This document outlines the Define, Measure, Analyze, Improve, and Control (DMAIC) phases of a Six Sigma project to improve quality levels for critical body-side subassembly dimensions at ABC Incorporated. The document defines the problem of dimensions not meeting Six Sigma quality levels, measures current defect levels, analyzes key variables and dimensions, and identifies improvements such as increasing press tonnage and adjusting clamp position based on design of experiments analysis. The goals are to minimize rework costs while achieving less than 3.4 defects per million for all dimensions.
ABC is not achieving Six Sigma quality levels for critical body side subassembly dimensions as requested by their customers. Through analysis, ABC determined that:
1) Setting the press tonnage above 935 would improve dimensions ASM_7Y and ASM_8Y.
2) Setting the clamp position to location 2 for ASM_9Y and ASM_10Y would improve those dimensions.
3) Re-machining the A-pillar die to shift the mean of dimension A_3Y would also shift ASM_3Y to meet specifications.
With these recommended changes, ABC expects the process performance and quality levels to significantly improve for all critical dimensions.
ABC is not achieving Six Sigma quality levels for critical body side subassembly dimensions as requested by their customers. Through analysis, ABC determined that:
1) Setting the press tonnage to over 935 would improve dimensions ASM_7Y and ASM_8Y.
2) Setting the clamp position to location 2 for ASM_9Y and ASM_10Y along with other optimized settings would improve those dimensions.
3) Re-machining the A-pillar die to shift the mean of dimension A_3Y would also shift ASM_3Y to meet specifications.
With these recommended changes, ABC expects the process performance and quality levels to significantly improve for all critical
This document provides guidance on calculating and interpreting the process capability index Cpk. It defines Cpk as a ratio that compares the specification tolerance to the process variation expressed in terms of standard deviations. It explains how to calculate Cpk and discusses factors that influence Cpk values such as sample size, process centering, and measurement uncertainty. The document also provides examples of the expected defective parts per million that correspond to different Cpk values and factors to consider when improving Cpk, such as machine, tooling, workholding, and workpiece variables.
Javier Garcia - Verdugo Sanchez - Six Sigma Training - W1 Process CapabilityJ. García - Verdugo
The document discusses process capability analysis and metrics. It provides information on calculating and interpreting process capability ratios Cp and Cpk using Minitab. Key steps in building a capability study include identifying rational subgroups, collecting a short-term dataset of 30-50 points, and analyzing the data to determine if the process is stable and normally distributed. Process capability can be estimated using pooled standard deviation for potential capability or overall standard deviation for true process capability.
This document provides an overview of process capability and how to calculate it. Process capability is a measurement of how well a process is performing compared to customer requirements. It is calculated by collecting process data, checking if the data is normally distributed, and using formulas to determine metrics like Cp, Cpk which indicate if the process mean and variability are able to meet specifications. If a process is found to be incapable, actions would be taken like process improvement projects to address performance gaps.
Statistical Process Control (SPC) is a method to maintain good product quality, control costs, and improve processes. It involves collecting data on key quality characteristics (4Ms+1E: Man, Machine, Method, Material, Environment) and analyzing the data using control charts to detect process variations. Control charts establish control limits to monitor a process over time, and identify processes that are unstable or out of control. Process improvement tools like Six Sigma aim to reduce process variations and keep performance stable through methods like DMAIC (Define, Measure, Analyze, Improve, Control). SPC helps detect machine faults, gain production awareness, and achieve good business reputation through consistent quality.
The document discusses process capability and defines key terms related to process capability. It provides the standard formula for process capability using 6 sigma and explains how process capability is compared to specification limits. It then discusses different process capability indices including Cp, Cpk, and Cpm. It explains how these indices measure both potential and actual process capability. The document also discusses limitations of the Cp index and the use of Cpk to address process centering. It describes how to calculate confidence intervals for process capability ratios and discusses some key process performance metrics.
- Six Sigma is a quality methodology that aims for near perfection with 3.4 defects per million opportunities. It was developed by Motorola in 1987.
- Key concepts include process capability index (Cp), process variation, and specification limits. A Cp of 2.0 or higher is needed to achieve Six Sigma quality.
- The DMAIC methodology is used for improving existing processes and focuses on defining problems, measuring processes, analyzing causes, improving processes, and controlling future performance. DFSS designs new processes at Six Sigma quality levels using approaches like DMADV.
ABC is not achieving Six Sigma quality levels for critical body side subassembly dimensions as requested by their customers. Through analysis, ABC determined that:
1) Setting the press tonnage above 935 would improve dimensions ASM_7Y and ASM_8Y.
2) Setting the clamp position to location 2 for ASM_9Y and ASM_10Y would improve those dimensions.
3) Re-machining the A-pillar die to shift the mean of dimension A_3Y would also shift ASM_3Y to meet specifications.
With these recommended changes, ABC expects the process performance and quality levels to significantly improve for all critical dimensions.
ABC is not achieving Six Sigma quality levels for critical body side subassembly dimensions as requested by their customers. Through analysis, ABC determined that:
1) Setting the press tonnage to over 935 would improve dimensions ASM_7Y and ASM_8Y.
2) Setting the clamp position to location 2 for ASM_9Y and ASM_10Y along with other optimized settings would improve those dimensions.
3) Re-machining the A-pillar die to shift the mean of dimension A_3Y would also shift ASM_3Y to meet specifications.
With these recommended changes, ABC expects the process performance and quality levels to significantly improve for all critical
This document provides guidance on calculating and interpreting the process capability index Cpk. It defines Cpk as a ratio that compares the specification tolerance to the process variation expressed in terms of standard deviations. It explains how to calculate Cpk and discusses factors that influence Cpk values such as sample size, process centering, and measurement uncertainty. The document also provides examples of the expected defective parts per million that correspond to different Cpk values and factors to consider when improving Cpk, such as machine, tooling, workholding, and workpiece variables.
Javier Garcia - Verdugo Sanchez - Six Sigma Training - W1 Process CapabilityJ. García - Verdugo
The document discusses process capability analysis and metrics. It provides information on calculating and interpreting process capability ratios Cp and Cpk using Minitab. Key steps in building a capability study include identifying rational subgroups, collecting a short-term dataset of 30-50 points, and analyzing the data to determine if the process is stable and normally distributed. Process capability can be estimated using pooled standard deviation for potential capability or overall standard deviation for true process capability.
This document provides an overview of process capability and how to calculate it. Process capability is a measurement of how well a process is performing compared to customer requirements. It is calculated by collecting process data, checking if the data is normally distributed, and using formulas to determine metrics like Cp, Cpk which indicate if the process mean and variability are able to meet specifications. If a process is found to be incapable, actions would be taken like process improvement projects to address performance gaps.
Statistical Process Control (SPC) is a method to maintain good product quality, control costs, and improve processes. It involves collecting data on key quality characteristics (4Ms+1E: Man, Machine, Method, Material, Environment) and analyzing the data using control charts to detect process variations. Control charts establish control limits to monitor a process over time, and identify processes that are unstable or out of control. Process improvement tools like Six Sigma aim to reduce process variations and keep performance stable through methods like DMAIC (Define, Measure, Analyze, Improve, Control). SPC helps detect machine faults, gain production awareness, and achieve good business reputation through consistent quality.
The document discusses process capability and defines key terms related to process capability. It provides the standard formula for process capability using 6 sigma and explains how process capability is compared to specification limits. It then discusses different process capability indices including Cp, Cpk, and Cpm. It explains how these indices measure both potential and actual process capability. The document also discusses limitations of the Cp index and the use of Cpk to address process centering. It describes how to calculate confidence intervals for process capability ratios and discusses some key process performance metrics.
- Six Sigma is a quality methodology that aims for near perfection with 3.4 defects per million opportunities. It was developed by Motorola in 1987.
- Key concepts include process capability index (Cp), process variation, and specification limits. A Cp of 2.0 or higher is needed to achieve Six Sigma quality.
- The DMAIC methodology is used for improving existing processes and focuses on defining problems, measuring processes, analyzing causes, improving processes, and controlling future performance. DFSS designs new processes at Six Sigma quality levels using approaches like DMADV.
Statistical Process Control (SPC) is an industry-standard methodology for measuring and controlling quality during the manufacturing process. Quality data in the form of Product or Process measurements are obtained in real-time during manufacturing. This data is then plotted on a graph with pre-determined control limits. Control limits are determined by the capability of the process, whereas specification limits are determined by the client's needs.
The document discusses statistical quality control (SQC) and its three categories: descriptive statistics, statistical process control (SPC), and acceptance sampling. SQC aims to understand and reduce variation in processes. Variation can come from common or assignable causes. Process capability compares process variability to specifications using indexes like Cp, Cpk, Pp, and Ppk. These indexes indicate if a process is capable of meeting customer requirements within specifications. SQC tools can also be applied to services by defining quantifiable service quality measurements.
This document provides an overview of statistical quality control (SQC). It describes the three main categories of SQC as descriptive statistics, statistical process control (SPC), and acceptance sampling. Key aspects of SPC covered include identifying sources of variation, using control charts for variables and attributes, calculating process capability indices, and the concepts of six-sigma quality. Acceptance sampling is introduced as inspecting a sample from a batch to determine if the entire batch meets quality standards.
This document provides an overview of statistical quality control (SQC). It describes the three main categories of SQC as descriptive statistics, statistical process control (SPC), and acceptance sampling. Control charts are discussed as a key SPC tool used to monitor processes and identify variations. The concepts of process capability, six sigma quality levels, and acceptance sampling plans are also introduced.
Process Capability: Step 4 (Normal Distributions)Matt Hansen
This document provides instruction on assessing the capability of a process that follows a normal distribution. It discusses key metrics like Cp, Cpk, Pp and Ppk which measure process performance relative to customer specifications. The document also explains how to calculate and interpret process capability metrics like DPMO from the output of a process capability analysis in Minitab.
Vitaletti Leonardo, Elica Motors - Design for Six Sigma ed applicazioni minit...GMSL S.r.l.
Leonardo Vitaletti from FIME presented on applying Design for Six Sigma and Minitab applications to a motor project. He summarized using the DMAIC approach of define, measure, analyze, improve, and control. Key aspects included defining objectives, measuring current performance, analyzing the stamping process using DOE, improving through optimization, and controlling with control charts. Analysis found hot injection stamping reduced variability compared to other methods. DOE identified significant factors and their interactions. Optimization reduced oscillation and imbalance predictions. Future work includes improving correlation of intermediate parameters and defining acceptability windows.
The document discusses a project to reduce failures of heat sink mounted components by improving the heat sink assembly process. It aims to determine the highest risk component, identify correlations between field data and production data, set goals to reduce defects by 50%, and analyze the current process capability to identify non-capable processes in need of improvement.
Design and Performance Analysis of Mechanical Hydro Pneumatic Suspension SystemIRJET Journal
The document describes the design and analysis of a mechanical hydro pneumatic suspension system. It aims to improve ride comfort in light commercial vehicles. The proposed system uses a helical coil spring with varying pitch, hydraulic and pneumatic cylinders filled with oil and nitrogen gas respectively. SolidWorks simulations show von Mises stresses below yield strength and displacements under 0.01 mm, indicating the design can withstand loads of 3000N with a safety factor of 2. Calculations confirm the damping ratio meets standards for an under-damped suspension providing a smooth ride. In summary, the suspension system design utilizes mechanical, hydraulic and pneumatic components to enhance ride quality in small commercial vehicles.
Validation of a Fast Transient Solver based on the Projection MethodApplied CCM Pty Ltd
This paper presents a fast transient solver suitable for the simulation of incompressible flows. The main characteristic of the solver is that it is based on the projection method and requires only one pressure and momentum solve per time step. Furthermore, advantage of using the projection method in the formulation is the particularly efficient form of the pressure equation that has the Laplacian term depending only on geometric quantities. This form is highly suitable for the high
performance computing that utilises the Algebraic Multi-grid Method (AMG) as the coarse levels produced by the algebraic multi-grid can be stored if the grid is not changing. Fractional step error near the boundaries is removed by utilising the incremental version of the algorithm. The solver is implemented using version
5.04 of the open source library, Caelus. Accuracy of the solver was investigated through several validation cases.The results indicate the solver is accurate and has good computational efficiency.
This chapter discusses process and measurement system capability analysis. It defines process capability as the uniformity of a process output. Process capability analysis estimates how well a process output will meet specification limits using metrics like Cp, Cpk, and Cpm. These metrics assume the output follows a normal distribution and the process is in statistical control. The chapter also discusses using histograms, probability plots, and control charts to analyze process capability, as well as designing experiments to determine sources of variability. It concludes by discussing measuring and accounting for measurement system variability separately from process variability.
This document provides an overview of Six Sigma methodology. It discusses that Six Sigma aims to reduce defects to 3.4 per million opportunities by using statistical methods. The Six Sigma methodology uses the DMAIC process which stands for Define, Measure, Analyze, Improve, and Control. It also outlines several statistical tools used in Six Sigma like check sheets, Pareto charts, histograms, scatter diagrams and control charts. Process capability and its measures like Cp, Cpk are also explained. The document provides examples to demonstrate how to calculate these metrics and interpret them.
This document provides an overview of Six Sigma methodology. It discusses that Six Sigma aims to reduce defects to 3.4 per million opportunities by using statistical methods. The Six Sigma methodology uses the DMAIC process which stands for Define, Measure, Analyze, Improve, and Control. It also outlines several statistical tools used in Six Sigma like check sheets, Pareto charts, histograms, scatter diagrams, and control charts. Process capability and its measures like Cp, Cpk are also defined. The document aims to explain the key concepts and tools used in Six Sigma to improve quality and processes.
This document provides an overview of process quality control using statistical process control (SPC) and statistical quality control (SQC) approaches. It defines SPC and SQC, noting that SPC focuses on controlling process inputs through variables while SQC monitors outputs through attributes. The document outlines key learning objectives around these topics. It also defines key terms like process quality control and discusses the difference between SPC and SQC. Additionally, it covers process capability analysis using Minitab and controlling process inputs and monitoring outputs. Overall, the document serves as training material on quality control tools and techniques with a focus on SPC and SQC.
Six Sigma Methods and Formulas for Successful Quality ManagementIJERA Editor
This document discusses Six Sigma methods and formulas for quality management. It begins by introducing Six Sigma and defining key terms like defects per million opportunities and standard deviation. It then presents the Six Sigma implementation process (DMAIC), outlining the Define, Measure, Analyze, Improve, and Control phases. In the Define phase, projects are chartered and teams assembled. The Measure phase involves collecting data and doing capability analyses. The Analyze phase uses statistical tests like t-tests and ANOVA to find sources of variation. Formulas for measures of central tendency, dispersion, hypothesis testing, and process capability are also provided.
Quality is defined as customers' perception of how well a product or service meets their expectations. There are three types of quality: quality of design, quality of performance, and quality of conformance. Statistical quality control uses statistical techniques to control, improve, and maintain quality. Control charts are used to determine if a process is in or out of control by monitoring for random or assignable variation. Process capability indices like Cp and Cpk compare process variability to specification limits to determine if a process is capable of meeting specifications.
1. The document presents a Lean Six Sigma project to reduce liquid particle counts (LPC) in plastic injection molded ramp components produced by ultrasonic washing and drying processes.
2. Baseline data shows the current processes have high variability and are not capable of meeting a new, stricter LPC specification required by customers.
3. The project aims to improve the washing processes for two representative ramp products to achieve a process mean LPC lower than 70% of the new specification by analyzing sources of variation and implementing process improvements.
Iaetsd position control of servo systems using pidIaetsd Iaetsd
This document discusses using PID controllers tuned with soft computing techniques for position control of servo systems. It analyzes tuning PID controllers for a 3rd order plant model of a servo motor using the Ziegler-Nichols (ZN) method, genetic algorithm (GA), and particle swarm optimization (PSO). The step responses show that PSO provides the best performance with the fastest rise and settling times and lowest overshoot and errors. While ZN is easy to apply, GA and especially PSO give better results for controlling the servo motor's position.
This document discusses process capability analysis. It defines specification and tolerance limits as boundaries that define conformance for manufacturing or service operations. Process capability indices like Cp, Cpk, CPU, CPL and Ppk are used to determine if a process's natural variation can meet specifications. Cp measures a process's potential to meet specifications based on its spread. Cpk incorporates both mean and standard deviation. CPU and CPL measure if the process mean is centered between the specification limits. Ppk indicates actual long-term process performance meeting specifications. Maintaining capable processes with indices above 1 ensures high quality and uniform output.
This presentation was provided by Rebecca Benner, Ph.D., of the American Society of Anesthesiologists, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
Statistical Process Control (SPC) is an industry-standard methodology for measuring and controlling quality during the manufacturing process. Quality data in the form of Product or Process measurements are obtained in real-time during manufacturing. This data is then plotted on a graph with pre-determined control limits. Control limits are determined by the capability of the process, whereas specification limits are determined by the client's needs.
The document discusses statistical quality control (SQC) and its three categories: descriptive statistics, statistical process control (SPC), and acceptance sampling. SQC aims to understand and reduce variation in processes. Variation can come from common or assignable causes. Process capability compares process variability to specifications using indexes like Cp, Cpk, Pp, and Ppk. These indexes indicate if a process is capable of meeting customer requirements within specifications. SQC tools can also be applied to services by defining quantifiable service quality measurements.
This document provides an overview of statistical quality control (SQC). It describes the three main categories of SQC as descriptive statistics, statistical process control (SPC), and acceptance sampling. Key aspects of SPC covered include identifying sources of variation, using control charts for variables and attributes, calculating process capability indices, and the concepts of six-sigma quality. Acceptance sampling is introduced as inspecting a sample from a batch to determine if the entire batch meets quality standards.
This document provides an overview of statistical quality control (SQC). It describes the three main categories of SQC as descriptive statistics, statistical process control (SPC), and acceptance sampling. Control charts are discussed as a key SPC tool used to monitor processes and identify variations. The concepts of process capability, six sigma quality levels, and acceptance sampling plans are also introduced.
Process Capability: Step 4 (Normal Distributions)Matt Hansen
This document provides instruction on assessing the capability of a process that follows a normal distribution. It discusses key metrics like Cp, Cpk, Pp and Ppk which measure process performance relative to customer specifications. The document also explains how to calculate and interpret process capability metrics like DPMO from the output of a process capability analysis in Minitab.
Vitaletti Leonardo, Elica Motors - Design for Six Sigma ed applicazioni minit...GMSL S.r.l.
Leonardo Vitaletti from FIME presented on applying Design for Six Sigma and Minitab applications to a motor project. He summarized using the DMAIC approach of define, measure, analyze, improve, and control. Key aspects included defining objectives, measuring current performance, analyzing the stamping process using DOE, improving through optimization, and controlling with control charts. Analysis found hot injection stamping reduced variability compared to other methods. DOE identified significant factors and their interactions. Optimization reduced oscillation and imbalance predictions. Future work includes improving correlation of intermediate parameters and defining acceptability windows.
The document discusses a project to reduce failures of heat sink mounted components by improving the heat sink assembly process. It aims to determine the highest risk component, identify correlations between field data and production data, set goals to reduce defects by 50%, and analyze the current process capability to identify non-capable processes in need of improvement.
Design and Performance Analysis of Mechanical Hydro Pneumatic Suspension SystemIRJET Journal
The document describes the design and analysis of a mechanical hydro pneumatic suspension system. It aims to improve ride comfort in light commercial vehicles. The proposed system uses a helical coil spring with varying pitch, hydraulic and pneumatic cylinders filled with oil and nitrogen gas respectively. SolidWorks simulations show von Mises stresses below yield strength and displacements under 0.01 mm, indicating the design can withstand loads of 3000N with a safety factor of 2. Calculations confirm the damping ratio meets standards for an under-damped suspension providing a smooth ride. In summary, the suspension system design utilizes mechanical, hydraulic and pneumatic components to enhance ride quality in small commercial vehicles.
Validation of a Fast Transient Solver based on the Projection MethodApplied CCM Pty Ltd
This paper presents a fast transient solver suitable for the simulation of incompressible flows. The main characteristic of the solver is that it is based on the projection method and requires only one pressure and momentum solve per time step. Furthermore, advantage of using the projection method in the formulation is the particularly efficient form of the pressure equation that has the Laplacian term depending only on geometric quantities. This form is highly suitable for the high
performance computing that utilises the Algebraic Multi-grid Method (AMG) as the coarse levels produced by the algebraic multi-grid can be stored if the grid is not changing. Fractional step error near the boundaries is removed by utilising the incremental version of the algorithm. The solver is implemented using version
5.04 of the open source library, Caelus. Accuracy of the solver was investigated through several validation cases.The results indicate the solver is accurate and has good computational efficiency.
This chapter discusses process and measurement system capability analysis. It defines process capability as the uniformity of a process output. Process capability analysis estimates how well a process output will meet specification limits using metrics like Cp, Cpk, and Cpm. These metrics assume the output follows a normal distribution and the process is in statistical control. The chapter also discusses using histograms, probability plots, and control charts to analyze process capability, as well as designing experiments to determine sources of variability. It concludes by discussing measuring and accounting for measurement system variability separately from process variability.
This document provides an overview of Six Sigma methodology. It discusses that Six Sigma aims to reduce defects to 3.4 per million opportunities by using statistical methods. The Six Sigma methodology uses the DMAIC process which stands for Define, Measure, Analyze, Improve, and Control. It also outlines several statistical tools used in Six Sigma like check sheets, Pareto charts, histograms, scatter diagrams and control charts. Process capability and its measures like Cp, Cpk are also explained. The document provides examples to demonstrate how to calculate these metrics and interpret them.
This document provides an overview of Six Sigma methodology. It discusses that Six Sigma aims to reduce defects to 3.4 per million opportunities by using statistical methods. The Six Sigma methodology uses the DMAIC process which stands for Define, Measure, Analyze, Improve, and Control. It also outlines several statistical tools used in Six Sigma like check sheets, Pareto charts, histograms, scatter diagrams, and control charts. Process capability and its measures like Cp, Cpk are also defined. The document aims to explain the key concepts and tools used in Six Sigma to improve quality and processes.
This document provides an overview of process quality control using statistical process control (SPC) and statistical quality control (SQC) approaches. It defines SPC and SQC, noting that SPC focuses on controlling process inputs through variables while SQC monitors outputs through attributes. The document outlines key learning objectives around these topics. It also defines key terms like process quality control and discusses the difference between SPC and SQC. Additionally, it covers process capability analysis using Minitab and controlling process inputs and monitoring outputs. Overall, the document serves as training material on quality control tools and techniques with a focus on SPC and SQC.
Six Sigma Methods and Formulas for Successful Quality ManagementIJERA Editor
This document discusses Six Sigma methods and formulas for quality management. It begins by introducing Six Sigma and defining key terms like defects per million opportunities and standard deviation. It then presents the Six Sigma implementation process (DMAIC), outlining the Define, Measure, Analyze, Improve, and Control phases. In the Define phase, projects are chartered and teams assembled. The Measure phase involves collecting data and doing capability analyses. The Analyze phase uses statistical tests like t-tests and ANOVA to find sources of variation. Formulas for measures of central tendency, dispersion, hypothesis testing, and process capability are also provided.
Quality is defined as customers' perception of how well a product or service meets their expectations. There are three types of quality: quality of design, quality of performance, and quality of conformance. Statistical quality control uses statistical techniques to control, improve, and maintain quality. Control charts are used to determine if a process is in or out of control by monitoring for random or assignable variation. Process capability indices like Cp and Cpk compare process variability to specification limits to determine if a process is capable of meeting specifications.
1. The document presents a Lean Six Sigma project to reduce liquid particle counts (LPC) in plastic injection molded ramp components produced by ultrasonic washing and drying processes.
2. Baseline data shows the current processes have high variability and are not capable of meeting a new, stricter LPC specification required by customers.
3. The project aims to improve the washing processes for two representative ramp products to achieve a process mean LPC lower than 70% of the new specification by analyzing sources of variation and implementing process improvements.
Iaetsd position control of servo systems using pidIaetsd Iaetsd
This document discusses using PID controllers tuned with soft computing techniques for position control of servo systems. It analyzes tuning PID controllers for a 3rd order plant model of a servo motor using the Ziegler-Nichols (ZN) method, genetic algorithm (GA), and particle swarm optimization (PSO). The step responses show that PSO provides the best performance with the fastest rise and settling times and lowest overshoot and errors. While ZN is easy to apply, GA and especially PSO give better results for controlling the servo motor's position.
This document discusses process capability analysis. It defines specification and tolerance limits as boundaries that define conformance for manufacturing or service operations. Process capability indices like Cp, Cpk, CPU, CPL and Ppk are used to determine if a process's natural variation can meet specifications. Cp measures a process's potential to meet specifications based on its spread. Cpk incorporates both mean and standard deviation. CPU and CPL measure if the process mean is centered between the specification limits. Ppk indicates actual long-term process performance meeting specifications. Maintaining capable processes with indices above 1 ensures high quality and uniform output.
Similar to Implementing Six Sigma Quality at Better Body Manufacturing .ppt (20)
This presentation was provided by Rebecca Benner, Ph.D., of the American Society of Anesthesiologists, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
🔥🔥🔥🔥🔥🔥🔥🔥🔥
إضغ بين إيديكم من أقوى الملازم التي صممتها
ملزمة تشريح الجهاز الهيكلي (نظري 3)
💀💀💀💀💀💀💀💀💀💀
تتميز هذهِ الملزمة بعِدة مُميزات :
1- مُترجمة ترجمة تُناسب جميع المستويات
2- تحتوي على 78 رسم توضيحي لكل كلمة موجودة بالملزمة (لكل كلمة !!!!)
#فهم_ماكو_درخ
3- دقة الكتابة والصور عالية جداً جداً جداً
4- هُنالك بعض المعلومات تم توضيحها بشكل تفصيلي جداً (تُعتبر لدى الطالب أو الطالبة بإنها معلومات مُبهمة ومع ذلك تم توضيح هذهِ المعلومات المُبهمة بشكل تفصيلي جداً
5- الملزمة تشرح نفسها ب نفسها بس تكلك تعال اقراني
6- تحتوي الملزمة في اول سلايد على خارطة تتضمن جميع تفرُعات معلومات الجهاز الهيكلي المذكورة في هذهِ الملزمة
واخيراً هذهِ الملزمة حلالٌ عليكم وإتمنى منكم إن تدعولي بالخير والصحة والعافية فقط
كل التوفيق زملائي وزميلاتي ، زميلكم محمد الذهبي 💊💊
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A Visual Guide to 1 Samuel | A Tale of Two HeartsSteve Thomason
These slides walk through the story of 1 Samuel. Samuel is the last judge of Israel. The people reject God and want a king. Saul is anointed as the first king, but he is not a good king. David, the shepherd boy is anointed and Saul is envious of him. David shows honor while Saul continues to self destruct.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
Leveraging Generative AI to Drive Nonprofit InnovationTechSoup
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
Implementing Six Sigma Quality at Better Body Manufacturing .ppt
1. D M A I C
Define Measure Analyze Improve Control
D
Define
M
Measure
A
Analyze
I
Improve
C
Control
Implementing Six Sigma Quality
at Better Body Manufacturing
2. 2
D M A I C
Define Measure Analyze Improve Control
Dimension DPM
ASM_7Y 172475
ASM_8Y 85824
ASM_3Y 19786
ASM_9Y 3874
ASM_10Y 776
ASM_6Y 4
Overview
ABC Incorporated (ABC) is not achieving Six Sigma quality levels for all critical
Body-Side Sub-Assembly dimensions as requested by their customers.
Ensure that all critical body-side subassembly dimensions are within Six Sigma
quality levels of < 3.4 DPM. Cp 2.0 and Cpk 1.67.
• Change tonnage to > 935 to correct ASM_7Y and ASM_8Y
• Set clamp position to location 2 for ASM_9Y and ASM_10Y
• Re-machine A-pillar die to correct A_3Y and ASM_3Y
• Determined the correlation between body side and assembly dimensions.
• Evaluated the significance of Tonnage > 935 for ASM_7Y & ASM_8Y.
• Conducted a DOE for Clamp position for ASM_9Y & ASM_10Y.
0
50000
100000
150000
200000
ASM_7Y
ASM_8Y
ASM_3Y
ASM_9Y
ASM_10Y
ASM_6Y
DPM
3. 3
D M A I C
Define Measure Analyze Improve Control
Problem Statement & The Goal
ABC Incorporated’s customer wants ABC to apply Six Sigma problem solving
methodology to insure that the body side subassembly is achieving Six Sigma quality
levels of less than 3.4 defects per million for all critical body side subassembly
dimensions.
ABC needs an improvement strategy that minimizes the rework costs while achieving the
desired quality objective. ABC’s goal is to produce module subassemblies that meet the
customer requirements and not necessarily to insure that every individual stamped
component within the assembly meets it original print specifications – sub-system
optimizations vs. local optimization.
+
+
A-Pillar
Reinforcement
B-Pillar
Reinforcement
Body Side Outer
+
+
A-Pillar
Reinforcement
B-Pillar
Reinforcement
Body Side Outer
D
Define
4. 4
D M A I C
Define Measure Analyze Improve Control
Measure Phase
Key Variables:
Assembly process variables:
Weld Pattern (density), Clamp Location, and Clamp Weld Pressure
Stamping process variables (body side):
Press Tonnage, Die Cushion Pressure, Material Thickness
Body Assembly Dimensions ASM_1Y through ASM_10Y
M
Measure
4
776
172475
85824
19786
3874
0
50000
100000
150000
200000
ASM_7Y ASM_8Y ASM_3Y ASM_9Y ASM_10Y ASM_6Y
DPM
Assembly Dimensions with Highest Defects
5. 5
D M A I C
Define Measure Analyze Improve Control
Resolution alternatives (based upon past experience):
1. Make adjustments to assembly process settings
2. Reduce variation of components through better control of stamping
process input variables
3. Rework stamping dies to shift component mean deviation that is off
target and causing assembly defects
Target Performance Level:
All ten critical assembly dimensions at Six Sigma quality level of 3.4 DPM.
Cp 2.0 and Cpk 1.67
Fish Bone and P-Diagrams:
Understanding potential causes of defects. From this we pick the assembly and
component dimensions that require further analysis
Analyze Phase A
Analyze
6. 6
D M A I C
Define Measure Analyze Improve Control
For our analysis we will do a DOE to check
for levels that contribute to better quality
product.
Weld Pattern
(density)
Clamp
Location
Operator
Machine
Materials
Methods
Clamp Weld
Pressure
Press
Tonnage
Die Cushion
Pressure
Material
Thickness
Training
Yield
Strength
Elastic
Limit
Environment
Temperature
Humidity
Quality
Component
Variability
Inspection
Process Gage R&R
Body
Assembly
Analyze Phase A
Analyze
Body Side Sub-Assembly
Stamping Process
Outputs
Body Side Sub-Assemblies at
Six Sigma quality levels
Control Variables
Clamp Location Press Tonnage
Weld Density Die Pressure
Clamp Pressure
Error
States
Dimensional
defects
Noise Variables
Environment
Inherent Variation
Inputs
Material Thickness
Yield Strength
7. 7
D M A I C
Define Measure Analyze Improve Control
Analysis of ASM_7Y and ASM_8Y
2 7 12
0.0
0.5
1.0
Subgroup Number
ASM_7Y
Number of runs about median:
Expected number of runs:
Longest run about median:
Approx P-Value for Clustering:
Approx P-Value for Mixtures:
Number of runs up or down:
Expected number of runs:
Longest run up or down:
Approx P-Value for Trends:
Approx P-Value for Oscillation:
4.00000
7.00000
5.00000
0.03464
0.96536
6.00000
7.66667
3.00000
0.10778
0.89222
Run Chart for ASM_7Y
2 7 12
0.0
0.5
1.0
Subgroup Number
ASM_8Y
Number of runs about median:
Expected number of runs:
Longest run about median:
Approx P-Value for Clustering:
Approx P-Value for Mixtures:
Number of runs up or down:
Expected number of runs:
Longest run up or down:
Approx P-Value for Trends:
Approx P-Value for Oscillation:
4.00000
7.00000
5.00000
0.03464
0.96536
8.00000
7.66667
2.00000
0.59781
0.40219
Run Chart for ASM_8Y
Analyze Phase A
Analyze
Conclusion: BS_7Y and ASM_7Y are following a similar trend.
A correlation chart to study this further shows high correlation.
(Pearson correlation, R of 0.701).
0.0 0.5 1.0
0.0
0.5
1.0
ASM_8Y
ASM_7Y
XY Plot of ASM_8Y and ASM_7Y
8. 8
D M A I C
Define Measure Analyze Improve Control
Analyze Phase A
Analyze
-0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8
LSL USL
Capability Analysis of B_7Y
USL
Target
LSL
Mean
Sample N
StDev(Within)
StDev(Overall)
Cp
CPU
CPL
Cpk
Cpm
Pp
PPU
PPL
Ppk
PPM< LSL
PPM> USL
PPMTotal
PPM< LSL
PPM> USL
PPMTotal
PPM< LSL
PPM> USL
PPMTotal
0.70
*
-0.70
0.11
36
0.0788122
0.0791215
2.96
2.50
3.43
2.50
*
2.95
2.49
3.41
2.49
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Process Data
Potential (Within) Capability
Overall Capability Observed Performance Exp. "Within" Performance Exp. "Overall" Performance
Within
Overall
Capability of B_7Y
-1.0 -0.5 0.0 0.5 1.0 1.5 2.0
LSL USL
Capability Analysis of BS_7Y
USL
Target
LSL
Mean
Sample N
StDev(Within)
StDev(Overall)
Cp
CPU
CPL
Cpk
Cpm
Pp
PPU
PPL
Ppk
PPM< LSL
PPM> USL
PPMTotal
PPM< LSL
PPM> USL
PPMTotal
PPM< LSL
PPM> USL
PPMTotal
0.700000
*
-0.700000
0.899444
36
0.149640
0.383691
1.56
-0.44
3.56
-0.44
*
0.61
-0.17
1.39
-0.17
0.00
666666.67
666666.67
0.00
908706.09
908706.09
15.33
698400.06
698415.39
Process Data
Potential (Within) Capability
Overall Capability Observed Performance Exp. "Within" Performance Exp. "Overall" Performance
Within
Overall
698416 DPM
0 DPM
0.5 1.0 1.5
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
ASM_7Y
BS_7Y
XY Plot of ASM_7Y and BS_7Y
Conclusion: B_7Y has 0 ppm compared to ~700K
DPM in BS_7Y.
Furthermore, BS_7Y shows strong correlation on
dimension ASM_7Y. (Pearson correlation, R of
0.786).
Capability of BS_7Y
9. 9
D M A I C
Define Measure Analyze Improve Control
905 915 925 935 945
0.5
1.0
1.5
Tonnage
BS_7Y
XY Plot of Tonnage vs. BS_7Y
XY Plot of Tonnage vs. BS_7Y
Conclusion: Tonnage values above 935 greatly improves BS_7Y and brings it closer
to the mean. Let’s see what impact this has on ASM dimensions 7Y, 8Y, 9Y, and
10Y by creating a subset of the data looking only at Tonnage > 935.
Analyze Phase A
Analyze
10. 10
D M A I C
Define Measure Analyze Improve Control
Analyze Phase A
Analyze
-1.0 -0.5 0.0 0.5 1.0
LSL USL
Capability Analysis of ASM_7Y at Tonnage > 935
USL
Target
LSL
Mean
Sample N
StDev (Within)
StDev (Overall)
Cp
CPU
CPL
Cpk
Cpm
Pp
PPU
PPL
Ppk
PPM < LSL
PPM > USL
PPM Total
PPM < LSL
PPM > USL
PPM Total
PPM < LSL
PPM > USL
PPM Total
1.00
*
-1.00
0.09
12
0.163174
0.147855
2.04
1.86
2.23
1.86
*
2.25
2.05
2.46
2.05
0.00
0.00
0.00
0.00
0.01
0.01
0.00
0.00
0.00
Process Data
Potential (Within) Capability
Overall Capability Observed Performance Exp. "Within" Performance Exp. "Overall" Performance
Within
Overall
-1.0 -0.5 0.0 0.5 1.0
LSL USL
Capability Analysis of ASM_8Y at Tonnage > 935
USL
Target
LSL
Mean
Sample N
StDev (Within)
StDev (Overall)
Cp
CPU
CPL
Cpk
Cpm
Pp
PPU
PPL
Ppk
PPM < LSL
PPM > USL
PPM Total
PPM < LSL
PPM > USL
PPM Total
PPM < LSL
PPM > USL
PPM Total
1.00000
*
-1.00000
-0.12833
12
0.101825
0.089161
3.27
3.69
2.85
2.85
*
3.74
4.22
3.26
3.26
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Process Data
Potential (Within) Capability
Overall Capability Observed Performance Exp. "Within" Performance Exp. "Overall" Performance
Within
Overall
-1.0 -0.5 0.0 0.5 1.0
LSL USL
Capability Analysis of ASM_9Y at Tonnage > 935
USL
Target
LSL
Mean
Sample N
StDev (Within)
StDev (Overall)
Cp
CPU
CPL
Cpk
Cpm
Pp
PPU
PPL
Ppk
PPM < LSL
PPM > USL
PPM Total
PPM < LSL
PPM > USL
PPM Total
PPM < LSL
PPM > USL
PPM Total
1.00000
*
-1.00000
0.52083
12
0.206010
0.177098
1.62
0.78
2.46
0.78
*
1.88
0.90
2.86
0.90
0.00
0.00
0.00
0.00
10010.77
10010.77
0.00
3408.51
3408.51
Process Data
Potential (Within) Capability
Overall Capability Observed Performance Exp. "Within" Performance Exp. "Overall" Performance
Within
Overall
-1.0 -0.5 0.0 0.5 1.0
LSL USL
Capability Analysis of ASM_10Y at Tonnage > 935
USL
Target
LSL
Mean
Sample N
StDev (Within)
StDev (Overall)
Cp
CPU
CPL
Cpk
Cpm
Pp
PPU
PPL
Ppk
PPM < LSL
PPM > USL
PPM Total
PPM < LSL
PPM > USL
PPM Total
PPM < LSL
PPM > USL
PPM Total
1.00
*
-1.00
0.39
12
0.215541
0.187663
1.55
0.94
2.15
0.94
*
1.78
1.08
2.47
1.08
0.00
0.00
0.00
0.00
2326.72
2326.72
0.00
576.00
576.00
Process Data
Potential (Within) Capability
Overall Capability Observed Performance Exp. "Within" Performance Exp. "Overall" Performance
Within
Overall
Conclusion: Setting Tonnage to greater than 935 resulted in ASM_7Y and ASM_8Y
meeting the goal of <3.4 DPM. ASM_9Y and ASM_10Y require further analysis.
Impact this has on ASM dimensions 7Y, 8Y, 9Y & 10Y on Tonnage
11. 11
D M A I C
Define Measure Analyze Improve Control
DOE for Response Variable ASM_9Y
• DOE factorial analysis shows Clamp Position is the only significant factor in
determining ASM_9Y dimension
DOE Response Optimization for ASM_9Y
• Set Clamp Position to Location 2 (level 1)
• Optimizer recommends setting Weld Density to 1.33 weld per inch (level 1),
but this appears to be a robust parameter, which could be changed for the benefit
of process without reducing quality if processing time or cost shows a benefit.
• Optimizer recommends setting Clamp Pressure to 2100 psi (level 1), but this
appears to be a robust parameter, which could be changed for the benefit of process
without reducing quality if processing time or cost shows a benefit.
• Run additional tests at recommended settings to confirm results
• Weld Density and Clamp Pressure are robust parameters and can be set to optimize
the process capability to maximum level and lowest cost.
Analyze Phase A
Analyze
Input Variable Proposed ASM_9Y Setting Proposed ASM_10Y Setting
Clamp Location Location 2 Location 2
Weld Density (welds per X inches) 1.33 1.33
Clamp Pressure 2100 psi 2100 psi
12. 12
D M A I C
Define Measure Analyze Improve Control
Analyze Phase A
Analyze
DOE for Response Variable ASM_10Y
• DOE factorial analysis shows Clamp Position is also the only significant
factor in determining ASM_10Y dimension
DOE Response Optimization for ASM_10Y
• Setting clamp to location 2 also improves ASM_10Y
• Recommend same settings used to improve ASM_9Y to improve process
capability which also allows for no changes to machine setup and helps reduce
possible process concerns
• Run additional tests at recommended settings to confirm results
• Weld Density and Clamp Pressure are robust parameters and can be set to optimize
the process capability to maximum level and lowest cost.
13. 13
D M A I C
Define Measure Analyze Improve Control
DOE for Response Variable ASM_3Y
• DOE factorial analysis shows that no factors are significant
• Response Optimization shows no solution for response optimizer
Observe Process Capability of A_3Y and BS_3Y
• ASM_3Y and A_3Y have a similar mean shift in the -Y direction
Correlation of Output Variables
• No dimensional correlations appear to exist between ASM_3Y and
A_3Y or BS_3Y
Stepwise Regression Analysis of BS_3Y
• Tonnage and Die Pressure appear to be significant in determining
dimension BS_3Y
• Tonnage values < 920 may improve BS_3Y
• Die Pressure appears to have no clear correlation to BS_3Y
Analyze Phase A
Analyze
14. 14
D M A I C
Define Measure Analyze Improve Control
Process Capability of BS_ 3Y and ASM_3Y at Tonnage < 920
• Created subset of body data looking only at dimensions with Tonnage < 935
• Tonnage < 920 appears to improve the mean of BS_3Y slightly, but has no
impact on improving the mean of ASM_3Y.
-1.0 -0.5 0.0 0.5 1.0
LSL USL
Capability Analysis of ASM_3Y
USL
Target
LSL
Mean
Sample N
StDev (Within)
StDev (Overall)
Cp
CPU
CPL
Cpk
Cpm
Pp
PPU
PPL
Ppk
PPM < LSL
PPM > USL
PPM Total
PPM < LSL
PPM > USL
PPM Total
PPM < LSL
PPM > USL
PPM Total
1
*
-1
0
36
0.0851436
0.0971725
3.91
3.91
3.91
3.91
*
3.43
3.43
3.43
3.43
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Process Data
Potential (Within) Capability
Overall Capability Observed Performance Exp. "Within" Performance Exp. "Overall" Performance
Within
Overall
Die remachined to move mean +0.80
Capability of A_3Y and ASM_3Y with +0.80
mm mean offset
• Manipulate data for A_3Y and ASM_3Y
by +0.80 mm to simulate re-machining
• Process capability shows 0 defects for
A_3Y and ASM_3Y with this mean offset
Analyze Phase A
Analyze
15. 15
D M A I C
Define Measure Analyze Improve Control
Analyze Phase A
Analyze
Conclusions
• From the analysis of ASM_7Y and ASM_8Y we can conclude that:
• Setting tonnage > 935 results in ASM_7Y and ASM_8Y meeting the goal
• Analyzing ASM_9Y and ASM_10Y helps determine that:
• Setting clamp position to location 2, weld density to 1 weld every 1.33”
and clamp pressure to 2000 psi helps with dimensions ASM_9Y and
ASM_10Y
• Analyzing ASM_3Y helps us conclude that:
• Re-machine A-Pillar die to move A_3Y to nominal – which could cause
BS_3Y to shift towards nominal – effectively shifting ASM_3Y to nominal
16. 16
D M A I C
Define Measure Analyze Improve Control
With the recommended changes the process performance will improve significantly
Dimension Mean StDev
Overall
DPM_Obsv DPM_Within DPM_Exp Pp Ppk Cp Cpk
ASM_1Y -0.035 0.165 0 0 0 2.01 1.94 2.47 2.39
ASM_2Y 0.259 0.152 0 0 1 2.20 1.63 2.31 1.71
ASM_3Y 0.000 0.097 0 0 0
ASM_4Y 0.009 0.115 0 0 0 2.90 2.87 3.53 3.50
ASM_5Y -0.330 0.145 0 0 2 2.30 1.54 3.72 2.50
ASM_6Y -0.284 0.160 0 1 4 2.08 1.49 2.24 1.60
ASM_7Y 0.090 0.148 0 0 0 2.25 2.05 2.04 1.86
ASM_8Y -0.128 0.089 0 0 0 3.74 3.26 3.27 2.85
ASM_9Y 0.521 0.180 0 0 0
ASM_10Y 0.395 0.191 0 0 0
A
Analyze
Analyze Phase
17. 17
D M A I C
Define Measure Analyze Improve Control
Recommendations for improving the process:
• Set Tonnage to above 935 to improve ASM_7Y & ASM_8Y
• Set Clamp to Location 2 to improve ASM_9Y & ASM_10Y
• Re-machine the A-Pillar die to move the mean of A_3Y to nominal which in turn
will move ASM_3Y to nominal
Implement the above recommendations and run additional samples to verify results.
I
Improve
Improve Phase
18. 18
D M A I C
Define Measure Analyze Improve Control
Control Phase C
Control
Recommended controls :
• Implement a gauge on the body side component press to monitor tonnage
• Implement an alarm and shut-off feature on the body side press if tonnage
falls below 935 tons
• Implement poke-yoke clamping fixture that ensures clamp is always in
Position 2
• Establish an affordable control plan for ongoing monitoring of the 10
critical assembly dimensions.
19. 19
D M A I C
Define Measure Analyze Improve Control
Summary
ABC Incorporated is not achieving Six Sigma quality levels for all critical Body-
Side Sub-Assembly dimensions as requested by their customers. BBM needs to
apply Six Sigma problem solving methodology to establish an improvement strategy
that minimizes rework costs, yet achieves the desired quality objective.
• Implement a gauge on the body side component press to monitor tonnage
• Implement an alarm & shut-off feature on body side press if tonnage falls below 935
• Implement poke-yoke clamping fixture that ensures clamp is always in Position 2
• Establish control plan for ongoing monitoring of the 10 critical assembly dimensions.
• Set Tonnage to above 935 to improve ASM_7Y & ASM_8Y
• Set Clamp to Location 2 to improve ASM_9Y & ASM_10Y
• Re-machine the A-Pillar die to move the mean of A_3Y to nominal
Bring the key process output variables within Six Sigma quality level of < 3.4 DPM.
Cp 2.0 and Cpk 1.67