The document discusses Failure Mode and Effects Analysis (FMEA) as part of the Six Sigma methodology. It explains that FMEA is implemented during the "Analyze" phase of the DMAIC cycle to help identify process tasks and product features that are prone to failure. The document then outlines the steps for conducting an FMEA, including compiling a failure list, determining failure impacts and rates, establishing detection probabilities, and developing action plans to address high-risk failures. It stresses that FMEA is a priority tool to improve business processes by enabling teams to proactively identify and eliminate likely defects before they impact customers.
Six Sigma is a methodology that aims to reduce defects and variation in processes. It uses a data-driven, five-phase approach called DMAIC (Define, Measure, Analyze, Improve, Control) to optimize processes. Six Sigma defines quality as 3.4 defects per million opportunities. It uses statistical tools and aims for near-zero defect rates through the elimination of defects from processes. Projects are led by Belts (Black, Green, etc.) who are trained in Six Sigma tools and methods.
The document provides an overview of the Define-Measure-Analyze-Improve-Control (DMAIC) methodology, which is Six Sigma's most common problem-solving approach. It consists of five steps: Define the problem, Measure current performance, Analyze to find the root cause, Improve by implementing a solution, and Control to ensure continued benefits. The approach aims to reduce variation and bring processes back into alignment with customer requirements. Key requirements that determine the methodology's activities include understanding the problem definition, current metrics and their trends, identifying root causes, implementing corrective actions, and controlling improvements over time.
How to solve problems (or at least try) with 8DStefan Kovacs
This document provides an overview of the 8D problem solving method. It begins with the goals of learning how to use 8D for problem solving. It then discusses the history and development of the 8D method at Ford Motor Company in the 1980s. The document also includes definitions of key terms used in 8D. It describes the typical steps and process flow for 8D problem solving. Finally, it provides details of an 8D procedure used at an X factory, outlining the initiation and requirements for conducting 8D analyses.
The document discusses quality costs and methodologies for managing quality including Six Sigma and Lean Six Sigma. It defines quality costs as costs associated with non-achievement of quality requirements and outlines four types: prevention, appraisal, internal failures, and external failures. It then explains the five phases (DMAIC) of the Six Sigma methodology for process improvement: define, measure, analyze, improve, and control. Finally, it provides details on integrating Lean and Six Sigma approaches.
- Six Sigma is a data-driven approach to process improvement originally developed by Motorola in 1986. It aims to reduce defects and variability in processes by identifying and removing causes of errors or defects.
- Key aspects include a focus on quantifiable financial returns, strong management support, and a defined infrastructure of experts ("Black Belts", "Green Belts" etc.) who lead Six Sigma projects using statistical methods.
- Projects follow either the DMAIC methodology (Define, Measure, Analyze, Improve, Control) to improve existing processes or DMADV (Define, Measure, Analyze, Design, Verify) to design new processes/products. The goal is to achieve no more than 3.
Six Sigma aims to reduce process variation and defects to below 3.4 per million opportunities. It was developed by Motorola in the 1980s and uses data-driven methods like defining problems, measuring processes, analyzing root causes, improving processes, and controlling future performance. The core elements are process improvement, design/redesign, and management. Process improvement uses a five step DMAIC approach of define, measure, analyze, improve, and control problems in existing processes.
DMAIC addressed Bearnson S-N tracking for all product.Bill Bearnson
1) The document describes the DMAIC process for continuous improvement. It consists of five steps: Define, Measure, Analyze, Improve, and Control.
2) An example project at L-3 CSW aimed to track configurations of delivered products to reduce rework, diagnose issues more quickly, and improve service. They lacked a centralized database to record hardware changes.
3) The root cause was high production demand requiring faster turnaround times. Records of configurations and changes were kept in multiple places, causing delays and redundant work to diagnose returned units. The project combined data from five sources into a searchable master database to improve traceability of configurations.
This document discusses various quality frameworks including the Capability Maturity Model (CMM), Six Sigma, and Total Quality Management (TQM).
The CMM is a framework that helps software organizations gain control of their processes through five levels of process maturity. Six Sigma is a data-driven philosophy for reducing variation and defects. It emphasizes achieving six standard deviations between the mean and nearest specification limit. TQM is a customer-focused and process-oriented approach that aims for continuous improvement and achieving zero defects.
Six Sigma is a methodology that aims to reduce defects and variation in processes. It uses a data-driven, five-phase approach called DMAIC (Define, Measure, Analyze, Improve, Control) to optimize processes. Six Sigma defines quality as 3.4 defects per million opportunities. It uses statistical tools and aims for near-zero defect rates through the elimination of defects from processes. Projects are led by Belts (Black, Green, etc.) who are trained in Six Sigma tools and methods.
The document provides an overview of the Define-Measure-Analyze-Improve-Control (DMAIC) methodology, which is Six Sigma's most common problem-solving approach. It consists of five steps: Define the problem, Measure current performance, Analyze to find the root cause, Improve by implementing a solution, and Control to ensure continued benefits. The approach aims to reduce variation and bring processes back into alignment with customer requirements. Key requirements that determine the methodology's activities include understanding the problem definition, current metrics and their trends, identifying root causes, implementing corrective actions, and controlling improvements over time.
How to solve problems (or at least try) with 8DStefan Kovacs
This document provides an overview of the 8D problem solving method. It begins with the goals of learning how to use 8D for problem solving. It then discusses the history and development of the 8D method at Ford Motor Company in the 1980s. The document also includes definitions of key terms used in 8D. It describes the typical steps and process flow for 8D problem solving. Finally, it provides details of an 8D procedure used at an X factory, outlining the initiation and requirements for conducting 8D analyses.
The document discusses quality costs and methodologies for managing quality including Six Sigma and Lean Six Sigma. It defines quality costs as costs associated with non-achievement of quality requirements and outlines four types: prevention, appraisal, internal failures, and external failures. It then explains the five phases (DMAIC) of the Six Sigma methodology for process improvement: define, measure, analyze, improve, and control. Finally, it provides details on integrating Lean and Six Sigma approaches.
- Six Sigma is a data-driven approach to process improvement originally developed by Motorola in 1986. It aims to reduce defects and variability in processes by identifying and removing causes of errors or defects.
- Key aspects include a focus on quantifiable financial returns, strong management support, and a defined infrastructure of experts ("Black Belts", "Green Belts" etc.) who lead Six Sigma projects using statistical methods.
- Projects follow either the DMAIC methodology (Define, Measure, Analyze, Improve, Control) to improve existing processes or DMADV (Define, Measure, Analyze, Design, Verify) to design new processes/products. The goal is to achieve no more than 3.
Six Sigma aims to reduce process variation and defects to below 3.4 per million opportunities. It was developed by Motorola in the 1980s and uses data-driven methods like defining problems, measuring processes, analyzing root causes, improving processes, and controlling future performance. The core elements are process improvement, design/redesign, and management. Process improvement uses a five step DMAIC approach of define, measure, analyze, improve, and control problems in existing processes.
DMAIC addressed Bearnson S-N tracking for all product.Bill Bearnson
1) The document describes the DMAIC process for continuous improvement. It consists of five steps: Define, Measure, Analyze, Improve, and Control.
2) An example project at L-3 CSW aimed to track configurations of delivered products to reduce rework, diagnose issues more quickly, and improve service. They lacked a centralized database to record hardware changes.
3) The root cause was high production demand requiring faster turnaround times. Records of configurations and changes were kept in multiple places, causing delays and redundant work to diagnose returned units. The project combined data from five sources into a searchable master database to improve traceability of configurations.
This document discusses various quality frameworks including the Capability Maturity Model (CMM), Six Sigma, and Total Quality Management (TQM).
The CMM is a framework that helps software organizations gain control of their processes through five levels of process maturity. Six Sigma is a data-driven philosophy for reducing variation and defects. It emphasizes achieving six standard deviations between the mean and nearest specification limit. TQM is a customer-focused and process-oriented approach that aims for continuous improvement and achieving zero defects.
The document provides an overview of Six Sigma management. It defines Six Sigma as a statistical concept that measures quality in terms of defects, with the goal of 3.4 defects per million opportunities. It describes the Six Sigma phases of Define, Measure, Analyze, Improve, and Control (DMAIC). Key tools for Six Sigma include process mapping, design of experiments, measurement system analysis, and control plans. Critical roles include Champions, Master Black Belts, Black Belts, and Green Belts. Implementing Six Sigma can help reduce costs and improve customer satisfaction, quality, and competitive advantage.
- Total Quality Management (TQM) is a philosophy involving customer satisfaction, employee involvement, and continuous improvement. It uses tools like control charts and the Plan-Do-Check-Act cycle.
- Six Sigma is a data-driven approach to process improvement originally developed by Motorola to reduce defects. It uses a five-step methodology of Define, Measure, Analyze, Improve, and Control.
- Quality circles involve small groups of employees who meet regularly to identify and solve work-related problems in order to improve organizational performance and motivate employees. They aim to enhance quality, productivity, safety, and reduce costs.
The document discusses SAP's AcceleratedSAP methodology for implementing SAP solutions. It describes the five phases of the methodology - Project Preparation, Business Blueprint, Realization, Final Preparation, and Go Live & Support. Each phase has specific goals and deliverables aimed at successfully implementing SAP and achieving business benefits. The methodology provides structure, guidance, and tools to help projects be on time, on budget and deliver business goals.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Six Sigma is a method for improving business processes and reducing defects. It aims to decrease process variation to improve quality, profits, and customer satisfaction. The methodology was developed by Motorola in 1986 and uses statistical tools. There are two approaches - DMAIC focuses on improving existing processes, following the define, measure, analyze, improve, control steps. DMADV is for new product or process design and follows define, measure, analyze, design, verify steps. Six Sigma has been successfully applied across many industries to design and control manufacturing, engineering, financial, supply chain, and healthcare processes.
The document discusses Six Sigma, a quality improvement methodology. It was developed at Motorola in the 1980s and focuses on reducing defects. Six Sigma uses statistical methods and aims for near perfect processes with fewer than 3.4 defects per million opportunities. It establishes roles like Champions, Black Belts and Green Belts to lead projects. Key aspects covered include the DMAIC (Define, Measure, Analyze, Improve, Control) process for improving existing processes and DMADV (Define, Measure, Analyze, Design, Verify) for developing new processes. Tools like process mapping, control charts and data analysis are used. The document also provides an example case study on using Six Sigma to reduce temporary labor expenses.
The document summarizes a seminar report on Six Sigma Lean production methodology and implementation. It includes the following sections: literature review on previous research; introduction to Six Sigma methodology including the DMAIC cycle of Define, Measure, Analyze, Improve, Control; discussion of Lean production; current scenario and case study examples of implementation; and challenges to Six Sigma Lean production. The literature review covered requirements, challenges, and approaches like the Toyota Way. Key aspects of the Six Sigma DMAIC cycle are defined such as critical quality parameters, measurement systems analysis, design of experiments, and statistical process control.
Management of time uncertainty in agileijseajournal
Agile software development represents a major departure from traditional methods of software
engineering. It had huge impact on how software is developed worldwide. Agile software development
solutions are targeted at enhancing work at project level. But it may encounter some uncertainties in its
working. One of the key measures of the resilience of a project is its ability to reach completion, on time
and on budget, regardless of the turbulent and uncertain environment it may operate within. Uncertainty of
time is the problem which can lead to other uncertainties too. In uncertainty of time the main issue is that
the how much delay will be caused by the uncertain environment and if the project manager comes to know
about this delay before, then he can ask for that extra time from customer. So this paper tries to know about
that extra time and calculate it.
The document provides an introduction to the DMAIC process used in Six Sigma. It consists of 5 phases: Define, Measure, Analyze, Improve, and Control. Each phase involves specific tasks and tools. Define involves identifying problems and goals. Measure establishes baselines. Analyze identifies root causes using tools like fishbone diagrams. Improve develops and tests solutions. Control institutionalizes improvements through control charts and process ownership. Tools across phases include brainstorming, control charts, Pareto charts, and more to systematically solve quality problems.
This is a practical guide for sprint development based on the OutSystems Delivery Method.
It helps you focus some of the main challenges found when using Agile in the field:
- Your sprints often start not being ready?
- Delivering at sprint end is always struggle?
Then you should take a look!
Target audience: Agile Project Managers (including Engagement and Delivery Managers)
Six sigma control in total quality management copyVijay Vuriti
This document provides an overview of Six Sigma and its role in Total Quality Management. Six Sigma is a data-driven approach to process improvement that aims to reduce defects to 3.4 per million opportunities. It uses statistical methods like DMAIC to define problems, measure processes, analyze data, improve processes, and control variables. Six Sigma projects can generate cost savings of 5-20% annually while improving quality, reducing cycle times, and developing employee skills. However, they also require costs for training, consulting, improvements, and software tools.
Six Sigma is a data-driven methodology used to improve processes and eliminate defects. It was developed at Motorola in 1987 and uses a define-measure-analyze-improve-control (DMAIC) cycle. Green and Black Belts are certified to lead Six Sigma projects through this cycle, first defining problems, measuring key aspects, analyzing root causes, improving processes, and controlling changes. The document provides an overview of Six Sigma and its goals, deployment, methods like DMAIC and DMADV, integration with other methodologies, and the activities involved in each stage of the DMAIC cycle.
Six Sigma aims to reduce defects to 3.4 defects per million opportunities through a methodology focused on eliminating process variation. It was pioneered by Motorola and GE and involves defining processes, measuring quality, analyzing sources of defects, improving processes, and controlling variation. Implementing Six Sigma requires leadership commitment, communication, and training to drive change. Benefits include cost reduction, less waste, improved quality and understanding of customer needs. It has been successfully applied across industries like manufacturing, healthcare, and aerospace.
This document describes the development of a quality control system for Tucksin Engineering Sdn. Bhd. A group of 5 students created INTIMaP 1.0, a quality measurement system using Microsoft Excel. The system measures quality across 5 areas - design process, efficiency, compliance, key performance, and fabrication process. It generates data, analyzes errors and areas for improvement, and tracks changes over time. The document outlines the system's objectives and measurements. Example analysis using project size data is shown to demonstrate the system.
Six Sigma is a data-driven approach to improving processes by identifying and removing defects. It aims for near perfect process quality. The goal is to improve end products or services by reducing errors. Six Sigma refers to producing only 3.4 defective parts per million.
Motorola first introduced Six Sigma in the 1970s to address quality issues. It connects quality improvement to cost reduction. The concepts were officially formulated in 1986 and have grown in popularity since. Six Sigma uses two methods: DMAIC for improving existing processes and DMADV for designing new defect-free processes. It is applied across entire organizations rather than just specific teams.
The document then provides a case study example of a company using the DMAIC
The document outlines an agenda for an FMEA training workshop. It discusses Failure Mode and Effects Analysis (FMEA), including its history, purpose, and process. FMEA is a methodology used to ensure potential problems are addressed in product and process development. The agenda includes explaining FMEA, its use as a design tool, the development process, management's role, team member responsibilities, and examples. It provides details on FMEA scope, functions, failure modes, effects, occurrence, detection, and criticality analysis. The workshop aims to train participants on effectively developing and applying FMEAs.
The document discusses various tools used for Corrective and Preventive Actions (CAPA), including DMAIC, Rubric, 8D, and DFSS. DMAIC is a five-step process for problem solving: Define, Measure, Analyze, Improve, Control. It involves defining problems, collecting data, analyzing causes, improving solutions, and controlling to prevent recurrence. Other tools discussed provide frameworks for investigating problems, finding root causes, and verifying solutions.
This document provides an overview and outline of a 6-Sigma training review. It covers 7 topics: Six Sigma overview, team process and logistics, introduction to project charter, project charter continued, measurement, pre-project planning, and data analysis. Topic 1 defines Six Sigma and its goals of minimizing process variation. It also describes the Six Sigma levels and defect rates. The document outlines the DMAIC process and expectations of learning skills, a problem-solving approach, and collaborative work.
This document discusses using design failure mode and effects analysis (dFMEA) to improve quality function deployment (QFD) and the theory of inventive problem solving (TRIZ) for computational simulation of component durability. It provides background on dFMEA processes and outlines how dFMEA can be applied to optimize durability simulation by considering potential failure modes and their causes early in the design process. Robustness tools like p-diagrams and boundary diagrams are also discussed as complementary to dFMEA for preventing failures through robust design.
BPM (Business Process Management) IntroductionIntegrify
An introduction to BPM for teams looking to improve business processes through business process management (BPM). This is an abridged version of the full BPM guide.
AI for Legal Research with applications, toolsmahaffeycheryld
AI applications in legal research include rapid document analysis, case law review, and statute interpretation. AI-powered tools can sift through vast legal databases to find relevant precedents and citations, enhancing research accuracy and speed. They assist in legal writing by drafting and proofreading documents. Predictive analytics help foresee case outcomes based on historical data, aiding in strategic decision-making. AI also automates routine tasks like contract review and due diligence, freeing up lawyers to focus on complex legal issues. These applications make legal research more efficient, cost-effective, and accessible.
The document provides an overview of Six Sigma management. It defines Six Sigma as a statistical concept that measures quality in terms of defects, with the goal of 3.4 defects per million opportunities. It describes the Six Sigma phases of Define, Measure, Analyze, Improve, and Control (DMAIC). Key tools for Six Sigma include process mapping, design of experiments, measurement system analysis, and control plans. Critical roles include Champions, Master Black Belts, Black Belts, and Green Belts. Implementing Six Sigma can help reduce costs and improve customer satisfaction, quality, and competitive advantage.
- Total Quality Management (TQM) is a philosophy involving customer satisfaction, employee involvement, and continuous improvement. It uses tools like control charts and the Plan-Do-Check-Act cycle.
- Six Sigma is a data-driven approach to process improvement originally developed by Motorola to reduce defects. It uses a five-step methodology of Define, Measure, Analyze, Improve, and Control.
- Quality circles involve small groups of employees who meet regularly to identify and solve work-related problems in order to improve organizational performance and motivate employees. They aim to enhance quality, productivity, safety, and reduce costs.
The document discusses SAP's AcceleratedSAP methodology for implementing SAP solutions. It describes the five phases of the methodology - Project Preparation, Business Blueprint, Realization, Final Preparation, and Go Live & Support. Each phase has specific goals and deliverables aimed at successfully implementing SAP and achieving business benefits. The methodology provides structure, guidance, and tools to help projects be on time, on budget and deliver business goals.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Six Sigma is a method for improving business processes and reducing defects. It aims to decrease process variation to improve quality, profits, and customer satisfaction. The methodology was developed by Motorola in 1986 and uses statistical tools. There are two approaches - DMAIC focuses on improving existing processes, following the define, measure, analyze, improve, control steps. DMADV is for new product or process design and follows define, measure, analyze, design, verify steps. Six Sigma has been successfully applied across many industries to design and control manufacturing, engineering, financial, supply chain, and healthcare processes.
The document discusses Six Sigma, a quality improvement methodology. It was developed at Motorola in the 1980s and focuses on reducing defects. Six Sigma uses statistical methods and aims for near perfect processes with fewer than 3.4 defects per million opportunities. It establishes roles like Champions, Black Belts and Green Belts to lead projects. Key aspects covered include the DMAIC (Define, Measure, Analyze, Improve, Control) process for improving existing processes and DMADV (Define, Measure, Analyze, Design, Verify) for developing new processes. Tools like process mapping, control charts and data analysis are used. The document also provides an example case study on using Six Sigma to reduce temporary labor expenses.
The document summarizes a seminar report on Six Sigma Lean production methodology and implementation. It includes the following sections: literature review on previous research; introduction to Six Sigma methodology including the DMAIC cycle of Define, Measure, Analyze, Improve, Control; discussion of Lean production; current scenario and case study examples of implementation; and challenges to Six Sigma Lean production. The literature review covered requirements, challenges, and approaches like the Toyota Way. Key aspects of the Six Sigma DMAIC cycle are defined such as critical quality parameters, measurement systems analysis, design of experiments, and statistical process control.
Management of time uncertainty in agileijseajournal
Agile software development represents a major departure from traditional methods of software
engineering. It had huge impact on how software is developed worldwide. Agile software development
solutions are targeted at enhancing work at project level. But it may encounter some uncertainties in its
working. One of the key measures of the resilience of a project is its ability to reach completion, on time
and on budget, regardless of the turbulent and uncertain environment it may operate within. Uncertainty of
time is the problem which can lead to other uncertainties too. In uncertainty of time the main issue is that
the how much delay will be caused by the uncertain environment and if the project manager comes to know
about this delay before, then he can ask for that extra time from customer. So this paper tries to know about
that extra time and calculate it.
The document provides an introduction to the DMAIC process used in Six Sigma. It consists of 5 phases: Define, Measure, Analyze, Improve, and Control. Each phase involves specific tasks and tools. Define involves identifying problems and goals. Measure establishes baselines. Analyze identifies root causes using tools like fishbone diagrams. Improve develops and tests solutions. Control institutionalizes improvements through control charts and process ownership. Tools across phases include brainstorming, control charts, Pareto charts, and more to systematically solve quality problems.
This is a practical guide for sprint development based on the OutSystems Delivery Method.
It helps you focus some of the main challenges found when using Agile in the field:
- Your sprints often start not being ready?
- Delivering at sprint end is always struggle?
Then you should take a look!
Target audience: Agile Project Managers (including Engagement and Delivery Managers)
Six sigma control in total quality management copyVijay Vuriti
This document provides an overview of Six Sigma and its role in Total Quality Management. Six Sigma is a data-driven approach to process improvement that aims to reduce defects to 3.4 per million opportunities. It uses statistical methods like DMAIC to define problems, measure processes, analyze data, improve processes, and control variables. Six Sigma projects can generate cost savings of 5-20% annually while improving quality, reducing cycle times, and developing employee skills. However, they also require costs for training, consulting, improvements, and software tools.
Six Sigma is a data-driven methodology used to improve processes and eliminate defects. It was developed at Motorola in 1987 and uses a define-measure-analyze-improve-control (DMAIC) cycle. Green and Black Belts are certified to lead Six Sigma projects through this cycle, first defining problems, measuring key aspects, analyzing root causes, improving processes, and controlling changes. The document provides an overview of Six Sigma and its goals, deployment, methods like DMAIC and DMADV, integration with other methodologies, and the activities involved in each stage of the DMAIC cycle.
Six Sigma aims to reduce defects to 3.4 defects per million opportunities through a methodology focused on eliminating process variation. It was pioneered by Motorola and GE and involves defining processes, measuring quality, analyzing sources of defects, improving processes, and controlling variation. Implementing Six Sigma requires leadership commitment, communication, and training to drive change. Benefits include cost reduction, less waste, improved quality and understanding of customer needs. It has been successfully applied across industries like manufacturing, healthcare, and aerospace.
This document describes the development of a quality control system for Tucksin Engineering Sdn. Bhd. A group of 5 students created INTIMaP 1.0, a quality measurement system using Microsoft Excel. The system measures quality across 5 areas - design process, efficiency, compliance, key performance, and fabrication process. It generates data, analyzes errors and areas for improvement, and tracks changes over time. The document outlines the system's objectives and measurements. Example analysis using project size data is shown to demonstrate the system.
Six Sigma is a data-driven approach to improving processes by identifying and removing defects. It aims for near perfect process quality. The goal is to improve end products or services by reducing errors. Six Sigma refers to producing only 3.4 defective parts per million.
Motorola first introduced Six Sigma in the 1970s to address quality issues. It connects quality improvement to cost reduction. The concepts were officially formulated in 1986 and have grown in popularity since. Six Sigma uses two methods: DMAIC for improving existing processes and DMADV for designing new defect-free processes. It is applied across entire organizations rather than just specific teams.
The document then provides a case study example of a company using the DMAIC
The document outlines an agenda for an FMEA training workshop. It discusses Failure Mode and Effects Analysis (FMEA), including its history, purpose, and process. FMEA is a methodology used to ensure potential problems are addressed in product and process development. The agenda includes explaining FMEA, its use as a design tool, the development process, management's role, team member responsibilities, and examples. It provides details on FMEA scope, functions, failure modes, effects, occurrence, detection, and criticality analysis. The workshop aims to train participants on effectively developing and applying FMEAs.
The document discusses various tools used for Corrective and Preventive Actions (CAPA), including DMAIC, Rubric, 8D, and DFSS. DMAIC is a five-step process for problem solving: Define, Measure, Analyze, Improve, Control. It involves defining problems, collecting data, analyzing causes, improving solutions, and controlling to prevent recurrence. Other tools discussed provide frameworks for investigating problems, finding root causes, and verifying solutions.
This document provides an overview and outline of a 6-Sigma training review. It covers 7 topics: Six Sigma overview, team process and logistics, introduction to project charter, project charter continued, measurement, pre-project planning, and data analysis. Topic 1 defines Six Sigma and its goals of minimizing process variation. It also describes the Six Sigma levels and defect rates. The document outlines the DMAIC process and expectations of learning skills, a problem-solving approach, and collaborative work.
This document discusses using design failure mode and effects analysis (dFMEA) to improve quality function deployment (QFD) and the theory of inventive problem solving (TRIZ) for computational simulation of component durability. It provides background on dFMEA processes and outlines how dFMEA can be applied to optimize durability simulation by considering potential failure modes and their causes early in the design process. Robustness tools like p-diagrams and boundary diagrams are also discussed as complementary to dFMEA for preventing failures through robust design.
BPM (Business Process Management) IntroductionIntegrify
An introduction to BPM for teams looking to improve business processes through business process management (BPM). This is an abridged version of the full BPM guide.
AI for Legal Research with applications, toolsmahaffeycheryld
AI applications in legal research include rapid document analysis, case law review, and statute interpretation. AI-powered tools can sift through vast legal databases to find relevant precedents and citations, enhancing research accuracy and speed. They assist in legal writing by drafting and proofreading documents. Predictive analytics help foresee case outcomes based on historical data, aiding in strategic decision-making. AI also automates routine tasks like contract review and due diligence, freeing up lawyers to focus on complex legal issues. These applications make legal research more efficient, cost-effective, and accessible.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Build the Next Generation of Apps with the Einstein 1 Platform.
Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Software Engineering and Project Management - Software Testing + Agile Method...Prakhyath Rai
Software Testing: A Strategic Approach to Software Testing, Strategic Issues, Test Strategies for Conventional Software, Test Strategies for Object -Oriented Software, Validation Testing, System Testing, The Art of Debugging.
Agile Methodology: Before Agile – Waterfall, Agile Development.
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
Prediction of Electrical Energy Efficiency Using Information on Consumer's Ac...PriyankaKilaniya
Energy efficiency has been important since the latter part of the last century. The main object of this survey is to determine the energy efficiency knowledge among consumers. Two separate districts in Bangladesh are selected to conduct the survey on households and showrooms about the energy and seller also. The survey uses the data to find some regression equations from which it is easy to predict energy efficiency knowledge. The data is analyzed and calculated based on five important criteria. The initial target was to find some factors that help predict a person's energy efficiency knowledge. From the survey, it is found that the energy efficiency awareness among the people of our country is very low. Relationships between household energy use behaviors are estimated using a unique dataset of about 40 households and 20 showrooms in Bangladesh's Chapainawabganj and Bagerhat districts. Knowledge of energy consumption and energy efficiency technology options is found to be associated with household use of energy conservation practices. Household characteristics also influence household energy use behavior. Younger household cohorts are more likely to adopt energy-efficient technologies and energy conservation practices and place primary importance on energy saving for environmental reasons. Education also influences attitudes toward energy conservation in Bangladesh. Low-education households indicate they primarily save electricity for the environment while high-education households indicate they are motivated by environmental concerns.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Digital Twins Computer Networking Paper Presentation.pptxaryanpankaj78
A Digital Twin in computer networking is a virtual representation of a physical network, used to simulate, analyze, and optimize network performance and reliability. It leverages real-time data to enhance network management, predict issues, and improve decision-making processes.
2. INTRODUCTION
• Six Sigma is all about eliminating errors and defects that details
the customer’s experience. And, this is where FMEA comes into
the picture, helping predict possible failures that will impact
customer experience.
• Majorly, FMEA is implemented during the ‘Analyze’ phase of
the SIX SIGMA DMAIC CYCLE.
• It helps project teams to identify product features and process
tasks that are prone to failure. With data analysis and reports,
FMEA provides project teams with enhanced product quality and
reduces errors by modifying the existing processes.
3. SIX SIGMA DMAIC CYCLE
DMAIC Stands for:-
• DEFINE
• MEASURE
• ANALYZE
• IMPROVE
• CONTROL
4. DEFINE
• As the first stage in DMAIC, Define is arguably the most important. The first action it calls for, much like Six Sigma,
is articulate the problem you face in a clear way. This can be anything, for example, you may experience slow
production times, depleted costs, or decrease in quality. It all depends on you and your business. The purpose of
Define is to help you come up with a focused problem statement as well as a measure of success or failure to
support that statement.
State YourProblem
• Stating your problem may seem simple enough, but it involves a lot of additional effort on your part. A deep understanding of your business
and its goals are essential here, as you will need to be able to describe them accurately and in detail, along with any potential resources you
have to assist you. This is also an ideal time to identify future resources you may need, as acquiring them up front can help avoid similar
problems in the future.
• You will also need to consider the overall project scope – including the duration of the project, affected areas/areas that require attention, as
well as costs to you – and a high-level timeline to determine how to proceed. A charter document is the best way to capture all of this
information – write down everything that you know at the moment, collect and collate all of your current knowledge.
5. DEFINE
Produce a Charter Document
• A charter document of this nature should include the project scope (with a statement, overall metric, and project
metric), potential opportunities (including savings, profits, improvements, baseline performance, and goal
performance), project status (historically, currently, and as predicted, with analyses for product returns and root
causes), and finally any actions and/or open issues. This information is essential to the Define stage, which relies
on clear, accurate qualitative data to function. Using the knowledge you have compiled, you will be able to define
the following issues:
• Your Problem – Starting with the issue at hand puts everything else into perspective. Look at what the problem is,
what it does, what it affects. Consider where it may stem from and how it can be resolved.
• Consider your customers – The customer is the backbone of your business. You rely on them for profit, just as they
rely on your for excellent products and services. Consider how the problem affects them. For example, if a
problem causes your product to become defective, it will affect customer satisfaction.
• Critical Process Outputs – Critical process outputs such as Voice of the Customer (VOC) and critical to quality
(CTQs) are also important. Once this is completed, any anomalous or missing data you can seek to clarify. You can
also focus on setting objectives to be met by the conclusion of the project, as well as putting together a project
team dedicated to overseeing and implementing the proposed changes yielded by the DMAIC process.
6. MEASURE
• The capability and stability of the project Y is established by the top Belt. This
individual will also conclude the ability to measure the Y. When the project is obviously
outlined with a clearly quantifiable Y, the process is scrutinized to define the Key
Process Steps, as well as the Key Inputs for every process.
• Once the Key Input list is produced, the Belt will determine the possible influence on
CTQs that every input has in connection to the faults that are being produced within
the process. Each Key Inputs is then prioritized to create a “short list” that can be
scrutinized more precisely.
• The “short list” will be used by the Belt to narrow down the possible methods that
could result in an error or how the input might become flawed. FMEA is the most
effective system for this process. Any input failures can quickly be defined and
preventive action strategies established.
• Having correct metrics is an important part of the Measure phase. Therefore, it is vital
that metrics be validated as reliable during the Measure phase, so that the progress of
the project can be accurately monitored. Tracking project metrics is best done by
using Business Process Charting methodologies.
7. ANALYZE
• In this phase, the group will use analysis to isolate the reasons for
errors that need to be corrected.
• the Analyzephase will provide insight on how to remove the space between the currentlevel of
performance and theanticipated level. Thisencompasses realizingwhy deficiencies are producedby
ascertaining the crucial variables which are apt to generate process variation.
• A frequent misconception individuals have about the Six Sigma
methodology is that the DMAIC process requires too much time to
realize improvements. The reality is that “quick hits” are often
discovered very early in the project.
• In most cases these improvements are established prior the Analyze
phase. Major developments may not happen until the data related to
the process is scrutinized. This is when the revolutions occur. The
Analysis methods used in Six Sigma are designed to expose more
problematic resolutions.
8. IMPROVE
• Although this phase can be the most challenging, it is also one of the
most enjoyable.
• The Analyze phase provides the causes of problems. Now in the
Improve phase the group can determine innovative new
improvement solutions. In most instances basic process testing and
simulation provide the group large achievements in this stage.
• The group also ascertains the results of required improvements not
being completed, as well is the outcome if improvements take an
extended amount of time.
9. CONTROL
• Thefour stages prior to this one will determine how successful we arein the Control phase.
• we utilized the correct change management strategies, such as
identifying the key stakeholders; if we did then a successful
control should be within our grasp.
• The purpose of the Control phase is to establish tools that will
ensure the key variables stay within the accepted variances over
the long run. At this point, the group will create a formula for
handing off the process which would include response
procedures and educational information to guarantee the
performance and long-term project savings.
• The group would complete the phase by establishing the next
phases for supplementary Six Sigma process improvement
prospects.
10. STEPS FOR IMPLEMENTATION OF FMEA
• Projectteams applyFMEA in a series of steps:
• Compile afailurelist foreachandevery step.
• Measure failureimpacts.
• Determinefailurerates.
• Establishprobabilityof discovering errors.
11. APPLICATIONS FOR FAILURE MODE AND EFFECTS ANALYSIS
• FEMA can be applied either on processes on a product, thus making
the technique a flexible one in Six Sigma methodologies. Common
uses of FMEA in the environment include:
• Designing new processes
• Reworking on existing processes
• Building new environment for existing processes
• Proactively following up on problem-solving
• Determining preliminary processes
• Defining and Designing the system and product functionality
12. FMEAS MODULES
FMEA MSRs
This unique FMEA focuses on the potential failure cause that might occur when a customer is operating on a product. Here, the study is done
on the effect of failure caused on a system and regulatory compliance
System FMEA
This FMEA is used to analyze a compilation of subsystems. It focuses on integrations, interactions and interfaces.
Machinery FMEA
This FMEA is used to assess and increase the reliability of machinery. The module leads to incorporate prevention measures, automated fail-
safe shutdown techniques and reduce both planned and unplanned downtimes.
Software FMEA
This FMEA module studies the programming logic that goes into generating reports or control associate processes.
Service FMEA
This FMEA is concerned and conducted with transactions. Since the transaction is considered a process, the FMEA technique is utilized heavily
in this field.
13. WHEN TO APPLY FMEA?
• FMEA is a necessary methodology for situations related to services, products and processes. Thesituations can be newor being adapted or redesigned for multiple
purposes. It is worth undertakingFMEA periodically throughthelife of the process, product or service to ensure it is effective.
• FMEA should be used:
• When a module is being designed or redesigned following a quality function development.
• When an existing module is being applied ina new eco-system.
• Before the development of control plans for a newor modified process.
• When planningimprovement goals for an existing module.
• When assessing a failure in an existing module.
• To periodically assess a module.
14. HOW TO CREATE AN FMEA
1. Review the process: Map the entire process, capturing every activity.
2. Identify failure modules: Brainstorm all the potential failures/errors for each process.
3. List the cause of failure: For every failed module, list its cause on the output of the process step.
4. Assign severity score: Rank each failure module to show how severe it was on a scale of 1 to 10.
5. Assign each module an occurrence score: Rank each failure module as to how often it will occur on a scale of 1 to
10.
6. Assign each item a detection score: Rank each failure mode as to how easy the errors can be detected by the
oncoming customer in the process on a scale of 1 to 10.
7. Calculate RPN score: For each failure mode, give RPN score. This will allow you to focus on the highest risk score
module first.
8. Develop an action plan: For each high scoring RPN, note who is doing what when close to eliminating the failure
modes.
9. Recalculate new RPNs: Once the action has been completed, recalculate the new RPN numbers. Repeat this
process if further action is needed. (in case the RPN score is too high).
15. HOW TO COMPLETE A FAILURE MODES AND EFFECT ANALYSIS(FMEA)
Every product or process is subject to different types or modes of failure and the potential failures all have
consequences or effects.
The FMEA is used to:
• Identify the potential failures and the associated relative risks designed into a product or process.
• Prioritize action plans to reduce those potential failures with the highest relative risk.
• Track and evaluate the results of the action plans.
16.
17. THE STEPS TO COMPLETE A FMEA ( FAILURE MODES AND EFFECTS
ANALYSIS)
18.
19. Consider the Potential Failure Modes for each component and its corresponding function.
A potential failure mode represents any way the component or process step could fail to perform its intended function
or functions.
20. Determine the Potential Failure Effects associated with each failure mode. The effect is related directly to the ability
Of that specific component to perform its intended function.
1. The effect should be stated in terms meaningful to product or system performance.
2. If the effects are defined in general terms, it will be difficult to identify (and reduce) true potential risks.
21. For each failure mode, determine all the Potential Root Causes. Use tools classified as root cause analysis tool, as well as the best knowledge and
experience of the team.
22. For each cause, identify Current Process Controls. These are tests, procedures, or mechanisms that you now have in place to keep failures from
reaching the customer.
23. Assign a Severity ranking to each effect that has been identified.
• The severity ranking is an estimate of how serious an effect would be should it occur.
• To determine the severity, consider the impact the effect would have on the customer, on downstream operations, or on the employees operating the process.
The severity ranking is based on a relative scale ranging from 1 to 10.
• A “10” means the effect has a dangerously high severity leading to a hazard without warning.
24. For each cause, identify Current Process Controls. These are tests, procedures, or mechanisms that you now have in place to keep failures from
reaching the customer.
25.
26. Assign the Occurrence Ranking
• The Occurrence ranking is based on the likelihood, or frequency, that the cause (or mechanism of failure) will occur.
• Once the cause is known, capture data on the frequency of causes. Sources of data may be scrap and rework reports, customer complaints,
and equipment maintenance records.
27.
28. Assign the Detection Rankings
• To assign detection rankings, identify the process or product related controls in place for each failure mode and then
assign a detection ranking to each control. Detection rankings evaluate the current process controls in place.
• A control can relate to the failure mode itself, the cause (or mechanism) of failure, or the effects of a failure mode.
To make evaluating controls even more complex, controls can either prevent a failure mode or cause from occurring or
detect a failure mode, cause of failure, or effect of failure after it has occurred.
29.
30. RISK PRIORITY NUMBER(RPN)
• The RPN is the Risk Priority Number. The RPN gives us a relative risk ranking. The higher the RPN, the higher the potential risk.
• The RPN is calculated by multiplying the three rankings together. Multiply the Severity ranking times the Occurrence ranking times the Detection ranking
• Calculate the RPN for each failure mode and effect
Prioritize the Risks by Sorting the RPN from Highest Score to Lowest Score. This will help the team determine the most critical inputs and the causes for their failure.
31. DEVELOP ACTION PLAN
• Taking action means reducing the RPN. The RPN can be reduced by lowering any of the three rankings (severity,
occurrence, or detection) individually or in combination with one another
32. Who is Responsible:
• This is a very important step in Taking Action!
• Be sure to include person(s) responsible and the deadline
33. Take Action:
• The Action Plan outlines what steps are needed to implement the solution, who will do them, and when they will be completed.
• Most Action Plans identified during a PFMEA will be of the simple “who, what, & when” category.
• Responsibilities and target completion dates for specific actions to be taken are identified.
34. • Recalculate the Resulting RPN :
• This step in a PFMEA confirms the action plan had the desired results by calculating the resulting RPN.
• To recalculate the RPN, reassess the severity, occurrence, and detection rankings for the failure modes after the action plan has
been completed.
35. SUMMARY
FEMA is a priority list that needs to be taken into account to improve a business process. Implementing the FMEA methodology helps project
teams identify the most likely source of failures. Seasoned Six Sigma project teams know that detection, severity and occurrence of defects,
impacts heavily on customer experience. Thus, with FMEA, the potential defects can be identified easily and can be weeded out before they
come to fruition.