Control chart
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A control chart is a graph used to study how a process changes over time by plotting data points in time order. It has a central line for the average, and upper and lower control limits determined from historical data. By comparing current data to these lines, you can determine if a process is in or out of control. Control charts are used for controlling ongoing processes, predicting expected outcomes, determining process stability, and analyzing patterns of variation from special or common causes. The basic components are a centerline, lower and upper control limits, and performance data plotted over time. The basic procedure is to determine the time period, collect and plot data, and look for out of control signals to investigate and document the cause and correction.
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Here are the key steps to construct a C-chart for this example:
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Does this help explain the basic approach to constructing a C-chart? Let me know if you need any clarification or have additional questions.
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This document provides an overview of control charts, which are statistical process control tools used to monitor manufacturing processes. It discusses the key elements of control charts, including center lines, control limits, data collection sections and how points inside or outside the control limits indicate whether a process is in or out of statistical control. The document also describes different types of control charts for variables and attributes data and their functions in improving process performance over time. Specifically, it focuses on X-bar and R charts, which are used when measurements are collected in subgroups, and how statistical data is required to construct these charts, including determining the number of subgroups.
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lecture 5 SPC Charta.pptx
This document discusses control charts, which were originally developed by Walter Shewhart in 1924 to distinguish between common and special causes of variation in processes. It provides background on Shewhart and the origins of control charts. The summary then explains that control charts can monitor characteristics measured on nominal, interval, or ratio scales using attributes or variables control charts. Attributes charts like c-charts and p-charts are used for categorical data, while variables charts like X-bar and R charts are used for continuous data. The document also discusses average run length and the performance of control charts.
Statstical process control
Statistical process control (SPC) is a method that uses statistical methods to monitor processes and ensure they operate efficiently. Key tools in SPC include control charts, which graph process data over time and establish upper and lower control limits to detect assignable causes of variation. Control charts come in two main types - variables charts that monitor quantitative measurements like weight or temperature, and attributes charts that count defects. The advantages of SPC include increased stability, predictability, and ability to detect attempts to improve processes. SPC has various applications in pharmaceutical manufacturing for monitoring characteristics like drug potency, fill weight, and microbial counts.
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A Practical Guide to Selecting the Right Control Chart eBook
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The document discusses different types of peer review processes used for code and document review. It describes peer review inspection, peer review walkthrough, and peer review offline. For peer review inspection, it outlines the roles involved including author, inspector, moderator, reader, and recorder. It also explains the different phases of the peer review inspection process.
Runchart
- A run chart is a line graph that displays observed data over time and can show how a process is running, revealing important early information before large amounts of data are collected, though it cannot determine process stability.
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- The document includes an example run chart tracking the number of test cases executed by individual employees in a week, with dots distinguishing test case complexity.
Pareto diagram
A Pareto chart is a type of chart that contains both bars and a line graph used to identify priorities. It arranges data in descending order by bars to show individual values and uses a line graph to show cumulative totals. This technique helps identify the major causes that need to be addressed to resolve most problems. For example, a Pareto chart shows employee salaries at a company with HR earning the most at $130,000 and accounting for 41.26% of the total salary amount.
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Checklist
This document discusses checklists, one of the seven basic quality tools. It defines a checklist as a comprehensive list of important actions or steps to be taken in a specific order, which can be in the form of questions or tasks. Checklists are useful when collecting data that can be repeatedly observed, from production processes, or to track frequencies and patterns of events. Examples of checklists are provided. Benefits of using checklists include organization, motivation, productivity, creativity, delegation, and safety. References are also included.
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This document provides an overview of cause and effect analysis and cause and effect diagrams. It explains that cause and effect analysis is a technique used to identify all possible causes associated with a problem or effect. A cause and effect diagram visually illustrates the results of this analysis and shows the relationships between potential causes. The document outlines how to construct a cause and effect diagram, including defining the problem, drawing the fishbone structure, and analyzing the diagram to identify the root cause.
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Control chart
- 2. Sun technologies.Sun technologies. _____________________________________ _ What is a control chart? _____________________________________ • The control chart is a graph used to study how a process changes over time. Data are plotted in time order. • A control chart always has a central line for the average, an upper line for the upper control limit and a lower line for the lower control limit. • Lines are determined from historical data. By comparing current data to these lines, you can draw conclusions about whether the process variation is consistent (in control) or is unpredictable (out of control, affected by special causes of variation).
- 3. Sun technologies.Sun technologies.Sun technologies. _____________________________________ _ When to use a control chart? _______________________ ______________ • Controlling ongoing processes by finding and correcting problems as they occur. • Predicting the expected range of outcomes from a process. • Determining whether a process is stable (in statistical control). • Analyzing patterns of process variation from special causes (non-routine events) or common causes (built into the process). • Determining whether the quality improvement project should aim to prevent specific problems or to make fundamental changes to the process.
- 4. Sun technologies.Sun technologies.Sun technologies. _____________________________________ _ Basic components of control charts ______________________________________ • A centerline, usually the mathematical average of all the samples plotted. • Lower and upper control limits defining the constraints of common cause variations. • Performance data plotted over time.
- 5. Sun technologies.Sun technologies.Sun technologies. _____________________________________ _ Control Chart Basic Procedure ______________________________________ 1.determine the appropriate time period for collecting and plotting data. 2.collect data, construct your chart and analyze the data. 3.look for “out-of-control signals” on the control chart. When one is identified, mark it on the chart and investigate the cause. Document how you investigated, what you learned, the cause and how it was corrected.
- 6. Sun technologies.Sun technologies.Sun technologies. _____________________________________ _ Example: ______________________________________
- 7. Sun technologies.Sun technologies.Sun technologies. ______________________________________ ______________________________________