Six sigma simply explained


Published on

Six sigma explained in easy language and with basic concepts

Published in: Business, Technology
  • Be the first to comment

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

Six sigma simply explained

  1. 1. Presented By: Gaurav Awasthi
  2. 2. NEED AND SCOPE OF SIX SIGMA Every generation of business strives for a new level of quality.  The quality program that is currently in vogue and being widely used and recognized by industry is the Six Sigma program.  Six Sigma is a relatively new program, and was only started in 1986.  It was first put into implementation at Motorola, but is now in use by most large corporations.  Some of these other large companies include GE, Honeywell, and Bank of America. 
  3. 3. SOME STATISTIC BACKGROUND BEFORE LEARNING ABOUT SIX SIGMA You are probably already familiar with the concepts of average, standard deviation, and Gaussian distribution.  However, they are very important concepts in Six Sigma, so they are reviewed in the next section. 
  4. 4. AVERAGE  The equation for calculating an average is shown below. where average xi = measurement for trial i N = number of measurements  This equation relates to Six Sigma because it is the value that you aim for when you are creating your product.
  5. 5. SIGNIFICANCE OF AVERAGE IN SIX SIGMA The average is combined with the specification limits, which are the limits that determine if your product is in or out of spec.  The wider the specification limits are, the more room for deviation from the average there is for your product.  A product specification would be written like this: 10 ± 2 mm  Where the first number (10) represents the average and the second number (2) represents the amount of error allowable from the average. Thus, your product can range from 8 to 12 mm for this example. 
  6. 6. STANDARD DEVIATION  The equation for standard deviation is shown below.  where σ = standard deviation, and the other variables are as defined for the average. For each measurement, the difference between the measured value and the average is calculated. This difference is called the residual. The sum of the squared residuals is calculated and divided by the number of samples minus 1. Finally, the square root is taken.  
  7. 7. SIGNIFICANCE OF STANDARD DEVIATION FOR SIX SIGMA The standard deviation is the basis of Six Sigma.  The number of standard deviations that can fit within the boundaries set by your process represent Six Sigma.  The number of errors that you can have for your process as you move out each standard deviation continues to decrease. 
  8. 8. DEFECTS PER MILLION AND SIX SIGMA TABLE  The table below shows the percentage of data that falls within the standard deviations and the amount of defects per sigma, in terms of "Defects Per Million Opportunities" or DPMO. Defects per 100 Defects per 10000 Defects per million Success rate Sigma Value (σ) 93 9330 933000 7% 0.0 69 6910 691000 31% 1.0 31 3090 309000 69.1% 2.0 7 668 66800 93.32 3.0 1 62 6210 99.379 4.0 2 233 99.9767 5.0 3.4 99.99966% 6.0
  9. 9. GAUSSIAN OR NORMAL DISTRIBUTION The normal, or Gaussian, distribution is a family of continuous probability distributions  These distribution functions are defined by two parameters: a location (most commonly the "mean",μ), and a scale (most commonly the "variance", σ2).  It is observed that most random variable distributions takes the shape of Gaussian distribution over a fairly long time. 
  10. 10. GAUSSIAN DISTRIBUTION EXPLAINED FURTHER…  Above are 4 examples of different distributions given different values for mean and standard deviation. An important case is the standard normal distribution shown as the red line. The standard normal distribution is the normal distribution with a mean of 0 and a variance of 1. It is symmetrical about the mean, μ.
  11. 11.  Suppose we have a process where we make a product of a certain concentration and we have good control over the process.  After analyzing a set of data from a time period we see that we have a standard deviation of only 0.01 and our product concentration is required to be within 0.05. In order to say our product is essentially defect-free, 4.5 standard deviations away from the average must be less than our required product tolerance (± 0.05). In this case 4.5 standard deviations is equal to 0.045 and our product tolerance is 0.05.
  13. 13. 1.EVERYTHING WE DO IS A PROCESS Absolutely everything that we do, at work or at play, is a process. Each process has a start, a stop(and therefore a time taken), inputs in from suppliers and outputs out to customers, and things that happen during the process steps.  Business processes usually perform an action on the main entity passing through the process, to physically change it and add value in the eyes of the customer.  Manufacturers use processes to add value to products, and service industries use processes to deliver value-added services. 
  14. 14. 2. EVERY PROCESS HAS MEASURABLE CHARACTERISTICS All of these processes change entities and such changes can be measured.  Measurements can be of input or output characteristics such as number, size, weight or type.  Measurements can be of continuous data items such as time, money, size, or they can be of discrete data items such as integer counts.  The process itself will have requirements for the inputs, and the customers will have requirements of the outputs. Even if we are interested in measuring such intangible things as customer satisfaction, we can still do this using customer surveys. 
  15. 15. 3. Measurements follow a frequency distribution Frequency distributions are histograms showing how many measurements fall within a given range of data.  The range of the data is divided into 'bins' (normally equally sized), and each data point is allocated to the corresponding 'bin'. By plotting the number in each bin against the data range, a frequency histogram will be produced. With a lot of data, the overall envelope shape can be clearly shown as a nice smooth curve. 
  16. 16. 4. THE MOST COMMON FREQUENCY DISTRIBUTION IS NORMAL DISTRIBUTION    Many different types of distribution have been observed and investigated. However there is one distribution which occurs so often naturally that is has been named the Normal Distribution because it is the one you will normally meet. The characteristics of the Normal Distribution are well understood. The centre point is the mean or average (half the measurements are above, and half below). The curve is like a 'bell shape' getting closer and closer to zero but never quite reaching the line. The 'fatness' (variation) of the curve is measured by the standard deviation
  17. 17. 5. MOST OBSERVATIONS FALL WITHIN THREE SIGMA  The interesting thing about the Normal Distribution is that 68% of all measurements fall within one sigma either side of the mean. This is both mathematically proven and a practically experienced result.  In fact, if you take all measurements that fall within three sigma of the mean - that is between (mean + 3 sigma) to (mean - 3 sigma), you will have 99.74% of all outcomes.  In practical terms - if you measure the shoe sizes of the entire population, the plotted measurements will look like the Normal Distribution, with a mean (M) and a sigma (S). Almost 100% of all people will have shoe sizes from M-3S to M+3S, so if you make shoes you can satisfy 99.74% of all your customers with just this range of shoe sizes.
  18. 18. 6. CUSTOMERS HAVE EXPECTATIONS OF PROCESS PERFORMANCE      Measure any process and you will find that almost everything you measure looks like the Normal Distribution Each measurement has variation, and that is a fundamental fact of our universe. Customers will have expectations about the outcome - such as how long it takes, and how well the output suits their needs. For example Customers of a bank expect to queue to reach the cashier in perhaps 3 minutes or less. Customer expectation can be determined, and processes can be measured. How well do they match up?
  19. 19. 7. THREE SIGMA IS THE STANDARD    Since we only miss 0.26% of the time if we aim for +/- (plus or minus) three sigma, this has become the accepted standard for manufacturing quality since about 1920. Manufacturers look at what the requirements are, and set the process up so that the outcome has a mean and sigma to fit within these requirements. If the customer has an upper and a lower limit on their requirements or expectations, then the best situation is where the mean is exactly between the customer limits, and the distance between the mean and either limit is three sigma.
  20. 20. 8. THREE SIGMA IS FAILURE 7% OF THE TIME     Unfortunately life is not quite that simple. The problem is that perfection only lasts a short while, and machines often change as parts wear or shift and the variation begins to increase, so less and less of the outcome meets customer requirements. Today manufacturing and services are becoming more and more complex with hundreds of process steps and thousands of parts. Each bit of the process may deliver at 99% success, but as each part relies on what has gone before the failures soon multiply, and only a very small fraction of the final product gets through without any failure at all. In reality, three sigma often fails customer requirements 7% of the time. This is not 99.74% as we might think but just 93% customer satisfaction.
  21. 21. 9. SIX SIGMA MEANS FAILURE LESS THAN 4 IN A MILLION Six Sigma quality does three major things to shake up the status quo: 1. It measures quality in terms of the number of standard deviations (Sigma) between the mean and limits for a process measure. 2 It focuses totally on the customer, and lets the customer decide what matters and lets the customer determine the acceptable limits. 3 It moves the target from three sigma to six sigma. That is a shift from 66,700 to under 4 Defects Per Million Opportunities.   With the limits set by the customer (and not the process owner), and with six standard deviations between mean and limits, failure is experienced by the customer only 3.4 times in every million opportunities, even when process wear and change is accounted for. Six Sigma quality is about measurable total customer satisfaction.
  22. 22. 10. SIX SIGMA IS A PHILOSOPHY, METHODOLOGY AND A QUALITY METRIC  Six Sigma stands for a measure of customer quality - and it stands for a philosophy of giving customers what they want each and every time (zero defects, or as close as you can get). It also stands for a methodology that can be used to change processes and company culture to enable companies to deliver Six Sigma quality.  Six Sigma quality methodology uses the very best from existing Total Quality Management together with Statistical Process Control and Measurement, and strong Customer Focus, and therefore impacts on three key areas: the process, the employee, and the customer.
  23. 23. HOW TO IMPLEMENT SIX SIGMA - METHODOLOGIES Six Sigma projects follow two project methodologies These methodologies, composed of five phases each, bear the acronyms DMAIC and DMADV.  DMAIC is used for projects aimed at improving an existing business process.  DMADV is used for projects aimed at creating new product or process designs.
  24. 24. DMAIC The DMAIC project methodology has five phases:  Define the system, the voice of the customer and their requirements, and the project goals, specifically.  Measure key aspects of the current process and collect relevant data.  Analyze the data to investigate and verify cause-and-effect relationships. Determine what the relationships are, and attempt to ensure that all factors have been considered. Seek out root cause of the defect under investigation.  Improve or optimize the current process based upon data analysis using techniques such as design of experiments, poka yoke or mistake proofing, and standard work to create a new, future state process. Set up pilot runs to establish process capability.  Control the future state process to ensure that any deviations from target are corrected before they result in defects. Implement control systems such as statistical process control, production boards, visual workplaces, and continuously monitor the process.
  25. 25. DMADV The DMADV project methodology, known as DFSS ("Design For Six Sigma"), features five phases:      Define design goals that are consistent with customer demands and the enterprise strategy. Measure and identify CTQs (characteristics that are Critical To Quality), product capabilities, production process capability, and risks. Analyze to develop and design alternatives Design an improved alternative, best suited per analysis in the previous step Verify the design, set up pilot runs, implement the production process and hand it over to the process owner(s).
  27. 27. SIX SIGMA IMPLEMENTATION ROLES Six Sigma identifies several key roles for its successful implementation.  Executive Leadership includes the CEO and other members of top management. They are responsible for setting up a vision for Six Sigma implementation. They also empower the other role holders with the freedom and resources to explore new ideas for breakthrough improvements.  Champions take responsibility for Six Sigma implementation across the organization in an integrated manner. The Executive Leadership draws them from upper management. Champions also act as mentors to Black Belts.  Master Black Belts, identified by champions, act as in-house coaches on Six Sigma. They devote 100% of their time to Six Sigma. They assist champions and guide Black Belts and Green Belts.  Black Belts operate under Master Black Belts to apply Six Sigma methodology to specific projects. They devote 100% of their valued time to Six Sigma. They primarily focus on Six Sigma project execution and special leadership with special tasks, whereas Champions and Master Black Belts focus on identifying projects/functions for Six Sigma.  Green Belts are the employees who take up Six Sigma implementation along with their other job responsibilities, operating under the guidance of Black Belts.
  28. 28. CRITICISMS OF SIX SIGMA Lack of originality  Role of consultants  Over-reliance on (statistical) tools  Stifling creativity in research environments  Lack of systematic documentation  Criticism of the 1.5 sigma shift 