Control charts, also known as Shewhart charts are tools used to determine if a manufacturing or business process is in a state of statistical control. The control chart was invented by Walter A. Shewhart, (also known as the father of statistical quality control) while working for Bell Labs in the 1920s. The company's engineers were seeking to improve the reliability of their telephony transmission systems. The engineers had realized the importance of reducing variation in a manufacturing process. Shewhart framed the problem in terms of Common and special causes of variation and in 1924 introduced the control chart as a tool for distinguishing between the two. Dr. Shewhart created the basis for the control chart and the concept of a state of statistical control by carefully designed experiments. Control charts in simpler terms are graphs used to study how a process changes over time. Data are plotted in time order. A control chart consists of 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. It includes points representing a statistic of measurements including things like mean, range or proportion of a quality characteristic in samples taken from the process at different times the data. By comparing current data to these historical lines, one can draw conclusions about whether the process variation is consistent (in control) or is unpredictable (out of control). The standard or standard deviation of the statistic is also calculated using all the samples. The chart may have other optional features, including labels such as upper and lower warning limits, drawn as separate lines, typically two standard errors above and below the centre line. When interpreting control charts, we must understand that it represents a picture of a process overtime. To effectively use control charts, one must be able to interpret the picture.
...Module 10
CONTROLCHARTCONTROLCHART
1
Basic Tools for Process Improvement
What is a ControlChart?
A controlchart is a statistical tool used to distinguish between variation in a process resulting from common causes and variation resulting from special causes. It presents a graphic display of process stability or instability over time (Viewgraph 1). Every process has variation. Some variation may be the result of causes which are not normally present in the process. This could be special cause variation. Some variation is simply the result of numerous, everpresent differences in the process. This is common cause variation. ControlCharts differentiate between these two types of variation. One goal of using a ControlChart is to achieve and maintain process stability. Process stability is defined as a state in which a process has displayed a certain degree of consistency in the past and is expected to continue to do so in the future. This consistency is characterized by a stream of data falling within control limits based on plus or minus 3 standard deviations (3 sigma) of the centerline [Ref. 6, p. 82]. We will discuss methods for calculating 3 sigma limits later in this module. NOTE: Control limits represent the limits of variation that should be expected...
...Statistical quality control (SQC)
The application of statistical techniques to measure and evaluate the quality of a product, service, or process.
Two basic categories:
I. Statistical process control (SPC):
 the application of statistical techniques to determine whether a process is functioning as desired
II. Acceptance Sampling:
 the application of statistical techniques to determine whether a population of items should be accepted or rejected based on inspection of a sample of those items. Quality Measurement: Attributes vs Variables
Attributes:
Characteristics that are measured as either "acceptable" or "not acceptable", thus have only discrete, binary, or integer values.
Variables:
Characteristics that are measured on a continuous scale.
Statistical Process Control (SPC) Methods
Statistical process control (SPC) monitors specified quality characteristics of a product or service so as: To detect whether the process has changed in a way that will affect product quality and To measure the current quality of products or services.
Control is maintained through the use of controlcharts. The charts have upper and lower
control limits and the process is in control if sample measurements are between the limits.
ControlCharts for Attributes P Charts  measures...
...INDUSTRIES: STATISTICAL QUALITY CONTROL
INSTRUCTION
PPG Cleveland was the largest automotivecoating manufacturing plant in the world with annual production exceeding 18 million gallons. The plant was a convertertype operation, which assembled and processed raw materials into hundreds of primers and topcoats for application at automotive assembly plants throughout the United States.
When a sample of paint from the production process was received in the Cleveland qualitycontrol lab, it was tested for physical and visual properties. Important physical properties included viscosity, nonvolatile solids, VOC, and film cure.
OBJECTIVE OF THIS ASSIGNMENT
Given the exhibits of some of the physical properties, Charles Orange has to interpret the controlcharts for E5657 and also develop and interpret controlcharts for other property data such as E5657 Grind Time, DPX 1715 Solids and VOC, DCT8914 Package viscosity, DCT8914 Spray viscosity.
ANALYSIS
1. Interpretation of Figure 2
1) Objective of PGG
The controlcharts were applied to the grinding process in an effort to reduce extremely high batchtobatch grindtime differences. So what we should focus on is the average ranges of the sample.
2) Data Analysis
From Xbar chart of Figure 2, there are only five points (12.5833, 18.6667, 12.9167, 17.9167, and 12.9167) are close...
...CHAPTER
Statistical Quality Control
Before studying this chapter you should know or, if necessary, review 1. 2. Quality as a competitive priority, Chapter 2, page 00. Total quality management (TQM) concepts, Chapter 5, pages 00 – 00.
6
LEARNING OBJECTIVES After studying this chapter you should be able to
1 2 3 4 5 6 7 8 9
Describe categories of statistical quality control (SQC). Explain the use of descriptive statistics in measuring quality characteristics. Identify and describe causes of variation. Describe the use of controlcharts. Identify the differences between xbar, R, p, and ccharts. Explain the meaning of process capability and the process capability index. Explain the term Six Sigma. Explain the process of acceptance sampling and describe the use of operating characteristic (OC) curves. Describe the challenges inherent in measuring quality in service organizations.
CHAPTER OUTLINE
What Is Statistical Quality Control? 172 Links to Practice: Intel Corporation 173 Sources of Variation: Common and Assignable Causes 174 Descriptive Statistics 174 Statistical Process Control Methods 176 ControlCharts for Variables 178 ControlCharts for Attributes 184 CCharts 188 Process Capability 190 Links to Practice: Motorola, Inc. 196
Acceptance Sampling 196 Implications for Managers 203...
...Statistical process control (SPC) is the application of statistical methods to the monitoring and control of a process to ensure that it operates at its full potential to produce conforming product. Under SPC, a process behaves predictably to produce as much conforming product as possible with the least possible waste. While SPC has been applied most frequently to controlling manufacturing lines, it applies equally well to any process with a measurable output. Key tools in SPC are controlcharts, a focus on continuous improvement and designed experiments.
Much of the power of SPC lies in the ability to examine a process and the sources of variation in that process using tools that give weight to objective analysis over subjective opinions and that allow the strength of each source to be determined numerically. Variations in the process that may affect the quality of the end product or service can be detected and corrected, thus reducing waste as well as the likelihood that problems will be passed on to the customer. With its emphasis on early detection and prevention of problems, SPC has a distinct advantage over other quality methods, such as inspection, that apply resources to detecting and correcting problems after they have occurred.
In addition to reducing waste, SPC can lead to a reduction in the time required to produce the product or service from end to end. This is partially due to a diminished likelihood...
...ControlChartsControlCharts are use to distinguishes between specialcause or commoncause of variation that is present in a
process.
There are two basic types of controlcharts:
Variables

Quantitative data (Measured)
Attributes

Qualitative data (Counted)
Variable ControlCharts
Use actual measurements for charting
Types:
Average & Rangecharts
Median & Range charts
Average & Standard deviation charts
Individual & Moving Range charts
Run Charts
Attribute ControlCharts
Use pass/fail or go/nogo judgment
Types :p  chart
np  chart
c  chart
u  chart
OBJECTIVES OF VARIABLE CONTROLCHARTS
For quality improvement.
To determine the process capability.
For decisions in regard to product specifications.
For current decisions in regard to the production process.
For current decisions in regard to recently produced
items.
Average & Range charts ( and R)
Guidelines for subgroup sizes (n):
1.
As n increases the CL become closer to central line.
2.
As n increases the inspection cost per subgroup
increases.
3.
Distributions for averages of subgroups are nearly
normal for n = 4
4.
If...
...Process Control: GMS401 F2013
In GMS401 we study 2 types:
1. Inspection for variables —there is typically one dimension most indicative of QUALITY or lack of Quality of an item being studied for compliance to a Quality Standard. Here it is a dimension such as the contents of a jar of fruit jam, the size of a pair of shoes etc. These are called Xbar and R charts. One calculates Xbarbar and Rbar averages and these are the centre lines of the SPC runcharts that will be drawn. The charts MUST have these centre lines PLUS upper and lower control AND range limits. The points on these graphs MUST be joined so that a reader can follow the level of quality versus centre lines and control limits over time and look for trends and potential outofcontrol conditions.
The data will be in a set of readings typically taken at say onehour intervals. The number of readings taken each hour is the sample size–for example 4 jars of jam in the exercise book. The sample size of 4 is used in calculating the control limits and for determining the value of the statistical constants used in these calculations such as A2, D3,D4.
The “number of samples” is 10 but the “sample size” is 4.
The 10 samples will be plotted on a graph but the number 10 in this case is NOT used in the calculation of control limits when looking up the A2,D3, and D4 values.
In...