This document provides an overview of statistical process control (SPC). It defines a process and discusses process controls, resources, inputs, outputs, and monitoring and measurement in SPC. It contrasts defect detection and prevention models, explaining that defect prevention is proactive, avoids waste, uses small samples, provides timely feedback, and is cost effective. The document also discusses sources of variation, including common and special causes, and defines concepts like stable processes and process capability. It outlines principles and objectives of SPC and introduces techniques for process control like mistake proofing, inspection, and using statistical tools like control charts and histograms.
The document discusses efforts to align the Failure Mode and Effects Analysis (FMEA) methods of the German VDA and American AIAG standards. A working group with representatives from automakers and suppliers met over several years to harmonize differences in how the VDA and AIAG FMEA manuals evaluate potential failures. The group standardized criteria for severity, occurrence, and detection ratings and developed common approaches for design and process FMEAs. The ultimate goal is to create a single, global FMEA process that satisfies customers in both Europe and North America.
Dear All, I have prepared this presentation to get a better understanding of Statistical Process Control (SPC). This is a very informative presentation and giving information about the History of SPC, the basics of SPC, the PDCA approach, the Benefits of SPC, application of 7-QC tools for problem-solving. You can follow this technique in your day to day business working to solve the problems. Thanking you.
- Statistical process control (SPC) is a method for monitoring and controlling a process to ensure it operates at its full potential and produces conforming product. Variation exists in all processes and SPC helps distinguish between natural and uncontrolled variation.
- SPC was pioneered in the 1920s and applied during World War II to improve quality. Control charts are a key SPC tool used to monitor processes over time and identify factors causing non-random variation. The two main types are x-bar charts for variables and R charts for dispersion. Proper application of SPC can reduce waste and costs while improving customer satisfaction.
The document provides information on Advanced Product Quality Planning (APQP) and Production Part Approval Process (PPAP). It discusses the APQP process which consists of four phases - planning, product design, process design, and validation. The goal of APQP is to plan quality in from the beginning to reduce costs and ensure customer satisfaction. PPAP is then described as the process used to get formal approval for a production part by providing evidence that the manufacturing process is capable of meeting requirements. It involves a first article inspection and data submission from a production run.
This document provides an introduction to statistical process control (SPC). It discusses the limitations of inspection and why SPC is better. It explains that SPC allows monitoring of processes to detect changes before defective products are produced. Various control chart templates are shown and key SPC concepts are defined, including sources of variation, the central limit theorem, and using average and range to monitor process behavior over time. Examples are provided to illustrate variability, distributions, and how control charts can be used.
The document discusses measurement systems analysis and gage reliability and repeatability (R&R) studies. It describes the components of a measurement system, how to conduct an R&R study to determine variability sources, and criteria for ensuring gage capability and precision. A case study example illustrates improving bore diameter measurement reliability for valve bodies by switching from dial calipers to a self-centering bore gauge.
Dear All, This is very comprehensive training on application of 7QC tools in industry. There is now a common demand in every industry to improve and control the process by achieving product quality with integrity. These 7-QC tools are very useful to fulfil industry demand by controlling the process. I am expecting your kind suggestions and comments to improve my presentation further. Thanks a lot everyone for your time to read this presentation. I hope it will definitely give some value addition in your routine life. Thanking you!
The document discusses process capability and assessing whether a process is capable of meeting customer requirements. It provides definitions of key terms like capable process, process capability ratios (Cp and Cpk), and discusses the differences between short-term and long-term capability studies. Short-term studies look at random variation over days/weeks using 30-50 data points, while long-term studies examine non-random sources of variation over weeks/months using 100-200 data points. The document warns that capability assessments only indicate potential performance if the process is stable and in control.
The document discusses efforts to align the Failure Mode and Effects Analysis (FMEA) methods of the German VDA and American AIAG standards. A working group with representatives from automakers and suppliers met over several years to harmonize differences in how the VDA and AIAG FMEA manuals evaluate potential failures. The group standardized criteria for severity, occurrence, and detection ratings and developed common approaches for design and process FMEAs. The ultimate goal is to create a single, global FMEA process that satisfies customers in both Europe and North America.
Dear All, I have prepared this presentation to get a better understanding of Statistical Process Control (SPC). This is a very informative presentation and giving information about the History of SPC, the basics of SPC, the PDCA approach, the Benefits of SPC, application of 7-QC tools for problem-solving. You can follow this technique in your day to day business working to solve the problems. Thanking you.
- Statistical process control (SPC) is a method for monitoring and controlling a process to ensure it operates at its full potential and produces conforming product. Variation exists in all processes and SPC helps distinguish between natural and uncontrolled variation.
- SPC was pioneered in the 1920s and applied during World War II to improve quality. Control charts are a key SPC tool used to monitor processes over time and identify factors causing non-random variation. The two main types are x-bar charts for variables and R charts for dispersion. Proper application of SPC can reduce waste and costs while improving customer satisfaction.
The document provides information on Advanced Product Quality Planning (APQP) and Production Part Approval Process (PPAP). It discusses the APQP process which consists of four phases - planning, product design, process design, and validation. The goal of APQP is to plan quality in from the beginning to reduce costs and ensure customer satisfaction. PPAP is then described as the process used to get formal approval for a production part by providing evidence that the manufacturing process is capable of meeting requirements. It involves a first article inspection and data submission from a production run.
This document provides an introduction to statistical process control (SPC). It discusses the limitations of inspection and why SPC is better. It explains that SPC allows monitoring of processes to detect changes before defective products are produced. Various control chart templates are shown and key SPC concepts are defined, including sources of variation, the central limit theorem, and using average and range to monitor process behavior over time. Examples are provided to illustrate variability, distributions, and how control charts can be used.
The document discusses measurement systems analysis and gage reliability and repeatability (R&R) studies. It describes the components of a measurement system, how to conduct an R&R study to determine variability sources, and criteria for ensuring gage capability and precision. A case study example illustrates improving bore diameter measurement reliability for valve bodies by switching from dial calipers to a self-centering bore gauge.
Dear All, This is very comprehensive training on application of 7QC tools in industry. There is now a common demand in every industry to improve and control the process by achieving product quality with integrity. These 7-QC tools are very useful to fulfil industry demand by controlling the process. I am expecting your kind suggestions and comments to improve my presentation further. Thanks a lot everyone for your time to read this presentation. I hope it will definitely give some value addition in your routine life. Thanking you!
The document discusses process capability and assessing whether a process is capable of meeting customer requirements. It provides definitions of key terms like capable process, process capability ratios (Cp and Cpk), and discusses the differences between short-term and long-term capability studies. Short-term studies look at random variation over days/weeks using 30-50 data points, while long-term studies examine non-random sources of variation over weeks/months using 100-200 data points. The document warns that capability assessments only indicate potential performance if the process is stable and in control.
The document discusses five core quality tools: APQP (Advanced Product Quality Planning), FMEA (Failure Modes and Effects Analysis), PPAP (Production Part Approval Process), MSA (Measurement Systems Analysis), and SPC (Statistical Process Control). It provides a brief overview of each tool, noting that APQP is used to develop products that satisfy customers, FMEA ensures potential problems are considered, PPAP ensures products meet specifications, MSA assesses measurement systems, and SPC enables process control and improvement. The document emphasizes that these five tools are considered core tools for quality management.
7 QC Tools are simple statistical tools used for problem solving. Nilesh Arora presented basics of 7 QC Tool training and details about Pareto Diagram.
The document discusses Failure Mode and Effects Analysis (FMEA) and how to conduct a Process FMEA, including defining the scope, identifying potential failures and their causes and effects, and establishing current process controls. It provides examples and templates to help participants understand how to properly perform a Process FMEA. The goal is to enable participants to effectively use FMEA to achieve robust capable designs and processes.
The document provides an overview of failure mode and effects analysis (FMEA). It discusses the history and evolution of FMEA from its origins in the aerospace industry in the 1960s to the current AIAG VDA FMEA Handbook published in 2019. The document outlines the seven step approach of the new handbook, including planning, structure analysis, function analysis, optimization, risk analysis, failure analysis, and documentation of results. It also summarizes some of the major changes between the previous AIAG 4th edition and new handbook, such as replacing RPN with action priority and revising the rating tables.
The document provides information on Advanced Product Quality Planning (APQP) and its 5 phases: 1) Plan and Define Program, 2) Product Design and Development, 3) Process Design and Development, 4) Product and Process Validation, and 5) Feedback, Assessment and Corrective Action. It describes the objectives and key activities that should be completed in each phase of the APQP process.
The document provides information about SPC (Statistical Process Control) training conducted by Hopez Institute. It includes the course content which covers topics like process definition, defect detection vs prevention, statistics fundamentals, variation and causes of variation, control charts for variables and attributes. It also discusses the history and evolution of SPC, which was pioneered by Walter Shewhart. The document aims to help participants understand why SPC is important and how it can be applied to processes.
This document outlines the tools and activities used in the Measure phase of a Lean Six Sigma DMAIC project. It includes reviewing project documents, validating measurements, identifying quick wins, collecting baseline data, conducting an MSA, analyzing process capabilities, and documenting conclusions. The tools covered are process mapping, data collection planning, operational definitions, basic statistics, histograms, control charts, and calculating sigma levels.
This document provides an overview of Failure Mode and Effects Analysis (FMEA). FMEA is a systematic method used to evaluate potential failure modes in a design, process or service and their causes and effects. It involves analyzing potential failures, their likelihood and severity, and identifying actions to address potential failures with high risk priority numbers. The document defines key terms in FMEA like severity, occurrence, detection and risk priority number. It also outlines the FMEA process, including steps to identify potential failure modes, effects, causes, current controls and priority actions.
This document outlines a measurement system analysis (MSA) project to evaluate a measurement gauge. It will:
1. Determine which measurement system and gauge will be studied according to the MSA plan and gauge repeatability and reproducibility analysis.
2. Establish test procedures in different departments to analyze the gauge's repeatability and reproducibility.
3. Determine the number of sample parts, repeated readings, and operators used in the study.
The document also provides background definitions and concepts for measurement system analysis, including accuracy, precision, bias, repeatability, reproducibility and their impacts on process variation. Sources of variation are identified.
This document provides an overview of Failure Mode and Effects Analysis (FMEA). It discusses that FMEA is a systematic group activity to recognize and evaluate potential failures, identify actions to address failures, and document findings. The document outlines the different types of FMEAs, including Design FMEA and Process FMEA. It also describes the typical steps to conduct a Process FMEA, including developing a process flow, identifying failure modes and their causes and effects, and estimating the risk priority number. The FMEA is presented as a team tool to prevent failures.
This document provides an overview and introduction to quality management systems and the ISO 9001:2015 and IATF 16949:2016 standards. It discusses key aspects of quality management including the basics of a QMS, requirements of the ISO and IATF standards, differences between the versions, transitioning processes, and implementing risk-based thinking. The document is intended to educate participants on quality management system requirements and certification.
This document discusses measurement system analysis (MSA), including attribute MSA. It defines key MSA terms and describes the importance, types, and steps of attribute MSA. The document provides examples of calculating kappa value, miss rate, and false rate from attribute MSA data to evaluate measurement system capability. Reasons for attribute MSA failure include issues with appraisers or inspection processes.
Six Sigma Statistical Process Control (SPC) Training ModuleFrank-G. Adler
The Statistical Process Control (SPC) Training Module v4.0 includes:
1. MS PowerPoint Presentation including 129 slides covering Introduction to Process Control, Types of Histograms, Measures of Location & Variability, Process Control Charts, Process Control Limits, Out-of-Control Criteria, Sample Size & Frequency, Out-of-Control Action Plan, Process Control Plan, and 6 Workshop Exercises.
2. MS Excel Confidence Interval Analysis Calculator making it really easy to calculate Confidence Intervals (mean value, standard deviation, capability indices, defect rate, count) and perform a Comparison of two Statistics (mean values, standard deviations, defect rates, counts).
3. MS Excel Process Control Plan Template
The document discusses Process Failure Modes and Effects Analysis (PFMEA) which analyzes manufacturing and assembly processes to identify potential failure modes caused by process deficiencies. A PFMEA includes a process flow diagram, failure analysis matrix, and process control plan. It assumes the design is valid, analyzes failure causes and effects, and recommends actions to eliminate root causes and detect failures. Benefits include improved processes, performance monitoring, and prioritizing resources to ensure process improvements benefit customers.
ABOUT THE TRAINING PROGRAM :-
Failure Mode and Effects Analysis or FMEA is a structured technique to analyze a process to determine shortcomings and opportunities for improvement. By assessing the severity of a potential failure, the likelihood that the failure will occur, and the chance of detecting the failure, dozens or even hundreds of potential issues can be prioritized for improvement.
DESIGNED FOR :-
Sr. Engineer, Engineer, Supervisor and Foreman engaged in maintenance, operation, Store, Supply chain, Quality, Safety and Engineering activities.
OBJECTIVE :-
Employees completing this training will be able to effectively participate on an FMEA team and can make immediate contributions to quality and productivity improvement efforts.
This document provides an overview and agenda for a Failure Mode Effects Analysis (FMEA) training session. The agenda includes introductions, discussions of Design FMEA (DFMEA) and Process FMEA (PFMEA), exercises, and a closing survey. The document also provides background information on FMEA including its history, purpose, benefits, and typical format/elements such as functions, potential failures, effects, severity, causes, detection, and actions. FMEA is presented as a systematic method to proactively identify and prevent potential product and process failures before they occur.
The document provides an overview of measurement system analysis (MSA) techniques for both variable and attribute gages. It describes the average-range method and ANOVA method for analyzing variable gages, and the short method, hypothesis test analysis, and long method for attribute gages. Acceptability criteria are outlined for determining if a measurement system is capable of measuring process variation.
The document provides information about Production Part Approval Process (PPAP). It discusses what PPAP is, its purpose, when it is required, benefits of PPAP submissions, elements of a PPAP submission including a production warrant, submission levels, and definitions of risk. Key points covered are that PPAP is used to reduce risks prior to product release, it provides evidence that requirements are understood and the process is capable of production, and it manages change and ensures product conformance.
Statistical analysis process- dr. a.amsavelAmsavel Vel
1. Statistical analysis is a problem solving tool that helps process raw data into useful information for decision making. It involves collecting, organizing, and interpreting numerical data.
2. Statistical tools like control charts, histograms, Pareto charts, cause-and-effect diagrams, and brainstorming can be used to identify problems, analyze causes, prioritize issues, monitor processes, and drive improvement.
3. Process capability analysis compares the natural variation in a process to specification limits to determine if a process is capable of meeting requirements and stable enough for improvement.
This document outlines the key deliverables, steps, tools, and stakeholders involved in a Define, Measure, Analyze, Improve, Control (DMAIC) project. The project follows the standard DMAIC framework with key deliverables defined for each phase such as process maps, measurement plans, data analysis, improvement plans, and control plans. Tollgates and progress are tracked throughout the project. A variety of statistical and process improvement tools are listed to guide the project team in their work. Stakeholder involvement is also defined using RACI (responsible, accountable, consulted, informed) charts. The overall goal is to map out the end-to-end process for completing a DMAIC project and achieving performance improvements
STATISTICAL PROCESS CONTROL satyam raj.pptxSatyamRaj25
This document provides an overview of statistical process control (SPC). It defines SPC as the application of statistical methods to measure and analyze variation in a process. The document discusses the importance of SPC in reducing waste and costs while improving quality and uniformity. It also describes key SPC tools like control charts and process capability analysis. Control charts help monitor processes for common and special causes of variation, while process capability analysis compares process performance to product specifications to ensure quality.
The document discusses five core quality tools: APQP (Advanced Product Quality Planning), FMEA (Failure Modes and Effects Analysis), PPAP (Production Part Approval Process), MSA (Measurement Systems Analysis), and SPC (Statistical Process Control). It provides a brief overview of each tool, noting that APQP is used to develop products that satisfy customers, FMEA ensures potential problems are considered, PPAP ensures products meet specifications, MSA assesses measurement systems, and SPC enables process control and improvement. The document emphasizes that these five tools are considered core tools for quality management.
7 QC Tools are simple statistical tools used for problem solving. Nilesh Arora presented basics of 7 QC Tool training and details about Pareto Diagram.
The document discusses Failure Mode and Effects Analysis (FMEA) and how to conduct a Process FMEA, including defining the scope, identifying potential failures and their causes and effects, and establishing current process controls. It provides examples and templates to help participants understand how to properly perform a Process FMEA. The goal is to enable participants to effectively use FMEA to achieve robust capable designs and processes.
The document provides an overview of failure mode and effects analysis (FMEA). It discusses the history and evolution of FMEA from its origins in the aerospace industry in the 1960s to the current AIAG VDA FMEA Handbook published in 2019. The document outlines the seven step approach of the new handbook, including planning, structure analysis, function analysis, optimization, risk analysis, failure analysis, and documentation of results. It also summarizes some of the major changes between the previous AIAG 4th edition and new handbook, such as replacing RPN with action priority and revising the rating tables.
The document provides information on Advanced Product Quality Planning (APQP) and its 5 phases: 1) Plan and Define Program, 2) Product Design and Development, 3) Process Design and Development, 4) Product and Process Validation, and 5) Feedback, Assessment and Corrective Action. It describes the objectives and key activities that should be completed in each phase of the APQP process.
The document provides information about SPC (Statistical Process Control) training conducted by Hopez Institute. It includes the course content which covers topics like process definition, defect detection vs prevention, statistics fundamentals, variation and causes of variation, control charts for variables and attributes. It also discusses the history and evolution of SPC, which was pioneered by Walter Shewhart. The document aims to help participants understand why SPC is important and how it can be applied to processes.
This document outlines the tools and activities used in the Measure phase of a Lean Six Sigma DMAIC project. It includes reviewing project documents, validating measurements, identifying quick wins, collecting baseline data, conducting an MSA, analyzing process capabilities, and documenting conclusions. The tools covered are process mapping, data collection planning, operational definitions, basic statistics, histograms, control charts, and calculating sigma levels.
This document provides an overview of Failure Mode and Effects Analysis (FMEA). FMEA is a systematic method used to evaluate potential failure modes in a design, process or service and their causes and effects. It involves analyzing potential failures, their likelihood and severity, and identifying actions to address potential failures with high risk priority numbers. The document defines key terms in FMEA like severity, occurrence, detection and risk priority number. It also outlines the FMEA process, including steps to identify potential failure modes, effects, causes, current controls and priority actions.
This document outlines a measurement system analysis (MSA) project to evaluate a measurement gauge. It will:
1. Determine which measurement system and gauge will be studied according to the MSA plan and gauge repeatability and reproducibility analysis.
2. Establish test procedures in different departments to analyze the gauge's repeatability and reproducibility.
3. Determine the number of sample parts, repeated readings, and operators used in the study.
The document also provides background definitions and concepts for measurement system analysis, including accuracy, precision, bias, repeatability, reproducibility and their impacts on process variation. Sources of variation are identified.
This document provides an overview of Failure Mode and Effects Analysis (FMEA). It discusses that FMEA is a systematic group activity to recognize and evaluate potential failures, identify actions to address failures, and document findings. The document outlines the different types of FMEAs, including Design FMEA and Process FMEA. It also describes the typical steps to conduct a Process FMEA, including developing a process flow, identifying failure modes and their causes and effects, and estimating the risk priority number. The FMEA is presented as a team tool to prevent failures.
This document provides an overview and introduction to quality management systems and the ISO 9001:2015 and IATF 16949:2016 standards. It discusses key aspects of quality management including the basics of a QMS, requirements of the ISO and IATF standards, differences between the versions, transitioning processes, and implementing risk-based thinking. The document is intended to educate participants on quality management system requirements and certification.
This document discusses measurement system analysis (MSA), including attribute MSA. It defines key MSA terms and describes the importance, types, and steps of attribute MSA. The document provides examples of calculating kappa value, miss rate, and false rate from attribute MSA data to evaluate measurement system capability. Reasons for attribute MSA failure include issues with appraisers or inspection processes.
Six Sigma Statistical Process Control (SPC) Training ModuleFrank-G. Adler
The Statistical Process Control (SPC) Training Module v4.0 includes:
1. MS PowerPoint Presentation including 129 slides covering Introduction to Process Control, Types of Histograms, Measures of Location & Variability, Process Control Charts, Process Control Limits, Out-of-Control Criteria, Sample Size & Frequency, Out-of-Control Action Plan, Process Control Plan, and 6 Workshop Exercises.
2. MS Excel Confidence Interval Analysis Calculator making it really easy to calculate Confidence Intervals (mean value, standard deviation, capability indices, defect rate, count) and perform a Comparison of two Statistics (mean values, standard deviations, defect rates, counts).
3. MS Excel Process Control Plan Template
The document discusses Process Failure Modes and Effects Analysis (PFMEA) which analyzes manufacturing and assembly processes to identify potential failure modes caused by process deficiencies. A PFMEA includes a process flow diagram, failure analysis matrix, and process control plan. It assumes the design is valid, analyzes failure causes and effects, and recommends actions to eliminate root causes and detect failures. Benefits include improved processes, performance monitoring, and prioritizing resources to ensure process improvements benefit customers.
ABOUT THE TRAINING PROGRAM :-
Failure Mode and Effects Analysis or FMEA is a structured technique to analyze a process to determine shortcomings and opportunities for improvement. By assessing the severity of a potential failure, the likelihood that the failure will occur, and the chance of detecting the failure, dozens or even hundreds of potential issues can be prioritized for improvement.
DESIGNED FOR :-
Sr. Engineer, Engineer, Supervisor and Foreman engaged in maintenance, operation, Store, Supply chain, Quality, Safety and Engineering activities.
OBJECTIVE :-
Employees completing this training will be able to effectively participate on an FMEA team and can make immediate contributions to quality and productivity improvement efforts.
This document provides an overview and agenda for a Failure Mode Effects Analysis (FMEA) training session. The agenda includes introductions, discussions of Design FMEA (DFMEA) and Process FMEA (PFMEA), exercises, and a closing survey. The document also provides background information on FMEA including its history, purpose, benefits, and typical format/elements such as functions, potential failures, effects, severity, causes, detection, and actions. FMEA is presented as a systematic method to proactively identify and prevent potential product and process failures before they occur.
The document provides an overview of measurement system analysis (MSA) techniques for both variable and attribute gages. It describes the average-range method and ANOVA method for analyzing variable gages, and the short method, hypothesis test analysis, and long method for attribute gages. Acceptability criteria are outlined for determining if a measurement system is capable of measuring process variation.
The document provides information about Production Part Approval Process (PPAP). It discusses what PPAP is, its purpose, when it is required, benefits of PPAP submissions, elements of a PPAP submission including a production warrant, submission levels, and definitions of risk. Key points covered are that PPAP is used to reduce risks prior to product release, it provides evidence that requirements are understood and the process is capable of production, and it manages change and ensures product conformance.
Statistical analysis process- dr. a.amsavelAmsavel Vel
1. Statistical analysis is a problem solving tool that helps process raw data into useful information for decision making. It involves collecting, organizing, and interpreting numerical data.
2. Statistical tools like control charts, histograms, Pareto charts, cause-and-effect diagrams, and brainstorming can be used to identify problems, analyze causes, prioritize issues, monitor processes, and drive improvement.
3. Process capability analysis compares the natural variation in a process to specification limits to determine if a process is capable of meeting requirements and stable enough for improvement.
This document outlines the key deliverables, steps, tools, and stakeholders involved in a Define, Measure, Analyze, Improve, Control (DMAIC) project. The project follows the standard DMAIC framework with key deliverables defined for each phase such as process maps, measurement plans, data analysis, improvement plans, and control plans. Tollgates and progress are tracked throughout the project. A variety of statistical and process improvement tools are listed to guide the project team in their work. Stakeholder involvement is also defined using RACI (responsible, accountable, consulted, informed) charts. The overall goal is to map out the end-to-end process for completing a DMAIC project and achieving performance improvements
STATISTICAL PROCESS CONTROL satyam raj.pptxSatyamRaj25
This document provides an overview of statistical process control (SPC). It defines SPC as the application of statistical methods to measure and analyze variation in a process. The document discusses the importance of SPC in reducing waste and costs while improving quality and uniformity. It also describes key SPC tools like control charts and process capability analysis. Control charts help monitor processes for common and special causes of variation, while process capability analysis compares process performance to product specifications to ensure quality.
1) This document discusses Statistical Process Control (SPC), which uses statistical methods to monitor and control processes to ensure they operate at full potential. SPC aims to maximize conforming product output while minimizing waste.
2) Key aspects of SPC include understanding variation in processes, distinguishing between common and special causes of variation, using statistical tools like control charts to monitor processes and detect issues, and taking action to control processes and continually improve quality.
3) The document outlines the basic elements of a process control system, including gathering performance information, taking action on processes and outputs, and using feedback to maintain stability and reduce variation. It emphasizes prevention over detection to avoid waste.
This document provides an overview of control charts and statistical process control. It discusses how control charts can show if a process is stable or experiencing special causes of variation. The document outlines different types of control charts for variable and attribute data, including how to generate and interpret them. It also describes how control charts are used within the DMAIC improvement process to establish a stable, capable process and monitor critical metrics ongoing.
This document provides an overview of control charts and statistical process control. It discusses how control charts can show if a process is stable or experiencing special causes of variation. The document outlines different types of control charts for variable and attribute data, including how to generate and interpret them. It explains the role of control charts within the DMAIC improvement process for measuring process stability, establishing improved processes, and ongoing process monitoring and control.
This document provides an overview of statistical process control (SPC). SPC uses statistical techniques to monitor processes and detect changes, helping to prevent defects and drive continuous improvement. It aims to identify problems in production as early as possible through analysis of process capability. Control charts are a key tool in SPC, monitoring the average and variation of a process over time through control limits. Being in statistical control means a process's measurements vary randomly within control limits in a predictable way. Patterns outside the limits indicate the process is out of control due to assignable causes that need correction. Benefits of SPC and control charts include reduced scrap, preventing unnecessary adjustments, and providing diagnostic information.
Statistical process control (SPC) involves using statistical methods to monitor and control processes to ensure they operate optimally and produce conforming products. SPC was pioneered in the 1920s and involves understanding process variation, identifying sources of variation through tools like control charts, and eliminating sources of unacceptable variation. Control charts graph process data over time to distinguish common from special causes of variation and identify when a process is stable or needs correction. Process capability analysis determines if a process can meet specifications under natural variation. The goal of SPC is to reduce waste and costs through early problem detection and prevention.
Statistical process control (SPC) techniques apply statistical methods to measure and analyze variation in manufacturing processes. SPC uses control charts to distinguish between common cause variation inherent to the process and special cause variation that can be assigned to a specific reason. Control charts monitor process data over time against statistical control limits. Process capability analysis compares process variation to product specifications to determine if the process is capable of meeting specifications. Key metrics like Cp, Cpk and Cpm indices quantify a process's capability relative to the specifications. For a process to have a valid capability analysis, it must meet assumptions of statistical control, normality, sufficient representative data, and independence of measurements.
This document discusses quality control, quality assurance, and statistical process control. It defines quality assurance as organized arrangements to ensure products meet requirements, quality control as testing and documentation to ensure a product's quality, and statistical process control as monitoring quality through statistical methods. The concepts of SPC were developed in the 1920s and help reduce process variation through techniques like control charts that establish control limits based on the mean and standard deviation. Process variability is important to understand and control limits placed at three standard deviations are effective at detecting shifts or instability.
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.
This document discusses quality management, quality assurance, quality control, and good manufacturing practices (GMP) in the pharmaceutical industry. It provides definitions of key terms from organizations like WHO and describes the relationships between quality management, quality assurance, quality control, and GMP. Quality is defined as fitness for use, freedom from defects, and meeting customer/regulatory requirements. Quality assurance involves all arrangements to ensure a product meets quality standards, while quality control specifically refers to testing and release procedures.
This presentation provides an overview of control charts, including what they are, their purposes and advantages, different types of control charts, and how to construct and interpret them. Control charts graphically display process data over time to determine whether a manufacturing or business process is in a state of statistical control. The presentation discusses variable and attribute control charts, and specific charts like X-bar and R-bar charts. It provides examples of how to calculate control limits and plot data on a chart, and how to interpret results to determine if a process is capable or needs improvement. A case study example analyzing wait time data from a hotel management company is also reviewed.
Internal Quality Control Lecture MD General 2014 Course, Clin Path Ain Shams ...Adel Elazab Elged
The document discusses internal quality control in clinical laboratories. It defines key terms like quality control, quality assurance, and quality management. It explains the importance of internal quality control in ensuring accurate and reliable test results. Quality control involves running control samples alongside patient samples and using statistical tools like control charts and Westgard rules to monitor the analytical process and ensure it is in control. Factors that could indicate the process is out of control are also summarized.
This document discusses statistical process control and monitoring validated processes. It describes determining what processes need to be monitored based on risk assessments and process validations. It also covers the basics of statistical process control including common and assignable cause variation, setting control limits, and using tools like X-bar and R charts to monitor processes. Western Electric rules for determining when a process is out of control are also summarized.
Statistical Quality Control involves using statistical techniques to control quality by inspecting products and processes to determine if they meet quality standards. W. Edward Deming advocated for this approach to reduce variation and achieve consistency. There are three main categories of statistical quality control: descriptive statistics, acceptance sampling, and statistical process control (SPC). SPC involves measuring quality characteristics over time and charting the results to identify variations and determine whether a process is stable and in control. Control charts are a key tool in SPC, as they graph data over time and can be used to differentiate between common cause variation and special cause variation.
Statistical Quality Control (SQC) uses statistical techniques to control quality by inspecting products and processes to determine if they meet quality standards. It aims to reduce variation through the identification and elimination of special causes of variation, while allowing for natural or common cause variation. SQC includes descriptive statistics, acceptance sampling, and statistical process control (SPC). SPC involves measuring quality characteristics over time and charting the results to identify uncommon variations that could indicate problems.
This document discusses various tools used in the improvement and control phases of quality management. In the improvement phase, tools like brainstorming, flow charts, Pareto charts, failure mode and effects analysis, stakeholder analysis, single minute exchange of dies, benchmarking, design of experiments, 5S's method, and kaizen are explained. In the control phase, control charts, standard operating procedures, standardization, and statistical process control are some of the tools discussed along with their purpose and examples. References used for the information are also listed.
Understanding Catalytic Converter Theft:
What is a Catalytic Converter?: Learn about the function of catalytic converters in vehicles and why they are targeted by thieves.
Why are They Stolen?: Discover the valuable metals inside catalytic converters (such as platinum, palladium, and rhodium) that make them attractive to criminals.
Steps to Prevent Catalytic Converter Theft:
Parking Strategies: Tips on where and how to park your vehicle to reduce the risk of theft, such as parking in well-lit areas or secure garages.
Protective Devices: Overview of various anti-theft devices available, including catalytic converter locks, shields, and alarms.
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Recent Trends: Current trends and patterns in catalytic converter thefts to help you stay aware of emerging hotspots and tactics used by thieves.
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5. Defect Detection Vs. Prevention
• What is a defect?
• Defect is deviation from the engineering
specifications.
• In reality defect is deviation from the targeted
value of a characteristics.
• The closer we go to the target , more is the
customer satisfied and farther we go, we have
dissatisfied customer.
SPC
5
7. 7
Defect Detection
• It uses customer as final inspector
• Is reactionary
• Tolerates waste such as scrap/rework
• Relies on inspection, audits, or checks
of large samples of output
• Treats all defects the same
• Focuses on specs.
• Involves action only on output
• Provides late feedback for defect
• Detection is not cost effective
SPC
8. 8
Voice of process
Every process generates information that can be
used to measure it. The most effective way to take
advantage of this is to:
determine the process quality characteristics &
respective target value
Collect data about process.
Compare process characteristics to pre-established
target values.
Act based on results of comparison.
Following this procedure results in early feedback
which leads to improvement
SPC
10. 10
Defect Prevention
Is pro-active
Avoids waste
Uses small samples of product and
process information
Is analytically based
Discriminates between potential
defects based on causes
Involves action on the process or
process parameters
Provides timely feedback
It is cost effective
Focuses on target value
SPC
11. 11
Test for 100% inspection
You are given a paper and in the
paragraph written there , you have
to find out how many times letter
‘ f ‘ has appeared in the paragraph.
Time for visual inspection is 2
minutes
SPC
12. 12
What is statistics?
Even though theory behind statistics is hard to
understand, we use it everyday extensively.
To talk about sports.
To shop intelligently.
To complete tasks at home or at work.
SPC
13. 13
Few fundamentals of Statistics
Mean - It is arithmetic average of all
observations.
Mode -It is most commonly occurring
observations.
Median - It is the middle observation
when all observations are arranged
in order of magnitude
SPC
14. 14
SPREAD
Spread is how far apart the ends of
the group are.
Bowling average
4, 2 , 6 , 0 , 3 , 5 , 2 , 1 , 3 , 4
Average / mean = 3 Spread = 6
Income Rs.
400, 200 , 660 , 90 , 300 , 250 , 100 , 300 , 480 ,180
Average / Mean = 296 Spread=570
Another term that relates spread is Standard Deviation
SPC
15. 15
Standard Deviation
is a statistical measure of spread or variation that
is present in the group data.
To calculate SD(Sigma : )there are six steps
1. Calculate the average of all the observations.
2. Subtract this average from each observations.
3. Square each number obtained in step 2.
4. Find the sum of all numbers obtained in step 3.
5. Divide the number obtained instep 4 by number of
observations minus one.
6. Take the square root of the number obtained in 5.
= i=1 (xi-)2 = Average; n= no.of observations
n-1
SPC
17. Variation
• Variation is Natural
• No two things are alike or identical
• Variation is the cause of inconsistency
• Variation is reason that components do
not always fit together
• Variations is the reason that quality levels of same
product ,produced on the same line using parts of
same suppliers are different.
Our goal is to manage Variation
SPC
17
18. To manage Variation we must understand it.
• Where from variation comes ?
• Variation can result from different procedures
used by a manufacturer or an operator.
• Variation can result from different machines used
by us.
• Variation can result from wear in mechanism of
machine.
• Variation can result from different batches of
material used.
Variation can be result of endless list of reasons.
SPC
18
19. Main sources of variation
• In all processes variation is inevitable ,difference
between individual outputs of a process.
• A process contains many sources of variability.
• The main sources of variability are Machine ,
Method , Material , Men, Environment etc.
SPC
19
20. Variation
There are two causes of variation
Common cause
Special causes
• Common Causes :- A source of variation that affect all
the individual values of the process output and
inherent in the process itself and can not be
eliminated totally.
• Special Causes:- A source of variation that is
intermittent, unpredictable, unstable. These causes
can be identified and can be eliminated permanently.
SPC
20
21. Variation
Characteristics of processes with only common
causes of variation
The processes are stable I.e. under control
Process outcomes are predictable
The effects of common cause variation can only
be eliminated by action on the system.
Common causes of variation
Are associated with the majority 85% of process
concerns
Arise from many small sources.
SPC
21
22. Variation
If only common causes of variation are present ,the
output of a process forms a distribution that is stable
over time. It is predictable.
Variation from common cause is predictable.
Variation from common cause is stable
The shape is unchanged over time
SPC
22
23. Variation
• If special causes of variation are present ,the
output of a process forms a distribution that is not
stable over time. It is not predictable.
• Variation from special cause is unpredictable.
• Variation from special cause is unstable
The mean changes over time
The spread changes over time
• The shape varies over time
SPC
23
24. Variation
• If special causes of variation are present ,the
output of a process forms a distribution that is not
stable over time. It is not predictable.
• Variation from special cause is unpredictable.
• Variation from special cause is unstable
The mean changes over time
The spread changes over time
• The shape varies over time
SPC
24
25. Stable Process
• A stable process is the one with no indication of
special cause of variation.
• Behaviour of stable process is predictable
• A process may be stable, but may produce defective
output.
• A stable process is not an end to improvement.
SPC
25
26. Variation
• The variation due to common causes contributes to
85 % and variation due to special causes contributes
to 15 %.
• Common causes are management responsibility &
hence 85 % it is management responsibility to reduce
variation.
• Only 15% is workers responsibility.
SPC
26
27. Principles & Objectives of SPC:
-- Variation is inevitable
-- Variation is predictable
-- Variation is measurable
• Statistical process control: 10% is statistics and 90% is product & process
knowledge
• SPC means applying statistics for output of a process in order to control the
process
• SPC is preventive technique
• Economic control over process
SPC
27
28. • PROCESS CONTROL:- A process is said to be operating in state of
statistical control when the only source of variation is common causes.
• PROCESS STABILITY:- A process is said to be stable when the process
is in control and variation is constant with respect to time.
• PROCESS CAPABILITY:- The measure of inherent variation of the
process when it is in stable condition is called as process capability
• OVER ADJUSTMENT:- It is the practice of adjusting each deviation from
the target as if it were due to a special cause of variation in the
process. If stable process is adjusted on the basis of each
measurement made, then adjustment comes an additional source of
variation.
SPC
28
29. Techniques for Process control:
• Mistake proofing :- In this technique 100% process control is achieved by
sealing all types of failures by using modern techniques to get defect free
product. Here causes are prevented from making the effect.
• 100 % Inspection:- In this technique 100% checking of all the parameters
of all products have been done to get defect free product. Here only
defect is detected.
• Statistical Process Control:- In this Statistical technique such as Control
Chart, Histogram etc are used so as to analyse the process and achieve
and maintain state of statistical control to get defect free product. Causes
are detected and prompting CA before detect occurs.
SPC
29
30. It is a technique whose aim is to prevent defective work
being produced by focusing on the process rather than on
the final product ,by using various statistical tools.
SPC provides the operator an opportunity of
correcting/tuning the process in time.
It encourages continual improvement ,which is reflected in
the product & the equipment.
Statistical Methods are not only control charts. Some of the
tools include
Pareto Diagrams
Check sheets
Scatter Diagram
Design of Experiments
Histogram
SPC
30
31. Histogram
• It provides the information about the distribution of
data
• It provides information about the spread or
variation of data.
• It provides information about the location or
centrality of distribution.
• to display large amounts of data values in a relatively
simple chart form.
• to tell relative frequency of occurrence.
• to easily see the distribution of the data.
• to see if there is variation in the data.
• to make future predictions based on the data.
SPC
31
32. Histogram Making
Step 1: Gather Data
Step 2 : Tabulate data
Step 3 : Count the number of data points ‘ n ‘
Step 4 : Determine the Range
Range (R) = Max. reading - Min. reading
Step 5 : Determine class width ‘ k ’
k = R/ Root of n
Step 6 : Determine class boundaries
Step 7 : Determine frequency .. Number of readings
in each class boundary.
Step 8 : Draw histogram with Class width on X axis
and frequency on Y axis.
SPC
32
36. Types of data:
• Variable data – Are quantitative data that can be
counted. Eg. – Distance, diameter, thickness,
hardness, length etc.
• Attribute data – Are qualitative data that can not
be counted. Eg. – Colour, texture, microstructure,
go-no-go checking results, yes/no results
SPC
36
37. SPC
1 2 3 4 5 6 7 8
Sample Number
Sample
Mean
UCLx
LCLx
UCLr
Control Charts
38. Control Charts:
• Variable Charts – Commonly X-Bar – R Chart is used. X Bar-R
chart (Average and Range) explains process data in terms of both
its spread (piece to piece variability) and its location (process
average).
Other variable control charts:
Average and SD Chart
Median and Range Chart
Individual and Moving Range Chart
• Attribute Charts – Pass/Fail type measurements.
P Chart for proportion of nonconforming
nP Chart for number of nonconforming
c Chart for number of non conformities
u Chart for Non conformities per unit
SPC
38
39. Control Charts: Benefits
• Can be used by operators for ongoing control of a process
• Can Help the process perform consistently, predictably for quality
and cost
• Allow the process to achieve
- Higher Quality
- Lower Unit cost
• Provide a common language for discussing the performance of the
process
• Distinguish special from common causes of variation, as guide to
local action or action on the system
SPC
39
40. Control Charts
In ‘ Statistical Process Control ‘ we have to
Gather Data
Plot data
Calculate and plot control limits
Interpret for process control
corrective action
Calculate process capability
Continual Improvement
SPC
40
41. DATA PLOTTED OVER TIME
MONITORED
CHARACTERISTIC
UCL
Center Line
LCL
UCL = Upper Control Limit / LCL = Lower Control Limit
Plotted Data
SPC
Key Component - Control Charts
42. Control Charts for Variables
Average and Range Charts
(X bar and R)
SPC
42
43. X bar- R Chart
• Preparatory Steps
– Establish an environment suitable for action
– Define process
– Define characteristics to be charted
– Define measurement system, do MSA
– Minimize unnecessary variation
SPC
43
44. X bar- R Chart
• Gather Data
• Choose sub group size
o Sensitivity increases with the sub group size
o Cost of sampling increases with size
o Normally sub group size can be 4 or 5
o Subgroups should be collected often enough & at appropriate
times such that they should reflect the potential opportunities for
change.
For example :Different shifts, Different operators, Different material
lots
o No. of subgroups :
Ensure that the major sources of variation are captured
Suggested is 25 nos. or more
SPC
44
45. X bar- R Chart
• Collect the data
• Calculate average (X bar) and range (R) of each
sub-group
• X bar = (X1+X2+……+Xn)/n
• R = Xhighest - Xlowest
SPC
45
46. X bar- R Chart
• Collect the data
• Calculate average (X bar) and range (R) of each
sub-group
• X bar = (X1+X2+……+Xn)/n
• R = Xhighest - Xlowest
SPC
46
Part Operation Other Details
Measurement
SN Date Time
X1 X2 X3 X4
Mean
(X
bar)
Range
(R)
1 12/12 10.25 35 40 32 33 35.0 8
2 12/12 13.45 46 42 40 38 41.5 8
3 12/12 15.34 34 40 34 36 36.0 6
…..
25 15/12 10.30 38 34 44 40 39 10
47. X bar- R Chart
Select proper scales for control charts
Plot Averages and Ranges on control charts
Calculate X double bar and R bar:
X double bar = (X1 bar + X2 bar + ………..+Xn bar) / n
R bar = (R1 + R2 +……….+ Rn) / n
SPC
47
48. X bar- R Chart
• Calculate control limits
• For Average control chart
Upper Control Limit, UCL = X(Double bar) + A2 x R bar
Lower Control Limit, LCL = X(Double bar) - A2 x R bar
• For range control chart
Upper Control Limit, UCL = D4 x R bar
Lower Control Limit, LCL = D3 x R bar
SPC
48
49. X bar- R Chart
• Calculate control limits
• For Average control chart
Upper Control Limit, UCL = X(Double bar) + A2 x R bar
Lower Control Limit, LCL = X(Double bar) - A2 x R bar
• For range control chart
Upper Control Limit, UCL = D4 x R bar
Lower Control Limit, LCL = D3 x R bar
SPC
49
Sub Group
Size
A2 D4 D3
2 1.880 3.267 0
3 1.023 2.527 0
4 0.729 2.282 0
5 0.577 2.115 0
6 0.483 2.004 0
7 0.419 1.924 0.076
50. X bar- R Chart
Draw lines for the following on the control chart
X double bar
R bar
control limits
SPC
50
53. X bar- R Chart
SPC
53
UCL
LCL
1 Sigma (Zone C)
2 Sigma (Zone B)
3 Sigma (Zone A)
1 Sigma (Zone C)
2 Sigma (Zone B)
3 Sigma (Zone A)
The Item
We Are
Measuring
TIME
54. X bar- R Chart
SPC
54
The Item
We Are
Measuring
TIME
1 Sigma
2 Sigma
3 Sigma
1 Sigma
2 Sigma
3 Sigma
60-75%
90-98%
99-99.9%
UCL
LCL
Rules of Standard Deviation
“Where should the data lie?”
55. X bar- R Chart
What does Out-of-Control mean?
Tests for Detecting Lack of Control
(For variable control charts)
1) One point more than 3 sigmas from center line- beyond control line
2) Seven points in a row on same side of center line
3) Seven points in a row, all increasing or all decreasing
4) Fourteen points in a row alternating up and down
5) Two out of three points more than two sigmas from center line (Same
side)
6) Four out of five points more than one sigma from center line (Same
side)
7) Fifteen points in a row within 1 sigma of center line (Either side)
8) Eight points in a row more than one sigma from center line (Either
side)
SPC
55
56. X bar- R Chart
SPC
56
30
20
10
0
10
5
0
-5
Observation Number
Individual
Value
I Chart for C1
X=0.2800
3.0SL=5.416
-3.0SL=-4.856
UCL
LCL
One point more than 3 sigmas from center line
57. X bar- R Chart
SPC
57
30
20
10
0
10
0
-10
Observation Number
Individual
Value
I Chart for C1
X=0.000
3.0SL=9.000
-3.0SL=-9.000
UCL
LCL
Fifteen points in a row within 1 sigma of center line
(either side)
58. X bar- R Chart
SPC
58
Therefore, based on what you know so far, what percent of data points
should fall between the upper control limit (UCL) and lower control
limit (LCL) if your process is in-control?
99 to 99.9 %
UCL
LCL
TIME
59. X bar- R Chart
Corrective Action
Find and address special causes
Use
Pareto analysis or
Cause and effect analysis or
Any problem solving technique
SPC
59
What should you do if you determine that your process is
“Out of Control?”
60. X bar- R Chart
Corrective Action
Take corrective action on identified special causes
Exclude any out of control points for which special causes
have been found & removed
Recalculate & plot the process average and control limits
Confirm that all data points show control when compared
to the new limits
Otherwise repeat the cycle of corrective action
SPC
60
61. X bar- R Chart
Ongoing Control
Ensure that process is in control
(For trial control limits)
Adjust the process to the target, if the process
center is off target
Extend the control limits to cover future periods
Monitor for ongoing control
SPC
61
62. X bar- R Chart
Prepare X bar - R chart using following
SPC
62
10.4 13.0 10.4 7.4 6.3 6.1 11.9 9.6 8.2 8.8
10.6 8.6 11.9 11.0 10.0 6.6 8.2 9.5 12.4 9.4
9.6 10.3 8.9 9.1 10.6 11.7 9.4 11.4 10.9 9.3
10.9 9.4 9.1 8.7 11.2 9.7 10.2 9.2 9.8 8.4
9.3 10.5 10.6 8.5 9.5 10.4 10.4 9.8 9.9 5.8
63. X bar- R Chart
SPC
63
10
9
8
7
6
5
4
3
2
1
Subgroup 0
12
11
10
9
8
7
Sample
Mean
Mean=9.660
UCL=11.78
LCL=7.541
8
7
6
5
4
3
2
1
0
Sample
Range
R=3.674
UCL=7.768
LCL=0
Xbar/R Chart for C1
64. Control Charts for Attributes
p Chart for proportion Nonconforming
SPC
64
65. p Chart
• It is important that
– Each component / part / or item being checked is
recorded as either conforming or non conforming
– Results of these inspections are grouped on a
meaningful basis, and non conforming items are
expressed as a decimal fraction of the subgroup
size.
– meaningful basis, and non conforming items are
expressed as a decimal fraction of the subgroup
SPC
65
66. p Chart
• Choose sub group size
Sensitivity increases with the sub group
size
Cost of sampling increases with size
Normally sub group size can be 50
to 200 or more
– are expressed as a decimal fraction of the subgroup
SPC
66
67. p Chart
Subgroups should be collected often enough & at
appropriate times such that they should reflect the
potential opportunities for change.
For example :
Different shifts
Different operators
Different material lots
No. of subgroups :
Ensure that the major sources of variation are captured
Suggested is 25 nos. or more
SPC
67
68. p Chart
Typical Data Collection Sheet
– are expressed as a decimal fraction of the subgroup
size
SPC
68
art Operation Other Details
SN Date Time
Sample (n)
No. of non
conforming
items in a
sample (np)
Proportion
nonconforming
(p = np/n)
1 12/12 10.25
2 12/12 13.45
3 12/12 15.34
…..
25 15/12 10.30
69. p Chart
• Select proper scales for control charts
• Plot values of p for each subgroup
on control charts
• Calculate Process Average Proportion Nonconforming
(p bar)
n1p1, n2p2 : no. of non conforming items in subgroup
n1,n2 : Subgroup size
SPC
69
p bar = (n1p1 + n2p2 + …+nkpk)
(n1 + n2 + …+ nk)
70. p Chart
Formula for Control Limits
– are expressed as a decimal fraction of the subgroup size
SPC
70
Upper Control Limit, UCLp = p bar + 3 * Sq. root {p bar (1-p bar)}
Sq. root (n)
Lower Control Limit, LCLp = p bar - 3 * Sq. root {p bar (1-p bar)}
Sq. root (n)
n is constant sample size
n
n is constant sample size
71. p Chart
Formula for Control Limits
– are expressed as a decimal fraction of the subgroup size
SPC
71
Upper Control Limit, UCLp = p bar + 3 * Sq. root {p bar (1-p bar)}
Sq. root (n bar)
Lower Control Limit, LCLp = p bar - 3 * Sq. root {p bar (1-p bar)}
Sq. root (n bar)
n is not constant sample size
It is varying between +/- 25%
72. p Chart
Formula for Control Limits
– are expressed as a decimal fraction of the subgroup size
SPC
72
If n is not constant sample size and varying beyond +/-25%,
then recalculate the precise control limits by using following
formula
Upper Control Limit, UCLp = p bar + 3 * Sq. root {p bar (1-p bar)}
Sq. root (n)
Lower Control Limit, LCLp = p bar - 3 * Sq. root {p bar (1-p bar)}
Sq. root (n)
73. p Chart
SPC
73
Draw lines for the following on the control chart
Process average (p bar)
control limits
74. p Chart
Interpret for Process Control
1) One point more than 3 sigmas from center line
2) Nine points in a row on same side of center line
3) Six points in a row, all increasing or all decreasing
4) Fourteen points in a row alternating up and down
To be interpreted same as charts for variables
SPC
74
75. p Chart
Interpret for Process Control
1) Find and correct special causes
2) Recalculate control limits
3) Monitor for on going control
Refer slides in X bar - R chart section
SPC
75
78. SPC
78
Control Limits vs. Specification Limits
• Process Control Limits are calculated based on data from the
process itself
• They are based on +/- 3 (99.73% of the process variation is
expected to fall between these limits)
• Product Specification Limits ARE NOT found on the control chart
• Understanding how the process matches up against customer
requirements IS important to know
To determine how the process performs to Customer Expectations, a
Process Capability Study is required
79. SPC
79
Control Limits vs. Specification Limits
TWO BIG CONTROL CHART ERRORS
1) Putting specification limits on a Control Chart
2 ) Treating UCL and LCL as a specification limit
When you do either of these the control chart becomes just an
inspection tool - it’s no longer a control chart
UCL / LCL are not directly tied to customer defects !
80. Relationship between Control Limits & Spec. Limits
USL
LSL
+ 3
- 3
Process natural limits are inside specification limits
and the process is centered nominal
Nominal
SPC
81. 81
Relationship between Control Limits & Spec. Limits
USL
LSL
+ 3
- 3
Process natural limits are inside specification limits
and the process is not centered nominal
Nominal
SPC
82. 82
Relationship between Control Limits & Spec. Limits
USL
LSL
+ 3
- 3
Process natural limits are outside specification limits
and the process is centered nominal
Nominal
Out of
spec
Out of
spec
SPC
83. 83
Relationship between Control Limits & Spec. Limits
USL
LSL
+ 3
- 3
Process natural limits are outside specification limits
and the process is not centered nominal
Nominal
SPC
84. 84
PROCESS CAPABILTY RATIOS
SPC
Cp & Cpk are two key measures of process capability
Cp = USL – LSL ie Total Tolerance
6 Sigma Process Spread
Cpk = Min X bar - LSL , USL – X bar Cpk accounts for process centering and spread
3 sigma 3 sigma
Cpk will always be equal to or less than Cp
Standard Deviation, Sigma = R bar/ d2
d2 is a constant varying by sample size,
n 2 3 4 5 6 7 8 9 10
d2 1.13 1.69 2.06 2.33 2.53 2.70 2.85 2.97 3.08
85. 27 mm = LSL USL = 33 mm
xbar = 30 mm
s = 1
3 3
Cp = __________
s s
C
USL - LSL
6
p
s
SPC
85
86. 3 3s
s
xbar = 35 mm
s = 1 mm
Cp = __________
C
USL - LSL
6
p
s
27 mm = LSL USL = 33 mm
SPC
87. CpU = ___________
CpL = ___________
Cpk = ____________
3 3 s
s
xbar = 35 mm
s = 1 mm
28 mm = LSL USL = 33 mm
SPC
88. SPC
Pre control Chart :
Out of specifications
Out of specifications
Specifications
Specifications
½ Specifications
½ Specifications
89. SPC
Pre control: The logic:-probability based on normal distribution
Specifications/2
GREEN ZONE
Yellow zone Yellow zone
RED ZONE RED ZONE
1/14 for 1
point out of
green zone
1/14 * 1/14
= 1/196 for 2
consecutive
points
If even one point falls in Red zone, Stop and reset!!
90. SPC
Pre control: The logic:-contd…
If two consecutive points are outside the two pre control
lines : May mean that the process variation has increased!
Use a sample of two consecutive measurements A & B. If A
is green , continue. If A is yellow check B and if B is also
yellow, STOPAND INVESTIGATE.
To qualify the process / set up:
Take two consecutive measurements. If both Green, O.K. If
one yellow, Restart the count. If both Yellow, Reset.
91. 92
Short term Capability Study
SPC
Short term capability :
Based on measurements collected from say one operating run. The
data are analyzed and checked further for the state of statistical
control.
If no special causes are found, a short term capability index can be
calculated.
This is useful for PPAP submissions, machine capability studies,
check for process modifications, etc.
Process Performance indices calculated are called Pp and Ppk
92. 93
Short term Capability Study
SPC
Pp & Ppk Cp &Cpk
PROCESS
PERFORMANCE INDEX
PROCESS CAPABILITY
INDEX
USED DURING INITIAL
PROCESS STUDY
DURING PPAP
ONGOING PROCESS
CAPABILITY STUDY
CAN BE CAPTURED
FOR STABLE AND
CHRONICALLY
UNSTABLE PROCESSES
USED ONLY FOR
STABLE PROCESSES
93. 94
Short term Capability Study
SPC
Pp & Ppk Cp &Cpk
CAPTURES VARIATION
DUE TO BOTH
COMMON & SPECIAL
CAUSES
CAPTURES VARIATION
DUE TO COMMON
CAUSES ONLY
SIGMA IS CALCULATED
USING n-1 FORMULA
USING ALL INDIVIDUAL
READINGS
SIGMA IS CALCULATED
USING R bar / d2
FORMULA
Ppk > 1.67 Cpk 1.33 – 1.67