 people, pain reduced by 10 points; for 1 person, pain reduced by 50 points.
Group B: For 99 people, pain reduced by 10 points; for 1 person, pain increased by 50 points.
 
The average is a 10.4 point improvement in group A and a 9.4 improvement in group B. Based on the average, the treatment in group A looks better but, in reality, for 99% of people, both treatments are equally effective.
 
In this case, the mean is misleading since two people with extreme results are pulling the average up or down, so it does not represent the population as a whole. This is what will happen when there is a ‘skewed’ distribution because of extreme outliers.
 
In a similar way, two groups can have the same mean result, even though most participants do better on treatment A but there is just one patient who does really well on treatment B, as in the example below. 

 
 3. The mean does not show how many people had a significant improvement
Another way in which the mean can be misleading is that it does not give us information about how a patient’s improvement relates to whether they had an significant change. Sometimes this change is referred to as the minimum significant difference.
From the example below, the average of group C seems better than group D since they improved by 5 points on average compared to 4 points in group D.

However, if a minimum 7 point change is needed to be significant in the patient, no patient in group C experienced a significant change but 2 people in group D did. In terms of the proportion of patients who have a significant improvement, group D is better than group C.
 
The average in this example is misleading as it does not show which patients have an significant change. When the average is higher in one group, it does not mean that the treatment was better for the other group, since most patients do not experience the ‘average effect’.
 
Averages are meant to be a measurement across a diverse group of samples. The main purpose of averages is to measure changes over time in the same sample group.
 
Three common errors that can happen by following only the averages :
 
1)     Any data set will have outliers. These outliers tend to skew the average of the dataset to “pull” it in their direction.
2)     Many people tend to think of “average” as “typical”,  the problem with that assumption is that, there are many, exceptions to “typical.” For example, the average obesity rate in the United States, which is often cited as “about 50%.” If one researches the question, they will discover average obesity rates vary widely based on factors such as age, level of educational, profession, genetics, etc. As such, everyone is categorized differently by these factors resulting in many different average obesity rates depending on which factors describe those being measured.
3)     Averages of often wrongly used is in characterizing individual scenarios. It is statistical err to apply the average of a group of data points to a single point and expect it to be true. Even assuming data as normally distributed, the probability that any one data point will be the same as the average is 50% — the same as a random guess.
 
Solution:
 
The problem is “Customers do not feel the average — they feel the variation”. The in-depth view of the business is based on average based measures of our performance. Customers don’t weigh us on averages, they feel the variance in each performance, service, each product, each interaction we have on the phone, each correspondence we have through email or a letter, and every other process that touches the customer in one way or other — online or offline.
Customers expect a consistent, predictable business processes that deliver world-class levels of quality. They feel the difference, not the average.
 
A few examples
For qualitative measures, the feel, Visual, taste, smell et., of a product, we do not think in terms of an “average”. Instead, the customers weigh them by relative measures and the difference between them.
For Example ,
1)        “the coffee from Cloudbucks tasted better than that from Coffeenight, (or) the perfume Wildrock smells good compared to the Axel perfume.” The customers feel the difference, not the average. That’s the reason we define Quality based on Customer’s perception.
 
For quantitative measures, variation and not the average, that works well. The customers tend to perceive the variation from the previous event.
For Example,
1)        ”The last time I ordered from EKart, I received that package in 3 business days; but, on other times I receive my order usually after 6 days on standard shipping”
Ekart may calculate and report their average lead time as 3 days which may not account the instances when the lead time were more than 3 days (dissatisfied customer) and those events when it was less than 3 days (happy customer).
 
2)        ”Yesterday when I went to PizzaHouse during lunch hour, I was in and out of there within 30 minutes, but on other days I’m there for my whole lunch hour”
PizzaHouse may advertise as their average service time as 30min, which may not account those events when it went >30 min (dissatisfied customer) & those events when it was <30 min (happy customer).
 
In general, the organizations that report averages, are not concerned about both happy customer and the Dissatisfied customer. They only race towards the average customer satisfaction.
 
“On average, our customers are 90% satisfied”- a popular tagline by some businesses and organizations.
 
If I have a bad experience with a vendor, I don’t cognitively step back and think that I have been a customer for this vendor during last 10 instances, and I had only one bad experience, so far, so on average I’m 90% satisfied.
 
Customers experience good or bad, as it is and their future behavior, purchases and recommendations are shaped by these experiences. To further add to it, cognitive biases such as negative and recency bias often interject and give disproportionate weight to these positive or negative experiences, reinforcing them and the resulting behavior that follows it.
 
In probability theory and statistics, the coefficient of variation (CV), also known as relative standard deviation (RSD),[citation needed] is a standardized measure of dispersion of a probability distribution or frequency distribution. It is often expressed as a percentage, and is defined as the ratio of the standard deviation to the mean (or its absolute value. The CV or RSD is widely used in analytical chemistry to express the precision and repeatability of an assay. It is also commonly used in fields such as engineering or physics when doing quality assurance studies and ANOVA gauge R&R. In addition, CV is utilized by economists and investors in economic models.
 
It shows the extent of variability in relation to the mean of the population. The coefficient of variation should be computed only for data measured on scales that have a meaningful zero (ratio scale) and hence allow relative comparison of two measurements (i.e., division of one measurement by the other). The coefficient of variation may not have any meaning for data on an interval scale.
 
Hence Standard Deviation, Variance and Co-efficient of Variation are some of the alternatives to the averages, while presenting the performance to the Customers.

DOE was first proposed by R.Fisher in 1920s. As all factors should be taken into account, Full Factorial Designs (FFD) will undoubtedly give the most accurate results. However, this method is not practically preferred, as it includes too much experiments, which is time consuming and costly. 
             For example, when 4 factors with 2-levels, we have to manage 16 experiments (2^n  = 24). But in practice, the number factors may not be as small as 4. For example; 15 factors with 2-levels need 2^15 = 32768 experiment to do. Therefore, to reduce the number of experiments, fractional factorial design has been developed.
 
1. Taguchi Method (TM)
                  Genichi Taguchi simplified classical DOE by using orthogonal arrays (OA). Taguchi created new methods on the improvement of product and process, which includes, "Taguchi Loss Function" and product/process design with three approaches - "System, Parameter and Tolerance Design". He simplified the Fisher's DOE by using Orthogonal Arrays (OA).
                  He used the signal/noise (SN) ratio to reduce variation  in the experimental design.  TM also used the SN ratio, which is used to predict the loss of quality, to maximize the robust design’s objective function. SN ratio takes the test results’ mean and variance.
 
2. Shainin Method (SM)
                  The modern approach to the DOE is Shainin Methodology. This strategy is based on detection of the one, two or three dominant causes of the process variations by focusing on a problem response.
                  Dorian Shainin developed this method to reduce the process output variability, It is simple, relatively easy to understand and implement, but uses the combination of powerful statistical techniques, to make it more reliable and faster to achieve results.
                  In this method, the problem of the poor quality and causes of this problem are identified by the colors of Green, Red and Pink. These parameters, named Red X, Pink X and Pale Pink X, are ranked based on Pareto Principle.
 
Green Y: Indicates special quality characteristics that are important to customers
Red X: Indicates the dominant cause of the variation and it contains at least 50% of the causes of variation (Green Y) 
Pink X: Indicates the secondary cause to the overall variation. It includes 20-30% of the Green Y.
Pale Pink X: Indicates the tertiary important reason. It causes to 10-15 % of the Green Y .
 
With SM, the analysis variation can be reduced by 75% to 95% for the causes of the Green Y (Red X, Pink X and Pale Pink X). SM has mainly 12 techniques, of which, 9 are  problem solving and 3 are controlling and preventing any repetition of the solved problems.
 

 
Comparison between Taguchi DOE Vs Shainin DOE methods:
 

Also, the Pro's and Con's of the 2 methods are listed below and it helps to choose the best appropriate method, based on the requirement
 

Some of the risks associated with Shainin DOE method is listed below:
 
1. This method focuses only on the analysis of mean response and does not take into account the variability of 2 different responses.
2. It can help only upto 70%-80% reduction of the problem, as it focuses on Vital few. The impact of the remaining causes are to be accounted with further more iterations.
3. Grouping of the causes and progressive elimination method, may result in eliminating some significant causes.
 

In 1994, Robert Dilts proposed The Disney Creativity Strategy (also known as Disney Brainstorming Method or Disney Creativity Method) that created this 100 billion kingdom. This is an effective roadmap for the incubation of many products and projects for thirty years. The Disney strategy use three step approach to identify project goals, clear paths, and avoid risks as shown in the Venn diagram below.
 

 
Disney’s Creativity Thinking Process
                    Disney’s strategy works based on 3 roles — to generate ideas, evaluate ideas, construct and criticize a plan of action structured in three thinking styles: dreamers, realizers, and critics.
 
                    This creative process unlocks the mind’s capabilities to dream and form unexpected ideas and solutions for existing problems, which may not be applicable in reality. This method emphasis on the balance between the dream and reality in order to enhance the viability of the solution proposed.
                    The three creative thinking styles are – dreamers, realizers, and critics.
In the first mode, the group uses divergent thinking to conceive creative and radical ideas, to brainstorm ideal solution. In the next mode, the group takes a realizer viewpoint. Here, the ideas left by the dreamers are reviewed through convergent thinking by the pragmatic realists and they select the best idea to create an action plan.  The final viewpoint is that of critics, where they review the plan made by the realists in order to identify weaknesses, obstacles or risks.
 
The dreamer
Any creative idea starts with a dream, devoid of the realism and practicality. In conventional meetings, this dreaming style is halted by reality. The first stage allows the team to share their dream without no restrictions or criticism, thus creating a pool of creative ideas. 
Following questions are asked by the dreamer:
1.    What do we want?
2.    What is the solution?
3.    How do we imagine the solution?
4.    What are the benefits of applying this solution?
 
The realist
The team switches the place and mode to think in a more logical style. Now, the attendees pretend that the dream is possible and start putting plans to turn the imaginary ideas into an achievable action plan. This is a constructive approach and it includes questions such as the following:
1.    How can we apply this idea in reality?
2.    What is the action plan to apply the idea?
3.    What is the timeline to apply this idea?
4.    How to evaluate the idea?
 
The Critic
The critic thinking mode tends to discover the limitations of applying the idea and how to overcome it. Now, the team provides a constructive critique for the idea in order to find the weak points and solve it in the final solution. Following questions are asked by the critics:
1.    What could be wrong with the idea?
2.    What is missing?
3.    Why cannot we apply it?
4.    What are the weaknesses in the plan?
5.    How to Create Disney’s Creativity Strategy in Team Afford?
6.    Why does this strategy work?
 
Disney used the same strategies to keep his staff coordinated towards a particular project.  He literally moved each of his staff between 3 rooms. Each room had a different function.
 
Room 1 Was The Dreamer Room – 

 
 
Room 2 Was The Realist Room – 

 
 
Room 3 Was The Critic Room – 

 
 
                   We can use readily available Disney Creative Strategy templates available in the visual collaboration platforms, such as Miro or Klaxoon.
 
Advantages of Disney's Creative Method Vs De Bono's Six Thinking Hats:

1. It is a step by step approach where it starts with Dream, Reality check & Criticism, where as, Six thinking hats doesn't follow step by step approach.
2.No confrontation of ideas or difference of opinion, as every one will be going through each phase together. In Six thinking hats method, people can still disagree strongly and there can even be conflicts about different perspectives.
3. This uses both divergent and convergent way of thinking whereas Six thinking hats use only convergent way of thinking.
4. Six thinking hats is a time consuming activity compared to Disney's method.
 
Disadvantages of Disney's Creative Method Vs De Bono's Six Thinking Hats:

1. There is a Blue Hat which will manage the ideas generation process in Six thinking hats, which is missing in the Disney's method.
2. Six thinking hats is more of a pragmatic approach where as in Disney's method a lot of time is spent on impractical ideas and how to make them realistic.

The Reliability Block Diagram is used for industrial and commercial power systems. It is a graphical presentation of a system diagram based on reliability or functional logic; i.e., connecting subsystems or components according to their function or reliability relationship. The significance of RBD is that it is easy to read. It is easily understood by customers who purchase the critical power systems, by the people who sell the systems, by engineers who design and test the systems, and by managers who make decisions on the systems. With knowledge of the system design, engineers can easily construct, verify, and modify the RBD, and also communicate with those of different functions.  It is generally drawn from top-down, left-right approach, depicting the system element functions. The 3 types of RBD are Series, Parallel and Combination of Series & Parallel elements. 
 

                                              An example of Reliability block diagram
Reliability is typically calculated as below:
Reliability = e-λt
λ (lambda) = Failure rate = 1/MTBF
t =  time in cycles, hours, miles, etc.
e = natural logarithm = 2.71828
 
RBD can be used in the following phases of DMADV:
Analyze Phase - To identify the to identify potential areas of poor reliability and where improvements can be made to lower the failure rates for the equipment Design Phase - To construct / design the system & subsystems thus improving the reliability from the functional perspective Verify Phase - To check if the newly designed system / subsystem is able to eliminate the potential bottlenecks identified with respect to reliability are improved.   This provides a useful benchmark to refer to when looking back at how the system used to perform, and whether the system’s current performance is as expected and is based on the model created.