Basic & Simple Quality management tools


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Basic & Simple Quality management tools

  1. 1. Quality management tools
  2. 2. Seven Basic Tools of Quality   The Seven Basic Tools of Quality is a designation given to a fixed set of graphical techniques identified as being most helpful in troubleshooting issues related to quality. They are called basic because they are suitable for people with little formal training in statistics and because they can be used to solve the vast majority of quality-related issues .
  3. 3. The seven tools are  Cause and effect diagram (also known as the "fishbone" or Ishikawa diagram)  Check sheet  Control chart  Histogram  Pareto chart  Scattered diagram  Stratification (alternately, flow chart or run chart)
  4. 4. The Seven Basic Tools stand in contrast to more advanced statistical methods such as survey sampling, acceptance sampling, statistical hypothesis testing, design of experiments, multivariate analysis, and various methods developed in the field of operations research.
  5. 5. Ishikawa diagrams (also called fishbone diagrams, herringbone diagrams, cause-and-effect diagrams, or Fishikawa) are causal diagrams created by Kaoru Ishikawa (1968) that show the causes of a specific event. Common uses of the Ishikawa diagram are product design and quality defect prevention, to identify potential factors causing an overall effect. Each cause or reason for imperfection is a source of variation . Causes are usually grouped into major categories to identify these sources of variation.
  6. 6. The categories typically include:  People: Anyone involved with the process  Methods: How the process is performed and the specific requirements for doing it, such as policies, procedures, rules, regulations and laws  Machines: Any equipment, computers, tools, etc. required to accomplish the job  Materials: Raw materials, parts, pens, paper, etc. used to produce the final product  Measurements: Data generated from the process that are used to evaluate its quality  Environment: The conditions, such as location, time, temperature, and culture in which the process operates.
  7. 7. Cause and effect diagram for defect XXX
  8. 8. Check sheet The check sheet is a form (document) used to collect data in real time at the location where the data is generated. The data it captures can be quantitative or qualitative. When the information is quantitative, the check sheet is sometimes called a tally sheet. The check sheet is one of the so-called Seven Basic Tools of Quality Control.
  9. 9. Format The defining characteristic of a check sheet is that data are recorded by making marks ("checks") on it. A typical check sheet is divided into regions, and marks made in different regions have different significance. Data are read by observing the location and number of marks on the sheet.
  10. 10. Check sheets typically employ a heading that answers the Five Ws: Who filled out the check sheet What was collected (what each check represents, an identifying batch or lot number) Where the collection took place (facility, room, apparatus) When the collection took place (hour, shift, day of the week) Why the data were collected
  11. 11. Function To check the shape of the probability distribution of a process To quantify defects by type To quantify defects by location To quantify defects by cause (machine, worker) To keep track of the completion of steps in a multistep procedure (in other words, as a checklist)
  12. 12. Frequency distribution for film coater
  13. 13. Quality Control Checksheet Example
  14. 14. Control chart Control charts, also known as Shewhart charts (after Walter A. Shewhart) or process-behavior charts, in statistical process control are tools used to determine if a manufacturing or business process is in a state of statistical control
  15. 15. Xbar chart for a paired xbar and R chart
  16. 16. Chart details A control chart consists of: Points representing a statistic (e.g., a mean, range, proportion) of measurements of a quality characteristic in samples taken from the process at different times [the data] The mean of this statistic using all the samples is calculated (e.g., the mean of the means, mean of the ranges, mean of the proportions) A centre line is drawn at the value of the mean of the statistic The standard error (e.g., standard deviation/sqrt(n) for the mean) of the statistic is also calculated using all the samples Upper and lower control limits (sometimes called "natural process limits") that indicate the threshold at which the process output is considered statistically 'unlikely' and are drawn typically at 3 standard errors from the centre line
  17. 17. The chart may have other optional features, including: Upper and lower warning or control limits, drawn as separate lines, typically two standard errors above and below the centre line Division into zones, with the addition of rules governing frequencies of observations in each zone Annotation with events of interest, as determined by the Quality Engineer in charge of the process's quality
  18. 18. Types of charts Chart Process observation Process observations relationships Process observations type Size of shift to detect Xbar and R chart Quality characteristic measurement within one subgroup Independent Variables Large (≥ 1.5σ) Xbar and S chart Quality characteristic measurement within one subgroup Independent Variables Large (≥ 1.5σ) Shewhart individual control chart (ImR chart or XmR chart) Quality characteristic measurement for one observation Independent Variables† Large (≥ 1.5σ) Three-way chart Quality characteristic measurement within one subgroup Independent Variables Large (≥ 1.5σ) p-chart Fraction nonconforming within one subgroup Independent Attributes† Large (≥ 1.5σ) np-chart Number nonconforming within one subgroup Independent Attributes† Large (≥ 1.5σ) c-chart Number of nonconformances within one subgroup Independent Attributes† Large (≥ 1.5σ) u-chart Nonconformances per unit within one subgroup Independent Attributes† Large (≥ 1.5σ)
  19. 19. Chart EWMA chart Process observation Exponentially weighted moving average of quality characteristic measurement within one subgroup Process observations relationships Process observations type Size of shift to detect Attributes or variables Small (< 1.5σ) Independent CUSUM chart Cumulative sum of quality characteristic measurement within one subgroup Independent Attributes or variables Small (< 1.5σ) Time series mode Quality characteristic measurement within one subgroup Autocorrelated Attributes or variables N/A Regression control chart Quality characteristic measurement within one subgroup Dependent of process control variables Variables Large (≥ 1.5σ)
  20. 20. Histogram In statistics, a histogram is a graphical representation of the distribution of data. It is an estimate of the probability distribution of a continuous variable and was first introduced by Karl Pearson
  21. 21. Uses Histograms are used to plot the density of data, and often for density estimation: estimating the probability density function of the underlying variable.
  22. 22. Histogram of arrivals per minute
  23. 23. Pareto chart A Pareto chart is a type of chart that contains both bars and a line graph, where individual values are represented in descending order by bars, and the cumulative total is represented by the line.
  24. 24. Scattered Diagram A scatter plot, scatterplot, or scattergraph is a type of mathematical diagram using Cartesian coordinates to display values for two variables for a set of data. The data is displayed as a collection of points, each having the value of one variable determining the position on the horizontal axis and the value of the other variable determining the position on the vertical axis . This kind of plot is also called a scatter chart, scattergram, scatter diagram, or scatter graph.
  25. 25. Stratified sampling In statistics, stratified sampling is a method of sampling from a population
  26. 26. Advantages If the population is large and enough resources are available, usually one will use multi-stage sampling. In such situations, usually stratified sampling will be done at some stages. However the main advantage remains stratified sampling being the most representative of a population.
  27. 27. Disadvantages Stratified sampling is not useful when the population cannot be exhaustively partitioned into disjoint subgroups. It would be a misapplication of the technique to make subgroups' sample sizes proportional to the amount of data available from the subgroups, rather than scaling sample sizes to subgroup sizes (or to their variances, if known to vary significantly. by means of an F Test). Data representing each subgroup are taken to be of equal importance if suspected variation among them warrants stratified sampling. If subgroups' variances differ significantly and the data need to be stratified by variance, then there is no way to make the subgroup sample sizes proportional (at the same time) to the subgroups' sizes within the total population. For an efficient way to partition sampling resources among groups that vary in their means, their variances, and their costs, see "optimum allocation"