Statistical Process Control

A process can be described as a transformation of set of inputs into desired outputs. Inputs PROCESSOutputs What is a process?

Data Collection We need information to arrive at a conclusion about a problem. Information is the outcome of the data collected. Statistical process control relies on the data and its analysis. There are many type of data = Measures where the metric is composed of a classification in one of two (or more) categories is called Attribute data. -Good/Bad -Yes/No =Measures where the metric consists of a number which indicates a precise value is called Variable data. -Time -Miles/Hr

4 Population Vs. Sample (Certainty Vs. Uncertainty) ã A sample is just a subset of all possible values  A whole set of data is called a population population sample Hence any statistics, such as mean and standard deviation, are just estimates of the true population parameters. ã Since the sample does not contain all the possible values, there is some uncertainty about the population. Hence any statistics, such as mean and standard deviation, are just estimates of the true population parameters.

5 WHY STATISTICS? THE ROLE OF STATISTICS ……… Statistics is the art of collecting, classifying, presenting, interpreting and analyzing numerical data, as well as making conclusions about the system from which the data was obtained.

6 Descriptive Statistics Descriptive Statistics is the branch of statistics which most people are familiar. It characterizes and summarizes the most prominent features of a given set of data (means, medians, standard deviations, percentiles, graphs, tables and charts.

7 Inferential Statistics Inferential Statistics is the branch of statistics that deals with drawing conclusions about a population based on information obtained from a sample drawn from that population.

8 WHAT IS THE MEAN? ORDERED DATA SET The mean is simply the average value of the data. n=12 x i   2 meanx x n i     Mean

9 WHAT IS THE MEDIAN? ORDERED DATA SET If we rank order (descending or ascending) the data set,we find the value half way (50%) through the data points and is called the median value. Median is the middle data point of a serious. Median Value Median 50% of data points

10 WHAT IS THE MODE? ORDERED DATA SET If we rank order (descending or ascending) the data set We find that a single value occurs more often than any other. This is called the mode.. Mode

11 WHAT IS THE RANGE? ORDERED DATA SET The range is a very common metric. To calculate the range simply subtract the minimum value in the sample from the maximum value. Range MaxMin Rangexx MAX MIN  459()

12 WHAT IS THE VARIANCE/STANDARD DEVIATION? The variance (s 2 ) is a very robust metric. The standard deviation(s) is the square root of the variance and is the most commonly used measure of dispersion.  s XX n i       .. DATA SET X X n i     XX i  -5-(-.17)= (-.17)= (-.17)= (-.17)=.17 1-(-.17)= (-.17)= (-.17)=4.17 (-4.83)2=23.32 (-2.83)2=8.01 (-.83)2=.69 (.17)2=.03 (1.17)2=1.37 (3.17)2=10.05 (4.17)2=

Measures performance of a process Uses mathematics (i.e., statistics) Involves collecting, organizing, & interpreting data Objective: Regulate product quality Used to –Control the process as products are produced –Inspect samples of finished products Statistical Process Control (SPC)

Steps of a Process Control System are  Define  Measure  Comparing with a standard  Evaluation of the sample for its acceptability  Corrective action  Evaluate corrective action CONTROL CHART

Control Charts Control Charts is a graph used to assess and maintain the stability of the production process. These charts describe where the process is in terms of current performance and helps the resources to work with the process to make decisions to enhance the future quality of products and services. Control Charts serve two basic functions: 1. Decision-making: These charts help the supervisor in deciding the course of action to be initiated for the information revealed from the process. ex. investigate for potential problems, adjust the process, allow the process to run as it is. 2.Problem–Solving: By observing the patterns on the chart, the supervisor can determine what adjustments need to be made to bring the process ‘in control’.

Essential features of a control chart Time Variable Values Upper Control Limit Central Line Lower Control Limit CONTROL CHART

Show changes in data pattern –e.g., trends Make corrections before process is out of control Show causes of changes in data –Assignable causes Data outside control limits or trend in data –Natural causes Random variations around average Control Chart Purposes

1. Characteristics for which you focus on defects 2. Classify products as either ‘good’ or ‘bad’, or count # defects –e.g., radio works or not 3. Categorical or discrete random variables AttributesVariables Quality Characteristics 1. Characteristics that you measure, e.g., weight, length 2. May be in whole or in fractional numbers 3. Continuous random variables

Types of Control Charts for Attribute Data DescriptionTypeSample Size Control Chart for proportion non conforming units p ChartMay change Control Chart for no. of non conforming units in a sample np ChartMust be constant Control Chart for no. of non conformities in a sample c ChartMust be constant Control Chart for no. of non conformities per unit u ChartMay Change CONTROL CHART

Control Charts R Chart Variables Charts Attributes Charts ` X Chart P C Control Chart Types

Type of variables control chart –Interval or ratio scaled numerical data Shows sample means over time Monitors process average and tells whether changes have occurred. These changes may due to 1. Tool wear 2. Increase in temperature 3. Different method used in the second shift 4. New stronger material Example: Weigh samples of coffee & compute means of samples; Plot X Chart

Type of variables control chart –Interval or ratio scaled numerical data Shows sample ranges over time –Difference between smallest & largest values in inspection sample Monitors variability in process, it tells us the loss or gain in dispersion. This change may be due to: 1. Worn bearing 2. A loose tool 3. An erratic flow of lubricant to machine 4. Sloppiness of machine operator Example: Weigh samples of coffee & compute ranges of samples; Plot R Chart

Construction of X and R Charts Step 1: Select the Characteristics for applying a control chart. Step 2: Select the appropriate type of control chart. Step 3: Collect the data. Step 4: Choose the rational sub-group i.e Sample Step 5: Calculate the average ( X) and range R for each sample. Step 6: Cal Average of averages of X and average of range(R)

Construction of X and R Charts Steps 7:Cal the limits for X and R Charts. Steps 8: Plot Centre line (CL) UCL and LCL on the chart Steps 9: Plot individual X and R values on the chart. Steps 10: Check whether the process is in control (or) not. Steps 11: Revise the control limits if the points are outside.

From Tables X Chart Control Limits Sub group average X = x 1 + x 2 +x 3 +x 4 +x 5 / 5 Sub group range R = Max Value – Min value

From Tables R Chart Control Limits

Problem8.1 from TQM by V.Jayakumar Page No 8.5

Type of attributes control chart –Nominally scaled categorical data e.g., good-bad Shows % of nonconforming items Example: Count # defective chairs & divide by total chairs inspected; Plot –Chair is either defective or not defective p Chart for Attributes

p Chart p = np / n where p = Fraction of Defective np = no of Defectives n = No of items inspected in sub group p= Avg Fraction Defective = ∑np/ ∑n = CL

z = 3 for 99.7% limits p Chart Control Limits

Purpose of the p Chart Identify and correct causes of bad quality The average proportion of defective articles submitted for inspection,over a period. To suggest where X and R charts to be used. Determine average Quality Level.

Problem Problem 9.1 Page no 9.3 TQM by V.Jayakumar

np CHART P and np are quiet same Whenever subgroup size is variable,p chart is used. If sub group size is constant, then np is used. FORMULA: Central Line CL np = n p Upper Control Limit, UCL np = n p +3√ n p (1- p ) Lower Control Limit, LCL np = n p -3 √ n p (1- p ) Where p = ∑ np/∑n =Average Fraction Defective n = Number of items inspected in subgroup.

Problem Problem No 9.11 page No 9.11 in TQM by V.Jayakumar

Type of attributes control chart –Discrete quantitative data Shows number of nonconformities (defects) in a unit –Unit may be chair, steel sheet, car etc. –Size of unit must be constant Example: Count no of defects (scratches, chips etc.) in each chair of a sample of 100 chairs; Plot c Chart

Use 3 for 99.7% limits c Chart Control Limits