In the name of Allah,the Most Beneficient, Presented by Nudrat Rehman Roll# 07-47

Chapter 3, Quality Improvement Tools Total Quality Management by Poornima M Charantimath

WHAT IS A CONTROL CHART? A statistical tool used to distinguish between process variation resulting from common causes and variation resulting from special causes.

SPECIAL CAUSES Common causes

ELEMENTS OF CONTROL CHART Center Line represents mean operating level of process Each point is usually a sample statistic (such as subgroup average) of the quality characteristic LCL & UCL are vital guidelines for deciding when action should be taken in a process

TWO KINDS OF VARIATIONS  Natural Variations:  Due to common causes, purely random, unidentified.  Assignable Variations:  Variability where causes can be identified.

VARIATIONS

Developing Control Charts 1.Prepare – Choose measurement – Determine how to collect data, sample size, and frequency of sampling – Set up an initial control chart 2.Collect Data – Record data – Calculate appropriate statistics – Plot statistics on chart

3.Determine trial control limits – Center line (process average) – Compute UCL, LCL 4.Analyze and interpret results – Determine if in control – Eliminate out-of-control points – Recomputed control limits as necessary

5.Use as a problem-solving tool – Continue to collect and plot data – Take corrective action when necessary 6.Compute process capability

Out-Of-Control Process when a sample point (e.g., mean in an X-bar chart) falls outside the control lines, you have reason to believe that the process may no longer be in control.

Types of Control Charts Control Charts for Variables X-bar chart (mean chart) R chart (range chart) S chart (Sigma chart/Standard Deviation) Control Charts for Attributes p chart np chart c chart u chart

Control Charts for Variables X-bar chart: In this chart the sample means are plotted in order to control the mean value of a variable (e.g., size of piston rings, strength of materials, etc.). R chart: In this chart, the sample ranges are plotted in order to control the variability of a variable. S chart: In this chart, the sample standard deviations(a measure of the variation between individuals on a variable) are plotted in order to control the variability of a variable.

Control Charts for Attributes P chart: In this chart, we plot the percent of defectives (per batch, per day, per machine, etc.) Np chart: In this chart, we plot the number of defectives (per batch, per day, per machine) as in the C chart. C chart: In this chart, we plot the number of defectives (per batch, per day, per machine, per 100 feet of pipe, etc.),but assumes that defects of the quality attribute are rare.

U chart: In this chart we plot the rate of defectives, that is, the number of defectives divided by the number of units inspected (e.g., feet of pipe, number of batches).

Advantages of attribute control charts allow quick summaries of various aspects of the quality of a product the engineer may simply classify products as acceptable or unacceptable, based on various quality criteria. Thus, attribute charts sometimes bypass the need for expensive, precise devices and time-consuming measurement procedures. Also, this type of chart tends to be more easily understood by managers

Advantages of variable control charts Variable control charts are more sensitive than attribute control charts. Therefore, variable control charts may alert us to quality problems before any actual "unacceptables" (as detected by the attribute chart) will occur.

Control Chart Applications Establish state of statistical control Monitor a process and signal when it goes out of control Determine process capability

SELECTION OF CONTROL CHART

QUESTIONS