The document discusses a project to reduce failures of heat sink mounted components by improving the heat sink assembly process. It aims to determine the highest risk component, identify correlations between field data and production data, set goals to reduce defects by 50%, and analyze the current process capability to identify non-capable processes in need of improvement.
An introduction to performing Measurement Systems Analysis with SigmaXL
Established in 1998, SigmaXL Inc. is a leading provider of user friendly Excel Add-ins for Lean Six Sigma graphical and statistical tools and Monte Carlo simulation.
SigmaXL® customers include market leaders like Agilent, Diebold, FedEx, Microsoft, Motorola and Shell. SigmaXL® software is also used by numerous colleges, universities and government agencies.
Our flagship product, SigmaXL®, was designed from the ground up to be a cost-effective, powerful, but easy to use tool that enables users to measure, analyze, improve and control their service, transactional, and manufacturing processes. As an add-in to the already familiar Microsoft Excel, SigmaXL® is ideal for Lean Six Sigma training and application, or use in a college statistics course.
DiscoverSim™ enables you to quantify your risk through Monte Carlo simulation and minimize your risk with global optimization. Business decisions are often based on assumptions with a single point value estimate or an average, resulting in unexpected outcomes.
DiscoverSim™ allows you to model the uncertainty in your inputs so that you know what to expect in your outputs.
An introduction to performing Measurement Systems Analysis with SigmaXL
Established in 1998, SigmaXL Inc. is a leading provider of user friendly Excel Add-ins for Lean Six Sigma graphical and statistical tools and Monte Carlo simulation.
SigmaXL® customers include market leaders like Agilent, Diebold, FedEx, Microsoft, Motorola and Shell. SigmaXL® software is also used by numerous colleges, universities and government agencies.
Our flagship product, SigmaXL®, was designed from the ground up to be a cost-effective, powerful, but easy to use tool that enables users to measure, analyze, improve and control their service, transactional, and manufacturing processes. As an add-in to the already familiar Microsoft Excel, SigmaXL® is ideal for Lean Six Sigma training and application, or use in a college statistics course.
DiscoverSim™ enables you to quantify your risk through Monte Carlo simulation and minimize your risk with global optimization. Business decisions are often based on assumptions with a single point value estimate or an average, resulting in unexpected outcomes.
DiscoverSim™ allows you to model the uncertainty in your inputs so that you know what to expect in your outputs.
Standard procedure by DMAIC approach to find solution of a problem. This covers a case study on production loss during processing & steps how to overcome this issue.
TIME SERIES ANALYSIS USING ARIMA MODEL FOR FORECASTING IN R (PRACTICAL)Laud Randy Amofah
Time series refers to a set of observations on a particular variable recorded in time sequence. This time sequence or space can be hourly, daily, weekly, monthly, quarterly or yearly. The dataset that will be used is the daily-minimum-temperatures-in-me.csv you can download it from Kaggle. The libraries that will be used for the model in time series are series and forecast.
Mba om 14_statistical_qualitycontrolmethodsNiranjana K.R.
Topic: Operations Management, Degree: MBA, Semester: II Syllabus: Mysore University. Date : Jan 2015.
Please note: This was prepared as a teaching aid. Not for commercial purposes. Sharing to spread the knowledge of operations management. Note : Copyright belongs to respective owners. List of top references used to prepare these slides given.
If you have any questions, comments, improvement suggestions, Email to: niranjanakoodavalli@gmail.com
Thermal Resistance Modelling with SimScaleSimScale
Thermal resistance network models are thermal models that represent the complex characteristics of an electrical component in a simple form that can be used to carry out thermal analysis in many ways. We can also use thermal resistance models in CFD to obtain better results without adding excessive complexity to the simulation.
Learn what thermal resistance network modelling is, how you can use them in SimScale, and how you can find the resistance values with a detailed thermal model if the manufacturer has not provided them.
Failure Diagnostic and Performance Monitoring, it is a part of CM program for airlines, it is addressing the condition of components of the aircraft, either it is initial , random or wear failures.
Standard procedure by DMAIC approach to find solution of a problem. This covers a case study on production loss during processing & steps how to overcome this issue.
TIME SERIES ANALYSIS USING ARIMA MODEL FOR FORECASTING IN R (PRACTICAL)Laud Randy Amofah
Time series refers to a set of observations on a particular variable recorded in time sequence. This time sequence or space can be hourly, daily, weekly, monthly, quarterly or yearly. The dataset that will be used is the daily-minimum-temperatures-in-me.csv you can download it from Kaggle. The libraries that will be used for the model in time series are series and forecast.
Mba om 14_statistical_qualitycontrolmethodsNiranjana K.R.
Topic: Operations Management, Degree: MBA, Semester: II Syllabus: Mysore University. Date : Jan 2015.
Please note: This was prepared as a teaching aid. Not for commercial purposes. Sharing to spread the knowledge of operations management. Note : Copyright belongs to respective owners. List of top references used to prepare these slides given.
If you have any questions, comments, improvement suggestions, Email to: niranjanakoodavalli@gmail.com
Thermal Resistance Modelling with SimScaleSimScale
Thermal resistance network models are thermal models that represent the complex characteristics of an electrical component in a simple form that can be used to carry out thermal analysis in many ways. We can also use thermal resistance models in CFD to obtain better results without adding excessive complexity to the simulation.
Learn what thermal resistance network modelling is, how you can use them in SimScale, and how you can find the resistance values with a detailed thermal model if the manufacturer has not provided them.
Failure Diagnostic and Performance Monitoring, it is a part of CM program for airlines, it is addressing the condition of components of the aircraft, either it is initial , random or wear failures.
Response Surface Regression - a useful tool for data mining, historical data analysis, and identifying critical factors in your process optimization efforts.
The Prediction Of Time Trending Techniques. Is It A Reasonable Estimate?Gan Chun Chet
Time prediction (modelling) techniques use to analyze machine setup (performance) time. These time series techniques are compared with probability and categorization method, found to be coherent.
Also with reference to Noise Prevention in Factories training dated 20 March 2012 at IEM Penang (noise calculation).
In this paper, we propose a new technique for implementing optimum controller for a conical tank. The objective of the controller is to maintain the level inside the process tank in a desired value. Hence an attempt is made in this paper as Internal Model Based PID controller design for conical tank level control. For each stable operating point, a first order process model was identified using process reaction curve method. The real time implementation is done in Simulink using MATLAB. The experimental results shows that proposed control scheme have good set point tracking and disturbance rejection capability.
six sigma DMAIC approach for reducing quality defects of camshaft binding pro...Niranjana B
Data collection for 11 months revealed that 26% of the defects are due to improper camshaft binding. The six sigma approach involves DMAIC approach with statistical tools involved in each stage. The main root are identified and improvements are implemented. The quality is improved by reducing the number of defects
PID Tuning for Near Integrating Processes - Greg McMillan DeminarJim Cahill
Greg McMillan shares how to reduce tuning time for near integrating processes.
Recorded video version available for viewing at: http://www.screencast.com/t/NmUxZTBiNTg
RGT is a planned test-analyze-and-fix (TAAF) process in which End Unit is tested under actual, simulated, or accelerated environments to disclose design deficiencies and defects. It is intended to provide a basis for early incorporation of corrective actions and for verification of their effectiveness, thus promoting reliability growth. RGT is intended to correct failures that reduce operational effectiveness and failures that increase maintenance and logistics support costs.
Similar to Six Sigma Project = Internet Sample (20)
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
3. Major Field Heat Sink type of Component Problem is the Voltage regulator ICX3400 used on CA Chassis . Define FIELD DATA
4. Define In Process Data There is a direct CORROLATION between the FIELD Data (previous Page) and the In Process Data (above) showing that CA ICX3400 is the component that most fails . Based On this the Team will Focus on Improving the In Process indexes for CA Chassis ICX3400 since by doing so we can Safely State that we will Improve the RELIABILITY RISK FACTOR IN THE FIELD of all Heat Sink Type Components regardless of Family . The Improvement will come from a Stable Heat Sink Assy. Process and the required Control that this team will set .
5. Define In Process Data There is a direct CORROLATION between the FIELD Data (see Page 3) and the In Process Data (above) showing that CA ICX3400 is the component that most fails . Based On this the Team will Focus on Improving the In Process indexes for CA Chassis ICX3400 since by doing so we can Safely State that we will Improve the RELIABILITY RISK FACTOR IN THE FIELD of all Heat Sink Type Components regardless of Family . The Improvement will come from a Stable Heat Sink Assy. Process and the required Control that this team will set .
6. GAP = 0.18 Goal = 50% defect reduction (in PPM) Define Project Goal
10. Measure Gage R&R Analyze The Short Term Method was used for Gage R&R since No Repetitions Can be Done on the Same Part (Torque) . And as can be seen our Gage R&R % Tolerance is 64.9 . NOT ACCETABLE Gage R&R Analyze All Conditions being the same we have variance between Operator and Operator The ROOT CAUSE of the Variance Needs to Be Found and Eliminated Before continuing with this project .
11. Measure Gage R&R #2 After the first Gage R&R Study produced Unsatisfactory results the Process was Study Closely . Was was noticed that operator at times do the TORQUE operation and than they RE-Torque Same part Again and Again . At this time the operator was instructed in the proper Techniques doing the operation . Basically torque until the clutch engages for the first time at this time do not re-torque (No double Clicks) Yielding the following Gage R&R . This Gage R&R is now acceptable since it is <30% .
12. Analyze Process Capability Study Plan GUN # & Operator 10 Samples Start Normal Distribution ? YES No Increase Sample size Graph Process Capability Process Capable ? YES No Investigate & Eliminate Variance This is the Process Capability Plan for the Heat Sink Assy. Area . Each Gun/ Operator Combination used for Assembly of CA Heat Sinks sinks will be study . If any are found to be Not Capable of operating within its Specs. The Assignable Cause will be INVESTIGATED , ELIMINTAED and CONTROLLED . This arrangement is required since this area is configured as a CELL Type work environment were each station does its complete Assy.
13. Analyze Tools To Be used In The Study Cal. Lab. Results for this Instrument. OK to use for Gun Set-up Torque Meter for gun Set-up Applied Torque Auditor Cal. Lab. Results for this Instrument. Show well suited to use for studying Applied Torque
14. Analyze Current Process Capability The Normality test above tells us that our process has a Normal distribution characteristics thus our 10 samples can be used to graph a process capability .
15. Analyze Current Process Capability Study The above shows that Gun 05 coupled with operator “ANA” are Capable of producing parts with Applied Torque within the specified Limits . Combined with Operator “ANA” CONCLUSION
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