This document summarizes a data mining project aimed at reducing gas porosity in castings to improve quality and increase throughput. The team analyzed a dataset with 39 attributes and 172 samples related to the casting process using various modeling techniques in Weka. Their best model was a J48 decision tree that showed the most influential factors on porosity were the molding team, assembling team, and level of FeMnSi. Comparing to previous sponsor findings, the team concluded their results agreed while providing additional insights to help the sponsor focus quality improvement efforts.
Briggs & Stratton implemented JDA's warehouse labor management system to standardize work methods, set productivity goals, and more accurately measure performance at its distribution center. This helped reduce headcount by 18% while increasing throughput. Productivity improved from 67% to over 100% of standards. Implementing the system in packaging operations increased productivity there by 20% within two weeks. Overall the system helped realize over $1 million in annual labor savings.
The document discusses the design and testing of a 3D printer for additive manufacturing of solid rocket propellant. It describes the project objectives of printing solid propellant and comparing its mechanical properties to traditionally cast propellant. It then outlines the printer design which uses a laser to sinter layers of sucrose and potassium nitrate powder in a powder bed. Test results show the printer can achieve layer thicknesses within specifications but that printed propellant is less dense and more brittle than cast propellant. A thermal model is developed to predict sintering behavior and set safe laser operation settings.
Six Sigma is a data-driven methodology for process improvement originally developed by Motorola. It involves defining a project goal, measuring key aspects of the current process, analyzing data to determine root causes of defects, improving the process by addressing causes, and controlling future process variation. The document provides an overview of Six Sigma and its development, then gives an example project summary involving improving calcium levels in a product. The project uses Six Sigma tools like process mapping, measurement systems analysis, data analysis, design of experiments, and risk analysis to select and validate factors influencing calcium and develop improvements.
The document provides details about a Six Sigma project conducted at Kennametal India Ltd. to reduce internal scrapping of carbide inserts. The project team analyzed insert manufacturing processes using tools like SIPOC, flowcharting and Pareto analysis. Data was collected on various defects like damages, unaccounted losses etc. to identify key causes of scrapping. The DMAIC approach was used, with the 'Define' phase focusing on problem definition, CTQ identification and process mapping to scope the Six Sigma implementation project at Kennametal.
This document summarizes the results of a cost of quality analysis conducted at an IT client, Beta Company. Over several years, Beta improved its software development processes, implemented formal inspections, and began measuring cost of quality. This led to a dramatic increase in the number of defects found during testing, a decrease in defects found in production, and over $6 million in cost savings and avoidance. Beta also found that implementing Fagan inspections helped find over 85% of defects early in the requirements and design phases, reducing rework costs compared to similar projects without inspections.
This is the Dissertation Part-I in support of my intended research work. It has presentation in support of my research methodology, timelines and expected results
The CMMI (Capability Maturity Model Integration) is a process improvement maturity model that provides best practices for developing products and services. It consists of practices that cover the product lifecycle from conception to delivery and maintenance. CMMI provides a consistent framework for process improvement that addresses productivity, performance, costs, and stakeholder satisfaction. It has two representations - staged which uses defined levels of process improvement, and continuous which characterizes improvements for individual process areas. CMMI consists of process areas that contain required components like specific goals and expected components like specific practices.
Machine learning can help decrease steelmaking costs by optimizing ferroalloy use. The author details a case study where a recommender service was developed to minimize ferroalloy amounts while ensuring steel quality requirements are met. By building probabilistic models from historical production data, the service provides ferroalloy quantity recommendations that save an estimated $4 million annually. Key challenges included gaining operator acceptance and iteratively improving models to balance cost savings with physical accuracy. Ongoing work aims to expand the models' capabilities and further optimize production costs.
Briggs & Stratton implemented JDA's warehouse labor management system to standardize work methods, set productivity goals, and more accurately measure performance at its distribution center. This helped reduce headcount by 18% while increasing throughput. Productivity improved from 67% to over 100% of standards. Implementing the system in packaging operations increased productivity there by 20% within two weeks. Overall the system helped realize over $1 million in annual labor savings.
The document discusses the design and testing of a 3D printer for additive manufacturing of solid rocket propellant. It describes the project objectives of printing solid propellant and comparing its mechanical properties to traditionally cast propellant. It then outlines the printer design which uses a laser to sinter layers of sucrose and potassium nitrate powder in a powder bed. Test results show the printer can achieve layer thicknesses within specifications but that printed propellant is less dense and more brittle than cast propellant. A thermal model is developed to predict sintering behavior and set safe laser operation settings.
Six Sigma is a data-driven methodology for process improvement originally developed by Motorola. It involves defining a project goal, measuring key aspects of the current process, analyzing data to determine root causes of defects, improving the process by addressing causes, and controlling future process variation. The document provides an overview of Six Sigma and its development, then gives an example project summary involving improving calcium levels in a product. The project uses Six Sigma tools like process mapping, measurement systems analysis, data analysis, design of experiments, and risk analysis to select and validate factors influencing calcium and develop improvements.
The document provides details about a Six Sigma project conducted at Kennametal India Ltd. to reduce internal scrapping of carbide inserts. The project team analyzed insert manufacturing processes using tools like SIPOC, flowcharting and Pareto analysis. Data was collected on various defects like damages, unaccounted losses etc. to identify key causes of scrapping. The DMAIC approach was used, with the 'Define' phase focusing on problem definition, CTQ identification and process mapping to scope the Six Sigma implementation project at Kennametal.
This document summarizes the results of a cost of quality analysis conducted at an IT client, Beta Company. Over several years, Beta improved its software development processes, implemented formal inspections, and began measuring cost of quality. This led to a dramatic increase in the number of defects found during testing, a decrease in defects found in production, and over $6 million in cost savings and avoidance. Beta also found that implementing Fagan inspections helped find over 85% of defects early in the requirements and design phases, reducing rework costs compared to similar projects without inspections.
This is the Dissertation Part-I in support of my intended research work. It has presentation in support of my research methodology, timelines and expected results
The CMMI (Capability Maturity Model Integration) is a process improvement maturity model that provides best practices for developing products and services. It consists of practices that cover the product lifecycle from conception to delivery and maintenance. CMMI provides a consistent framework for process improvement that addresses productivity, performance, costs, and stakeholder satisfaction. It has two representations - staged which uses defined levels of process improvement, and continuous which characterizes improvements for individual process areas. CMMI consists of process areas that contain required components like specific goals and expected components like specific practices.
Machine learning can help decrease steelmaking costs by optimizing ferroalloy use. The author details a case study where a recommender service was developed to minimize ferroalloy amounts while ensuring steel quality requirements are met. By building probabilistic models from historical production data, the service provides ferroalloy quantity recommendations that save an estimated $4 million annually. Key challenges included gaining operator acceptance and iteratively improving models to balance cost savings with physical accuracy. Ongoing work aims to expand the models' capabilities and further optimize production costs.
This document discusses defining and tracking productivity metrics for an organization. It proposes identifying key metrics across teams to measure productivity gaps. It suggests developing a framework to collect data, analyze gaps, and deliver a report with optimization recommendations. Sample metrics are provided for engineering, development, sustainment, and quality assurance. Case studies demonstrate defining complexity-weighted productivity comparisons between global teams and addressing constraints impacting productivity.
Agile, qa and data projects geek night 2020Balvinder Hira
This document discusses quality assurance challenges on data projects. It provides an overview of a case study where a business wanted to price its products more intelligently based on external factors. It then describes the data science and engineering processes involved in building a price recommendation pipeline. This includes data collection, mapping, modeling, transformation, algorithm development, storage, and publishing. It outlines the various stages of testing quality analysts performed, such as data validation, algorithm testing, performance testing, and environment testing. Finally, it discusses some of the challenges of testing data projects and lessons learned.
This project applies the Six Sigma DMAIC methodology to improve a sand casting process at a foundry in Southern India that manufactures flywheel outer casings. In the Define phase, the problem of high defect rates is stated. In the Measure phase, defect data is collected and the baseline sigma level is calculated. In the Analyze phase, statistical tools like Pareto charts and fishbone diagrams are used to identify sources of variation. In the Improve phase, solutions are implemented to reduce defects. Experimental results show the optimized process reduced defects and increased sigma levels, validating the Six Sigma approach.
Processes are the building blocks of every organization. Yet, many organizations do not have consistent and repeatable processes. Research shows that projects managed using structured processes leveraging “best practices” consistently show higher performance than those that do not. This session focuses on a method from ISO to improve processes and eliminate defects. Assessing process capability demonstrably helps lower risk associated with the processes.
Main points covered:
• What is a Process Reference Model?
• What is process capability and how do I measure it?
• How to use a Process Assessment Model to assess processes?
Presenter:
Peter Davis is the Principal of Peter Davis Associates, a management consulting firm specializing in Governance, Security, and Audit. Prior to founding PDA, Mr. Davis’ private sector experience included stints with two large Canadian banks and a manufacturing company. He was formerly a principal in the Information Systems Audit practice of Ernst & Young. In the public sector, Mr. Davis was Director of Information Systems Audit in the Office of the Provincial Auditor (Ontario), where he had oversight audit responsibilities for all Ontario crown corporations, agencies and boards.
Mr. Davis has written or co-written 13 books including “Project Management Process Capability Assessment,” “Lean Six Sigma Secrets for the CIO,” and “Hacking Wireless Networks for Dummies.” Peter currently teaches COBIT 5 Foundation/Implementation/Assessor/Implementing NIST Cyber-security Framework using COBIT 5, ISO 20000 FC/LI/LA ISO 27001 LI/LA, ISO 27032 LM, ISO 27005 RM, and ISO 31000 RM.
Organizer: Ardian Berisha
Date: September 5th, 2018
Recorded webinar link: https://youtu.be/NECQ5Angadw
Performed predictive Data analytics for “Black Friday Sales Dataset” wherein the company wants to predict the purchase amount against the products using Rapid Miner Tool.
This interactive slideshow demonstrates step by step how we work with clients and how DCT works by providing a detailed case study, in which DCT was used to develop process to remarkably improve enzyme productivity within only 10 experiments.
The document provides test procedures for quality management of complaints against suppliers in SAP. It outlines 9 steps to create a quality notification, capture defects, define tasks, execute tasks, review tasks, document supplier analysis, and complete the notification. Tasks include notifying the supplier, defining corrective actions, processing tasks in a worklist, and updating the notification status.
How performance management can improve client satisfactionSkanska USA
Early in her construction career, Wendy (Li) MacLeod-Roemer realized there was significant room to improve construction delivery beyond traditional means. To help advance our industry, she decided to pursue a PhD in organization management to understand what changes would be most effective. She dedicated her thesis to exploring how performance management can transform construction projects. Here, Wendy – now one of our senior project managers – explains how her research shows that cost isn’t what is most important to clients.
This document provides an overview of Six Sigma and its application to software development. It discusses key Six Sigma concepts like DMAIC (Define, Measure, Analyze, Improve, Control), tools used in each phase, and how they can help improve processes and reduce defects in software development. It also covers process maturity models, different types of waste specific to software development, and how Six Sigma principles of data-driven problem solving can help organizations deliver higher quality software and improve customer satisfaction.
This document provides an overview of Six Sigma, including its objectives, methodologies, tools, and an example of its implementation. It describes Six Sigma as a set of techniques for process improvement developed by Motorola to reduce defects. The key methodologies are DMAIC for improving existing processes and DMADV for designing new processes or products. Both follow five phases: Define, Measure, Analyze, Improve/Design, and Control. The document also lists common Six Sigma tools and software and gives an example of how Catalent Pharma Solutions used Six Sigma processes to improve efficiency and prevent product losses.
This document outlines 47 project management processes grouped into 5 process groups based on the Project Management Body of Knowledge (PMBOK) Guide 5th Edition. The processes are: initiating, planning, executing, monitoring and controlling, and closing. An overview is provided for each process including typical inputs, tools and techniques, and outputs as defined in the PMBOK Guide. The document is intended as a high-level overview and does not replace reading the full PMBOK Guide.
THERE'S A NEW VERSION AVAILABLE: https://www.slideshare.net/ricardo.vargas/pmbok-guide-processes-flow-6th-edition
The 47 processes are separated into colors according to their respective knowledge areas. Only the main connections that are depicted in the PMBOK® Guide are shown in this process flow.
One of the most challenging problems that test managers face involves implementing effective, meaningful, and insightful test metrics. Data and measures are the foundation of true understanding, but the misuse of metrics causes confusion, bad decisions, and demotivation. Rex Black shares how to avoid these unfortunate situations by using metrics properly as part of your test management process. How can we measure our progress in testing a project? What can metrics tell us about the quality of the product? How can we measure the quality of the test process itself? Rex answers these questions, illustrated with case studies and real-life examples. Learn how to use test case metrics, coverage metrics, and defect metrics in ways that demonstrate status, quantify effectiveness, and support smart decision making. Exercises provide immediate opportunities for you to apply the techniques to your own testing metrics. Join Rex to jump-start a new testing metrics program or gain new ideas to improve your existing one.
The document discusses supplier performance evaluation methods for SMEs in Macedonia. It covers several key areas:
1. Supplier evaluation methods including categorical, weighted-point, and cost-ratio models. The weighted-point model is most commonly used.
2. Key performance indicators from literature including Dickson and Weber's criteria which focus on quality, delivery, price and other factors.
3. Approaches to evaluate suppliers such as linear weighting, total cost of ownership, and statistical models. Linear weighting is most popular.
4. The study's methodology used grounded theory and questionnaires. Findings showed manufacturing is the largest industry, evaluation processes are common, and key criteria include price, quality and delivery
The document outlines an operational excellence project approach that involves making processes and performance metrics visible through analysis of reports, value streams, and financial data. Key aspects of the project include establishing goals and roles, reviewing current management systems, identifying best practices, and installing ongoing audit and training processes to ensure sustainability. A variety of industrial engineering tools will be used such as process task analysis, resource utilization analysis, variance studies, line balancing, and action planning to pursue ongoing process improvement. The roles of the consulting firm and client are to jointly analyze information, diagnose opportunities, develop recommendations, and have the client implement and own the process changes.
This document provides an overview of database security concepts including confidentiality, integrity, and availability. It defines database security as protecting the confidentiality, integrity, and availability of data. Key concepts discussed include authentication, authorization, access control, data encryption, data privacy, auditing, and logging. The document also outlines security problems such as non-fraudulent threats from errors or disasters and fraudulent threats from authorized users abusing privileges or hostile agents attacking the system.
New for 2018 MRO master data auditing and cleansingDavid Thompson
New Workshop developed due to high number of company's inventory systems with poor data leading to high number of duplicates, lack of focus on cost reduction, excess downtime
The document discusses the benefits of software process improvement (SPI) and achieving higher maturity levels like CMMI Level 5. It provides examples of organizations that saw significant reductions in defects, costs and improvements in productivity after implementing SPI initiatives and achieving higher maturity levels. While SPI requires initial investments, it more than pays for itself through reductions in rework costs and improvements in productivity.
The document discusses key topics in operations management including Six Sigma, acceptance sampling, Taguchi loss function, House of Quality, and robustness. It provides details on Six Sigma such as its goal of reducing defects to 3.4 per million and the DMAIC methodology. Acceptance sampling uses statistical sampling to determine if production lots meet standards. The Taguchi loss function quantifies the costs of deviations from a target value. House of Quality is a tool that integrates customer needs into product development. Finally, robust design aims to create products that maintain performance over a wide range of conditions.
This document discusses defining and tracking productivity metrics for an organization. It proposes identifying key metrics across teams to measure productivity gaps. It suggests developing a framework to collect data, analyze gaps, and deliver a report with optimization recommendations. Sample metrics are provided for engineering, development, sustainment, and quality assurance. Case studies demonstrate defining complexity-weighted productivity comparisons between global teams and addressing constraints impacting productivity.
Agile, qa and data projects geek night 2020Balvinder Hira
This document discusses quality assurance challenges on data projects. It provides an overview of a case study where a business wanted to price its products more intelligently based on external factors. It then describes the data science and engineering processes involved in building a price recommendation pipeline. This includes data collection, mapping, modeling, transformation, algorithm development, storage, and publishing. It outlines the various stages of testing quality analysts performed, such as data validation, algorithm testing, performance testing, and environment testing. Finally, it discusses some of the challenges of testing data projects and lessons learned.
This project applies the Six Sigma DMAIC methodology to improve a sand casting process at a foundry in Southern India that manufactures flywheel outer casings. In the Define phase, the problem of high defect rates is stated. In the Measure phase, defect data is collected and the baseline sigma level is calculated. In the Analyze phase, statistical tools like Pareto charts and fishbone diagrams are used to identify sources of variation. In the Improve phase, solutions are implemented to reduce defects. Experimental results show the optimized process reduced defects and increased sigma levels, validating the Six Sigma approach.
Processes are the building blocks of every organization. Yet, many organizations do not have consistent and repeatable processes. Research shows that projects managed using structured processes leveraging “best practices” consistently show higher performance than those that do not. This session focuses on a method from ISO to improve processes and eliminate defects. Assessing process capability demonstrably helps lower risk associated with the processes.
Main points covered:
• What is a Process Reference Model?
• What is process capability and how do I measure it?
• How to use a Process Assessment Model to assess processes?
Presenter:
Peter Davis is the Principal of Peter Davis Associates, a management consulting firm specializing in Governance, Security, and Audit. Prior to founding PDA, Mr. Davis’ private sector experience included stints with two large Canadian banks and a manufacturing company. He was formerly a principal in the Information Systems Audit practice of Ernst & Young. In the public sector, Mr. Davis was Director of Information Systems Audit in the Office of the Provincial Auditor (Ontario), where he had oversight audit responsibilities for all Ontario crown corporations, agencies and boards.
Mr. Davis has written or co-written 13 books including “Project Management Process Capability Assessment,” “Lean Six Sigma Secrets for the CIO,” and “Hacking Wireless Networks for Dummies.” Peter currently teaches COBIT 5 Foundation/Implementation/Assessor/Implementing NIST Cyber-security Framework using COBIT 5, ISO 20000 FC/LI/LA ISO 27001 LI/LA, ISO 27032 LM, ISO 27005 RM, and ISO 31000 RM.
Organizer: Ardian Berisha
Date: September 5th, 2018
Recorded webinar link: https://youtu.be/NECQ5Angadw
Performed predictive Data analytics for “Black Friday Sales Dataset” wherein the company wants to predict the purchase amount against the products using Rapid Miner Tool.
This interactive slideshow demonstrates step by step how we work with clients and how DCT works by providing a detailed case study, in which DCT was used to develop process to remarkably improve enzyme productivity within only 10 experiments.
The document provides test procedures for quality management of complaints against suppliers in SAP. It outlines 9 steps to create a quality notification, capture defects, define tasks, execute tasks, review tasks, document supplier analysis, and complete the notification. Tasks include notifying the supplier, defining corrective actions, processing tasks in a worklist, and updating the notification status.
How performance management can improve client satisfactionSkanska USA
Early in her construction career, Wendy (Li) MacLeod-Roemer realized there was significant room to improve construction delivery beyond traditional means. To help advance our industry, she decided to pursue a PhD in organization management to understand what changes would be most effective. She dedicated her thesis to exploring how performance management can transform construction projects. Here, Wendy – now one of our senior project managers – explains how her research shows that cost isn’t what is most important to clients.
This document provides an overview of Six Sigma and its application to software development. It discusses key Six Sigma concepts like DMAIC (Define, Measure, Analyze, Improve, Control), tools used in each phase, and how they can help improve processes and reduce defects in software development. It also covers process maturity models, different types of waste specific to software development, and how Six Sigma principles of data-driven problem solving can help organizations deliver higher quality software and improve customer satisfaction.
This document provides an overview of Six Sigma, including its objectives, methodologies, tools, and an example of its implementation. It describes Six Sigma as a set of techniques for process improvement developed by Motorola to reduce defects. The key methodologies are DMAIC for improving existing processes and DMADV for designing new processes or products. Both follow five phases: Define, Measure, Analyze, Improve/Design, and Control. The document also lists common Six Sigma tools and software and gives an example of how Catalent Pharma Solutions used Six Sigma processes to improve efficiency and prevent product losses.
This document outlines 47 project management processes grouped into 5 process groups based on the Project Management Body of Knowledge (PMBOK) Guide 5th Edition. The processes are: initiating, planning, executing, monitoring and controlling, and closing. An overview is provided for each process including typical inputs, tools and techniques, and outputs as defined in the PMBOK Guide. The document is intended as a high-level overview and does not replace reading the full PMBOK Guide.
THERE'S A NEW VERSION AVAILABLE: https://www.slideshare.net/ricardo.vargas/pmbok-guide-processes-flow-6th-edition
The 47 processes are separated into colors according to their respective knowledge areas. Only the main connections that are depicted in the PMBOK® Guide are shown in this process flow.
One of the most challenging problems that test managers face involves implementing effective, meaningful, and insightful test metrics. Data and measures are the foundation of true understanding, but the misuse of metrics causes confusion, bad decisions, and demotivation. Rex Black shares how to avoid these unfortunate situations by using metrics properly as part of your test management process. How can we measure our progress in testing a project? What can metrics tell us about the quality of the product? How can we measure the quality of the test process itself? Rex answers these questions, illustrated with case studies and real-life examples. Learn how to use test case metrics, coverage metrics, and defect metrics in ways that demonstrate status, quantify effectiveness, and support smart decision making. Exercises provide immediate opportunities for you to apply the techniques to your own testing metrics. Join Rex to jump-start a new testing metrics program or gain new ideas to improve your existing one.
The document discusses supplier performance evaluation methods for SMEs in Macedonia. It covers several key areas:
1. Supplier evaluation methods including categorical, weighted-point, and cost-ratio models. The weighted-point model is most commonly used.
2. Key performance indicators from literature including Dickson and Weber's criteria which focus on quality, delivery, price and other factors.
3. Approaches to evaluate suppliers such as linear weighting, total cost of ownership, and statistical models. Linear weighting is most popular.
4. The study's methodology used grounded theory and questionnaires. Findings showed manufacturing is the largest industry, evaluation processes are common, and key criteria include price, quality and delivery
The document outlines an operational excellence project approach that involves making processes and performance metrics visible through analysis of reports, value streams, and financial data. Key aspects of the project include establishing goals and roles, reviewing current management systems, identifying best practices, and installing ongoing audit and training processes to ensure sustainability. A variety of industrial engineering tools will be used such as process task analysis, resource utilization analysis, variance studies, line balancing, and action planning to pursue ongoing process improvement. The roles of the consulting firm and client are to jointly analyze information, diagnose opportunities, develop recommendations, and have the client implement and own the process changes.
This document provides an overview of database security concepts including confidentiality, integrity, and availability. It defines database security as protecting the confidentiality, integrity, and availability of data. Key concepts discussed include authentication, authorization, access control, data encryption, data privacy, auditing, and logging. The document also outlines security problems such as non-fraudulent threats from errors or disasters and fraudulent threats from authorized users abusing privileges or hostile agents attacking the system.
New for 2018 MRO master data auditing and cleansingDavid Thompson
New Workshop developed due to high number of company's inventory systems with poor data leading to high number of duplicates, lack of focus on cost reduction, excess downtime
The document discusses the benefits of software process improvement (SPI) and achieving higher maturity levels like CMMI Level 5. It provides examples of organizations that saw significant reductions in defects, costs and improvements in productivity after implementing SPI initiatives and achieving higher maturity levels. While SPI requires initial investments, it more than pays for itself through reductions in rework costs and improvements in productivity.
The document discusses key topics in operations management including Six Sigma, acceptance sampling, Taguchi loss function, House of Quality, and robustness. It provides details on Six Sigma such as its goal of reducing defects to 3.4 per million and the DMAIC methodology. Acceptance sampling uses statistical sampling to determine if production lots meet standards. The Taguchi loss function quantifies the costs of deviations from a target value. House of Quality is a tool that integrates customer needs into product development. Finally, robust design aims to create products that maintain performance over a wide range of conditions.
A375 Example Taste the taste of the Lord, the taste of the Lord The taste of...franktsao4
It seems that current missionary work requires spending a lot of money, preparing a lot of materials, and traveling to far away places, so that it feels like missionary work. But what was the result they brought back? It's just a lot of photos of activities, fun eating, drinking and some playing games. And then we have to do the same thing next year, never ending. The church once mentioned that a certain missionary would go to the field where she used to work before the end of his life. It seemed that if she had not gone, no one would be willing to go. The reason why these missionary work is so difficult is that no one obeys God’s words, and the Bible is not the main content during missionary work, because in the eyes of those who do not obey God’s words, the Bible is just words and cannot be connected with life, so Reading out God's words is boring because it doesn't have any life experience, so it cannot be connected with human life. I will give a few examples in the hope that this situation can be changed. A375
The Book of Ruth is included in the third division, or the Writings, of the Hebrew Bible. In most Christian canons it is treated as one of the historical books and placed between Judges and 1 Samuel.
The Hope of Salvation - Jude 1:24-25 - MessageCole Hartman
Jude gives us hope at the end of a dark letter. In a dark world like today, we need the light of Christ to shine brighter and brighter. Jude shows us where to fix our focus so we can be filled with God's goodness and glory. Join us to explore this incredible passage.
The Enchantment and Shadows_ Unveiling the Mysteries of Magic and Black Magic...Phoenix O
This manual will guide you through basic skills and tasks to help you get started with various aspects of Magic. Each section is designed to be easy to follow, with step-by-step instructions.
Why is this So? ~ Do Seek to KNOW (English & Chinese).pptxOH TEIK BIN
A PowerPoint Presentation based on the Dhamma teaching of Kamma-Vipaka (Intentional Actions-Ripening Effects).
A Presentation for developing morality, concentration and wisdom and to spur us to practice the Dhamma diligently.
The texts are in English and Chinese.
Sanatan Vastu | Experience Great Living | Vastu ExpertSanatan Vastu
Santan Vastu Provides Vedic astrology courses & Vastu remedies, If you are searching Vastu for home, Vastu for kitchen, Vastu for house, Vastu for Office & Factory. Best Vastu in Bahadurgarh. Best Vastu in Delhi NCR
A Free eBook ~ Valuable LIFE Lessons to Learn ( 5 Sets of Presentations)...OH TEIK BIN
A free eBook comprising 5 sets of PowerPoint presentations of meaningful stories /Inspirational pieces that teach important Dhamma/Life lessons. For reflection and practice to develop the mind to grow in love, compassion and wisdom. The texts are in English and Chinese.
My other free eBooks can be obtained from the following Links:
https://www.slideshare.net/ohteikbin/presentations
https://www.slideshare.net/ohteikbin/documents
The forces involved in this witchcraft spell will re-establish the loving bond between you and help to build a strong, loving relationship from which to start anew. Despite any previous hardships or problems, the spell work will re-establish the strong bonds of friendship and love upon which the marriage and relationship originated. Have faith, these stop divorce and stop separation spells are extremely powerful and will reconnect you and your partner in a strong and harmonious relationship.
My ritual will not only stop separation and divorce, but rebuild a strong bond between you and your partner that is based on truth, honesty, and unconditional love. For an even stronger effect, you may want to consider using the Eternal Love Bond spell to ensure your relationship and love will last through all tests of time. If you have not yet determined if your partner is considering separation or divorce, but are aware of rifts in the relationship, try the Love Spells to remove problems in a relationship or marriage. Keep in mind that all my love spells are 100% customized and that you'll only need 1 spell to address all problems/wishes.
Save your marriage from divorce & make your relationship stronger using anti divorce spells to make him or her fall back in love with you. End your marriage if you are no longer in love with your husband or wife. Permanently end your marriage using divorce spells that work fast. Protect your marriage from divorce using love spells to boost commitment, love & bind your hearts together for a stronger marriage that will last. Get your ex lover who has remarried using divorce spells to break up a couple & make your ex lost lover come back to you permanently.
Visit https://www.profbalaj.com/love-spells-loves-spells-that-work/
Call/WhatsApp +27836633417 for more info.
How to Stop a Divorce and Save Your Marriage: Divorce Spells That Really Work...
Test presentation
1. Improving Casting Porosity with Data Mining Creed Darling Marcin Kuta Kyle Saginus ENMA 6060 Innovation & Technology December 7, 2009
2. Overview 1.0 Business Understanding 2.0 Data Exploration and Description 3.0 Modeling 4.0 Evaluation 5.0 Deployment 2
3. 1.0 Business Understanding 1.1 Business Objectives 1.1.1 Background 1.1.2 Business Objectives 1.1.3 Business Success Criteria 1.2 Assess Situation 1.3 Risks and Contingencies 1.4 Costs and Benefits 1.5 Data Mining Goals 1.5.1 Data Mining Goals 1.5.2 Data Mining Success Criteria 3
4. 1.1.1 Background A casting with high gas porosity is considered defective In the process of casting, defects are put back in the “melting pot” The loss of capital from defects is mostly overhead and reduced throughput Defects have to be reduced to reduce waste and increase throughput without increasing foundry capacity 4
5. 1.1.2 Business Objectives Primary Business Objective: Reduce gas porosity in castings to Improve quality of foundry castings Increase throughput Reduce waste Secondary Business Objective: Compare our data mining results obtained in this project against previous findings. 5
6. 1.1.3 Business Success Criteria Measurable Outcomes: Lower operating cost Improve throughput without additional capital expenditures Reduce waste 6
7. 1.2 Assess Situation Inventory of Resources 3 students on team Dataset with 39 attributes and 172 samples The team has access to Weka software Requirements, Assumptions, and Restraints The project deadline is December 7, 2009 The results need to state which variables should be altered and to what degree in order to improve quality of castings, reduce cost, reduce waste, and to improve throughput without increasing foundry capacity. 7
8. 1.3 Risks & Contingencies Communication with project sponsor might be difficult. The sponsor has published articles using data mining on our dataset Limited knowledge of casting process Data is data Our tools might limit our findings We have many different algorithms that can be employed 8
9. 1.4 Costs & Benefits There is no cost to execute this project as the project sponsor provided the data There is no direct financial benefit to the team; however, the information gained can help the project sponsor to understand critical variables that affect the quality of casting in a foundry environment, which can then increase throughput for the foundry 9
10. 1.5.1 Data Mining Goals To determine the variables (of the 39 provided) that have the greatest impact on reducing gas porosity in the sand castings in order to increase the quality of the cast part. 10
11. 1.5.2 Data Mining Success Criteria Compare our data to the findings of the project sponsor Our results agree with our sponsors but add something new 11
12. 2.0 Data Exploration and Description 2.1 Data Collection and Description 2.2 Data Quality 2.3 Data Selection 2.4 Data Integration 12
13. 2.1 Data Collection and Description Project sponsor collected over 6500 points of data 39 attributes and 172 samples Attribute Types: Elements in the final casting composition Information regarding the casting process Chronological data regarding the cast Data is a mixture of numerical and nominal 13
14. 2.2 Data Quality Upon review of a number of articles addressing casting procedures, it appears the data collected by the team’s sponsor is complete and it does not contain any significant errors. Further analysis of the provided data may reveal areas of investigation that could be broadened to provide complete findings. 14
15. 2.3 Data Selection In an article published by the sponsor it was discovered that 11 of the attributes were found to be unnecessary in the model Trials were run using our data mining tools with the attributes included and removed and only a small difference in error was observed We removed the eleven variables from the dataset 15
17. 2.4 Data Integration Created two new attributes from existing attributes Total Impurities – summation of FeMnSi, FeSi, FeCaSi, and Ca Total Al, Si, P – summation of Al, Si, P No formating issues were found with the data 17
18. 3.0 Modeling 3.1 Modeling Techniques 3.2 Build Model 3.3 Test Model 3.4 Assess Model 18
19. 3.1 Modeling techniques Experiments were conducted with each of the following algorithms to discover our best model One Rule Classifier Naïve Bayes Classifier J48 Decision Tree Classifier Multilayer Perception Neural Network Training sets were used with some discretization The dataset with the 11 attributes removed and integrated dataset were used for all of the experiments 19
20. 3.2 Build Model The experiments using the J48 Decision Tree classifier resulted in the best model for our dataset This algorithm was chosen to be used for analyzing our dataset The following slides show the details of building the model 20
24. 3.4 Assess Model 24 From the Weka output we can see that the model only misclassified 2.33% of the instances The classification results in the Confusion Matrix
25. 4.O Evaluation 4.1 Evaluate Results 4.2 Review Process 4.3 Next Steps 25
26. 4.1 Evaluate Results From the J48 Decision Tree we can see the attribute that has the largest impact on the porosity is the “molding team number” This indicates that the process of casting is very dependent on the workers This is also not a surprise and was already recognized by the sponsor The molding team attribute was removed from the dataset to see which attribute was the next most important 26
27.
28.
29. 4.2 Review Process Process seems to have followed all of the correct steps and has yielded acceptable results Results could be improved or new findings made by using a different software package with different algorithms 30
30. 4.3 Next Steps For continuous monitoring and improvement of the casting porosity while limiting excess data acquisition, it is suggested that data pertaining to casting failures should only be collected on the attributes appearing in the J48 Decision Tree 31
31. 5.0 Deployment Plan The completed model and results met our business objectives Send our final report and model to sponsor for Review Monitoring and maintenance 32
Welcome to our presentation on improving casting porosity with data mining. This presentation was completed for Marquette University’s Engineering Management course on Innovation and Technology by Creed Darling, Marcin Kuta, and Kyle Saginus.
Here is a quick overview of the presentation. We will start by first discussing our business understanding of what we planned to accomplish with this project. Then we will discuss the different aspects of the data and the model we developed to analyze it. We will conclude the presentation by evaluating our model in terms of accomplishing our business goals and the deployment of our model.
For the business understanding section of this presentation we will give a short background along with our objectives and success criteria. We will then present our team’s situation including our requirements and resources followed by the risks and contingency plans for the project. Finally we will talk about the costs and benefits of the project and how we planned to accomplish our business objectives through data mining.
Metal castings are used throughout several industries, varying anywhere from the construction industry to the aerospace industry. Depending upon the final application for the casting, overall quality is essential. Among other factors, castings can be considered defective when a certain level of gas porosity is present in the final product. A defective part is typically returned to the melting pot and recast. This might lead you to believe that defective castings don’t have a significant cost associated with them but a casting’s cost is usually about 10% material and 90% overhead, so a defective casting does have a major cost. Also each casting that is considered defective is a product that went through the entire process but did not make it out the door, so the foundry’s throughput is reduced. Reducing defective parts will increase a foundry’s throughput without spending more capital to increase capacity.
Based on the background our business objectives are simple. Our goal was to reduce the gas porosity in castings to improve the quality of castings, increase the throughput of the foundry, and reduce waste. Our project sponsor has already collected data and analyzed it using data mining techniques. It might seem then that the project is already complete but there are many different approaches in data mining and a new team can always add new insight. Our secondary business objective was to compare our findings to our sponsors and hopefully find similarities to ensure our approach is correct, but also to bring something new to the table.
For our business success criteria, based on our business objectives, we are focusing on the following measurable outcomes. By reducing the number of defects, our project will be successful if there is a lower operating cost, improved throughput in the absence of increased capacity, and reduced waste.
The resources available to the team are three students in the innovation and technology course, the dataset collected by the sponsor, and access to Weka data mining freeware.We were required to complete the project by December 7th (today),and the results found needed to meet the business objectives. Since we are not directly linked to the foundry we needed to make recommendations to our project sponsor as to what attributes in the dataset we were given have the largest impact on high casting porosity. The project sponsor will then employ the recommendations.
There are a couple risks associated with the project that were addressed at the beginning of the project and contingency plans were developed. Since our sponsor is from a foreign country it was assumed that it would be difficult to communicate with our sponsor, however it was thought shouldn’t inhibit our work as the sponsor had already published numerous articles on this data. Our team had a limited knowledge of the casting process, but in one sense it is not necessary to understand the process to analyze the data. Also when confirming if our recommendations were accurate the team had the sponsors findings to see if the analysis was headed in the right direction. The software package that we had available to us may not have the best model for this data, but there are enough algorithms in the software that the team felt it would able to find a model that is sufficient.
The team will not be compensated in any way for completing the project so there is no cost involved for our analysis, but there is also no direct benefit to the team aside from gaining experience in data mining. The foundry will however receive a benefit from our project by possibly improving the quality of their castings, and identifying areas of process improvement that increase the throughput of the foundry and reduce operating cost.
The teams data mining goal was to find the attribute(s) in the dataset that have the largest impact on high casting porosity resulting defective parts. With this knowledge the casting process can be altered to reduce the number of defective castings.
The team’s data mining success criteria depend mostly on the sponsor’s findings. We will know that we have found a good model when our results show findings similar to our sponsor’s. We want to add something to our sponsor’s knowledge of the data, so we will also consider the data mining to be successful when we have found something new.
Following the Business Understanding section, this segment of the presentation will focus on Data Exploration and Description. Here we will explore Data Collection and Description, Data Quality, Data Selection and Data Integration.
The team was provided with over 6500 points of data which were collected by the project sponsor. The data set consisted of 39 attributes derived from 172 samples. The attribute types provided insight into the physical nature of the final product by listing elements in the final composition of the casting. Other attributes provided valuable information into the casting process technology by listing information regarding the casting process. The last set of attributes characterized the casting process itself by listing chronological data regarding the cast. The data points were a mixture of numerical and nominal values.
Since the data points were provided by the project sponsor, data evaluation step was performed by the team in order to ensure adequate data quality. The team’s focus was placed on thorough review of published literature related to the topic of gas porosity in a foundry process. The article review supported the sponsor’s data definition and collection methods. Furthermore, it lead the team to believe that further analysis of the provided data may reveal areas of investigation that could be broadened to provide complete findings.
In parallel to data quality, data selection is a critical step in the data mining analysis. In one article published by the project sponsor it was discovered that 11 of the 39 attributes listed were found to provide no insight or value to the model. In order to ensure successful data selection progression, trials were run using various data mining tools and models with all attributes included and removed when a small difference in error was observed. In conclusion 11 attributes were removed from the data set that showed no influence on gas porosity in the final casting.
Out of the 39 attributes listed in this section, the following 11 attributes showed no influence on the model – those were labels as “Iron and Silicon Amount,” “Final %Al,” “Final %P”, “Nozzle Supplier Code,” “Pouring Order,” “Mould Quality,” “Core Coating Code,” “Molding Sand Code,” “Molding Coating Code,” “Environment Temperature Before Pour” and “Bar Test Casting Porosity”
The data integration and data review lead to the development of two new attributes from the data set. The two attributes were “Total Impurities consisted of summation of FeMnSi, FeSi, FeCaSi, Ca levels” and “Total of Al, Si,and P levels which consisted of summation of Al, Si and P.” No formatting issues were found with the data.
The next step within the project was to develop proper model which enabled the team to analyze the data points and derive appropriate conclusions related to the gas porosity issues found in the casting process. This section of the presentation will focus on Modeling Techniques, Building a Model, Testing a Model and Assessing of the Model.
In an effort to define and build the best model, the team has evaluated various algorithms found in Weka software. A number of simulations were conducted using One Rule Classifier, Naïve Bayes Classifier, J48 Decision Tree Classifier and Multilayer Perception Neural Network algorithm. In order to generate adequate results, the training sets were used with some level of discretization. The data set with the 11 attributes removed and the integrated data set were used for all of the experiments.
The simulation efforts using a number of different algorithms resulted in selection of J48 Decision Tree classifier as appropriate model for this project. The J48 algorithm generated best results and yielded the lowest classification error.
This is the J48 model set up. The model was generated using the software defaults with an exception of “saveinstancedata.” This feature was changed to true. This allowed the team to find out how each sample is classified after the building the J48 classification tree.
This table lists the number of misclassified instances. The original data set with 11 attributes removed showed the same results as the integrated data set. We can see that non discretized sets generated only 4 misclassified attributes, whereas the discretized data sets yielded as many as 13 misclassified instances.
This is the J48 classification tree generated by the model using the non-discretized, original dataset. We can see that the tree has 9 leaves, and that the total size of the tree is 23 elements. The size of the tree is important when testing the model. In general, the goal of the tree learner is to classify the most test samples correctly while reducing the tree size and the number of leaves. Considering the number of attributes in this data set, the model had done a good job in generating only 9 leaves and 23 elements.
The final step within the modeling phase consisted of model assessment. From the Weka output shown, it was observed that the model only misclassified 2.33% of the instances This value was derived from misclassification of 4 out of 172 samples available.