Quality_Control_Tools.ppt
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The document describes various quality control tools that can be used for process improvement. It lists 7 basic tools - Pareto diagram, cause and effect diagram, flow chart, check sheet, histogram, scatter diagram, and control chart. These are more numeric tools. It also lists 7 advanced tools - affinity diagram, activity node diagram, interrelationship diagraph, process decision program chart, matrix diagram, prioritization matrix, and tree diagram. These are described as being more managerial tools. Each tool is then described in more detail with examples on how and when they should be used.
Quality_Control_Tools.ppt
- 1. QUALITY CONTROL TOOLS FOR PROCESS IMPROVEMENT Flow Chart Check Sheet Pareto Diagram Cause & Effect Histogram Scatter Diagram Control Chart
- 2. 7 BASIC TOOLS 1.Pareto Diagram 2.Cause and Effect diagram 3.Flow Chart 4.Check Sheet 5.Histogram 6.Scatter Diagram 7.Control Chart 7 ADVANCED TOOLS 1.Affinity Diagram 2.Activity Node Diagram 3.Interrelationship Diagraph 4.Process Decision Program Chart 5.Matrix Diagram 6.Prioritization Matrix 7.Tree Diagram Tools for Process Improvement •Seven basic tools are more numeric •Seven advanced tools are more managerial
- 4. PROCESS FLOW CHART Description : A process flow chart is a representation of sequences of operation of a process from the beginning to the end through certain symbols. It records the series of events, activities and decisions in a form which can be easily understood and communicated to all.
- 7. Types of Flowcharts Linear Flowchart Displays the sequence of work steps that make up a process. Helps to identify rework and redundant or unnecessary steps. Deployment Flowchart Identifies the people involved at each step. Horizontal lines define customer-supplier relationships. Opportunity Flowchart Differentiates process activities as: • Value-added steps (VA) •Cost-added-only steps (CAO)
- 9. When to use Check Sheet •Use it when data is to be recorded manually, to ensure the data is accurately recorded and is easy to use later •Use it when the recording involves counting, classifying, checking or locating. •Use it when it is useful to see the distribution of measures as they are built up.
- 10. Check Sheets Step #1: Identify data/information needs Step #2: Decide on check sheet format Step #3: Design and produce the check sheet Step #4: Complete the check sheet
- 11. Example of a simple process check sheet. Model XYZC217 Batch (Batch Size = 100) failures 1 2 3 4 5 6 7 8 9 10 Power up 1 2 1 Boot up 6 4 2 1 2 Sink test 2 1 1 1 Case damage 1 1 2 Keyboard damage Monitor damaged 1 2 Bundled s/w included 3 1 3 Checked by pj am jj [j lm lm rm pj am pj
- 12. Example
- 13. • Example • The paint shop team in a car manufacturer had the objective of discovering and removing the main causes of paint defects (bubble, run and scuff) in doors. To achieve this, they concluded that they needed to determine the number and location of each type of defects. They also defined the process to capture data on one form for each paint lot. Paint bubbles were the most common problem, and were investigated first. The grouping led the team to investigate the paint programming, where it was discovered that the paint robot was hesitating at corners. Reprogramming the robot significantly reduced the number of errors. Further analysis found that varying paint viscosity was causing runs.
- 14. • Check-up Confirmation Check Sheet Car type Car registration Ford Focus W357 PHR Interior vacuumed √ Upholstery cleaned √ Dash board cleaned √ De odorised √ Body washed √ Washed waxed & Polished √ Under car washed √ Wheels washed √ Tyres blacked √ Comments: Front bumper badly scratched on delivery, this can not be covered Performed / Checked by Date Practical variations
- 16. A Histogram is a vertical bar chart that depicts the distribution of a set of data. Unlike Control Charts, which are discussed later, a Histogram does not reflect process performance over time. It's helpful to think of a Histogram as being like a snapshot, while a Run Chart or Control Chart is more like a movie
- 17. •Summarize large data sets graphically When to use it •Assist in decision making. •Compare process results with specification limits •Communicate information graphically.
- 18. The Histogram shows the frequency distribution across a set of measurements as a set of physical bars. The width of each bar is constant and represents a fixed range of measurements (called a cell, bin or class). The height of each bar is proportional to the number of measurements within that cell. Grouping a set of measurements into a Histogram
- 20. When to use it • Use it when selecting the most important things on which to focus, thus differentiating between the 'vital few' and the 'trivial many'. • Use it, rather than a Bar Chart or Pie Chart to show the relative priority of a set of numeric measurements.
- 21. What Is a Pareto Chart? • Bar chart arranged in descending order of height from left to right • Bars on left relatively more important than those on right • Separates the "vital few" from the "trivial many" (Pareto Principle)
- 22. Why Use a Pareto Chart? • Breaks big problem into smaller pieces • Identifies most significant factors • Shows where to focus efforts • Allows better use of limited resources
- 24. Quality Tool Cause and Effect Diagram (Fish Bone Diagram) CAUSE & EFFECT DIAGRAM
- 25. Cause and Effect Diagram • When to use it • Use it when investigating a problem, to identify and select key problem causes to investigate or address. • Use it when working in a group, to gain a common understanding of problem causes and their relationship. The fishbone diagram identifies many possible causes for an effect or problem. It can be used to structure a brainstorming session. It immediately sorts ideas into useful categories.
- 26. How to understand it
- 27. The default 'Four Ms'
- 28. Step #1: Define problem and determine the main causes (I,II,III,...) Use 5Ms: Man, Method, Machine, Material, Mother Nature Main Cause II Main Cause I Main Cause V Main Cause IV Main Cause III Problem
- 29. Step #2: Pick one main cause and ask “Why did the main cause occur?” Results in causes A, B, C, ... Work one category at a time… please! Main Cause I Why did the main cause occur? C A B Problem
- 30. Step #3: Pick one cause and ask “Why did cause A occur?” Results in causes A1, A2, A3,… Work one category at a time… please! Main Cause I Why did cause “A” occur? A A1 A2 A3 Problem
- 31. Step #4: Pick one cause and ask “Why?” 5 times End result is a probable cause Ask “Why?” 5 times? Main Cause I Why A2A1? A A2 Why A? A2A1A1 A2A1 A2A Why A2A? A2A1A Why A2A1A? Probable Cause Problem Step #5: Repeat Steps #2 through #4 until all causes have resulted in a probable cause
- 32. Step #6: Identify Root Causes Main Cause II Main Cause I Main Cause V Main Cause IV Main Cause III If one probable cause repeats… Root Cause A A C B Root Cause Problem Step #7: Develop an action plan to address root causes
- 33. Case Study The managing director of a weighing machine company received a number of irate letters, complaining of slow service and a constantly engaged telephone. Rather surprised, he asked his support and marketing managers to look into it. With two other people, they first defined the key symptom as 'lack of responsiveness to customers' and then met to brainstorm possible causes, using a Cause-Effect Diagram, as illustrated.
- 34. Cause and Effect Diagram Case Study
- 36. Scatter Diagram • A Scatter Diagram examines the relationships between data collected for two different characteristics. • Although the Scatter Diagram cannot determine the cause of such a relationship, it can show whether or not such a relationship exists, and if so, just how strong it is. • The analysis produced by the Scatter Diagram is called Regression Analysis.
- 37. When to use it • Use a Scatter Diagram to determine if there is correlation between two characteristics. • Use it only when both items being measured can be measured together, in pairs.
- 38. The Scatter Diagram helps to identify the existence of a measurable relationship between two such items by measuring them in pairs and plotting them on a graph, as below. This visually shows the correlation between the two sets of measurements.
- 41. Exercise Construct a scatter diagram and comment whether the marks scored in soldering certification program is correlated with the quality of soldering work Employee Number Soldering Certification Score (%) Soldering Quality (%) x y 1 62% 84% 2 66% 86% 3 69% 87% 4 70% 90% 5 71% 92% 6 72% 92% 7 72% 92% 8 72% 92% 9 73% 93% 10 73% 95%
- 42. y = 0.9375x + 0.2468 R² = 0.8941 82% 84% 86% 88% 90% 92% 94% 96% 60% 65% 70% 75% Soldering Quality (%) y Soldering Quality (%) y Linear (Soldering Quality (%) y)
- 44. Control Chart • The control chart is a graph used to study how a process changes over time. Data are plotted in time order. • A control chart always has a central line for the average, an upper line for the upper control limit and a lower line for the lower control limit. These lines are determined from historical data. • By comparing current data to these lines, you can draw conclusions about whether the process variation is consistent (in control) or is unpredictable (out of control, affected by special causes of variation).
- 45. •Use when investigating a process, to determine whether it is in a state of statistical control and thus whether actions are required to bring the process under control. •Use it to differentiate between special and common causes of variation, identifying the special causes which need to be addressed first. •Use it as an ongoing 'health' measure of a process, to help spot problems before they become significant. When to use it
- 46. Concealed dynamics of Histogram A Histogram can be used to show the static distribution of a set of these measurements, but this does not show dynamic trends, for example where successive measurements may indicate a significant change within the process
- 47. A Control Chart usually has three horizontal lines in addition to the main plot line, as shown below .The central line is the average (or mean). The outer two lines are at three standard deviations either side of the mean. Thus 99.7% of all measurements will fall between these two lines. Mean and Control Limits
- 48. Significance in Control Charts
- 49. Creating a Control Chart Bottle Filling Process: 100th of a ML over 10ML
- 50. Find the mean of each subgroup Find the mean of all of the means from the previous step (X) Calculate the standard deviation (S) of the data points
- 51. Calculate the upper and lower control limits (UCL, LCL) using the following formula: UCL = CL + 3*S LCL = CL – 3*S The formula represents 3 standard deviations above and 3 standard deviations below the mean respectively.
- 52. Draw a line at each deviation. In the above example, there is a line drawn at one, two, and three standard deviations (sigma’s) away from the mean. Zone C is 1 sigma away from the mean (green). Zone B is 2 sigma away from the mean (yellow). Zone A is 3 sigma away from the mean (red).
- 53. Graph the X-bar Control Chart, by graphing the subgroup means (x- axis) verses measurements (y-axis). Your graph should look like something like this: Evaluate the graph to see if the process is out-of-control
- 54. Interpreting Control Charts One point is more than 3s from center line. Nine consecutive points Nine on same side of center line Six consecutive points, all increasing or all decreasing 9 consecutive points, alternating up & down 2 out of 3 consecutive points more than 2s from center line (same side) 4 out of 5 consecutive points more than 1s from center line (same side)