They say that a picture paints a thousand words, consider the columns of data below which show revenue by product and customer for the last 3 months. How easy is it to see which customer gives the most revenue?
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Lean Six Sigma
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A hypothesis test is a statistical test that is used to determine whether there is enough evidence in a sample of data to infer that a certain condition is true for the entire population. Hypothesis testing involves defining two mutually exclusive statements, and then using sample data to determine which statement is best supported by the facts. These two statements are called the null hypothesis and the alternative hypotheses. They are always statements about population attributes, such as the value of a parameter, the difference between corresponding parameters of multiple populations, or the type of distribution that best describes the population.
Hypothesis testing gives answers to practical questions such as:
...In most projects, most of the time we are taking samples and using inferential statistics to infer population parameters. Why? Because to take the whole population would be impractical, too costly or too time consuming. This of course means that there is a risk that our parameters do not represent the whole population. The question this raises is how big is the risk, or expressed another way, what real confidence do we have in the parameters we have calculated being representative of the whole population?
For example, we want to find the average height of men aged 40 in the United Kingdom, so we take a sample of 50 people at random and work out the mean and standard deviation. How likely is it that our calculated parameters are the same as the whole population? If they are not then how close are they likely to be?
...You don’t have to spend very long talking to Quality Management professionals, Six Sigma Black Belts, Consultants, Business School lecturers, and the like, on subjects such as Six Sigma, Business Excellence and Continuous Improvement before the word Culture crops up; particularly if you are discussing the reasons for the relative success or failure of these initiatives. It is also usually agreed without too much debate that an organisation’s culture is of major importance in these initiatives and - most would say - Critical to Quality. If it is Critical to Quality then of course we should measure it. However, not only do most people put measurement of culture in the “too difficult” file but also there is very little agreement about what culture actually is.
In spending much of my time working with organisations on the development of high performance teams and their leaders, I have often worked alongside a number of quality management professionals and in particular, those who are involved in Six Sigma initiatives. Six Sigma is another case in point of course where there is considerable debate about just how you would define it. Some put the emphasis on things like Cost of Quality, others on unbeatable measures, many emphasise the value of arriving at a common measure throughout the business e.g. DPMO (Defects Per Million Opportunities) with almost as many different emphases as people that you talk to. What I have found though is that all of the serious Six Sigma exponents, those who have invested heavily in the training of Black Belts and who are taking the initiative right the way through their companies, is an understanding from the start that the people issues are critical. There is also a growing awareness that having tackled the measurement and training issues that to achieve the next breakthrough probably means that the culture has to receive even more attention.
...In the article on 29 January 2015, we discussed Value Stream Mapping as a particular approach to understanding the Value Stream that ensures customer value is created using a high level process mapping approach. The article was about the first phase of Value Stream Mapping, creating an existing map.
As a reminder, Value Stream Mapping is a lean approach to improving the Value Stream, and as such has 4 main stages:
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