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This seminar is for all associates who will be using SPC on a regular basis and need more in-depth knowledge than provided in the SPC Awareness seminar. The SPC Basics seminar is a two to five day seminar. The seminar is tailored to the needs of the organization. There are 29 modules that can be included. Examples using data from your organization will be used an much as you need. The objective of the seminar is to teach participants how to effectively use SPC techniques to monitor processes and solve problems. The seminar can be built around the use of WinSPC Software, your-in house software or the old fashion way - with the calculator. An outline for a typical seminar is given below.
Module 1: Introduction to SPC and Problem Solving
This module introduces the workshop and tells attendees what to expect over the course of the seminar. It includes an introductory exercise to help people feel more at ease and to get to know each other better. An effective eight step problem solving model is introduced.
Module 2: Variation
In this module attendees will learn:
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The definition of quality. |
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What a process is and why we should focus on the process. |
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The prevention and detection modes of operation. |
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The definition of variation. |
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Why we must trace variation back to its source. |
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How we have handled variation in the past. |
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What common and special causes of variation are. |
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The Shewhart approach to handling variation. |
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The lessons of the red beads. |
This module is designed to develop an understanding of variation. Quality is defined first. We will then define what we mean by a process and why it is important that we focus on improving our processes. The definition of variation is then introduced. This includes examining how we have viewed variation in the past and how we will view variation in the framework of SPC. SPC views variation as coming from two distinct sources: special cause and common cause variation. Both of these sources of variation will be examined in detail. The concept of statistical control is also introduced in this module. It is important to know if your process is in statistical control because the action you take to improve your process depends on the state of statistical control. Two exercises (the red beads and the measurement of thumbs) help drive home the key concepts of the module.
Module 3: Types of Data
Control charts give us a picture of our process over time. This picture tells us when to leave our process alone (i.e., the process is in control) or when to look for a problem (i.e., an assignable cause is present). There are many different types of control charts. However, you can group control charts into two major categories. These two categories are distinguished by the type of data being charted. There are two types of data you can have: attributes data and variables data. Both these types of data are introduced in this module. With attributes data, there is a need to develop specific descriptions. These descriptions, which are called operational definitions, are also introduced in this module. For variables data, the standard deviation is an important measurement as well as the average. Both of these terms are explored. The normal distribution is also introduced.
Module4: Pareto Diagrams In this module attendees will learn:
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What a Pareto diagram is. |
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When to use a Pareto diagram. |
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How to construct a Pareto diagram. |
The Pareto diagram is a special type of bar chart used to determine which problem to work on first to improve a process. An Italian economist named Vilfredo Pareto developed the Pareto chart in the late 1800s. It is based on what is now called the Pareto principle. Pareto found that 80% of Italy 's wealth was held by only 20% of the people. This 80/20 rule is generally true for many things. For example, 80% of our problems are probably due to only 20% of the possible causes. The Pareto diagram allows us to separate the "vital few" from the "trivial many." This permits us to focus our time and resources where they will be most beneficial.
Module 5: Process Flow Diagrams with Cause and Effect Diagrams
In this module attendees will learn:
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What a process flow diagram is. |
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What a cause and effect diagram is. |
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How to construct and use both techniques. |
A process flow diagram should be drawn for every process being worked on. Construction of a process flow diagram should include the people actually doing the work in the process. They are a "living document" to be updated when the process changes. Creating a cause and effect diagram is fun and educational. These diagrams are usually constructed as a team or group activity to get ideas from as many people as possible. As a result of everyone working on the diagram together, everyone tends to gain some new knowledge. Cause and effect diagrams encourage new ideas about causes of problems by helping the group think about different categories of causes. The cause and effect diagram also indicates how much we know about our process. If the diagram is full, we know a lot about our process. If it is sketchy, chances are we don't have a good understanding of our process. Cause and effect diagrams should be living documents. That is, we should actively seek causes of problems and add to the diagram as time goes on.
Module 6: Histograms
In this module attendees will learn:
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What a histogram is. |
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When to use a histogram. |
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How to construct a histogram. |
Control charts present a picture of how a process varies over time. Histograms, on the other hand, present a picture of how the process "stacks up" over time. Histograms illustrate how many times a certain data value or range of data values occurred in a given time frame. Histograms provide estimates of the location, the spread and the shape of a distribution.
Module 7: Interpretation of Control Charts
In this module attendees will learn:
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An example of a process in control. |
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The general model used for control charts. |
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How control charts relate to a process. |
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How to recognize a process in statistical control. |
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The various tests for out of control situations. |
The control chart is a powerful tool for monitoring variation in a process. The chart allows you to determine when variation is simply due to random (common cause) variation or when the variation is due to special causes. How does a control chart tell if only common cause variation is present? This is determined by the data itself. Using the data, we compute a range of values we would expect if only common cause variation is present. The largest number we would expect is called the upper control limit. The smallest number we would expect is called the lower control limit. In general, if all the results fall between the smallest and the largest number and there is no evidence of nonrandom patterns, the process is in statistical control, i.e., only common cause variation is present.
Module 8: p Control Charts
Many customers today are examining quality from many different aspects. Not only do customers want a product that meets their expectations, but they also want quality in items associated with the product. These items include things such as accurate paperwork that might accompany a delivery, deliveries arriving on time, and having the phone answered when the customer calls. How can we monitor these types of situations over time? Attributes control charts are used to monitor variation over time in attributes data. The p chart, introduced in this module, is useful for determining the variation in yes/no type data, e.g., the paperwork is right or it is not. In this module attendees will learn:
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What a p chart is. |
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When to use a p chart. |
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How to construct and interpret a p chart. |
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How to handle varying subgroup sizes. |
Module 9: Control Charts
On occasion, there is a customer complaint. Sometimes someone gets injured on the job. Sometimes the warehouse does not have an item that is suppose to be in stock. These situations are examining counting type attributes data. Each count (customer complaint, injury or stock out) is considered a defect. The c chart is one technique to use for examining variation in counting type attributes data over time. The c chart is introduced in this module. In this module attendees will learn:
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What a c chart is. |
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When to use a c chart. |
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How to construct and interpret a c chart. |
Module 10: Individuals Charts
Suppose your process generates data on a very limit frequency. Maybe you only get data once a day, once a week or once every two weeks. How can we apply control charts to these types of data? In these instances, individuals control charts are useful. This type of chart is useful when you have only one data point at a time to represent a given situation.
In this module attendees will learn:
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What an individuals control chart is. |
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When to use an individuals control chart. |
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How to construct and interpret an individuals control chart. |
Module 11: Xbar-R Charts
In this module attendees will learn:
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What the Xbar-R chart is. |
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When to use the Xbar-R chart. |
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How to construct the Xbar-R chart. |
The Xbar-R chart is a type of control chart that can be used with variable data. Like most other variable control charts, it is actually two charts. One chart is for subgroup averages (Xbar) The other chart is for subgroup ranges (R). These charts are a very powerful tool for monitoring variation in a process and detecting changes in either the average or the amount of variation in the process.
Module 12: Control Strategies
In this module attendees will learn:
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What a control strategy is. |
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Who should develop the control strategy. |
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How the control strategy is updated. |
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The benefits of using a control strategy. |
The point you just plotted on your control chart is above the upper control limit. Your chart is telling you that there is a special cause present in your process. You are supposed to find out what caused this point to be above the upper control limit. Where do you start looking? There are thousands of things that could have caused this point to be above the upper control limit. A control strategy provides a method of helping you look for causes of out of control points.
Module 13 :Process Capability
In this module attendees will learn:
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The definition of process capability for attributes data. |
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The definition of process capability for variables data. |
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How to calculate the process capability. |
Process improvement is not bringing a process into statistical control. Bringing a process into statistical control is putting the process where it should be. Once the process is in statistical control, real efforts at process improvement can begin. Process capability is one method of measuring the effectiveness of a process in meeting standards or customer specifications as well as measuring process improvement efforts.
Module 14 :Measurement Systems
In this module attendees will learn:
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How measurement systems cause variability in processes. |
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How to view the measurement system itself as a process. |
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The definition of accuracy and precision. |
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How to determine and monitor the accuracy of measurement systems. |
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How to determine and monitor the precision of measurement systems. |
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How to determine how much of the total process variation is due to the measurement system. |
Collecting and analyzing data are a vital part of process improvement. It is important to be sure that the data we are collecting are accurate and precise. If not, we may not be able to see the effect of improvements we are trying to make in the process. In addition, we may be looking for a special cause in an operating unit when the real cause is in the measurement system.
Module 15 :Scatter Diagrams
Suppose you are faced with a problem. The process is in control, but the results are not acceptable. There is too much variation in the process (or perhaps it is operating at the wrong level or average). You need to find out what is causing the process to behave as it does. A cause and effect diagram has been constructed. This diagram lists all the possible causes of the problem. How do you determine what causes are really responsible for the variation. For example, is reaction yield influenced more by run time or pressure? One method of doing this is to use a scatter diagram. The scatter diagram is introduced in this module. In this module, attendees will learn:
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What a scatter diagram is. |
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When to use a scatter diagram. |
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How to construct a scatter diagram. |
Module 16: Failure Mode and Effect Analysis (FMEA)
In this module, attendees will learn how to use a failure mode and effect analysis to determine potential ways a process can fail. Action plans are then developed to prevent these potential failure modes from occurring. In this module attendees will learn:
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What a FMEA is. |
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When to use a FMEA. |
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How to construct a FMEA. |
Other Modules Which Can Be Added to Meet Your Needs
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Problem Solving Model |
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Strategy for Process Improvement |
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Data and Distributions |
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np Charts |
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u Charts |
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Xbar-s Charts |
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Moving Average/Moving Range Charts |
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Rational Subgrouping |
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Control Charts with Trend Lines |
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Cumulative Sum Charts |
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EWMA Charts |
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Comparing Processes |
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Nested Experimental Designs |
For more information on this seminar, please contact us ( sales@mqip.com .cn )
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MQIP Quality Management Intelligence Tank
Rm.1314, No.27 Shui Yin 2 nd Heng Rd. GuangZhou , China
p: 020-33689262,37597781, f: 020-37597782 |
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