RaghavendraRao

@Vishwadeep Khatri: Thanks VK - this will further enable us to align with the AI trends that is buzzing the industry, looking forward to add on this capability to my fresh Business Excellence MBB knowledge. Also this gives chance to explore how i have been trying to put my learnings into practice of completing MBB last year. Cant wait to learn more. Thanks for keeping us on speed!

I would consider myself as 50% theoretical and 50% practical. Ever since i have completed MBB last year mid, i have been focusing on improving this ratio towards practicality. Since i represent software development team, iam focusing on building excellence practices for software development. I have been revolving around Lean Software Development practices. I choose this as center of my focus because, it aligns well with expectation of leadership of software development community and the practitioners of the community. Cost, Speed, Quality being key focus area, lean also revolves around these key facets. I have been doing trainings and webinars on the subject. I have covered some of the MBB competencies in the content, like AHP and BVA. FMEA is another key add on. Book series from Mary Poependieck and Tom Popendieck on Lean Software Development, has been another inspiration after MBB. Have used these books as base for interventions with the teams. After every training iam working with the participants to implement relevant practices in their team. Through these implementations we are trying to build the culture of excellence. These use cases will also help to influence senior leadership to invest more on excellence culture. I have plans of proposing the launch of "Lean Software Development Practitioner" certification within organizations. Idea is to set up three level of training program that will have step by step learning of Lean methodologies for software development and take assessments, do post training implementation. This sets up long term plan for individuals and organization to make learning into a value for their customers. This will help me to move my ratio of 50:50 on theory: practice towards higher inclination towards practices that matter for my customers.

Agile leadership has been a decade journey now for us. Lean and Agile leadership are appropriately converging in the diagram. Agile encourages to break down the deliverables into smaller batch across shorter iteration to create minimum viable product for the customer. Lean does emphasize the aspects of batch processing, continuous flow of value to customer. Importance of information flow across all levels across value stream is another important aspiration of Agile and lean. Respect for people forms foundation for both the agile and lean ways of delivering value. Design thinking has larger portion of its convergence with Agile leadership. I can relate to this in our agile journey as we stepped up our agile maturity bringing in the product thinking aspirations into our delivery. Design thinking is at the heart of the product thinking, and also is closely connected with continuous discovery aspiration of product thinking. Continuous discovery aspiration encourages that we make it a ongoing pursuit to explore our product to understand how it is used by end users so that we can enhance the capability continuously. Creativity & Innovation is rightly converging with Agile, Lean and Design thinking - but only a small convergence. As part of Agile and Lean there is emphasis on innovation, we have been able to build this culture through our KAIZEN program, in last 7 years. But its been difficult to take it to next level getting buying in from all stakeholders. Its been tough to make creativity & Innovation a strong part of Agile and Lean journey. Continuous Improvement is understood as an core part of delivery practices, but maturing the effort to next level has been poorly invested. This needs attention by leadership team insisting on roadmap for the maturing creativity & Innovation as part of Agile, Lean and Product thinking dimensions. BPM (Business Performance Management) including BI (Business Intelligence) is sitting right at the heart of Lean and surprising to see it having only small convergence with Agile. Any Management System standards aspires BPM as its vision, enabling data based decision making has been one of the aspiration that we have talked about, again not been able to seriously invest and mature the practices. Got stuck with the data accuracy and definition issues and failed to leverage better possibilities of data. Local optimization is another challenge, where we have tried to focus on global metrics and tried to optimize locally to look good on global metrics, also some places too much focus on local metrics without visibility on its effect at global level has been challenge. Unless this is not accounted it will not be possible get the true benefit of BPM, which will in-turn lead to Agile and Lean looking weak and disconnect from business. In the journey of establishing BPM, we have tried Six Sigma DMAIC approaches, again its been difficult to penetrate these practices as culture due to same data and maturity challenges highlighted above. Six sigma was tried with intention of developing structured problem solving capability among the masses, system thinking capability, ability to unearth the hidden inefficiencies (like Lean wants) and to mature the system capability rather than focusing on superficial root cause analysis that revolve around people as the cause of all problem, which Lean recommends is not true. Large part of inefficiencies in the system is due to systemic issues that needs to be addressed by focusing on understanding and improving systemic capability. TOC (Theory Of Constraints) is a potential area that needs attention in industry, ultimately all inefficiencies boil down to the system constraints. Understanding system constraints and tackling them effectively will go long way in maturing the systems, beating the disadvantages of conventional problem solving that we have discussed earlier. There is not much focus on TOC as a focus area.

Neural networks are a mock of human brain where pattern understanding for given data is done via layers of neurons with each layer having specific number of neurons. Every neuron houses a function to add it's bit to overall decision or prediction of the network. Its applications can ben seen in predicting outcome of the process based on the input data derived from the same process.

Yes zero defects is achievable for short span of time but may not be reality in a long run. It also depends on the criticality of operations and the need of the zero defect. If the criticality is high and zero defects is the need of operation from all stakeholder point of view, then zero defects can be made a reality.

ARMI is a project management tool which is used to plan support required for project various stakeholders. This includes four level of support, Approver, Resource, Member, Interested party. Approver is one who signs of the project and help move it forward. Resources are those who contribute their skills to the project where as members are those who work for the project and interested party are those who need to know about the project. RACI is also a model that stands for Responsibility, Accountability, Consultation, and Informed from project stakeholder expectation. Approver is equivalent to accountable, resource is equivalent to consultation, and interested part is equivalent to inform. But member cannot be said as equivalent to responsible, as responsible by itself indicates the ownership to execute, whereas member do not really brings out that clarity. Also resource though are the ones which bring in skills to the project, but its not clearly highlighted where as Consultation in itself indicates it’s a expert who will support the project by the skill. So May be RACI which gives clear definition of Roles may be better than ARMI which is little ambiguous.

First possibility is to explore and see if the characteristics of this new product be benchmarked against any existing products. Also exploration can focus on arriving at optimal levels for these characteristics during design of the product itself. Design Of Experiments can be explored if applicable for the scenario to arrive at specification limits for optimal performance of the product. During customer voice gathering focus can be on finding out expected performance of the product, which during designing can be converted to quantified characteristics which can produce the expected experience for the customer. To make this process of defining specification limits for characteristics of an innovative new product effective, focus on vision of the product, derive business objectives from the vision, further derive operational objectives of critical characteristics required for the product and further explore on the specification limits for the product.

Rolled Throughput Yield is the product of yield at every stage of the process. It provides the probability of producing defect free outcome in a process with multiple steps. 100 percent Rolled Throughput Yield does not indicate that process is efficient. Rolled Throughput Yield does not consider the rework associated in the process, it only accounts if the process steps have produced good outputs. So there would be hidden waste in the form of rework which makes system inefficient. First Time Right yield would be useful to analyze the efficiency of the system as it accounts for rework in the process.

Taking process performances samples over different time zones will help to increase possibility of getting natural performance of the process. Educating the people involved on the impact of indulging with natural process performance of the process would also help. Subject Matter Experts assessment of the process performance for any flaws involved in natural flow of process can also help neutralize the process. Benchmarking the process performance against similar industry processes will help to understand the natural flow of process. Sampling process outcomes from area of experienced professional’s area also will help. Even benchmarking similar processes within organization will help to neutralize the effect. Monitor the process for sudden spike or fall in performance and analyze the reasons for this sudden change in process performance also will provide good insight in to the possibility of unnatural process.

Coefficient of Variation (CV) is used to compare variability between different measures. Coefficient of variation is the ratio of standard deviation to mean values. CV helps to assess the variability between tests conducted. Is sample X has 10% CV against sample Y which has 15% CV, then sample Y is having higher variance completed to sample X in relation to their mean. CV comes in handy in cases where the samples under comparison have mean values of different range. In this case just going by standard deviation may not provide better insight, so CV values will give better perspective as they account ratio between standard deviation and mean. One point to consider is CV will help only in case of positive values of ratio data, it will not be meaningful in case of interval scales like temperature measures in Celsius or Fahrenheit.

Primary limitation of FMEA as risk assessment method is subjectivity involved in assessing severity, occurrence and detectability. Though various industries have established the criteria to rate these parameters to arrive at Risk Priority Number, while the assessment is done for these parameter its largely dependent on the experience and perception of the assessor which would introduce subjectivity in to this assessment. Also while potential failure modes are assessed this methodology does not provide clear guidance to make sure that all possible failure modes are explored. It will again be dependent on the expert to assess all possible failure modes. Also this tool does not help expose potential future failure, apart from those that can be anticipated based on expert experience. FMEA also do not draw boundary in terms of scope of exercise, so they is possibility of too much information gathered and directionless to address all failure modes. Also other area in which FMEA can be misused is not having regular refinement of the FMEA would make it disconnected from current reality which is changing always.

Customer Relation Management in the context of my organization which is in to providing Information Technology to Insurance business of our mother organization is as follows. From IT team we have our IT director who is the representative interacting with IT director from onshore, who will interface with the Business director. This business director is the Customer Relation Manager who interacts between business and IT team. Here Customer Relation Management is a connecting bridge between business and IT team to bridge any gaps in communication and to make sure that customer needs are worked upon effectively. In case of telecom sector Customer Relation Management covers on call support to help customer with any issues faced at any point of time. Also in case of privileged customers there are special contact personnel engaged to address any special needs of these customers. In case of retail industry customer relationship management is about closely working with the customer and helping build business on regular basis. Customer Relation Management also owns the responsibility of exploring on the innovation and its needs based on market needs.

Cycle time is the time taken for execution of the process, point when work starts until the product is ready for delivery, whereas lead time is the end to end time from point where customer gives the order till customer gets the good or services. One of the confusing aspects is to choose the point of customer request initiation as that is the point where cycle time starts, in cases of multiple stakeholders involved, choosing this key point would be confusing point. Cycle time also is looked at as the time taken for completion of every key step in the process, this also adds to confusion. Inclusion of waiting time as part of cycle time is another confusion point. In case where customer is actively involved in the services process, where the customer reviews and provides continuous feedback then the lead time will be tricky to measure, as scope of the services may undergo modifications.

Improvement is a mindset not just for work force but for management included. Quality focus and productivity focus should be inculcated in to habit of everyone. Inclusion of everyone in this culture building process is the key. Goal setting should be cautiously handled only until it's a motivator for everyone. Management can take its vision, mission, objectives and goal little seriously in terms of what it takes for them to be part of this journey and drive it. Management should lookout for ways to lead with examples so that workforce follows them with will. Management should design the system which simply provides career path for people to drive them with purpose and passion in the process achieving the quality and productivity. Management should make sure that the big picture of where does the contribution of people fit in for end users be made known so that it helps people build the best for users and also appreciate the fact of being part of bigger purpose in life. It's heavily management's initiative.

Fishbone diagram is misused in the scenarios where process experts are not involved to bring out true causes of the effect. In case is all the required stakeholders from affected area are not involved then there is possibility of misrepresentation of facts in fishbone diagram. Selection of main bones of the fishbone analysis, key categories to analyze the problem if not done effectively then it leads to misuse. While the root cause is arrived at when a category of cause is analyzed if a detailed 5 why analysis is not done to arrive at core root cause then that leads to misuse as well. Another key thing to remember is correlation does not mean causation. So some in-depth thought process is not to arrive at appropriate causes of the problem.

Data segmentation is about breaking up larger data sets in to smaller ones of logical groups, based on specific parameter to help in focused analysis. Since data segmentation helps in categorizing similar data sets, It helps to focus down root causes of the problem to specific category of data, avoiding the possibility of inter influence of root causes. If different segment of data are involved in the target set, there is possibility of bias on the root causes of the problem, leading to improper diagnosis of the problem. Data segmentation also focusses on the accuracy of the data, which makes it a perfect scenario for perfect root cause analysis of the problem. Data segmentation also helps in breaking up of the complex problem in to manageable and uniform segments, facilitating effective root cause analysis.

Prevention costs are related to training and proactive initiatives taken up to avoid any issues, Appraisal costs are those associated with the reviews, inspection and audits, internal failure costs are those related to failure happening at supplier end and external failure costs are those that are realized at customer end. Achieving proper equilibrium among these goes hand in hand with the maturity of the system. There is a need to strike an equilibrium among between these costs. For a system starting its maturity journey key focus would be on prevention and appraisal costs to in turn have optimal internal failure and external failure costs. In the beginning of process maturity prevention costs and appraisal costs would be varied based on how failure costs are trending. As the system matures, prevention costs should be primary focus and appraisal costs and failure costs should get lean. In case of external failure cost being high, all other three costs need to be increased to make sure external impact is reduced. Approach to reach the best scenario is to focus on Quality Improvement programs, where people, process and technology are put in to efficient use. For this system maturity initiatives like CMMi, Agile approach and other suitable frameworks can be used. For solving critical quality problems Six sigma methodology can be put in place. To improve involvement of people at all level, KAIZEN programs can be looked at. As the system matures, all the costs gets reduced to optimal level. Best scenario would be to have zero external failure cost, low tolerance to be set for internal failure rate based on feasibility in the process and customer acceptance. Prevention cost to be kept at an optimal level considering the new process and people inducted in to process. Appraisal costs should also be ideally zero as the system in matured and best one.

Type 1 error is when Null is true but gets rejected, type 2 error is when null is false but gets accepted. When a producer becomes a consumer then type 1 and 2 error contexts will change. In case if competent candidate is rejected then type 1 error occur, where as if the incompetent candidate is hired then type 2 error.

8 disciplines of problem solving is a systematic approach of problem solving. Which encompasses establishing team, describing the problem, containment action identification, root cause identification, corrective action identification, and validation of corrective action, prevention of recurrence, verifying the solution and congratulating the team. 8D will be handy in situations that needs immediate attention, like critical customer complaints. 8D also can come in handy in situation where a six sigma project solution need to be validated or piloted, as validating interim containment action is key aspect of 8D.

In cases where LCL is below zero and in negative range it has to be taken as zero for practical purpose, but it indicates the asymmetrical and non-normal data, which indicates that tests run may have some problem or the pattern of data is difficult to analyze. This implies need of analyzing the outliers and study the process stability, and that’s the relevance of lower control limits. If focus is about reduction of defects to lowest level then LCL may not be relevant, focus would only be to operate below UCL and optimize it further down. If goal achievement is the only focus then LCL will not relevant, but if reduction of process variation in the focus then LCL comes to play.

Before getting in to crux of this question, little exploration on sigma long term and short term. Long term sigma (Zlt) is (USL-Xbar)/Standard Deviation or (Xbar-LSL)/Standard Deviation. Standard Deviation considered here is from long term data. Long term data comes from a population of data where special cause of variation is primarily dominant. In case of Zst (Sigma level short term) standard deviation comes from short term data. Short term data comes from a sample data, which primarily will have common cause of variations. Now to distinguish what is short term and long term data, reference point would be, once a special cause gets introduced in to a process that’s the transition point of short term to long term. Research reveals that, over period of time center of process moves by 1.5 times sigma towards USL which is termed as ‘Long term dynamic mean variation’. So Zlt = 1.5 + Zst or Zst = Zlt - 1.5. If the data set is continuous and long term data is available and there are no grouping of data then Zlt becomes reference. In case if the data can be logically grouped, then average weighted standard deviation of all groups can be taken as reference and Zst can be arrived at. Weightage for a group in this instance will depend on the number of samples in the group. Zlt can never be greater than Zst as special cause of variations are introduced in to data over long term. With above background it can be derived that Zst is the practical sigma level of the process considering sampling of data is the practical approach, to project this in to future Zlt can be derived considering 1.5 sigma shift. Similarly vice versa, if population data sigma level (long term sigma) can be derived then the short term sigma level can be derived using short term sigma level. Hence though there are direct method to arrive at short term and long term sigma level are available this method of conversion of one sigma value to other would help.

Outliers are the uncharacteristic aspect of the process, these will mask the original character of the process and mislead one who is studying the process. First step of process improvement is to act upon these outliers and subject them to analysis to segregate common cause and special causes of variation. So that it becomes very clear on what needs attention and how. Once the special causes of variations are confirmed, that they can be handled, then analysis excluding the outliers will help us to understand true capability of the process. Statistical Process Control and Monitoring using Control charts is the good method to identify the outliers, which helps us to assess stability of the process. Based on pattern of the data spread control chart helps to proactively assess the stability of the process. Stable process would be one where there are no possibilities of failure and is operating consistently within the control limits of the process. Even before getting in to detailed analysis of outliers using Control charts, Box plot techniques will help to perform initial refining of data outliers. Box plot gives a good visibility in to data spread and helps to do meaningful analysis of process performance.

Process starts its journey from ground zero where everything is reactive. First good step process journey takes if standardization at the local level focusing on specific area of work. Later the learnings from this area has to be shared to larger audience, that way standardization will stretch to more work area and standardized ways of working across larger audience to make sure that we do not reinvent the wheel. Later process takes its next step establishing quantifiable measures to remove subjectivity of the improvement and refine the improvement. Later the measurements are strengthened and used as a lever to predict the future of the process and to take proactive steps. Ultimately process takes the tip of the maturity pyramid by attaining continuous evolution by getting better than what it was earlier. So the journey keep going on. In this quest of process excellence, process can be compared with its own history and see the progress and assess how good the maturity is. Process can also look at industry baselines to get good perspective of the maturity. Process improvement and redesign is an integral part of the good evolving process, which needs motivation factor of assessing the maturity of the process and set the course towards betterment. So maturity assessment is a key part of Process excellence journey.

Voice of Customer is the spoken and unspoken voice of internal or external business customers. Whereas Voice of Business is spoken and unspoken voice of people who constitute the business like shareholders, corporate governance etc. Key conflict of interest arise as business focus on profit on long term basis whereas customer look for best products at lower costs. Reason of this gap also will point to lack in focus on customer and business perspective as part of company’s strategic planning. In case of well-established business there is more chance of this disconnect happening as there is more chance of business relaxing on the alignment to need of customer. Another scenario where the conflict of interest may happen is lack of business to catch up with technological advancement, which leads of lack of focus on this in strategic goals, which will surely leave customer behind.

Simple way to start off putting seven wastes of lean in to good use in the organization are as follows. Engage key leaders / business owners in educating about these seven wastes. Engage key folks from practitioner’s team and socialize about these seven wastes. Most importantly brain storm on what are the equivalent wastes that are prevalent in the organization. Taking example from my own company of IT, Defects, Over production, waiting, neglected talent, transportation, inventory, motion, excess processing would be the equivalent wastes of lean but there is eight one in the form of neglected talent. What we have started doing, is we have started identifying lean as a focused exercise under our KAIZEN program. These key practitioners representing Delivery Excellence team have been educating their groups on these lean wastes and we see that there are quite a few lean projects where teams are analyzing their process flows to understand their value chain. Measures are taken up by these teams to optimize these wastes in their process. Part of this exercise we as an organization are able to realize good amount of Returns On Investment, and when our business stakeholders confidence for this focus on waste elimination. Also we have enhanced this program by optimizing the processes by automating those processes that are not adding value.