In GMS-401 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 X-bar and R charts. One calculates X-bar-bar and R-bar averages and these are the centre lines of the SPC run-charts 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 out-of-control conditions.

The data will be in a set of readings typically taken at say one-hour 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 this type of SPC BOTH graphs must be drawn and examined. If a SINGLE POINT on either graph exceeds the upper or lower control or range limits, the process is said to be “out of control”. If NO points exceed the range or control limits, the process is said to be “in control”.

In addition the analyst will examine the graphs for patterns showing either expected random behaviour or the tendency towards an out-of-control condition. The QUALITY GURUS: Deming, Juran, Crosby, Taguchi, Feigenbaum were the originators and proponents of SPC. Their themes were:

...Control Charts
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...

...bility CentersHow rewarded Efficiency QualityProfits InnovationROI CreativityOverall CompanyPerformanceFocus Internal Macro-EnvironmentIndustry EnvironmentInternal
Statistical Process Control Charts
Statistical process control charts are a widely used quality management tool because theycan be applied in many different situations. When maintained in real time, these charts provide an early warning about quality problems. Most cost management and accountingliterature focuses on control charts with only a single variable, even though manyvariables can be measured for the same process.
Univariate
(one-variable) charts measure only one characteristic, while
multivariate
(many-variable) charts monitor more than one characteristic simultaneously. A singlevariable control chart can, under certain conditions, give misleading information whenmultiple variables are being measured concurrently. This article shows how a25
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multivariate control chart can be used to acquire more useful information about a processor activity when more than one characteristic is monitored at once.
Control Chart Elements
An SPCchart is a graph that shows the measurements of some characteristic of interest.This characteristic can be a qualitative or a quantitative attribute. In general, SPCcharts possess...

...Pie Charts
An important part of decision making is having a clear understanding of the information used to base decisions from. Charts can be valuable when a need to represent numerical data would benefit communicating information visually. Some of the most important aspects of a good chart are to select the right type of chart (or graph) that can best characterize the data, also, to keep the design simple in order for an audience to easily understand the information.
One of the most popular types of charts is the pie chart. The pie chart is used to visually represent the proportional value of individual parts to the whole. As the name describes, this is done by representing the numerical equivalence of each part as a piece of the whole pie, which in total equates to 100%. The Pennsylvania Department of Health (2001) says that pie charts are a good choice when a relatively small amount of parts, perhaps 3 to 7, need to be represented. With any more it becomes difficult to notice the differences in magnitude; thus, the pie chart loses its simplicity and impact. They can only be used when a total amount is known, one such example would be an election where the total of votes received by all candidates equals 100% of the votes. Or a budget where the total amount spending is divided in to categories such as labor, facilities costs, advertising, etc ...

...Basic Tools for Process Improvement
Module 10
CONTROL CHART
CONTROL CHART
1
Basic Tools for Process Improvement
What is a Control Chart?
A control chart 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, ever-present differences in the process. This is common cause variation. Control Charts differentiate between these two types of variation. One goal of using a Control Chart 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 from a process in a state of statistical control. When a process is in...

...Pareto Principle. Pareto charts provide facts and insights necessary for setting priorities. Pareto charts assist teams to focus on the smaller number of the causes of problems in order to aid in decision making. Pareto charts organize and display information. They are a form of vertical bar chart. Attributes are discussed. Suggestions on when to use a Pareto chart are made. Pareto analysis is one way to determine major causes of particular problems. A review is provided with suggestions for alternatives. The Pareto chart is a valuable decision making tool.
Tools & Techniques
Pareto Charts
As a decision-making tool, the Pareto chart provides facts and insights necessary for setting priorities. Vilfredo Pareto was an Italian economist credited with establishing what is now widely known as the Pareto Principle. It is also known as the "80/20 Rule" (iSixSigma, 2006). When Pareto discovered the principle in 1906, he established that 80% of the land in Italy was owned by 20% of the population. Later, Pareto discovered his principle was valid in other parts of his life, such as gardening. For example, 80% of his garden peas were produced by 20% of the peapods.
The "80/20 Rule is not literal. The ratios may vary. Rather than an even 80% to 20% ratio the exact percentage may be 82% to 18%, or 78% to 22%. However as a rule of thumb' it is common...

...Integrating SPC and EPC Methods for Quality Improvement.
Abstract: This term paper are mainly based on the research paper, which was written by Wei Jiang and John V. Farr. Process variations are classified into common cause and assignable cause variations in the manufacturing and services industries. Firstly, the authors pointed out attention, that Common cause variations are inherent in a process and can be described implicitly or explicitly by stochastic methods. Assignable cause variations are unexpected and unpredictable and can occur before the commencement of any special events. Statistical process control (SPC) methods has been successfully utilized in discrete parts industry through identification and elimination of the assignable cause of variations, while engineering process control methods (EPC) are widely employed in continuous process industry to reduce common cause variations. This paper provides a review of various control techniques and develops a unified framework to model the relationships among these well-known methods in EPC, SPC, and integrated EPC/SPC, which have been successfully implement in the semiconductor manufacturing.
Keywords: Automatic process control, chemical mechanical planarization, control charts, run-to run control, semiconductor manufacturing.
Introduction
Two categories of research and applications have been developed independetely to achieve process...

...
Statistical Analysis and Application of Charts
Presented To: Mam Ayesha IftikharPresented By: Hassan Bashir
Roll Number: bba02141016
Program : BBA
Semester : 2nd
Date: 19-Oct-2014
Research Questionnaire/ Objective:
Analysis of quantitative and qualitative data
Uses of appropriate charts under the specific/general scenario.
To ensure that statistical tools are the important for decision making.
Type of Data:
Quantitative Data
Qualitative data
Quantitative Data:
Quantitative data is data expressing a certain quantity, amount or range. Usually, there are measurement units associated with the data, e.g. meters, in the case of the height of a person.
Qualitative data:
Qualitative data is information about qualities; information that can't actually be measured. Some examples of qualitative data are the softness of your skin, the grace with which you run, and the color of your eyes. However, try telling Photoshop you can't measure color with numbers.
Charts:
Pie Chart
Line Chart
Histogram
Flow Chart
Time Line Chart
5518206524001. Pie Chart:
A pie chart is divided into sectors, illustrating numerical proportion. In a pie chart, the arc length of each sector (and consequently its central angle and area), is proportional to the quantity it represents. While it is named for its resemblance to a pie which has been...