Anita Upadhyay

Voice of customer (VOC) are the explicit and implicit needs, wants, expectations and priorities of internal and external customers. Some of the business priorities are total cost of ownership over time, productivity gains, etc. Some of the voice of customer for a software development firm customer are quick deliveries, zero defect deliveries, lowest cost, 24x7 availability, latest technology, most skilled team, extended warranty periods, flexibility, etc. Voice of business (VOB) are the explicit and implicit needs, wants, expectations and priorities of people who run the business namely shareholders, management, employees, etc). Some of the business priorities are profitability, return on investment etc. Some of the voice of business for a software development firm are repeat revenue, timely project closures, timely payments, minimal rework costs, optimal resource utilization, minimize manpower costs, employee satisfaction, etc. Since the priorities of business and customer are different being different entities with different purpose, their Y (priorities) and X (needs and expectations) may differ. While business / company exists for the purpose of making profit which is required to sustain and achieve its goals, it cannot do so at the cost of customer’s money. The same extends to the customer as in while he expects best services at lowest prices, it cannot do so at the cost of company’s sustenance. There may be some parameters which may be common however the acceptable measures may vary – for example both business and customer expect quality, however the level of quality that is acceptable to business and customer may be different. Voice of business and customer must be considered alongside when identifying the common priorities and designing the processes. Businesses must make sufficient efforts to address voice of customer and use competition analysis to understand how the competition performs on the said parameters before disregarding the voice of customer based on voice of business. Voice of customer must also be viewed against the company strategy to ensure alignment. Tools like Quality function deployment (QFD) or House of Quality helps businesses map and prioritize these conflicting priorities. Other relevant tools used in conjunction are balanced score card and goals means diagram which help view these priorities together.

Measures of central tendency like mean, median and mode are typically useful when spread is small. They are not representative of the data if the spread of data is high. Measures of spread or variation helps to describe the variability in data. One of the main reasons for importance of measures of spread is its relationship with measures of central tendency. It provides us understanding on how well the mean represents the data. Range is the simplest measure of spread and while it doesn’t stand out as measure that is often used, however it helps identify the boundaries of the data and also helps identify if any values are outliers instantly. Quartiles are a useful measure of spread because they are less affected by outliers or a skewed data set that the equivalent measures of mean and standard deviation. Therefore quartiles are often reported along with median. Interquartile range is popular measure that describes the difference between third and first quartile, therefore helping us understand the range of middle half of the values in data. Quartiles are however limited in that they do not take every score into account. The absolute deviation, standard deviation and variance address this concern as they show the amount of deviation of each score from the mean. Standard deviation is arrived at by calculating sum of deviation from the mean and divide by no. of measures. However in this case the negatives and positives cancel each other, to address this absolution deviation is used in which the sign is ignored when calculating the sum of deviation from mean and then dividing by no. of measures. Variance on the other hand uses sum of squared deviation from mean instead of ignoring the sign to eliminate the effective negatives. Both mean and standard deviation are highly influenced by extreme observations, they are more suitable for numerical data that is roughly symmetric. For asymmetric data the five number summary comprising of minimum, Q1, median, Q3 and maximum is more useful measure. Measures of central tendency and spread are often therefore used in conjunction to provide an overall description of set of data.

Business Excellence Sponsor is a crucial role in organizations that are driving large scale business excellence initiatives at organization level. Business Excellence Sponsor is the helm of these initiatives. He or she can really define and determine the extent of influence business excellence initiatives will have on the organization’s performance. He or she should be a person with rich experience in decades and in a role position that has extensive decision making capability and capacity. Considering the criticality of the role, he or she must have below characteristics to be able to drive business excellence initiatives brilliantly. - Ability to envision – strategic intelligence, prioritization - Ability to visualize the challenges of future - Ability to drive changes – operational intelligence - Ability to drive cultural changes – culture building - Ability to focus on value generation – business acumen - Alignment of business and social interest – social relevance - Ability to nurture leadership and coach – people skills - Ability to spread trust and empower – leadership - Ability to find win-win solutions – teamwork and conflict management, decision making - Never accepts status quo or never becomes complacent at any level of achievement - Good acceptance across the organization to influence decisions - Strong understanding of processes and master black belt

Rework is generally considered wasteful endeavor however for products that perform non-critical functions or products produced in masses at very low cost, rework may not be worthwhile (scrapping may be easier) or rework may be acceptable. Products that are related to health like pacemakers have to be done right first time. In such cases rework is undesirable. For certain industries like software industry zero defect is impractical and cannot be achieved. The cost to reach near zero defect is exponentially high and not justified against the risks associated with defects leaked. Products like nails – produced in masses, low cost of manufacturing and sale may be perfectly ok with scraping rework items and not create extraordinary processes to ensure zero rework as the cost may be far more than the saving from reduced rework. Again here it is impractical and undesirable. Finally the decision on what level of rework is acceptable and necessary is driven by cost of poor quality to intended user (hazard, risk associated), cost of rework (to fix identified defect), ability to fix the defect effectively like a fresh product without introducing other defects.

New generation tools and techniques allows us to measure practically everything and it is cheap to do so. This means that measure and analyze phase of DMAIC will no longer be impacted due to lack of sufficient data. Predictive modelling can be used in analyze and improve phase to make it more effective since the data availability is no longer a constraint. It will now become increasingly important to select relevant data from mountains of information available. It is not Lean Six Sigma vs BigData but BigData is an answer to a critical problem that plagued Six Sigma since its advent about data availability and analytics. Descriptive Analytics – Insight into Past Descriptive analytics is a preliminary stage of data processing that creates summary of historical data to yield useful information and possibly prepare for further analysis. It uses data aggregation and data mining to provide insights on what has happened. Descriptive Statistics is a method of organizing, summarizing, and presenting data in a convenient and informative way with aim of understanding what has happened or current situation and aids in descriptive analytics. The actual method used depends on what information we would like to extract. The tools and techniques covered in Six Sigma that are applied in Descriptive analytics are measures of central tendency like mean, median, mode and quartiles and measures of dispersion / variation like standard deviation, variance and range. This is reasonably well captured in Lean Six Sigma. Predictive Analytics – Understanding the future Predictive analytics uses statistical models and forecast techniques to understand the future and answer what could happen. It helps predict what could happen based on data and these predictions are not 100% certain and is this uncertainty is denoted in form of probability. It uses historical data available within organizations to identify patterns and apply statistical models to forecast customer behavior, purchase patterns, inventory and sales. Another common application is to compute credit score. It helps fill in information that is not available based on information available. The tools and techniques covered in Six Sigma that can be applied in Predictive Analytics are Hypothesis testing, correlation and regression. This is also reasonably well captured in Lean Six Sigma. Prescriptive Analytics – Advise on possible outcomes and how to influence it Prescriptive Analytics is a relatively new field that allows users to “prescribe” a no. of possible actions and guide them towards a solution. It helps to quantify effect of future decisions in order to advice on possible outcomes before actual decision is made. So it provides not just insight on what will happen but why it will happen in terms of actions that are required to ensure prediction is realized and how best to maximize benefits. It uses a combination of techniques and tools like business rules, algorithm, machine learning and computational modelling procedures which are applied against input from various sources like transactional, historical data, real time data feeds and big data. These are relatively complex and most companies aren’t applying it yet. The tools and techniques covered in Six Sigma that can be applied in Prescriptive Analytics are Design of Experiments and Simulation. This is captured in Lean Six Sigma however can be handled better with Prescriptive Analytics using BigData.

False alarm - there is no cause of concern but a concern has been flagged. So when this happens, the responsible person can validate the concern is not valid and no specific action may be required. However when false alarms occur frequently or repeatedly, there is a productivity loss in validating false alarms. For eg. 1. weather department raised an alarm of Hurricane Irma hitting Florida. As a result mass evacuation was ordered. However if Irma doesn’t cross Florida then it is false alarm – effort or cost of evacuation is a waste. 2. quality department raised alarm about a specific issue in Space Shuttle Challenger, so engineering team inspects the issue reported and confirms that this is not an issue... this is a false alarm.. in this case Space Shuttle Challenger will not face specific issues in its mission, though internally a cost overhead will be borne due to false alarm. Missed alert – an alert was needed but was missed to raise or an alert was sounded but person responsible missed to take action on it. In both cases the case was a genuine concern and due to lack of alert or lack of action on alert, concern was not attended to, leading to damages. For eg. 1. weather department didn’t raise an alert or alert was raised but no action was taken to inform public or evaluate about Hurricane Irma hitting Florida and Hurricane Irma actually hits Florida… this would be a disaster and result in heavy loss of life… 2. quality department misses to flag a major defect in the Space Shuttle Challenger or defect was reported but it was missed, then Space Shuttle Challenger will run into an issue in its mission... now this would an issue and may result in severe losses... The missed alert situation is definitely more alarming than false alarm and can result in more significant losses. However repeated false alarms can result in loss of productivity or result in cost overheads.

Fault Tree Analysis was first introduced by Bell Laboratories and a popular technique for reliability, maintainability and safety analysis. It is a top-down deductive failure analysis technique used to determine the various combinations of events / causes that could cause undesired events. In simple words it is a technique to decompose a problem and identify situation that may lead to failure. Usage Scenarios: - Understand the logic leading to top event or undesired state - Show compliance with input system safety / reliability requirements - Prioritize contributors to top event - Minimize and optimize resources Steps for performing FTA: - Step 1: Define scope of analysis - Step 2: Define the fault condition and top level failure - Step 3: Identify possible causes for top level failure to occur - Step 4: Consider the relationship between elements to decide logic gates o or (one event can cause failure alone) o and (requires multiple events to result in failure) o exclusive or (output occurs if exactly one event occurs) o priority and (output occurs if events occur in a specific sequence specified by a conditioning event) o inhibit (output occurs if event occurs under an enabling condition specified by a conditioning event) - Step 5: Continue to breakdown each level by repeating Step 3 and 4 - Step 6: Identify probability of the causes - Step 7: Analysis the fault tree Illustration Practical applications - Hospital uses FTA to identify how incorrect prescriptions may be given through combination of events - Airplane parts manufacturer uses FTA to identify critical faults that could lead to hazardous failure - NASA uses FTA for risk and reliability analysis post Challenger accident - Used for process hazard analysis in high hazard process industry - Software industry uses FTA for debugging and software testing Situations wherein FTA is not effective - Complex man-made systems with too many causes for failure - Scarce or insufficient data

By and large the above is true for all industries and processes however there are certain activities in all industries which may not directly add value to customer however indirectly contribute to the customer satisfaction or business success and therefore there is a need for a category called non-value add but necessary. These tasks or activities do not directly bring value to the customer however enable delivering value to customer. These are also activities that allow businesses to continue to stay in business. The kind of activities that fall into this category are: - Management activities in projects which involve creating plans and monitoring against plans, providing status reports, tracking metrics - Regulatory activities that are required for compliance - Requirements specifications in software industry which many a customers don’t particularly like but is a necessary ingredient to successful projects and happy customers The value must be looked at not from just customer perspective but also the viability of the entire business and therefore activities like training, payroll, accounting, audits are essential to successful business and must be considered in a category necessary non-value adding. The overall approach that would work with this situation is: o Customer Value Add to be OPTIMIZED o Necessary non-value add to be MINIMIZED o Non-Value add to be ELIMINATED

A process takes input(s) and perform a set of activities to produce an outcome or result. Innovation: The process of creating a product or service solution that delivers significant new customer value. The process begins with the selection of the customer and market, includes the identification and prioritization of opportunities, and ends with the creation of an innovative product or service. Lean Six sigma is a collaborative team effort to improve performance by systematically removing waste, reducing variation and increase predictability. Six Sigma and Innovation are complementary yet opposite. Refer table below for how they complement and contrast and how they can work together. Six Sigma Innovation Contrast · Reduce variation · Optimize processes and product trade-offs · Be precise and data driven · Make money now · Encourage diverse thinking · Break the mold so trade-offs are unnecessary · Create “emotional” experiences · Create opportunities to make money by taking risks How they complement Helps create customer loyalty through stable & capable processes Frees up resources by eliminating costs Making execution of a new idea more predictable --> creates foundation for innovation --> can be reinvested in innovation --> helps mitigate risks What’s common Both aim to meet customer expectations in cost effective manner How does it help if they come together Lean brings structure and predictability to innovation and sharpens the distinction between idea generation and development process. Lean can help bring discipline needed to develop and profit from new product and service offerings. Lean encourages focus on - listen to the voice of the customer, business and employee which will help innovate a profitable winning idea. Lean can help reduce risk. Caution Excessive adherence to Lean may kill innovation as people will not deviate from norm and thus no innovation. Reason / Necessity for them to come together Increasing competition, need to be more profitable and sustainable, pressure for cost cuts Case Study for co-existence Pixar is a live example of lean and innovation coexistence. Pixar believes in a method to the madness. Pixar has created a set of processes that emphasizes team based collaboration and continuous feedback loops to help overcome creative blocks and track deliverables. Pixar has setup structure that believes individual is as important to the team as team is to individual – this helps create structures that helps resolve roadblocks faster and encourages individuals to discuss problems and collaborate. They have practice of discussing daily progress and bottleneck / risks. Ideas that are likely to be doomed are therefore terminated early on. They nurture creative freedoms and yet reduce waste. Techniques that can be used Root cause analysis (RCA) Voice of customer (VOC) Quality function deployment (QFD) House of Quality (HOQ) Failure mode effects and analysis (FMEA) Value engineering analysis (VEA) XY Matrix, TRIZ method (Theory of Inventive Problem Solving) PUGH Matrix Design of Experiments (DOE) Mistake Proofing DMAIC and application of above tools at relevant phases. Conclusion: Not everyone is an advocate of Six Sigma. While the well thought-out process and constant benchmarking may seem to be restrictive however Six Sigma does not suppress free thinking. They complement each other and when used creatively and effectively can result in true benefits.

Kaizen, Kaikaku and Kakushin are three approaches within Lean which have their roots from Toyota. They work well together and have different areas of focus and magnitude of impact/risk. The table below provides differentiation and tips for their implementation. Kaizen - Kai – Change, Zen – Good Kaikaku – Radical Change Kakushin – Innovation Definition Evolutionary change for better focused on incremental improvements Revolutionary change focused on radical improvements Innovation, transformation, reform and renewal Focus Area Continual improvement of their processes Transformation of their organizational culture Bringing something new into existence People All levels including workers Executives and top management Top Management Risk / Impact Low Medium High Steps / Tips / Techniques · 5 S o Seiri – Sort out o Seition – organize o Seiso – shine the workplace o Seiketsu – standardization o Shitsuke – self-discipline · 7+1 Wastes o Transportation o Inventory o Motion o Waiting o Over-processing o Over-production o Defects o Skills under-utilization · Look for ways to make maximum contribution to ideal state – “What would be ideal customer experience” · Search for opportunities for radical improvements · Apply 80-20 rule to do more with less · Creative problem solving · Challenge assumptions · Ask What and why questions – think differently · Brainstorm creative solutions · Know how to sell radical ideas – overcome resistance · Think positively and act promptly · Follow radical improvements with continual improvements (Kaizen) · Attribute listing · Biomimicry · Brainwriting 6-3-5 · Challenge assumptions · Osborn checklist · Harvey cards · Lotus Blossom Technique · Redefinition · Reverse Brainstorming · Systematic Inventive Thinking · COCD Box · Force Field Analysis · Six Thinking Hats · Follow it with radical and incremental improvements Eg. · Reduce production time by implementing 5 S · Usability improvement in software that’s allows people to enter data with reduced no. of values to enter · Introduce new lighter material for vehicle body - reform production processes · Upgrade software with new technology which allows faster development, better performance and more features · Make simplified cars by cutting the number of parts in half · Extending software on multiple media allowing ease of access and seamless collaboration and eliminates duplication throughout the supply chain Conclusion: All the three techniques have different role in the lean journey and allows organization to identify and implement changes at different levels and magnitude of impact. Each of them are necessary and must be run in tandem for an organization to be truly lean and successful cause by just being innovative, a company may not be successful in long run as it may lose out on efficiency nor by just being a company which is strong on efficiency sustain itself in long run as lack of innovation will allow competitors who are innovative to beat it down in market.

In my view both the statements have their own place in the continuous improvement or excellence journey. "If you can't measure it, you can't manage it" - Peter Drucker. "It is wrong to suppose that if you can't measure it, you can't manage it. The most important numbers are unknown and unknowable" - Dr. W. Edward Deming Arguments For Measuring is a definite way of identifying whats working and what is not working. If something can be measured, then definitely it is easier to manage and manage effectively. Managing things that can be measured without measuring definitely hurts chances of success or extent of success. Eg. Losing Weight without stepping on weighing scale regularly Measuring everything of importance to management is not feasible however it must still be managed – otherwise these important aspects that cannot be measured are left unattended To put it in another way, we just cannot say what can’t be measured it not worth managing. Eg. ROI on raising children, satisfaction of serving society, reputation, creativity, relationships Arguments Against Focusing on numbers without proper understanding of concepts of variance and other statistical interpretations or using inappropriate measures can lead to inappropriate conclusions. For eg. measuring success of an event by no. of participants who attended alone – may or may not mean anything. You can’t know whether you are successful unless success is defined. It helps demonstrate progress and qualify success. For eg. employee satisfaction cannot be measured using a specific index or parameter however in absence of defining some way to measure it you may meet challenges. After detailed investigations, it appears that both gentlemen had strong measurements background and have faith in measurements systems and its importance, they however feel that not everything important can be measured. Some references - http://www.druckerinstitute.com/2013/07/measurement-myopia/ https://blog.deming.org/2015/08/myth-if-you-cant-measure-it-you-cant-manage-it/ Conclusion: Measurement YES, but ONLY measurement NO.

Cause is a variable which affects the outcome by increasing its chances or making it happen. Problem is the outcome of the cause. Scenario Understanding Conclusion Action A cause (X) may be necessary but not sufficient for a problem (Y) to occur. Problem (Y) occurs ONLY when cause (X) is TRUE Problem (Y) may not occur even when the cause (X) is TRUE Causes other than X (A, B, …) must occur together with X for problem Y to occur Fix the cause (X) to remove one of the factors causing the problem (Y) however this will not fix it so look for other causes (A, B, ..) that result in this problem (Y) and address them too to fix the problem (Y). A cause (X) may be sufficient but not necessary for a problem (Y) to happen. Problem (Y) occurs whenever cause (X) is TRUE Problem (Y) can occur even when cause (X) is not TRUE Cause X is a definite cause for the problem (Y), however other causes (A, B, … ) can also result in this problem (Y) Fix the cause (X) to address some scenarios in which the problem (Y) occurs. However look for other causes (A, B, ..) which may also result in this problem to fix it for all scenarios. A cause (X) may be neither sufficient nor necessary for a problem (Y) to occur. Problem (Y) may not occur even when cause (X) is TRUE Problem (Y) can occur even when cause (X) is not TRUE Cause X is not established as a reason for the problem (Y) Fixing cause (X) may not fix the problem (Y). Look for other causes (A, B, ..) which are likely to be more relevant cause for this problem (Y) A cause may be both sufficient and necessary for a problem to occur. Problem (Y) occurs ONLY when cause (X) is TRUE Problem (Y) occurs whenever cause (X) is TRUE Cause X is a definite cause for problem Y Fixing cause (X) may fix Y completely.

Excellence is a continuous endeavor of an individual or group of individuals to improve his or her performance with relation to previous self-performance and / or target performance. Excellence can be measured through relevant performance indicators with reference to the activity / process / business. Businesses that desire to excel inspire operational excellence through outstanding processes driven by excelling professionals. Excelling professionals who collaborate well and work together in a team create process excellence, excelling processes together create operational efficiency / excellence which in turn creates business excellence. Excellence has to be driven top down and bottom up for it to be really exceptional – which means it has to be a value that all individuals imbibe. Personal excellence is at individual level and focuses on excelling in activities performed by individuals. This is driven by individual initiatives to excel, standards laid down by embodying process and continuous improvement initiatives at activity level. Personal excellence is often driven by continuous improvement programs like Kaizen which allows organizations to instill excellence as a value at the lowest level. Process excellence is at a process (group of activities) level and focuses on overall outcome of the process (group of activities). This is driven by process team's initiative to excel and operational excellence which defines the expected outcome and continuous improvement initiatives for various processes. Process and business owners often drive these initiatives through implementation of various Lean Management. Operations excellence is focused on the organizations operations which largely focuses on principles, systems and tools that help deliver to meet customer requirements keeping both internal and external stakeholders happy. This is driven by operations unit initiatives to excel and business excellence which defines the vision and strategy that drives the entire organization. Operations owners often drives this through no. of initiatives under the umbrella of Business Process Management (BPM). Business excellence is at the organization level and focuses on achieving superior business performance that helps generate better shareholder value and business growth. This is driven by various external factors like completion and market apart from internal factors like business vision and strategy and involves active risk and change management. Top management often drives this through a broad spectrum of strategies, programs and techniques under the umbrella of Total Quality Management (TQM).