Six Sigma is a proven quality improvement method that gets results. By feel intimidated by the statistics vital to Six Sigma, but you don't need to be. 6. t- Tests. Where t-tests compare a mean to a target, or two means to each other, Minitab is the leading provider of statistical software for quality improvement and statistics. Six Sigma. Handbook. A Complete Guide for Green Belts,. Black Belts, and Managers at All Levels The material in this eBook also appears in the print version of this title: ISBN: , .. Gage R&R Analysis Using Minitab. .. topics addressing the details of the Lean Six Sigma DMAIC problem solving. Free PDF Lean Six Sigma and Minitab (4th Edition): The Complete Toolbox Guide for Business Improvement Books Online. Now in its 4th edition with over , copies sold, this Lean Six Sigma guide is firmly established as the practical guide for business improvement professionals.
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This paper presents a Six Sigma project conducted at a semiconductor company . results of the R&R study performed with Minitab© by ANOVA are shown in Study Var (6 * SD) %Study Var (%SV) %Tolerance (SV/Toler) %Process (SV/Proc ) .. FMEA-Potential Failure Mode and Effect Analysis: Reference Manual. 3rd. Lean Six Sigma and Minitab' Page Number changes for Edition 3 to Edition 4. Introduction. Edition 3. Edition 4 Notes. What is Six Sigma? 1. 1. DMAIC – A. minitab download filetype pdfpdf ke word tanpa, convert word document to pdf in sharepoint ,. High Point, aplikasi merubah format pdf Lean six sigma and.
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Lean Six Sigma Black Belt. The lists contain the courses we will offer during one of our upcoming terms, along with details such as the instructor, the start and end dates, the meeting times, and the meeting locations. Marshall has 6 jobs listed on their profile. Therefore it is essential to make a clear distinction between both uses of sigma.
One of the first steps GE took in working towards implementation of Six Sigma was to invite Mikel Harry, formerly a manager with Motorola and founder of the Six Sigma Academy, to talk to a group of senior employees. The presentation captured the imagination of Welch and his colleagues, the discipline of the approach being of particular appeal to engineers.
Examples of early Six Sigma success stories at GE, in both manufacturing and non-manufacturing situations, are reported by Welch , pp. Perez-Wilson states that Motorola had looked for a catchy name to shake up the organization when introducing the concept of variation reduction and that in Six Sigma they found it. A Six Sigma process, i.
Generation I — focus on elimination of defects and variation reduction, primarily in manufacturing. Spanned the period —, with Motorola being a good exemplar.
Generation II — in addition to the focus in the previous generation there was an emphasis on linking efforts to eliminate defects and reduce variation to efforts to improve product design and reduce costs. Spanned the period —, with General Electric a prime exemplar.
Generation III — since there has been an additional focus on value creation for both organizations and their stakeholders. These comments indicate that Six Sigma is still evolving. In the final section of their paper, on the future of Six Sigma, Montgomery and Woodall state: Six Sigma has become a widely used implementation vehicle for quality and business improvement.
It is logical to ask about its future. However, since Six Sigma is over 20 years old and implementations are growing worldwide, it is difficult to believe that it is simply a management fad. Identify core processes and key customers. Define customer requirements. Measure current performance. Prioritize, analyze, and implement improvements. Expand and integrate the Six Sigma system. A process or system may be represented by the schematic in Figure 1. For the process of baking biscuits examples of inputs are flour supplier, oven temperature and cooking time.
Some inputs are controllable by the baker, such as flour supplier and oven temperature.
Others, such as the moisture content of the flour, are not. Examples of performance measures are the proportion of broken biscuits and the height of a stack of 16 biscuits — the number in a standard pack. Problems with the next process of packaging the biscuits may arise because stack height is off target or because there is too much variation in stack height.
Xs are referred to as inputs, independent variables, factors and key process input variables. Uncontrollable inputs are often designated noise factors. Ys are referred to as performance measures, dependent variables or responses. Where performance measures are of vital importance to the customer, the phrases key process output variables or critical to quality characteristics are employed.
The first step is to define the problem or opportunity for improvement that is to be addressed. He also Hoerl , p. Six Sigma projects are normally selected as having potential in terms of major financial impact through facets of a business such as quality, costs, yield and capacity. Analyze — Analyze the preliminary data to document current performance or baseline process capability.
Begin identifying root causes of defects the Xs or independent variables and their impact. Improve — Determine how to intervene in the process to significantly reduce the defect levels. Several rounds of improvement may be required.
Control — Once the desired improvements have been made, put some type of system into place to ensure the improvements are sustained, even though additional Six Sigma resources may no longer be focused on the problem. Box 1. External consultants are often used to train the first group of Black Belts within an organization. A discussion of curricula for the training of Black Belts is given by Hoerl Many organizations look to Black Belts to progress to senior roles; for example, at SKF it is envisaged that future company leaders will be former Black Belts Johnstone, personal communication, Snee provides a Six Sigma project case study.
In terms of the process model shown in Figure 1. The use of bold symbols indicates that invariably a process has associated with it a set of Ys and a set of Xs so that Y represents Y1,Y2,Y3,.
Statistics provides a series of tools to aid the search for such knowledge — tools such as design of experiments and regression. Data display can be highly informative, so the topic will be emphasized. In many cases, process improvement is clearly evident from data display.
Much process improvement experimentation effort is wasted through the use of badly planned experiments, particularly experiments in which the effect of only a single X on the Ys is considered. The use of properly designed experiments can be of immense benefit, especially when the effects of the Xs are not independent of one another. Regression and correlation also provide tools for exploring and modelling relationships between Xs and Ys. Shewhart control charts have a major role to play and many important applications.
The example in Figure 1. The aim of the project was to reduce delays to assessment at a rapid access transient ischaemic attack and stroke clinic. In the measure and analyse phases of the project, the chart indicated that delay between patient referral and attendance at clinic averaged around 13 days.
In early , during the improve phase, a hotline to a consultant was introduced, open 24 hours a day, seven days a week. The consultant on call provided immediate advice and, if appropriate, made an appointment for a clinic visit. The chart indicated a reduction in the average delay from 13 days to around 3 days.
There is a danger that project improvement teams can become involved in measurement without considering the measurement process itself. In a chapter on the history of statistics and quality improvement, Jeroen de Mast , p. Minitab is without doubt a statistical software package that is easy to use and its creators claim to be the leading provider of software for statistics education and lean, Six Sigma, and quality improvement projects.
Essentially Minitab can provide all the methods referred to in the previous section — and much more. It provides the following capabilities and features: 1. Data and file management 2. Assistant new in Release 16 3.
Basic statistics 4. Graphics 5. Regression analysis 6. Analysis of variance 7. Design of experiments 8. Statistical process control 9. Measurement systems analysis Reliability and survival analysis Multivariate analysis Time series and forecasting Nonparametrics Tables Power and sample size Simulation and distributions Macros and customizability A number of quality tools are also provided, e.
The simulation facilities may be used to illustrate statistical concepts and theory requiring fairly advanced mathematics for a theoretical understanding. For a familiar process for the creation of a product, list inputs Xs and performance measures Ys. Indicate which inputs you view as being key, and identify those that can be controlled and those that cannot be controlled, or are not controlled, during routine operation, which are noise and which performance measures are critical.
Repeat for a process for the creation of a service. Note the availability of seven other Minitab 16 videos that you might find informative as you progress through the book.
Attributed to W Edwards Deming Overview Data are essential in the monitoring and improvement of processes and in the measure and control phases of Six Sigma projects. Such data are often obtained in time sequence. For example, a critical dimension might be measured every hour on each member of a sample of machined automotive components during production in a factory, the number of mortgage agreements completed successfully each day might be recorded by a building society. A run chart of such data can frequently be highly informative and forms the basis for some control charts.
The facility for calculation of derived data will also be introduced. The use of histograms for the display of data will be described, and widely used summary statistics that indicate location and variability defined. The chapter concludes with consideration of a variety of methods for data entry in Minitab, of data manipulation and of the detection of missing and erroneous data values. When was this value obtained?
What time period does that value represent? Because of this association with time there is information contained in the time-order sequence of the values. To recover this information you will need to look at your data in a time-ordered plot, which is commonly called a running record or time-series graph. In order to introduce running records or run charts, consider the time series of weights g of glass bottles in Table 2.
Bottle weight is a key process output variable in the food packaging industry.
Each bottle was formed in the same mould of the machine used to produce the bottles and the time interval between sampling of bottles was 15 minutes. The target weight is g and the production run was scheduled to run for a total of 12 hours. On opening Minitab the screen displayed in Figure 2. Two main windows are visible. The Session window displays the results of analyses in text format; initially it displays the date, time and a message of welcome.
It is also possible to perform tasks by entering commands in the Session window instead of using the Minitab menus. The Data window Table 2. It is possible to use multiple worksheets within a single project. Note that the blue band across the top of the Session window is a deeper colour than that across the top of the Data window, indicating that the Session window is currently active.
Take a moment to click on the blue bands to make the worksheet active and then the Session window active again. A third component, in addition to the Session and Data windows, of the new Minitab project just opened up, is the Project Manager, which is minimized at this stage. Note the corresponding icon labelled Proje.
Figure 2. Clicking on an icon makes the corresponding component active. Note that the message displayed indicates the project component associated with the icon and gives the keys that may be used to make the component active as an alternative to clicking on its icon. Enter all 25 of the weight data values in Table 2.
In order to access the dialog box for the creation of a run chart first click the Stat menu icon see Figure 2. The Run Chart dialog box can now be completed as shown in Figure 2. Highlight Weight g in the window on the left of the dialog box and click on the Select button so that Weight g appears in the window labelled Single column:.
Alternatively highlight Figure 2. Weight g and double-click. Enter Subgroup size: 1 in the appropriate window as each sample consisted of a single bottle. Had the weights of samples of four bottles been recorded every15 minutes then Subgroup size: 4 would have been entered.
Clicking the Options. Click OK to return to the main dialog box and then click OK again to display the run chart — see Figure 2. Those involved in running the process can learn about its performance from scrutiny of the run chart. Weight is plotted on the vertical axis, with the weight of each bottle represented by a square symbol and the symbols connected by dotted lines indicating the sequence of sampling. Had, for example, four bottles been weighed every 15 minutes then Minitab offers the choice of a run chart with symbols corresponding to either the mean or median of the weights of each sample or subgroup of four bottles.
The horizontal axis is labelled Observation — each sample of a single bottle may be thought of as an observation of the process behaviour. The horizontal line on the chart corresponds to the median weight of the entire group of 25 bottles weighed.
The median weight is Scrutiny of the run chart reveals 12 points above the median line, 12 points below the median line and the point Figure 2. The sample of five bottle weights The sample of six bottle weights In this case, where there is an even number of bottles in the sample, the median is taken as the value midway between the middle pair of values, i. The median of This is the sort of question on which statistics can shed light — further discussion of such questions and the tools available in Minitab to answer them appears in Chapter 7.
However, before considering the performance in relation to any target or specification limits, a much more fundamental question should be asked: is the process performing in a stable, predictable manner? The information displayed beneath the run chart is relevant. For an explanation, the Help facility provides the overview of run charts displayed in Figure 2.
One way to access this information is to click on the Help button in the bottom left-hand corner of the Run Chart dialog box — see Figure 2. Note also the provision of how to, example, data and see also links to further sources of information to aid the user to learn about run charts. In addition, an explanation of the dialog box items is given and a link to information on the options available in creating a run chart in Minitab via.
Links to related topics are also provided within the text — in the case of the run chart the related topics are subgroup and median. A process performing in a stable and predictable manner is said to exhibit common cause variation only and to be in a state of statistical control.
When a process is affected by special cause variation, i. Data for a process affected only by common cause variation exhibit randomness while data for a process do not. In order to conduct these one must scrutinize the P-values in the text boxes beneath the run chart. P-values will be explained in Chapter 7, but at this stage one need only know that it is generally accepted that any P-value less than significance level or a-value of 0.
For the weight data none of the P-values is less than 0. The run charts in Figures 2. In the first scenario, displayed in Figure 2. The line segment superimposed on the display indicates an apparent initial downward trend in weight. In the second scenario, displayed in Figure 2. The rectangle superimposed on the display indicates a period during which weight oscillates rapidly. In the third scenario, displayed in Figure 2. The rectangle superimposed on the display indicates a period during which there is an absence of weight values close to the median weight represented by the reference line — typical when mixtures occur.
In the fourth scenario, displayed in Figure 2. Two clusters — groups of points corresponding to bottles with similar weights — are indicated in the display. A process team should respond to evidence of special cause variation by taking steps to carry out a root cause investigation in order to determine the extraneous factor or factors affecting process performance.