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  1. 23 points

    Version 12

    4,556 downloads

    This is the Pre-Course study material for Lean Six Sigma Green Belt Training. Note: It is important to read this material before the training, as there will be a Quiz based on the pre-course material on the 1st day of the training.
  2. 19 points
    The most essential facet of being a manager, be it in any walk of life, is for sure the one skill that differentiates him/her from anybody else and that is: Decision-making. It is most definitely the power, rather the ability of a person to take prudent, relevant and effective decisions that set him/her a notch ahead in the race that the corporate run comprises. Thus, we not only need to know what or why, but how to make decisions, how to make the choices that we make, to be able to foresee what lies in store having made those choices and to be positive that the alternatives have an opportunity cost that is less likely to exceed the benefits from the plan A that we choose. The mind always works on matter, matter that is assimilated, accumulated, observed, absorbed and created. This matter more often than not comprises facts and figures that we come across over a period of time and it is this that formulates our opinions on issues and circumstances and should guide us in choosing the way forward. One’s experiences are a key to learning and avoid mistakes, but life is all about new challenges and it isn’t necessary that one’s challenge will be the other’s or that one’s solution will work out for the other, problems, solutions and hence decisions, vary, with time and tide, to each his own. Decision-making is a crucial skill for treading on the path to success. It is imperative thus that one take informed decisions, rather than impulsive or intuitive ones. This itself can be illustrated by the use of numerical figures in terms of probability of a decision being a thumbs-up or a downer based on whether it is based on gut-feeling or not, where a gutsy decision has a fair and square half chance of hitting the bulls-eye, an informed, data driven decision based on historical data can warrant a chance much greater than that of being a winner. Data analytics, a field of research, an active component of all industrial data processing and reporting, has become the buzz-word in the world of business. It is owing to the stress on hard core historical data projection and information related to past trends, their relevance, their cyclical patterns, any relational change between parameters and many other such ways of engineering data to deduce important facts and identify occurrences, areas of improvement, effect of factors and even have a decent foresight by extrapolating trends, assuming at times ceteris paribus. It is the mere fact that this subject is being undertaken as a specialization in some of the newer and premiere business schools in the country that relays its importance, its applicability and the extent to which it can play a role in making key decisions easier for every managerial level all courtesy the heavy data dependency, real data building up virtual images only for a clearer picture albeit a surer one. While the success of data driven decision making can itself be proved through data, its wonder lies in the fact that one can also prove the risk related to intuition based choices in a data driven manner. There is a reason the rational mind which processes such data is called analytical. It overcomes the biases of the emotional mind and loosens the shackles of a narrow-minded attitude towards things or the safe zone preference based on conservative beliefs, prevalent practices and recency effect of happenings. Strong and concrete data has the power to shake people out of the herd mentality and of Utopian expectations based on rosy optimism rather than hard-hitting realism. Mostly intuitive decisions tend to land people in false situations, because they are taken either in haste or due to ignorance. Either way, it makes waste due to the hurry in which it is taken without any deliberation or any heed to the consequence, totally reliant on luck for the outcome, well if good, expected if bad. In case of ignorance, the risk is even higher as the stakeholder in question very conveniently underestimates the potential risk and can in fact land in a whole lot of trouble, based on the decision ending up seeming gullible, irrational and more often than not irresponsible and unreliable. Thus we can say that data plays a key role, be it that of the number of germs that a disinfectant can kill or that of the amount of disposable income in the economy that drives consumption, be it the household consumer or the government bodies, be it the decision of keeping yourself healthy or the nation, it is data alone that can be relied upon for a less blurred vision of the future through the spectacles of the past. If such decisions were to be made on the flip of a coin, only probability data related to the outcomes of a coin flip can tell us which side we would be losing, the health of the nation or the its people, either of which states the vulnerability of intuition as a tool for making choices against rationality which is but obvious the only choice to make when deciding, anything, big or small, for the better and not remotely for the worse. Note - Visitors shall not be able to comment on this article until they are logged in.
  3. 13 points
    In the FMCG sector, our company is rated among the top 5 organisations both by the consumers and industry. Much of the success is attributed to the increased consumer confidence and round-the-clock shelf SKU availability of our product line. The role of our Purchase Division has always been at the core for achieving this mark. Over the years our team under your guidance has been able to keep the inventory at optimum levels thereby helping the Production Unit to make delivery on time. It is due to the careful execution of our fundamental function - 'Material Management' which ensures that raw material is procured from the suppliers at the right time and in right quantities. This is the key to success in our sector and thus any minuscule improvement towards this can play an important role in raising our organization's bar. Our well maintained database of daily Material Management activities can help us in this direction for making more accurate estimations. An approach-'Data Driven Decision Making' can be applied in which assessment data and background information can be used to take decisions related to planning activities. According to a study by MIT Center for Digital Business, organizations driven most by data-based decision making had 4% higher productivity rates and 6% higher profits. With data driven decision making we can deploy Just-In-Time as approach and Material Resource Planning as the method for waste minimization in the purchase cycle. To demonstrate the strategic, operational and financial advantages of DDDM approach, we can consider the following conventional process being carried out at our unit. Under usual circumstances, we have to keep a safety stock of inventory items so as to counter the market and supply chain uncertainties. These may include logistics delay, plant failures, supply side variability, demand side variability and others. This leads to estimations made with the help of brainstorming methods within the team and experiential knowledge that cumulatively determine the amount of inventory to be ordered. With the help of estimations, performance is delivered in terms of fulfilling various conditions like on time delivery, safety stock, safety time related to inventory items. But there could occur estimation errors during the process due to which stores or warehouses can have lower or excessive amounts of inventory. This can affect the overall process efficiency both in monetary and operational terms. To improve this scenario, Data driven decision making mode can be deployed. Considering the situation that we faced last month. The production unit had given a demand forecast of 100 packs for one of our products and keeping safety stock levels in mind, we had ordered 120 packs. But we received only 115 packs from the supplier due to production based variability at his end which had not been a factor of consideration in our process. Also, the actual demand turned out to be 118 packs which was closer to our safety stock levels. So, overall we faced a shortage of 3 packs. From this we can identify that there is a chance of around 4% variability in delivery from supplier's end. This data if incorporated in taking decisions before ordering again from the same supplier can help us in achieving more accurate outcomes. This was an example of countering lower inventory received from the supplier where DDDM could have helped otherwise. Considering another situation that occurred 6 months back where we had ordered around 200 packs where as the production had ordered for 120 packs only but finally picked up around 150 packs from the warehouse. We had a rough idea of up-scaling from demand side as well as in the costs from the supplier's end due to which we increased the order. Though a more accurate estimation using DDDM would have given an additional gain by reducing the wastage of the remaining underutilized 50 packs as well. Using DDDM as one of our modus operandi would be convenient and beneficial too as its output depends on the quality of data gathered and a well managed database already in place can help us in reaping maximum benefits out of this investment. Further its effectiveness depends on defining the questions to be considered before analyzing the data and with your experience level in this area, we can easily frame pertinent questions to get relevant results from the data. Data driven decision making would give us added advantages of faster processing, refocusing our resources to increase the yield, relevant data backing to explain our rationale behind purchase decisions to the management, foreseeing the opportunities and threats in the market and overall supply chain. Over a period of time, this approach can also lead to building a reliable group of suppliers giving us a competitive advantage gained by adopting a data backed strategic purchasing model. An early adoption of Data driven decision making would bring maturity to our supply chain infrastructure and resilience towards unforeseen circumstances so that we can quickly respond to them without compromising on financial and operational aspects. Note - Visitors shall not be able to comment on this article until they are logged in.
  4. 13 points
    As a Computer Illiterate growing up in the new millennium, I had tremendous difficulty making sense of how to do most of my stuff. Most of my decisions were snap, on-the-moment and intuitive ones. By the time I was in my teens, I had learnt my way around the big computer problem. My experiences and the Great Indian “Jugaad†mind-set had provided me with a sufficient enough method wherein I only worked on the computer when absolutely necessary. If there was any other way to do the task, any escape route, I gladly followed it. By now you must have realised that I was and am sceptic and data-averse. Data to me was always those unreadable files that eat a hell lot of my storage space. I was gaining proficiency in getting around this data problem and along came Big Data. Everyone from Google to Barrack Obama was using it.[1] As a student at one of the most prestigious B-Schools in the knowledge market, not only was I supposed to know what it was, but was expected to be able to tune in and utilise it to make a difference. Life has strange ways of getting back at us, mere mortals, and here was my customised gut wrenching sucker punch. The fighter in me knew I had to do this, but I did not find a way I could. A 3 month long research in the field led me to Data Driven Decision Making or D3M. Simply put, it is the pleasure of sipping a coffee in your office chair while you watch your computer work up some algorithms and provide you invaluable decision making tips to face some of your most frequently encountered problems. Immediately after, I experimented with the different sorts of decisions that can be made with D3M. The results were a true eye-opener. If we think of decision making as a broad spectrum from operational decisions at one end to strategic decisions at the other. Operational decisions can be characterized as highly structured, routine, short-term oriented and increasingly embodied in sophisticated software applications. On the other hand strategic decisions are taken by the top management and serve to set the long-term directions, policies and procedures of an organization. They tend to be complex and unstructured because of the uncertainty and risks that generally accompany longer term decisions. In between these two extremes, we can have varied decisions including non-routine ones in response to new or unforeseen circumstances beyond the scope of operational processes, and tactical decisions dealing with the necessary adjustments required to implement longer term strategies. [2] Half a decade ago, D3M could have only helped you with the more structured forms of decision making but no more. With the advent of Big Data, machines know much more about humans and human behaviour than humans themselves. Sample this, personal analytics can actually allowed me to analyse my Whatsapp chat history and find why most of the girls I chatted with refused me a date. The word cloud usage showed me that my texting skills were uninspiring to say the least with the most common words being “ok†and “yaâ€. I realised I needed to be more creative and engage better. What did not help was that most girls chatted in the window of 8-12pm whereas I had the habit of taking a short nap at the time. Also it helped me identify my most productive work hours and helped me plan out my work better. Thus far, all seemed well with D3M but on further research most early moving corporates into the field were still undecided on its benefits. A closer look exposed a distinctly similar pattern in them. Most early moving managers thought that D3M will help them save money or time or both. Research shows that they are misguided to say the least. D3M does neither, atleast not in the short term. What D3M does allow though is discovering solutions you never knew existed. What it will do though is finding needles in the haystack consistently. Another interesting insight was that D3M depends a lot on the data collection. Great collection leads to great results. All we are required to do is ensure data cleanliness, variety and velocity. In his book, Data Driven: Profiting from Your Most Important Business Asset, Prof. Thomas Redman summarizes the whole decision making via data as “Good decision makers follow at least three Bayesian principles. First, they bring as much of their prior experience as possible to bear in formulating their initial decision spaces and determining the sorts of data they will consider in making the decision. Second, for big, important decisions, they adopt decision criteria that minimize the maximum risk. Third, they constantly evaluate new data to determine how well a decision is working out, and they do not hesitate to modify the decision as needed.†After months of thorough investigation and experimentation, I have arrived at this conclusion, “To stay a step ahead in this ever competitive world, using Data Driven Decision making is a must. So let D3M take care of all the external data that you need to work with and let your mind focus on understanding “the data from insideâ€. That will surely lead you to not just success but contentment. References 1. http://swampland.time.com/2012/11/07/inside-the-secret-world-of-quants-and-data-crunchers-who-helped-obama-win/ 2. http://blogs.wsj.com/cio/2013/09/27/data-driven-decision-making-promises-and-limits/ Note - Visitors shall not be able to comment on this article until they are logged in.
  5. 12 points
    Data driven decision making (DDDM) As it is rightly said by Plato, “Necessity is the mother of inventionâ€; it comfortably fits in the life and business perspective. In today competitive world, we cannot even think of succeeding over others if we are not better than them. It is applicable in both day to day lives as well as in Businesses. In day to day life, we have numerous examples of us competing with our colleagues for ranks, posts or various other things. But it is the candidate which has better skills and qualities will always succeed. This thing is also applicable to a larger extent to businesses where every day lakhs and crores of transactions are performed. Any organization should use all its data available judiciously and all the decisions should be based on these data instead of personal beliefs. This process of making decisions based on data is called Data-Driven Decision Making. In a recently published article, “Data Science and its Relationship to Big Data and Data-Driven Decision Making,†Foster Provost and Tom Fawcett define Data-Driven Decision Making as “the practice of basing decisions on the analysis of data rather than purely on intuition.†Equally succinctly, they view data science “as the connective tissue between data-processing technologies (including those for big data) and data-driven decision making.†This DDDM is being viewed as a tool to help people make smarter, more effective decisions. Also according to Electronic Learning Assessment Resources (ELAR), a DDDM focus uses student assessment data and relevant background information, to inform decisions related to planning and implementing instructional strategies at the district, school, classroom, and individual student levels. Even the concept of Data literacy meaning “a person possesses a basic understanding of how data can be used to inform instruction†is closely inter-weaved with DDDM. We can say Data Literacy as an underlying technique of use of DDDM. Considering the reference from the research paper “Strength in Numbers: How Does Data-Driven Decision making Affect Firm Performance?†by Erik Brynjolfsson, MIT & NBER Lorin Hitt, University of Pennsylvania and Heekyung Kim, MIT. A detailed survey data on the business practices and information technology investments of 179 large publicly traded firms, it was found that firms that adopt DDD have output and productivity that is 5-6% higher than what would be expected given their other investments and information technology usage. Such surveys and studies have every now and then showed the importance of data in taking important managerial decisions. Even the share-market is not luck or belief based. It is based on various complex logics which have to interpreted using different other factors. So it is the need of the hour to work on the data analysis for having better forecasts, demands and market scenarios. If we see all the prospering companies of the world, they go by numbers. It is the challenge on the part of the management to lead the organization towards data-driven decision making. This DDDM is important because of the following reasons: 1. Commodity priced computing 2. Massive file system storage and retrieval technology 3. Bandwidth 4. Smart devices: Records are everywhere Keeping in mind these important factors, it become necessary for any company to take decisions very precisely as each and every decision has very long term effects on the company and its revenues. Recently, so many technologies have evolved including Big Data which have made the analysis of data far easier as it was earlier. Now even small information which is sort out of crap data is very useful for the organizations in taking future decisions. In last few year many new organizations had come up which provide services in the field of data analysis which indirectly helps the companies hiring them. Lots of social networking sites provide some data which are used by these analysts to provide related advertisements to the people. This explosion of decision making from personal instincts to data driven can be largely attached to Big Data. With the advent of Big Data, this has come out even more drastically and most of the companies shifting towards it. Lot of money is being invested in getting meaningful data out of bulk of data available in the companies. It’s not surprising that data-driven decision making is one of the most promising applications in the emerging discipline of data science. It has an explosive growth. There are large numbers of characteristics of Data which have to study before taking certain decision. These include variety, volume, velocity, veracity, variables and sources. In finding meaningful information from the raw data the following steps are to be followed: 1) Collect all data from various sources 2) Create a file of raw data and arrange properly 3) According to predefined index, interpret the data to make a data file 4) Analyze this data file generated This complete procedure is a basic process of DDDM. It has to be followed if the accurate analysis is required. In a nutshell, we can conclude that this data driven decision making is the need of the hour and each and every company should move towards it as soon as possible. It may be looking a tedious and unnecessary at the present moment but its long term effects are very soothing and beneficial for the entire company. References: · http://blogs.wsj.com/cio/2013/09/27/data-driven-decision-making-promises-and-limits/ · http://online.liebertpub.com/doi/full/10.1089/big.2013.1508 · www.a51.nl/storage/pdf/SSRN_id1819486.pdf‎ · https://www.950.ibm.com/events/wwe/grp/grp017.nsf/vLookupPDFs/Michael%20Kowolenko%20Presentation/$file/Michael%20Kowolenko%20Presentation.pdf Note - Visitors shall not be able to comment on this article until they are logged in.
  6. 7 points
    Most of the textbook definitions of Six Sigma talk about Six Sigma being a strategy, methodology, metric, philosophy etc. There is another definition that I use in my training programs frequently. Lean Six Sigma provides you with world best methods of data driven decision making. If you are working in an organization or running a business, it is inevitable that you shall need to use data in decision making. Some pointers that highlight the importance of data driven decision making are given below. Even if one uses brainstorming techniques with teams, one can generate and use data.The use of basic cause-effect approaches for analysis like fishbone diagram get strengthened only by effective use of data.All functions in an organization generate data of various kinds. It makes sense to learn correct methods and techniques that help in decision making for business success.Analysis of wastes in a process requires data (like cycle times, takt times, value added time, non value added time, productivity, etc)Finding whether internal/ customer requirements (or regulatory requirments) are met requires data driven techniques like hypothesis testing.Forecasting methods are essentially a set of data driven techniques.Comparison of two or more sets of data( for comparing vendors, technology, techniques, materials, processes, customer types, teams etc) is a common need.Market research or R&D makes us of data all the time.Problem Solving is strengthened by usage of data.Project Management requires data driven decisions.Performance management cannot be considered fair unless data is capured, analyzed and used properly.To get more ideas on how Lean Six Sigma is useful in a specific Industry or Functional area, please make use of tags present at the top right section (found after clicking on forum tab above).
  7. 6 points
    I suppose everyone agrees that if one is not good with numbers, career growth is likely to face a serious roadblock at one stage or the other. I have noticed several people who fear mathematics and this leads to certain problems in learning or applying Six Sigma. Many have already given up hope assuming that they can never cover up. Good news, however is that this weakness can be addressed by most people. It definitely needs a persistent effort to capture Mathematics concepts that are really important. Some of these are Algebra, Data Handling, Decimals, Equations, Exponents and powers, Fractions, Graphs, Integers, Mathematical modelling, Mathematical Reasoning, Probability, Proportions, Ratios, Rational Numbers and Statistics. If you are one of those who felt this way and wish to improve your math, I can provide you a step by step approach which shall broadly follow the sequence below. Plan study time for these topicsUse the uploaded materialStudy identified topics and answer questions provided in the text. Check your answers with answer key provided.Conquer your weakness and face the Six Sigma world more confidently.In case good number of people see value in such a sequence, I shall be putting in extra effort and make the content and sequence available to you free of cost. I have written this post just to know whether there are many people out there who really wish to use such content and approach. Reply to this post showing your interest so that I can view the count. Best Wishes, VK
  8. 6 points
    Data Driven Decision Making - injecting rationality in your gut feeling Sector: Banking Sector in India The data centricity of banking industry is the universal truth. Traditionally banking has been the one sector which handled the maximum data about any person that are particularly critical and dear to those persons as they deposit their trust with the banks in the form of their finances. In the recent times this has been further reinforced with the RBI implementing the “Know your Customer†mandate that aims to compulsorily maintain the customer data which should be relevant, concurrent & authentic. Despite the proliferation of such data, effective analytics and data mining techniques has been at its elusive best. The information industry has grown leaps and bounds and the remarkable advances in analytics software and its processing power aided by the cloud computing systems is just the tip of huge iceberg of potential that such data is capable of achieving. As the industry tries to grow out of the recent financial crisis towards the shady future of uncertainty, banking and retail banking in particular must inculcate the power of analytics in them to be able to improve decision making, indulge in constant innovations which ought to become the bread and butter for survival & be more compliant with the stringent financial regulatory environment that the RBI is supposed to impose for greater control. The siloed approach to banking should give way to enterprise wide resource planning (data being the most critical resource) for fostering greater transparency, efficiency & effectiveness through integration and unified image of the entire sector. This will also help in garnering greater customer trust & rejuvenate customer relationship which is the single most critical factor for survival in times of uncertainty, mistrust & risk. The recessionary trends have forced the clientele of banks to a more frugal approach to managing their funds. Careless consumption has been replaced by need based one and “ROI†has suddenly become the buzzword which never had such a great reputation except amongst the business houses. However it is interesting to note that despite reduced spending the world has not stopped itself from the adoption of latest technologies. Be it smart phones or social media presence the huge numbers are truly defiant of the existing economic conditions and its implications. Such behaviour re-confirms the value of innovations in today’s society besides such channels could provide source of huge data tapping which can help retail bankers to provide a more rewarding experience to their customers enhancing their brand loyalty. The usage of “Big Data†as the new window to the world of increased productivity, innovation & competition is important to be considered here. The rapid adoption of analytical tools would help banks process the information they have into market knowledge which would enable them to differentiate themselves through service excellence. It may sound contradictory that previous paragraphs talked of unified image and integration and now differentiation is promoted. Well, competition has been and will always be the root for future growth without which the need for existence of mankind comes under the radar. Rather we should look at a new dimension of competition – “Competition through Cooperation†where competitors would be on the same page with respect to technology and new inventions yet they would have to constantly evolve themselves to be relevant. Advanced analytics provides the banks with a new path of continuing business by overcoming the obstacles of risk and uncertainty, the prime growth drivers being stricter regulation, better risk management, effective strategising and stronger CRM. The various ways to achieve data salvation is revealed as below: The analytics software would speed up the financial and risk reporting services as required by the new norms as and when implemented by the government ensuring service delivery with no or minimal cost. The usage of Enterprise wide data architecture would provide a single version of banking creating transparency and restoring customer confidence. Data crunching would enforce better risk management by identifying malicious transactions and preventing its recurrence. Usage of technology to combine past and current data can help in predicting future scenarios with greater accuracy and provides an opportunity to face the shady future in a planned manner with confidence. Besides the data analytics tools may be used to boost revenues as well like, Customer data analytics – enhance service and bring more clients Investment analytics – improve lending process Process analytics – find process inefficiencies and take corrective measures thereby reducing costs, to name a few. [*]Data collection from various sources like KYC, social media websites etc... and it analysis using Big Data and relevant technologies can help in providing customized banking solutions, new financial products to suit customer needs & gather feedback on marketing campaigns launched. This would lead to greater customer satisfaction and tighter relationship. Mobile banking is the new brainchild of the banking sector that allows customers carry out transactions on the move. This means greater volume of transactions to be handled and the usage of analytic software to integrate data across channels become essential. Also multichannel banking is constantly evolving with the endeavour of providing cross channel banking across websites. So far the discussions lead us to the conclusion that usage of Data Driven Decision Making through Data Analytics & ERP is imperative to the future competitiveness in banking industry. But there are major speed breakers in the path to this rediscovery, which are as follows: Modifying existing IT infrastructures and the corresponding data migration might incur substantial initial costs Using analytics at the strategic level would require identification of relevant data and standardization of processes and data structures Resolving frequent data issues & inconsistencies that exists in the customer data in the baking domain Required expertise in analysing data points, process expertise & technical expertise is important Support & Initiative of key stakeholders Finally the embracing of analytics as a service depends on the internal culture and dynamics of the organization. Hence to successfully implement the same nurturing of the employees to convince them of the power of data driven decision making is very crucial. However the conviction in employees can be developed only if the leaders & top management of the enterprise believe in the vision of “Analytics as the future of bankingâ€. Hence the purpose of this article would be to inspire the top management, so that they can realize the importance of using data in their organizational decision making and inject rationality in their decision making. Note - Visitors shall not be able to comment on this article until they are logged in.
  9. 5 points

    Version

    1,239 downloads

    This is the Pre-Course study material for Lean Six Sigma Black Belt Training. Note: It is important to read this material before the training, as there will be a Quiz based on the pre-course material on the 1st day of the training.
  10. 5 points
    “A point of view can be a dangerous luxury when substituted for insight and understanding. – Marshall Mcluhan †We all have come a long way from industrial revolution where mass production and consumption was the order of the day. Over the years, the once not so knowledgeable consumer has transformed into a living storehouse of information. What has changed over the years? Why did the companies who were the industry leaders at one point of time failed miserably later on? What changed? The answer to all this lies in data. In our thirst to find newer markets for products and differentiate them from our competitors, we pushed the consumer towards seeking knowledge and then when he demanded, we failed to live up to standards. We pushed the customer to give more importance to value i.e. value today has become emergent as compared to the old times when it was measurable. Now the new age customer wants more and he/she will switch, the moment he/she finds more value from another product or another competitor. How do we live up to this? Given the rapid changes in technology and the rapid adoption of management information systems by almost all industries, we need not look further. We have the solutions but we are too thick skinned to look at it. Today’s customer is like a walking hard drive with copious amounts of data. What we need to do is analyse it, find patterns where none exist, combine it with the experience of mankind and predict the needs and demands of future backed by strong data analysis. If we stop and look back into the history what will be distinctly visible to us is the fact that companies that were present in the first list of Standard & Poor’s 500 only 74 remain in operation today. Almost half of the 25 companies that passed the rigorous test for inclusion in Tom Peters and Robert Waterman’s 1982 book “In Search of Excellenceâ€, have gone bankrupt or have shut down due to poor performance. The reason being that when organisations are thriving and successful, it creates an illusion that whatever gave them success in the past will work in the present and still remain valid in the future. They forget the fact that the world is constantly changing, constantly evolving and to survive they need to evolve with it too. Today there is no dearth of examples of companies that failed after achieving success on a grand scale, to quote a few, Wang Labs, Digital Equipment, Borders and Blockbusters and many more; of which many either fell after achieving industry leadership position or shrunk in size. The reason is not that they failed to act or didn’t know what was happening but the fact that they had all the data but didn’t analyse it. The patterns were there, had they analysed the data they already possessed they would have seen it. They believed that experience can upstage data but they failed to comprehend that with today’s rapidly changing global economy, it’s impossible for the human mind to absorb all factors that go into making sound decisions. Statistical analysis often uncovers correlations that no one could have predicted on the basis of their past experiences. We won’t be wrong in accepting the fact that data and statistical analysis isn’t perfect but who hasn’t made poor decisions on pure gut instinct!! It is said that sustainability is based on transforming data into analyzable information for insights and decision-making. This is where data driven decision making comes in and plays its part. (Cokins) Take for example the case of Wal-Mart as reported in New-York Times. This occurred when Hurricane Frances was moving across the Caribbean, threatening to directly hit on Florida’s Atlantic coast. Residents were making a rush for the higher ground, but at the same time, far away, in Bentonville, Ark., executives presiding over the Wal-Mart Stores decided that the situation opened a great window of opportunity for their newest data-driven weapons, Predictive technology. A week ahead of storms predicted landfall date, Linda M. Dillman Wal-Mart’s CIO at that point of time asked her staff to come up with forecasts based on what happened when Hurricane Charley struck several weeks earlier. Their experts mined for data and found that the stores would need certain products more than the usual batteries, flashlights, and bottles of water. All this brought out some very interesting facts about the pre-storm behaviour of consumers. Strawberry pop tarts and beer were the top-selling items. By predicting what was going to happen rather than waiting for it to happen, Wal-mart was able to deal with this unexpected situation in a holistic manner. It is said that if we can improve our ability to estimate for a given customer, we stand to gain by applying the same logic to millions of customers. This applies to many other areas where intense application of data science and data mining is seen like direct marketing, credit scoring, financial trading, help-desk management, fraud detection, search ranking, product recommendation and many more. An example for this would be the instant recommendations of Amazon.com and Netflix which are a result of split-second advertising decisions based on the items a user has previously seen or is viewing right now, which has resulted in increased revenues for both of them. The increasing amount of data generated by humans while doing their daily chores has lead us into a world where the purchases and habits of a consumer give us an insight into the behaviour of the consumer, how he reacts to various offers , what are his preferences, Is the customer a sort of person who will pay back loans. The algorithms have been designed and implemented which predict accurately how likely a person is to get diabetes. All this has a potential to give rise to platform of customization for every consumer i.e. mass customization. A recent study by MIT professor Erik Brynjolfsson has brought out the fact that most revolutions in science begin with better methods of measurement. When we can see new things, we are driven to seek answers and thus build new ways of thinking and operating. The main underlying part of his findings was the fact that organisations that relied on data driven decision making, enhanced their performance by 4 – 6 % viz–a-viz their peers in the same market. This figure has remained robust despite taking into account the contributions for labour, capital, purchased services and traditional IT investment. (Lohr) Another research conducted by Mr. Erik along with Mr. Lynn Wu, an assistant professor from Wharton Business School used publicly available web search data to predict housing-price-changes in metropolitan areas across the United States. They didn’t possess any special knowledge of the housing market when they began their study, but they reasoned that virtually real time search data would enable good near term forecasts about the housing market- and they were right. In fact their predictions proved to be more accurate than the official ones generated by the National Association of Realtors which had far more complex model but relied on slow changing historical data. (Brynjolfsson) Now if we analyze the business that exist today and have been running on intuition, tradition and convenience, we observe that they have scattered staff development programs, the budgetary decisions are based on prior practice and programs, Staff assignments are based on interest and availability and goal setting is based on votes , favourite initiatives or fads. In contrast, the companies that have adopted data driven decision making for the staff development programs are more focussed as an improvement strategy to address the problems identified by the analysis of data. Staff assignments are based on skills required for future as well as the present requirement. Goal settings are done on the basis of problems identified by data analysis and their possible explanations. One factor that we must consider is that with the ever increasing rapidly changing pace of technology, companies today have multiple points of contact with the customer i.e. blogs, call centres , customer reviews or simple comments on social media platforms which are all a valuable source about client segments , insights and behaviour. Today it can be observed that increasing competition, decreasing margins and availability of information has left many companies struggling for survival. The only way out seems to stem from the fact that it is very important today to analyse the vast amount of data being generated daily and convert it into actionable knowledge so that client needs are addressed adequately. (Mike Lynch) An example worth mentioning here is that of researchers at John Hopkins School of Medicine, who provide a classic example of how simple models and big data win over more elaborate analytic approaches. They used the data from Google Flu Trends, which is publicly available, to forecast surges in flu related emergency visits a week before similar warning were received from the Centres of Disease Control. Similarly, twitter updates provided accurate records similar to the official reports that were tracking the spread of cholera in Haiti after the 2010 earthquake. (Brynjolfsson) From the numerous examples quoted above, it can be substantiated that data analytics unlock huge amounts of useful information, which can be made more transparent in nature by use of Big Data. It is often seen that we search for data in forms other than electronic and then transfer it to electronic form. This effort represents a huge inefficiency; if we could the information directly from other forms to electronic form without human intervention it could save us a lot of resources and time. Our online financial transactions or any transaction for that matter, if and when collected over a period of time can bring out interesting facts about our choices and behaviour. Now if we apply the same to a huge organization, what we have is copious amount of data that can provide us with very accurate and detailed information about various aspects of the organization. This further helps the middle level and upper level management make meaningful and informed decisions regarding day-today decisions to designing long term plans for the organizations along with requisite data to support it. Data collected from consumer transactions can help us predict the behaviour of the customers and help the organisations in maintaining requisite inventory levels. They can also tailor and customize the products and services according to the needs of an individual customer without the customer making an additional effect in telling the provider about it. It also leads to narrower segmentation of customers. Data obtained when combined with power analytics techniques can be used to find hidden patterns which otherwise would remain hidden and help in predicting future needs , improvise after-sale services and meet the expectations of the consumers in a holistic manner. In short the transfer of information between consumer and manufacturer can be defined as the art of communicating; without actually communicating; as no formal/informal communication takes place between the parties’ involved; just observation, which leads to predicting the needs and aspirations of consumers. We need to understand that the data is not the only tool for the decision making. If applied in isolation without experience, the end result is a Frankenstein’s monster of business process and best practices woven together. As value increases its dependency on unique knowledge and human experience, objective data sources must be supplemented with perspectives from organizational cultures, communities of interest and other relationships. Only then can we take holistic and sound decisions. Bibliography (Debates, 2011) Bonabeau, E. (n.d.). Harvard Business Review . Retrieved 11 21, 2013, from hbr.org : http://hbr.org/2003/05/dont-trust-your-gut/ar/5 Bono, E. D. (n.d.). Search Quotes. Retrieved 11 21, 2013, from searchquotes.com: http://www.searchquotes.com/quotes/author/Edward_De_Bono/3/ Brynjolfsson, A. M. (n.d.). Harvard Business School . Retrieved 11 21, 2013, from Harvard Business School Website : http://hbr.org/2012/10/big-data-the-management-revolution Christensen, C. (n.d.). Design Intelligence. Retrieved from di.net: http://www.di.net/articles/the-innovators-dilemma-when-new-technologies-cause-great-firms-to-fail/ Cokins, G. (n.d.). analytics-magazine. Retrieved 11 21, 2013, from analytics-magazine.org : http://www.analytics-magazine.org/may-june-2012/577-corporate-decision-making-why-do-large-once-successful-companies-fail Debates, D. (2011). When it comes to important business decisions , should you trust your gut or follow the numbers . Deloitte Development LLC. Lohr, S. (n.d.). New York Times . Retrieved 11 21, 2013, from New York Times Website : http://www.nytimes.com/2011/04/24/business/24unboxed.html?_r=0 Mike Lynch, c. e. (n.d.). Financial Times . Retrieved 11 21, 2013, from Financial Times Website : http://www.ft.com/cms/s/0/62f37a4a-931c-11de-b146-00144feabdc0.html#axzz2lITs0aA5 Plisch, B. G. (2004, 02 04). WTN News . Retrieved 11 21, 2013, from WTN News Website : http://wtnnews.com/articles/557/ Tim McGuire, J. M. (n.d.). Ivey Business Journal . Retrieved 11 21, 2013, from iveybusinessjournal.com: http://iveybusinessjournal.com/topics/strategy/why-big-data-is-the-new-competitive-advantage#.Uo47PsXlbrN Wladawsky-Berger, I. (n.d.). Irving Wladawsky-Berger. Retrieved 11 21, 2013, from Irving Wladawsky-Berger blog: http://blog.irvingwb.com/blog/2013/09/data-driven-decision-making-promises-and-limitations.html Zwilling, M. (n.d.). Forbes . Retrieved 11 21, 2013, from Forbes.com: http://www.forbes.com/sites/martinzwilling/2012/03/25/too-many-leaders-still-rely-on-their-golden-gut/ Note - Visitors shall not be able to comment on this article until they are logged in.
  11. 5 points
    Once when asked what he does, if the data does not support his decision, John Maynard Keynes replied – “I change my opinion. What do you do?†In a haystack of information today, that one thing which helps organizations take sound decisions is the ‘analysis of data’. Often, companies find themselves in situations where from a variety of choices, they need to pick one. In such cases, data-driven decision making enables following a systematic procedure. A successful completion of any process begins from a decision well made. It forms the first step of any execution process, and is thereafter followed by modification, as and when changes in information arise. The question that one would ask then is – ‘If it is data that is needed, then exactly how much of it?’. Authors like James Taylor and Stephen Covey, in their writings explain keeping ‘the end in mind' before undertaking a course of action. They say that the goal is never to build on the data; rather it is to use the facts to make work easier. Thus, the perfect quantity would be one which helps an organization make ‘timely’ as well as ‘correct’ choices. But even a manager’s power to predict can do this job, isn’t it? On digging deeper we realize that the ‘intuition' we are referring to is nothing but the gut feeling that arises based on a manager’s experiences of the past, and thus its own roots lie in data as well. What people actually follow is ‘informed intuition' – that uses previous occurrences as its basis. This is justified, since not only is complete information necessary, but also alongside is corporate alignment and clarity. These days, a term that managers often hear is ‘big data’ – which refers not only to the volume of data available, but also to the variety of it and the rapid pace at which it alters. Big data brings with itself the complexities of processing and interpretation, causing confusion and delays. It is here that just-the-right-kind of filtration is needed, to separate what is relevant and what is not.Infact sometimes, even lesser amounts of data can lead to better decisions being taken. As they say, the ‘first impression’ can indeed be beneficial if taken as the ‘last impression’. This is exactly where the use of instinct comes into picture. Whether it be studying a consumer insight or predicting the future, it is a blend of analysis and a manager’s intuition that leads to the apt solution. In a post in Forbes, Robert Carraway, a professor at the Darden School of Business said that big data and the increasing use of frameworks require not less, but a higher amount of managerial insight to accompany them. There have been faults based on judgment (remember Google claiming to overtake Firefox when launching Chrome?) ; and so exist popular crisis due to over-reliance on data. The idea hence is to strike a balance between these two seemingly different ways that managers use to reach a conclusion. The more an association can accommodate diversity in terms of style, emotions and experiences, the higher is the probability of improved performance. In a nutshell, if a corporation can make sure that it has as members both left-brain and right-brain thinkers – it can strike the nail on its head! (*source for the cartoon : Google images) Note - Visitors shall not be able to comment on this article until they are logged in.
  12. 4 points
    By not being Mutual Exclusive By not being Collectively Exhaustive By not implementing the findings
  13. 4 points
    The story revolves around the growing of square watermelons in Japan. Japanese grocery stores had a problem. They are much smaller than their US counterparts and therefore don’t have room to waste. Watermelons, big and round, wasted a lot of space. Most people would simply tell the grocery stores that watermelons grow round and there is nothing that can be done about it. But some Japanese farmers took a different approach. ”If the supermarkets want a space efficient watermelon,” they asked themselves, “How can we provide one?” It wasn’t long before they invented the square watermelon. The solution to the problem of round watermelons wasn’t nearly as difficult to solve for those who didn’t assume the problem was impossible to begin with and simply asked how it could be done. It turns out that all you need to do is place them into a square box when they are growing and the watermelon will take on the shape of the box. This made the grocery stores happy and had the added benefit that it was much easier and cost effective to ship the watermelons. Consumers also loved them because they took less space in their refrigerators which are much smaller than those in the US - which resulted in the growers being able to charge a premium price for them. What does this have do with anything besides square watermelons? There are a five lessons that you can take away from this story which will help you in all parts of your life. Here are a few of them: 1.) Don’t Assume: The major problem was that most people had always seen round watermelons so they automatically assumed that square watermelons were impossible before even thinking about the question. Things that you have been doing a certain way your entire life have taken on the aura of the round watermelon and you likely don’t even take the time to consider if there is another way to do it. Breaking yourself from assuming this way can greatly improve your overall life as you are constantly looking for new and better ways to do things. 2.) Question Habits: The best way to tackle these assumptions is to question your habits. If you can make an effort to question the way you do things on a consistent basis, you will find that you can continually improve the way that you live your life. Forming habits when they have been well thought out is usually a positive thing, but most of us have adopted our habits from various people and places without even thinking about them. 3.) Be Creative: When faced with a problem, be creative in looking for a solution. This often requires thinking outside the box. Most people who viewed this question likely thought they were being asked how they could genetically alter water melons to grow square which would be a much more difficult process to accomplish. By looking at the question from an alternative perspective, however, the solution was quite simple. Being creative and looking at things in different ways in all portions of your live will help you find solutions to many problems where others can’t see them. 4.) Look for a Better Way: The square watermelon question was simply seeking a better and more convenient way to do something. The stores had flagged a problem they were having and asked if a solution was possible. It’s impossible to find a better way if you are never asking the question in the first place . Always ask if there is a better way of doing the things that you do and constantly write down the things you wish you could do (but currently can’t) since these are usually hints about steps you need to change. Get into the habit of asking yourself, “Is there a better way I could be doing this?” and you will find there often is. 5.) Impossibilities Often Aren’t: If you begin with the notion that something is impossible, then it obviously will be for you. If, on the other hand, you decide to see if something is possible or not, you will find out through trial and error.
  14. 4 points
    I observed that the scope of six sigma has increased it is now applicable in many industrial sectors. When Bill Smith created this methodology his main focus was defect reduction and improvement which brings excellence and process improvement, later on Quality formed the core of other methodology/framework too. I have seen that the approach of employees toward quality management has not yet changed, they still consider this as the job of quality unit, as for them quality means compliance, reporting, QMS manual and most importantly Audits. I found that organizations are spending good amount of money in quality related training/workshops/paperwork etc. but still are not successful in changing the approach of individuals. At high level, every organization’s policy talks about quality and its importance but at ground level due to fixed processes, departmentalized structure it become a task of small number of people, like managers and specifically of those who belongs to quality department, since it’s their job. Induction program- By definition an induction program is an important process for bringing staff into an organization. It provides an introduction to the working environment and the set-up of the employee within the organization. An induction program is part of an organizations knowledge management process and is intended to enable the new starter to become a useful, integrated member of the team. But I believe now organizations have a need to adopt a new approach of induction program: Organizations should provide Process Excellence/Quality training to each and every employee at the time of induction, this training they can design as per the requirements of their LOB (Line of Business). Also when induction of an employee gets conducted at that time we can tell employee that they are suppose to share their views (positive or negative) after six months from now about quality of process/operation to which they belongs (preferably through a presentation) so that this training of quality not become just a training and they feel involved with the things they have learned. This kind of treatment at induction will make employee responsible for his own quality as well as quality of his team and organization. Why this should be done at induction program because when we start with induction: 1. The new employee will feel himself linked with the quality department. 2. This will make new employee feel that he is responsible for the quality of operation to which he belongs. 3. This will convey a message to employee that organization has a serious approach towards quality. This approach will bring mutual benefits and this also links with the high level objective of each and every organization that is “alignment of individual objectives with organization’s objectiveâ€. Now the question comes since induction is for new employee what will happen to those who are already working? For this we need not to do anything but to ask them to attend this “Quality session†which we are conducting for new employees and they can join this from there and then. I believe that each and every individual should feel himself responsible for quality and there is a need to make quality a routine habit rather than an event.
  15. 4 points
    When fire broke out in an office, the reason identified was a burning cigarette butt. Few minutes later, a Master Black Belt was seen throwing burning cigarette butts at several places in the office. Aghast at his behavior, senior people summoned him up at the meeting room. He explained - Well, you see we cannot conclude unless we have enough samples!!!
  16. 3 points
    Lean Six Sigma learning can impact your future in several ways: Enhanced ability to make the right decisions - Professional and business success depends on this. Lean Six Sigma provides a set of world’s best decision approaches that have proved their worth in a wide variety of settings. It pays to learn these techniques. Personal worth - People who are able to solve key problems systematically are the most sought people in an organizational setting. Engaging others - It is possible for you to facilitate action based team learning if you gain this competence. Lean Six Sigma training is a kind of leadership training. Improvement expertise - You can succeed with process improvement initiatives (in any industry or function) if you begin with the right fundamentals and build up the blocks well over them. All the above result in career and business benefits. Please feel free to discuss this topic here.
  17. 3 points
    The difference lies in the definition iteself. DPMO (Defects per Million Opportunities) talk about the defects while DPPM (Defectives parts per Million) talks about the defectives. Take an example: A car manufacturer produces 1000 cars with each car having 50 checkpoints. Hence Units - 1000 Opportunities - 1000*50 = 50000 Assume, 10 cars are defective while the total number of defects are 250 (i.e. these 10 cars cumulatively failed on 250 checkppoints). Hence Defectives - 10 Defects - 250 DPMO = 250/50000*1000000 = 5000 DPPM = 10/1000*1000000 = 10000 Typically in a service industry, Customer is more concerned with DPPM. For a service provider it makes more sense to look at DPMO. Even if they start working with DPPM, they will eventually have to drill down to the defects that are rendering the service as defective. Not sure if the same logic applies to manufacturing, but even though DPPM is an excellent metric to track the performance of the manufacturing unit, for any improvements you still need to look at DPMO.
  18. 3 points
    They say that if you put a frog into a pot of boiling water, it will leap out right away to escape the danger. But if you put a frog in a kettle that is filled with water that is cool and pleasant and then gradually start heating the kettle until the water starts boiling, the frog will not become aware of the threat until it is too late. The frog's survival instincts are geared towards detecting sudden changes. Don’t most of us suffer from this short-sightedness too? Aren’t we always obsessing over short-term events and not taking cognisance of the bigger picture? This could be a major Six Sigma hindrance.
  19. 2 points
    Let us assume the talent pool available in the market is same for company A and company B. Company A decides to to use Six Sigma for improving their results from recruitment process while company B does not do this. Now we would like to understand how company A can get better results with Six Sigma. Let us assume that Company A goes through DMAIC in the following way. Define - Using appropriate Six Sigma tools, it defines the problem as inability to meet SLA for recruitment. This problem is converted into a charter document and discussed in a senior management meeting. The objective of the discussion in the meeting is to finalize whether this improvement project meets the criteria for approval as a Six Sigma project. As senior management gets convinced about the opportunity for improvement and sees the possibility of good return on investment through this project, the project gets approved with a leader assigned. A team is identified to work on the project with clear objective and a time frame of 4 months is provided to the team. These 4 months shall be used to select the right Six Sigma techniques on the issue, with right people working on the project and senior management support assured with resources (time, meeting places, communication support). Company B continues in a haphazard manner fire fighting as always. You might have noticed that well begun is half done for company A. Measure phase - Team at company A prepares a detailed process map of the current process, finalises the CTQ with an operational definition (bringing absolute clarity about when do we consider SLA as met and when is it considered as not met). It also checks and fixes the measurement system and brings visibility and accuracy (Company B does not know at a point in time how many people are on rolls, how many recruitment requests are pending and for what reasons). Company A now measures and documents its current process performance to be at sigma level = 2.3 and fixes a target of improvement at 3.0 Analyze phase - Using appropriate six sigma techniques, team at company A now identifies that various factors that influence the recruitment success. These are listed as - i. Understanding of hiring needs from departments properly. ii. Identifying the right approach for recruitment drive iii. Selecting the message (including introduction to company and career prospects) iv. Selecting the right media for recruitment. v. The short-listing mechanism. vi. Making the offer with terms and conditions vii. On-boarding and induction process. Analyze phase part II - Team at company A now identifies the factors that are critical to recruitment success. For this it carries out a survey on good (selected people or people who got an offer) people who did not join in a recent drive. Team uses certain techniques like FMEA, graphical techniques like box plot, hypothesis tests like the t-test to finalize on validate few critical factors that influence selection success. Company B meanwhile knows that there are several factors that influence the efficiency of recruitment process, but has no idea where to focus for improvement. Improve phase - Team at company A identifies solution alternatives using techniques like creative thinking, benchmarking. The team then selects the best solution using the appropriate technique out of pugh matrix, multi-voting, delphi technique etc. Before implementing the solution, the team finds that they can carry out a DOE to optimize the solution further. The new alternate process (improved process map) is documented, piloted, validated and implemented. The team records the benefits with the next recruitment drive. Company B continues as before. It has no mechanism to learn from its own mistakes or to learn from mistakes of others (and no data driven approach to reach conclusions, no management support to approve good recommendations when they arrive) Control phase - Team at company A puts in the right controls in the improved process so that the benefits can be sustained over a period of time. It carries out mistake proofing at few places, and creates a control plan with audit check-lists to ensure that desired behaviour changes does actually happen. Finally with a better process in place and removal of gaps, delays, errors, and ineffective communication (at the right places), Company A starts attracting the best talent more effectively. To put in in brief Six Sigma philosophy is about using a systematic, scientific, data driven approach that leads to the best possible process (the best does not remain best for long so there is a need for continual improvement and more improvement projects periodically) Six Sigma methodology provides us with world best tried and tested techniques and we select and use the appropriate ones for a specific improvement project. Benchmark Six Sigma Green Belt and Black Belt training gears you up with not just the right techniques but also provides the wisdom about when to select which one. Do let me know if you have more questions.
  20. 2 points
    Question - A forensic expert walks into a room with a bunch of dead Six Sigma Black Belts who show no visible signs of injury. What's the first thing he should check for? Answer: Poisson distribution.
  21. 2 points
    What is Lean?- This is the most commonly asked questions. Today I thought of posting the answer to this question here, so the people can easily refer to it. "Lean is a philosophy which focuses on two main concepts: The first is the elimination of wastes from all processes so that only steps that add value from a customer's point of view are the ones that are performed by the company. Lean focuses on continuous improvement where all employees in a company are constantly looking at their processes to eliminate waste. Lean provides some guidelines on how to look for these wastes-usually referred to as the eight wastes (Waiting, Over-production, Rework, Motion, Over-processing, Inventory, Transportation, and Human Underutilization). The concept of waste elimination requires a change in culture and mindset of its employees to encourage them to constantly look for and submit ideas for workplace improvement. Each small improvement made by the workforce may be inconsequential to the overall business but over a period of time, they add up and can be a huge source of competitive advantage to the company. The second main Lean concept is making things flow - making sure that the material or information moves smoothly within a company to meet the customer demand without getting stuck anywhere in the process. Continuous flow requires arrival of material or information just in time to when the material or information is needed in the right quantities and at the right time. This also implies that when the material or information arrives, it is immediately processes and passed on to the next step in the process. Of course, continuous flow just for the sake of making the product or information that no one needs is also a waste. So, continuous flow has to be linked with customer demand and material or information should be produced only on pull from the customer. Pull and continuous flow are difficult concepts to deploy in reality-there can be a number of issues that can crop up when implementing these initiatives. For example, the work that is done on the material or information should be defect free. If this were not the case, we would not be able to satisfy a waiting customer downstream. Since, ideally there is no excess inventory any production problems such as machine breakdowns may cause the customer to wait and disrupt the entire process. Both Lean and Six Sigma focus on business process improvement. Whereas, Six Sigma focuses on variation reduction to improve process performance, it is Lean which focuses on reduction of wastes to add the most value to the customers."
  22. 2 points
    The inductive inference consists in arriving at a decision to accept or reject a null hypothesis (H0) after inspecting only a sample from it. As such, an element of risk - the risk of taking wrong decisions is involved. In any test procedure the four possible mutually disjoint and exhaustive decisions are: Reject Null Hypothesis when actually it is not true, i.e. when Null is false Accept Null when it is true Reject Null when it is true Accept Null when it is false The decision in (I) & (ii) are correct decisions while the decisions (iii) & (iv) are wrong decisions. These decisions may be expressed in the following dichotomous table: Thus, in testing the hypothesis we are likely to commit two types of errors. The error of rejecting Null when Null is true is known as a Type I error and the error of accepting Null when Null is false (i.e. Alternate is true) is known as Type II error Remark: Type I & Type II error: We make type I error by rejecting a true null hypothesis We make type II error by accepting a wrong null hypothesis If we make P (Reject Null when it is true) = P (Type I Error) = Alpha (a) P (Accept Null when it is wrong) = P (Type II Error) = Beta (b.) Then a & b are also called the sizes of Type I error & Type II error respectively In the terminology of Industrial Quality Control while inspecting the quality of a manufactured lot, the Type I error amounts to rejecting a good lot and Type II error amounts to accepting a bad lot. Accordingly, a = P (Rejecting a good lot) b= P (Accepting a bad lot) The sizes of Type I & Type II errors are also known as Producer's risk & Consumer's risk respectively. An ideal test procedure would be one which is so planned as to safeguard against both these errors. But, practically, in any given problem, it is not possible to minimize both these errors simultaneously. An attempt to decrease a results in an increase in b & vice versa. In practice, in most of decision-making problems in business and social sciences, it is more risky to accept a wrong hypothesis than to reject a correct one, i.e., consequences of type II error are likely to be more serious than the consequences of type I error. Since for a given sample, both the errors cannot be reduced simultaneously, a compromise is made by minimizing more serious errors after fixing up the less serious error. Thus, we fix a, the size of type I error and then try to obtain a criterion which minimizes b, the size of type II error. Obviously, when Null is true, it ought to be accepted. Hence, minimizing b amounts to maximizing (1-b.), this is called the power of the test. Hence, the usual practice in testing of hypothesis is to fix a the size of type I error and then try to obtain a criterion which minimizes b, the size of type II error or maximizes (1-b.), the power of the test.
  23. 2 points
    Value is different from Quality. The focus of Lean Six Sigma is on Value! The difference can be understood by looking at how American Customer Satisfaction Index is calculated. Value considers the investment w.r.t deliverable and not just the happiness derived by consuming/ experiencing the product/service. The key point, again, is that Lean Six Sigma, is not about Quality but about Value (which leads to satisfaction, loyalty, repeats and referrals)
  24. 2 points
    Six Sigma Project: Focus on VOC(Voice of customer), CTC(Critical to customer), CTQ(Critical to Quality) by aiming the customer first Lean Project Focus on Waste elimination and faster delivery to custamer with lean inventory and single peice flow
  25. 2 points
    Here is good example for 80-20 rule. Here, it is even more lopsided. Apple owns 4% of the mobile phone market but controls a large share of the profit in the industry.
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