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NATIONAL PRODUCTIVITY COUNCIL WELCOMES YOU TO A PRESENTATION ON

NATIONAL PRODUCTIVITY COUNCIL WELCOMES YOU TO A PRESENTATION ON. CONTROL CHARTS By B.Girish Dy. Director. Three SQC Categories. Traditional descriptive statistics e.g. the mean, standard deviation, and range

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NATIONAL PRODUCTIVITY COUNCIL WELCOMES YOU TO A PRESENTATION ON

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  1. NATIONAL PRODUCTIVITY COUNCIL WELCOMES YOU TO A PRESENTATIONON CONTROL CHARTS By B.Girish Dy. Director

  2. Three SQC Categories • Traditionaldescriptive statistics • e.g. the mean, standard deviation, and range • Acceptance sampling used to randomly inspect a batch of goods to determine acceptance/rejection • Does not help to catch in-process problems • Statistical process control(SPC) • Involves inspecting the output from a process • Quality characteristics are measured and charted • Helpful in identifying in-process variations

  3. Statistical Process Control (SPC) • A methodology for monitoring a process to identify special causes of variation and signal the need to take corrective action when appropriate • SPC relies on control charts

  4. SPC Implementation Requirements • Top management commitment • Project champion • Initial workable project • Employee education and training • Accurate measurement system

  5. The Mean- measure of central tendency The Range- difference between largest/smallest observations in a set of data Standard Deviation measures the amount of data dispersion around mean Traditional Statistical Tools

  6. Normal distributions Skewed distribution Distribution of Data

  7. Sources of Variation • Common causes of variation • Random causes that we cannot identify • Unavoidable • e.g. slight differences in process variables like diameter, weight, service time, temperature • Assignable causes of variation • Causes can be identified and eliminated • e.g. poor employee training, worn tool, machine needing repair

  8. Common Causes Special Causes

  9. Histograms do not take into account changes over time. Control charts can tell us when a process changes

  10. Introduction to Control Charts • Important uses of the control chart • Most processes do not operate in a state of statistical control • Consequently, the routine and attentive use of control charts will identify assignable causes. If these causes can be eliminated from the process, variability will be reduced and the process will be improved • The control chart only detects assignable causes. Management, operator, and engineering action will be necessary to eliminate the assignable causes.

  11. Introduction to Control Charts • Monitor Variation in Data • Exhibit trend - make correction before process is out of control • A Process - A Repeatable Series of Steps Leading to a Specific Goal

  12. Introduction to Control Charts (continued) • Show When Changes in Data are Due to: • Special or assignable causes • Fluctuations not inherent to a process • Represent problems to be corrected • Data outside control limits or trend • Chance or common causes • Inherent random variations • Consist of numerous small causes of random variability

  13. Introduction to Control Charts Graph of sample data plotted over time Special Cause Variation Process Average  UCL Mean LCL Common Cause Variation

  14. Commonly Used Control Charts • Variables data • x-bar and R-charts • x-bar and s-charts • Charts for individuals (x-charts) • Attribute data • For “defectives” (p-chart, np-chart) • For “defects” (c-chart, u-chart)

  15. Introduction to Control Charts Popularity of control charts 1) Control charts are a proven technique for improving productivity. 2) Control charts are effective in defect prevention. 3) Control charts prevent unnecessary process adjustment. 4) Control charts provide diagnostic information. 5) Control charts provide information about process capability.

  16. Control Chart Selection Quality Characteristic variable attribute defective defect no n>1? x and MR constant sampling unit? yes constant sample size? yes p or np no n>=10 or computer? x and R yes no no yes p-chart with variable sample size c u x and s

  17. SPC Methods-Control Charts • Control Charts show sample data plotted on a graph with CL, UCL, and LCL • Control chart for variables are used to monitor characteristics that can be measured, e.g. length, weight, diameter, time • Control charts for attributes are used to monitor characteristics that have discrete values and can be counted, e.g. % defective, number of flaws in a shirt, number of broken eggs in a box

  18. Control Charts for Attributes –P-Charts & C-Charts • Use P-Charts for quality characteristics that are discrete and involve yes/no or good/bad decisions • Number of leaking caulking tubes in a box of 48 • Number of broken eggs in a carton • Use C-Charts for discrete defects when there can be more than one defect per unit • Number of flaws or stains in a carpet sample cut from a production run • Number of complaints per customer at a hotel

  19. Control Chart Design Issues • Basis for sampling • Sample size • Frequency of sampling • Location of control limits

  20. Developing Control Charts • Prepare • Choose measurement • Determine how to collect data, sample size, and frequency of sampling • Set up an initial control chart • Collect Data • Record data • Calculate appropriate statistics • Plot statistics on chart

  21. LTL UTL Red Zone Red Zone Green Zone nominal value Yellow Zones Pre-Control

  22. Control Limits UCL =Process Average + 3 Standard Deviations LCL = Process Average - 3 Standard Deviations X UCL + 3 Process Average - 3 LCL TIME

  23. Next Steps • Determine trial control limits • Center line (process average) • Compute UCL, LCL • Analyze and interpret results • Determine if in control • Eliminate out-of-control points • Recompute control limits as necessary

  24. Percentage of values under normal curve Control limitsbalance risks like Type I error Setting Control Limits

  25. Comparing Control Chart Patterns X X X Common Cause Variation: No Points Outside Control Limits Special Cause Variation: 2 Points Outside Control Limits Downward Pattern: No Points Outside Control Limits but Trend Exists

  26. Typical Out-of-Control Patterns • Point outside control limits • Sudden shift in process average • Cycles • Trends • Hugging the center line • Hugging the control limits • Instability

  27. Use x-bar and R-bar charts together Used to monitor different variables X-bar & R-bar Charts reveal different problems In statistical control on one chart, out of control on the other chart? OK? Control Charts for Variables

  28. Processes In Control

  29. Process Out of Control

  30. Shift in Process Average

  31. Identifying Potential Shifts

  32. Cycles

  33. Trend

  34. PROCESS STREAMS UCL Mixtures Sudden stability Center line LCL

  35. Interpreting Control Charts Description Example # 1 Example # 2 Interpretation Chart Process In Control Process Out of Control Run Chart points do not form a parti- cular pattern and lie within the upper and lower chart limits The process is stable, not changing. Doesn’t necesarily mean to leave the process alone. May be opportunities to improve the process and enjoy substantial benefits UCL 10 x 19 lcl 30 UCL 10 x 19 lcl 30 Chart points form a particular pattern OR one or more points lie beyond the uppor or lower chart limits UCL 10 x 19 lcl 30 Alerts us that the process is changing. Doesn’t mean you need to take a corrective action. May be relate to a change you have made. Be sureto identify the reason\(s) before taking any constructive actions(w) UCL 10 x 19 lcl 30 Chart points are on one side of the center line. The number of points in a run is called the “length of the run” Suggest the process has undergone a permanent change (positive or negative) and is now becoming stable. Often requires tha t you recompute the control lines for future interpre- tation efforts. UCL 10 x 19 lcl 30 UCL 10 x 19 lcl 30

  36. Interpreting Control Charts Description Example # 1 Example # 2 Interpretation Chart Trend Cycle Hugging Often seen after some change has been made. Helps tell you if the change(s) had a positive or negative effect. may also be part of a learning curve associated with some form of training A continued rise or fall in a series of points (7 or more consecutive points direction) 1 UCL 10 x 19 lcl 30 2 UCL 10 x 19 lcl 30 7 6 3 5 4 4 3 2 5 6 1 often relates to factors that influence the process in a predictable manner. Factors occur over a set time period and a positive/negative effect Helps determine future work load and staffing levels Chart ponts show the same pattern changes (e.g.rise or fall) over equal periods of time UCL 10 x 19 lcl 30 UCL 10 x 19 lcl 30 Suggests a different type of data has been mixed into the sub-group being sampled. Often need to change the sub-group, reassemble the data, redraw the control chart Chart points are close to the center line or to a control limit line (2 out of 3, 3 out of 4, or 4 out of 10.) UCL 10 x 19 lcl 30 CL 10 x 19 lcl 30 1/2 1/2 1/2 1/2

  37. When to Take Corrective Action • Corrective Action Should Be Taken When Observing Points Outside the Control Limits or when a Trend Has Been Detected • Eight consecutive points above the center line (or eight below) • Eight consecutive points that are increasing (decreasing)

  38. Out-of-Control Processes • If the Control Chart Indicates an Out-of-Control Condition (a Point Outside the Control Limits or Exhibiting Trend) • Contains both common causes of variation and assignable causes of variation • The assignable causes of variation must be identified • If detrimental to quality, assignable causes of variation must be removed • If increases quality, assignable causes must be incorporated into the process design

  39. In-Control Process • If the Control Chart is Not Indicating Any Out-of-Control Condition, then • Only common causes of variation exist • It is sometimes said to be in a state of statistical control • If the common-cause variation is small, then control chart can be used to monitor the process • If the common-cause variation is too large, the process needs to be altered

  40. Types of Error • First Type: • Belief that observed value represents special cause when, in fact, it is due to common cause • Second Type: • Treating special cause variation as if it is common cause variation

  41. Remember • Control does not mean that the product or service will meet the needs. It only means that the process is consistent (may be consistently bad). • Capability of meeting the specification.

  42. How to use the results • By eliminating the special causes first and then reducing the common causes, quality can be improved.

  43. Final Steps • Use as a problem-solving tool • Continue to collect and plot data • Take corrective action when necessary • Compute process capability

  44. Process Capability • Product Specifications • Preset product or service dimensions, tolerances • e.g. bottle fill might be 16 oz. ±.2 oz. (15.8oz.-16.2oz.) • Based on how product is to be used or what the customer expects • Process Capability – Cp and Cpk • Assessing capability involves evaluating process variability relative to preset product or service specifications • Cp assumes that the process is centered in the specification range • Cpkhelps to address a possible lack of centering of the process

  45. Three possible ranges for Cp Cp = 1, as in Fig. (a), process variability just meets specifications Cp ≤ 1, as in Fig. (b), process not capable of producing within specifications Cp ≥ 1, as in Fig. (c), process exceeds minimal specifications One shortcoming, Cpassumes that the process is centered on the specification range Cp=Cpkwhen process is centered Relationship between Process Variability and Specification Width

  46. The table below shows the information gathered from production runs on each machine. Are they all acceptable? Solution: Machine A Machine B Cp= Machine C Cp= Computing the Cp Value at Cocoa Fizz: three bottlingmachines are being evaluated for possible use at the Fizz plant. The machines must be capableof meeting the designspecification of 15.8-16.2 oz. with at least a process capability index of 1.0 (Cp≥1)

  47. Computing the Cpk Value at Cocoa Fizz • Design specifications call for a target value of 16.0 ±0.2 OZ. (USL = 16.2 & LSL = 15.8) • Observed process output has now shifted and has a µ of 15.9 and a σ of 0.1 oz. • Cpkis less than 1, revealing that the process is not capable

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