Microsoft Root Cause Analysis Webinar Introduction V2 987 RCA By Ops A La Carte
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Root Cause Analysis Webinar Phone: (702) 824-9512 Access code 204-987-863 Sponsored by Ops A La Carte DfR Solutions SigmaQuest INTRODUCTION • Thank you for joining us this morning (or afternoon) • In April of this year, we held our annual Reliability Symposium in Santa Clara, California, featuring 7 of our reliability seminars, and Root Cause Analysis (RCA) was one of these seminar. • Based on the response of that seminar, we decided to highlight RCA as our featured service in our newsletter last quarter http://www.opsalacarte.com/Newsletters/2008summer_news.htm and decided to hold a webinar to provide further information. • We invited two of our solutions partners – DfR Solutions and SigmaQuest – to participate in this webinar because their complementary offerings really help to portray a more complete view on RCA. INTRODUCTION There are over 700 people registered for this webinar so we obviously hit on a very hot topic. FORMAT • Four different experts will give presentations • At the beginning and end of each presentation, we will be asking “polling” questions to get a better idea on the make-up of the audience and your level of interest/experience. We will make these statistics available to the audience after the webinar is over. • During the discussion, feel free to ask any questions you’d like by typing into the question area on the right. FORMAT • At the end of each presentation, we will review all the questions that came in during that portion of the presentation and then will respond to as many as we have time for in the remaining portion of that section. • After the end of the webinar, there will be a short set of prepared survey questions. FORMAT • For any questions not answered in that time, we will respond to each person individually after the webinar is over. • If you think of a question after the end of the webinar, feel free to email it to me at mikes@opsalacarte.com and I will make sure to get the question to the correct panelist. FORMAT • At the end of the presentation, we will send you a follow-up email, thanking you for attending. • For those of you interested, we can also send a copy of the slides. • We will also provide you with a way to contact us if you need further information. PRESENTATIONS 0) 9:00-9:15am: Introductions 1) 9:00-11:00am: Understanding the Motivation and Basics of Root-Cause Analysis in Electronics. By: Jim McLeish, CRE, Senior Technical Staff, DfR Solutions 2) 11:00-11:45am: Understanding Techniques to Address Mechanical Components in the Evaluation of System Reliability. By: Cliff Lange, Ph.D., PE, Ops A La Carte 3) 11:45am-12:30pm: A Mechanical RCA Case Study. By: Kim Parnell, Ph.D., PE, Ops A La Carte 4) 12:30pm-1:00pm: Data Collection: An Important Aspect of RCA Investigation. By: Al Alaverdi, VP Technology, SigmaQuest PRESENTERS Presentation 1: Understanding the Motivation and Basics of RootCause Analysis in Electronics. Summary: Before successful Root-Cause Analysis can even start, organizations and individuals must understand the need to have basic problem solving skills, tools and knowledge of how problems occur and how they can be fixed. This portion of the webinar will discuss the fundamentals of RCA and cover some of the best practices in the electronics industry from the Physics of Failure point of view. Author: Jim McLeish, CRE, Senior Technical Staff, DfR Solutions Jim has 30 years of automotive Electrical/Electronics (E/E) experience. He has worked in systems engineering, design, development, production, validation, reliability and quality assurance of both components and vehicle systems. He holds three patents, is the author or co-author of three GM E/E validation and test standards and is credited with the introduction of Physicsof-Failure engineering techniques to GM. PRESENTERS Presentation 2: Understanding Techniques to Address Mechanical Components in the Evaluation of System Reliability Summary: In this portion of the webinar, we will first review the standard design guidelines for robust mechanical design. This is followed by a brief review of the critical elements of mechanical systems and the corresponding failure mechanisms. Then, we will go through a detailed review of RCA for a high temperature power plant creep failure and the analysis of fatigue of wind turbine blades. Author: Cliff Lange, Ph.D., PE, Ops A La Carte Cliff has 30 years of industry experience in both reliability engineering and root cause failure analysis. Most recently Dr. Lange spent 12 years developing reliability programs for the Semiconductor Equipment Manufacturing industry. He worked at General Electric Company and Exponent Failure Analysis where he gained extensive experience in finite element modeling and root cause analysis of structural, mechanical and electrical failures. PRESENTERS Presentation 3: A Mechanical RCA Case Study Summary: This portion of the webinar will provide an overview of a particularly spectacular process plant accident in Nevada. This incident became visible as a small fire which spread rapidly and ultimately ended with two devastating explosions. Through this case study, we will show how to develop a scenario and an initial sequence of events, modify scenarios based on new evidence, and identify the Root Cause of this accident and the sequence of events leading to the ultimate catastrophe. Author: Kim Parnell, Ph.D., PE, Ops A La Carte Kim specializes in failure analysis and reliability of mechanical systems. He is an expert in mechanical engineering design and behavior of systems ranging from biomedical devices, to electronic and miniature components, to power generation, automotive, and aerospace applications. Kim is an independent consultant and was previously a Senior Manager with Exponent where he analyzed and investigated accidents and failures in a variety of industries. Kim has MS and PhD degrees in Mechanical Engineering from Stanford. PRESENTERS Presentation 4: Data Collection: An Important Aspect of RCA Investigation Summary: A company needs a good data collection system that quickly and easily identifies and corrects the root cause for failures which result in warranty returns - to uncover emerging trends and patterns before they become issues. This, in turn, will provide a number of benefits which we will address in this portion of the webinar. Author: Al Alaverdi, VP Technology, SigmaQuest Al has over 20 years of experience in testing and manufacturing software development. Al is an expert at process engineering and in the development of tools to enhance product performance and manufacturing efficiencies. SPONSORS Ops A La Carte DfR Solutions SigmaQuest SPONSORS Ops A La Carte is a Professional Consulting Firm focused on Reliability Engineering Services, Reliability Management, and Reliability Education to assist you in developing and executing any and all elements of Reliability throughout your Organization and your Product’s Life Cycle. We work in the area of Electronics, Mechanical Systems, and Software. In the area of RCA, Ops A La Carte has performed countless root-cause analyses in the area of electronics, mechanics, and software. SPONSORS DfR Solutions has world-renowned expertise in applying the science of Reliability Physics to electrical and electronics technologies, and the company is a leading provider of quality, reliability, and durability (QRD) research and consulting for the electronics industry. DfR's integrated use of Physics-of-Failure (PoF) and Best Practices provides crucial insights and solutions early in product design and development, and throughout the product life cycle. In the area of RCA, DfR Solutions has their own failure analysis lab in Maryland and has performed over 250 root-cause investigations in the past 4 years SPONSORS SigmaQuest provides an on-demand suite of solutions that help companies build better products using business intelligence techniques for product design, manufacturing, supplier quality, repair and returns. Benefits are reduced warranty costs; improved product quality, lower costs of goods sold, and increased revenue and profits. In the area of RCA, SigmaQuest is well positioned because its solutions can be used for collecting failure data for use in the critical step of analyzing and gathering data/evidence. Understanding the Motivation and Basics of Root-Cause Analysis in Electronics Abstract: Before successful Root-Cause Analysis can begin, organizations and individuals must understand the need to have basic problem solving skills, tools and knowledge of how problems occur and how they can be fixed. This portion of the webinar will discuss the fundamentals of RCA and cover some of the best practices in the electronics industry from the Physics of Failure point of view. James McLeish, CRE © 2008 Background: Jim McLeish Education: Dual EE/ME MS in Electronics Control Systems ASQ-CRE (American Society of Quality - Certified Reliability Engineer) 32 years of Automotive, Military and Industrial Electrical/Electronics (E/E) Part 1: Product Design, Development, Systems Engineering & Production 3 Patents Electronic Control Systems EE System Engineering and Architecture Planning Product Engineering Management Part 2: Validation, Reliability, Quality Assurance, Warranty Problem Solving & Test Technology Development Part 3: Senior Technical Staff/Consulting Associate - Design for Reliability Solutions. © 2008 Variety of Management & Technical Leadership Positions: Principle Investigator for E/E Failure Analysis and Root Cause Problem Solving. E/E Manufacturing Process Optimization, Yield Improvement. Reliability Demonstration, Product Validation and Accelerated Testing. Field Return/ Warranty Analysis Design Reviews for Proactive Problem Prevention Society of Automotive Engineering (SAE) - Reliability Committee DOD MIL-HDBK-217 Update & Enhancement Tea 2 Background: DfR Solutions DfR Solutions is an Engineering- Laboratory Services and Consulting firm experienced in Physics of Failure based Quality, Reliability and Durability (QRD) research, consulting and applied science for electrical and electronics products and technologies. The DfR staff provide knowledge and science based solutions that maximize product integrity and accelerate product assurance activities. DfR captures the broad range of reliability and quality issues in electronics through the expansive expertise of our multi-discipline staff. Physicists, Material Scientists, Chemists and Electronic Engineers from Various Industry Segment. Over 500 failure analysis and root-cause investigations in the past 4 years, A world leader in failure analysis in electronics. Strong partnerships with the leading companies in the field of electronics, DfR strives to make our clients life easier by providing knowledge based solutions for electronic quality, reliability and durability issues. From component specifications and computer modeling based lifetime predictions. From robust design of products and process to accelerated product qualification. From technology insertion to RCA and failure analysis. 3 © 2008 1) Motivation for Root Cause Analysis The 1st rule of business is now: “The competitor who Consistently, Reliably and Profitably provides the greatest value to customers FIRST wins.” 2nd rule is: "There are NO OTHER RULES". In other words it’s Survival of the Fittest and the Best. Continuously Improvement is Essential to Becoming and Staying the Best or At Least Remaining Competitive. © 2008 4 1) Motivation For Root Cause Analysis - Continuous Improvement (C.I.) Continuously Improvement is the ongoing effort to improve products, services or processes, in order to advance the goals of an organization, business or society. A never ending effort to discover and eliminate: Inefficient process road block and bottle necks, Non value added activities, Problems, Either “incremental” improvement over time or “breakthrough” improvement all at once. Japanese Version Kaizen - “Change for the Better”. Examples of C.I. Tools Statistical Process Control 6 Sigma Quality Best Practices / Leasons Learned Process Optimization Problem Solving 5 © 2008 2) Introduction to Root Cause Analysis - Problem Solving Problem Solving is an integral part of cognitive thinking & decision making. It is essential to many aspect of daily live, it involves: Problem solving method examples: Trial-and-error Brainstorming Root Cause Analysis Problem Solving uses similar skills as: © 2008 Using tools to obtain relevant data, information and knowledge, Creating mental models of situations and how the world works, Make logical connections that lead to the formation of potential solution concepts, Evaluate the potential solutions against goals, constrains and desires. Solving a puzzle Detective work. 6 2) Problem Solving, Failure Analysis & Continuous Improvement Has Been the Basis of Engineering Since Humans First Make Tools & Structures Lessons Learned for Problem Solving During the Construction of the Early Step & Bent Pyramid Enabled the Ancient Egyptians To Later Build Bigger & Better Pyramids 7 © 2008 3) Introduction to Root Cause Analysis (RCA) Root Cause Analysis - is a category or problem solving methods that focus on identifying the ultimate underlying reason of why an event occurred. Based on the belief that problems are more effectively solved by correcting or eliminating the root causes, rather than merely addressing the obvious symptoms. The root cause is the trigger point in a causal chain of events, which may be natural or man-made, active or passive, initiating or permitting, obvious or hidden. Efforts to prevent or mitigate the trigger event are expected to prevent the outcome or at least reduce the potential for problem recurrence. RCA is a full-blown analysis that identifies the chain of physical and human related root cause(s) behind an undesirable event . This differs from basic troubleshooting and problem-solving processes that typically seek solutions to specific, relatively simple difficulties. The undesired event may be a product durability failure, a safety incident, a customer complaint, a quality defects, human error . . . etc. It helps focus CA/PA (Corrective Action / Preventive Action) efforts at the points of most leverage it is essential for pointing change management efforts in the right direction. © 2008 8 3) Introduction to Root Cause Analysis - Failure Analysis (FA) Failure Analysis is a subcategory of RCA techniques Systematic examination of “Failed Devices” to determine the root cause of failure. Use knowledge gained to improve technology, quality and reliability. Primarily associated with the physics and material science of mechanical, structural and E/E (Electrical /Electronic) devices and materials (i.e. hardware). Software FA is a growing subcategory involving computer science & programming. Forensic Engineering a subcategory that uses science and technology to investigate materials, structures, products or components that fail or malfunctions to establish facts for criminal or civil legal actions. 9 © 2008 3) Introduction to Root Cause Analysis - Failure Analysis (FA) Failure analysis is designed to: Identify the failure modes (the way the product failed), Identify the failure site (where in the product failure occurred), Identify the failure mechanism (the physical phenomena involved in the failure), Determine the root cause (the design, defect, or loads which led to failure) and recommend failure prevention methods FA begins with non-destructive techniques, then proceeds to destructive techniques. © 2008 10 3) Introduction to Root Cause Analysis - Section Summary The Hierarchary: Continously Improvemet Essential to being compeditive and advancing objectives. Problem Solving An important method for continuous Improvement. Root Cause Analysis One type or problem solving approach that works to identify not only what and how an undesired event occurred, but also why it happened so as to prevent reoccurance. Failure Analysis A broad subcategory of Root Cause Anaylsis techniques that can be used when failed or malfunctioning devices are available for examination. FA has many sub categories and specialists realated to the type of technologies and materials that failured. 11 © 2008 4) RCA Approaches, Management & Reporting Methods Root cause analysis is a generic term for diligent structured problem solving. Over the years various RCA techniques and management methods have been developed 5 of the most popular RCA approaches are: © 2008 The” 5 Whys” Technique The 8D (Eight Disciplines) Problem Solving Process Shainin Red “X” Statistical Problem Solving Six Sigma Physics of Failure / Reliability Physics 12 4.1) The 5 Why Approach 1) WHY? 2) WHY? 3) WHY? 4) WHY? 5) WHY? Mom, Why is the Sky Blue? Why Can’t we see God? Why is water wet? Why . . . 13 © 2008 4.1) The 5 Why Approach The 5 Why’s is a simple problem-solving technique developed by Toyota* to quickly get to the root of a problem. The 5 Why strategy involves looking at any problem and asking: “Why”? and “What caused this problem”? The answer to the first “why” must prompt another “why” and the answer to the second “why” must prompt another and so on. The rule of thumb is that the “Why” question must be asked & resolved at least 5 times in order to identify the true underlining root cause of the problem. Toyota’s Philosophy: A Rush to action that addresses only symptoms or a problem only produces temporary relief. Only after the “True” Root Cause has been identified can an “EFFECTIVE STRATEGY TO PERMANENTLY RESOLVE” the issue be developed. * Ref. “The Toyota Way, by Jeffrey K. Liker, McGraw-Hill 2004 © 2008 14 4.1) The 5 Why Approach Example: WHY WHY ISSUE OBVIOUS RESPONSE There is an oil spill on the floor This is a safety hazard, Clean it up A machine is leaking oil Fix the oil leak A gasket has failed Replace the gasket The gasket is made out of paper which breaks down quickly Find a better gasket Low cost paper gaskets were purchased instead of durable graphic or silicon gaskets Developed detailed specifications to provide better direction to purchasing Purchasing bonuses are based on up front cost savings not long term performance Change purchasing incentive policy to promote total value over short term savings WHY WHY WHY Toyota is known of not stopping at the technical issues. They continue until the root causes of organization, cultural & people motivation issues are also understood & addressed. 15 © 2008 4.1) The 5 Why Approach Vague Perception of Problem The “Funnel Model” Clarify Issues Phase 1) Identify the Issue Identify a Problem or Concern Issue Obvious Cause WHY Phase 2) Issue Investigation Cause WHY Cause WHY Cause WHY WHY Phase 3) Corrective Action Cause ROOT CAUSE Develop Corrective Action Evaluate / Verify Effectiveness Implement & Standardize Across the Organization © 2008 16 4.1) The 5 Why Approach - Summation Benefits Easy to remember, Simple to apply, Gets deeper into “Root Cause” than many other problem solving techniques, so better in the long run. Informal, flexible, open structure, little bureaucracy Organizations/users adapts to their own needs. Potential Issues / Concerns. More time consuming investigate than quick fix approaches. Sorting out issues with MORE THAN 1 CAUSE. Mistakes in developing/answering a “Why” question can mislead the investigation. Requires some Subject Matter Expertise Hardest part of 5-Whys is asking the right “Why” questions. Every organization does not have access to experts in every area. Depends on some knowledge of cause & effect. To ask the right questions, Know how to follow them up in order to reach the right conclusions. Novices can follow the wrong path. Informal, flexible, open structure, little bureaucracy = Little guidance. Repeatedly ask why can appear threatening to involved people. Fear of an inquisition and assigning blame. Self preservation instincts can lead to lack of cooperation or hiding information. 17 © 2008 4.2) 8D (Eight Disciplines) Team Problem Solving Process 8D is a problem-solving methodology that emphasize team synergy. Originated in 1974’s as part of MIL-STD-1520 Ford introduced and popularized the process within the Auto Industry in 1987. “Corrective Action & Disposition System for Nonconforming Material” First known as TOPS - “Team Oriented Problem Solving“. Evolved into today’s widely used 8D process. Philosophy - When a problem cannot be solved quickly by an individual, a team approach is the most effect way to resolve the situation. Team are more effective than the sum efforts of individuals working separately. Essential to assign the right members to each a team and support them. © 2008 Team members need to have the inclination and skills needed for each problem Team members need to be provided with the time and resourced need to solve the problem. 18 4.2) 8D Problem Solving Process (PSP) Project Initialization Root Cause Investigation Team Appropriate Problem Identified D4 – Determine & Verify Root Causes D1 - Select & Empower a Team Investigate and Select Most Likely Causes Identify Potential Root Causes D2 - Describe the Problem No D3 - Implement & Verify Interim Containment Actions Is the Potential Cause a Real Cause Yes Identify Potential Corrective Action Implement Corrective Acton D5 – Verify Corrective Action D6 – Implement Permanent Corrective Action D7 – Prevent Reoccurrences D8 – Congratulate the Team 19 © 2008 4.2) 8D PSP - Phase 1 Project Initialization Starting Point - An Appropriate Problem is Identified. 8D Method does not define how problem awareness is developed. Always use the right tool for the job: D1 - Use Team Approach Establish a small group of people with the collective knowledge, time, authority and skills to solve the problem, develop and implement corrective actions. Provide each team with an executive champion to report to and clear roadblocks. Each team requires a team leader to pace the process, lead meetings, coordinate team efforts. Intermix skills: problem solvers, technical knowledge, manuf. process, test, analysis . . . etc. Ensure team members have the inclination to work towards a common goal. D2 - Describe the Problem You can not fix a problem you don’t know what’s broke. Ensure problem warrant the resources of team PSP effort. Avoid one size fits all tool and processes. Avoid management dictates i.e. “all departments MUST deploy at least five 8D PSP per year”. Clearly describe the problem in measurable, specific terms. Clarify what, when, where and how much, impact to customers. Info will be needed later to measure corrective action effectiveness. D3 - Implement and Verify Short-Term Containment Actions Stop or limit the bleeding as quickly as possible. © 2008 Define and implement screens, extra Q.C procedures, Rework . . . other appropriate actions. To protect the customer & limit losses from the problem until a permanent C.A. is implemented. Verify effectiveness with data and enhance if necessary. 20 4.2) 8D PSP - Phase 2 - Root Cause Investigation 4. Determine and Verify Root Causes Phase where team conducts the actual root cause Investigation. Team applied experience and brain storm of preliminary information to identify potential causes. Team collects data, follows leads, performs analysis, authorizes test, apply statistics . . . etc. Specific procedures or tools not defined by the 8D process. Team empowered to follow the facts, apply their expertise and available resources to determine the best investigation approach. Identification of “true” root cause(s) must be verified, proven and documented by data not opinion) to proceed to corrective action activities. Concludes with team proposal for potential corrective action(s). 21 © 2008 4.2) 8D PSP - Phase 3 - Corrective Action 5. Verify Corrective Actions 6. Implement Permanent Corrective Actions Revise the product and/or process to implement the permanent fix Establish monitoring to make sure it’s working. If issues reoccurs implement additional controls or go back a few steps & try again. 7. Prevent Recurrence Select the best case or optimal corrective action. Perform test builds, process runs & evaluations to verify effectiveness & feasibility. Confirm that the selected CA effectively resolves the problem without side effects. Develop Corrective Action business case and obtain management approval. Improve practices & procedures to prevent recurrence of this & similar problems. Modify specifications, update training, document lessons learned, review work flow. 8. Congratulate Your Team © 2008 Recognize the collective efforts of your team. Publicize accomplishments, share knowledge & learning across the organization Going public with success spreads knowledge and learning. Letters of thanks, certificates of recognition. 22 4.2) 8D Sample Reports & Worksheets Many 8D report templates exist. Simple: “just the facts” & results documentation reports (Ref. Example right). Complex: “document every step” formats that include pages of worksheets for preferred tools (Ref. Following Example). No Universal Format Many format variations possible. Use what works for your products, organization & customers. 23 © 2008 4.2) 8D PSP Variation - The 5 Phase PSP Simplified Version of the 8D. Used to resolve & document less complex / everyday issues. That don’t require the resources or expertise of a team approach. Many Common Features: 1) Problem Description. 2) Immediate Actions. 2) Root Cause Conclusions. 4) Corrective Acton Plan (CAP) RCA Investigation Plan Optional. A Lesson Learned Opportunity? Part / Process & The System. 5) Verification/Validation of CAP. No Universal Format © 2008 Many format variations possible. Use what works for your products, organization & customers. 24 4.2) 8D/5 Phase Problem Solving Processes - Summation Benefits - Address a Number of 5 Why Concerns, Early initial problem containment counter balance time need for thorough RCA Drawing on team experience reduces potential for RCA errors. Team format expands potential to tap available subject mater expertise. Opportunity for novices to learn from more experienced personnel. Provides a formal PSP structure without dictating methods and tools. Easily converted into an 8D problem solving/ RCA report Example 8D worksheet/ report template (on following pages) provides: Team retain freedom to select tools and follows leads. Team members feel empowered, respected and appreciate trust. Sections for documenting outcome of all 8 steps. RCA Worksheet for 5 Why and Fishbone Cause & Effect diagrams. Status documentation Potential Issues / Concerns. 8D structure provides susceptibility to excessive bureaucracy & micro-management. Excessive status report updates detract from problem solving efforts. Process management personnel represent non-value added overhead. Management “throughput / efficiency” improvement efforts that degrade RCA effectiveness (teams will avoid time consuming hard problems to avoid poor performance ratings in systems that emphasize quantity over quality) Management with lackluster team recognition / congratulations 25 © 2008 4.3) Shainin Red “X” - Diagnostic Journey Eliminate Source of Cracking/Breakage of Vehicle Exterior Rear View Mirror Glass. Vehicle Relate A Red X Statistical “Journeyman” or “Master” start the process by organizing a team of problem stake holders. The team creates a problem definition tree diagram (similar to a fault tree minus the logic symbols). Use the diagram as a guide for evaluating the impact of each factor. Create a visual map of the issue or sequence of events that relate to or resulted in a failure. Included relevant issues & realistic contributing factors. Use progressive search questioning strategy a series of (yes/no) questions concerning degree contribution to reduce the field of suspects. Cross off the factors that are minor contributors to the outcome to eliminate them from serious consideration. The remaining factor in each category line are considered to be the factors worthy of detailed statistically evaluations. © 2008 Part Related Event Inop. (4) Defect (27) Cracked (11) Loose (3) Non Customer Induced (4) Falls Off (1) NTF (8) Customer Induced (7) Power Heated Mirrors (DL3/DL8/DFP)(4) Other Mirrors (0). Thin Line Cracks Driver’s Side Feature Other Types of Crack Both Pass Side Region to Region Same Glass Other Strategies) Crack in East-West Direction Crack in Other Directions Top of Glass . Center of Glass Bottom of Glass 26 4.4) Six Sigma (6σ) A methodology for “Improving Business Performance”. Pioneered by Motorola Q.A. manager Bill Smith (mid 80’s) who proved that: Manuf. lines with high in-process defects rates requiring Rework/Repairs (R/R) had higher field failure rates & warranty costs than lines with low repair rates. Low repair rate (build right on the first attempt) lines also had improved customer satisfaction that resulted in better sales. Lines with “better/tighter process capability” resulted in: Root Causes: Defect escapes from quality control systems. Inadvertent, hidden damage during addition handling, rework & retest. “Higher First Pass Quality” making them “More Efficient & Cheaper to Operate”, even if the better equipment had higher up front costs, due to: Less Effort & Costs for the “Hidden Factory” (Q.A, R/R & Root Cause). Improved efficiency from higher throughput. “Quality Pays” Even Better than Phil Crosby’s “Quality if Free” Philosophy. Enabled QRD professional to communicate in the native language of executive management: “Time and “$” Money” 27 © 2008 4.4) Six Sigma (6σ) 3σ Sigma σ is the symbol for Statistical Standard Deviation of the normal distribution (bell curve). The “σ” measurement scale define how much of process’s normal distribution is capable of being contained within required tolerance limit “ON THE FIRST PROCESS PASS”. Out of spec “defects” are measured in terms of Defects Per Million Opportunities (DPMO). Processes that operate at a “6σ” quality capability level produce < 3.4 DPMO “for each operation”. DPMO is related to process operations not the number of parts produced, Example: 6σ Spread A circuit board requires 100 component placement operations so 1,000,000 placements ~ 10,000 boards. The same board requires 500 solder joints so 1,000,000 soldering operations ~ 2,000 boards. 10,000 6σ boards would require no more than 3.4 placement repairs & 17 solder repairs. Wave soldering typ. run at 100-500 DPMO (4.78-5.19σ), Reflow soldering is typ, 25-100 DPMO (5.55-4.78σ). The goal is more capable processes that produce a tighter variation spread within the spec limits © 2008 σ ( Std Dev) Conversion Table σ In Spec Yield DPMO (Outliers) 1 2 3 4 5 6 30.85% 69.14% 93.32% 99.38% 99.9767% 99.99966% 691,462 308,534 66,807 6,210 233 3.4 6σ 4σ 3σ 28 4.4) Six Sigma (6σ) Improvement Processes DMAIC - Define, Measure, Analyze, Improve & Control The 6σ improvement system for: DMADV - Define, Measure, Analyze, Design & Verify The 6σ improvement system for: Developing new processes or products or Resolving design related problems. Also used in Design For Six Sigma (DFSS) a methodology for new produce development. Existing” processes related problems Sub-optimized process that fall below specification & yield expectations. Obvious similarities with the previously discussed 8D and 5 Phase PSP’s Different definitions and terms. Some differences in statistically tools. 29 © 2008 4.5) Physics of Failure - Definitions Physics of Failure (PoF also known as Reliability Physics). A Proactive, Science Based Engineering Philosophy. Development & Applied Science of Product Assurance Technology base on: A Formalized and Structured approach to Failure Analysis/Forensics Engineering that focuses on total learning and not only fixing a current problem. Material Science, Physics & Chemistry. Variation Theory & Probabilistic Mechanics. Up Front Understanding of Failure Mechanisms and Variation Effects. Knowing how & why things fail is equally important to understand how & why things work. Knowledge of how thing fail and the root causes of failures, enables engineers to identify and design out potential failure mechanisms in new products and solve problems faster. Provides scientific basis for evaluating usage life and hazard risks of new materials, structures, and technologies, under actual operating conditions. Applicable to the entire product life cycle © 2008 Design, Development, Validation, Manufacturing, Usage, Service. 30 4.5) PoF Grew Out of the Limitations of Statistics Based Reliability Prediction Fundamental Limitations Statistical probability should be used only when we lack knowledge of the situation and cannot obtain it at a reasonable cost. "Statistics are applicable only when: 1. You are unavoidably ignorant about a given issue, 2. Some action is necessary and cannot be delayed." Leonard Peikoff In Book & Lectures on The Art of Thinking In other words, if you're trying to determine a course of action: - Your best bet is to acquire knowledge and not to blindly use statistics to play the odds. 31 © 2008 4.5) A View of Quality, Reliability & Durability (QRD) Via The Traditional Product Life Cycle Failure Rate “Bath Tub” Curve Problem or Failure Rate Focuses on 3 Separate Phases with Separate Control & Improvement Strategies Average Repurchase Decision The Bath Tub Curve (Sum of 3 Independent Phenomena) But “True” Root Causes Can Be Disguised by Statistical Assumptions to Make QRD Easy to Administer This is an Inaccurate & Misleading Point of View Quality = Infant Mortality Durability = Wear Out (End of Useful Life) Reliability = Random or Chance Problems (Constant Unavoidable) Time 0 © 2008 1 Year 2 Years 3 Years 4 Years 5 Years 32 4.5) A “PoF FAILURE MECHANISM” Based “REALISTIC” View Reveals the True Interactive Relationships Between Q, R & D - Real failure rate curves are irregular, dynamic and full of valuable information, not clean smooth curves to simplify the data plots. Problem or Failure Rate Manuf. Variation & Error and Service Errors That Cause Latent Problems Throughout Life Weak Designs That Start to Wear Out Prematurely “Cause & Effect” Root Causes Can Be Disguised by Statistics Once Problems Are Accurately Categorized You Have a More Effective Point of View TRUE Random Problems Are Rare Once Correlated to “ACTS OF GOD & WAR” Time 0 1 Year 2 Years 3 Years 4 Years 5 Years 33 © 2008 4.5) Root Cause Implications of the Physics of Failure Point of view The focus of “Traditional Reliability Methods” on “Random/Chance Failures” conveys a perception that problems and failure are inevitable & unavoidable. “Resistance is Futile” The Physics of Failure approach emphasizes: An ordered understandable, predictable universe of cause & effect relationships. The role of root cause analysis problem solving for discovering, understanding and mastering these cause and effect relationships. Using RCA to build a “Compendium of Formalized, Institutionalized Knowledge” for Future Problem Prevention as well as for solving today’s problems. © 2008 34 4.5) Key PoF Terms and Definitions Failure Site : The location of potential failures, typically the site of a designed in: stress concentrator , design weakness or (designed in) material variation or defect. (process related or Inherent) Knowledge Used to Identify and Prioritized Potential Failure Sites and Risks in New Designs During PoF Design Reviews. 35 © 2008 4.5) 3 Generic PoF Failure Categories and Detection Methods GENERIC FAILURE CATEGORY Errors - Incorrect Operations & Variation Defects/Weaknesses. Missing parts, incorrect assembly or process. Process control errors (Torque, Heat treat). Design errors Missing functions, Inadequate performance. Inadequate strength. Overstress. Overheating. Voltage/Current Electro static discharge. Immediate yield, buckling, crack. Wearout/Changes, via Damage Accumulation. Friction wear. Fatigue. Corrosion. Performance changes/parameter drift © 2008 TYP. FAILURE DETECTION Quality Assurance Immediate or Latent defects Performance Capability Assessments Stress-Life Durability Assessments 36 4.5) Generic PoF Failure Categories 1) Overstress - When Loading Stress Exceed Material Strength STRESS/ STRENGTH Variation of Design’s Material Strengths - Related to Process Capabilities Typical Deterministic (Nominal) Analysis 4 How well σ do you | Understand 9 9 & Design For % Strengths t & Stresses? i l e 3 σ | 9 3 % t i l e DESIGN MARGIN SAFETY FACTOR 2 σ | 6 9 % t i l e UNRELIABILITY = Probability that Load Exceed Strength Stress Variation of Usage & Environments Loads & Their Interactions FREQUENCY OF OCCURRENCE 37 © 2008 4.5) Generic PoF Failure Categories 2) Errors and Variation Issues (They Are Everywhere) Errors Broadest Category Errors in Design, Manufacturing, Usage & Service. Missing knowledge Human factor Issues. Variation Fine line between excessive variation & out right errors. Both related to various quality issues. Interface Equipment Manufacturing equipment wear out & failure could be related to maintenance errors. Weak material could be raw material variation or insufficient heat treat processing errors. Equipment process capabilities limitation or operator set up error. Design & Process People Performance Usage Material Environment Noise Factors © 2008 38 4.5) Generic PoF Failure Categories 3) Wearout - Damage Accumulation In Materials 3. Strain : Instantaneous changes (materials\structural) due to loading, different loads interact to contribute to a single type of strain. 2. Stress The distribution/ transmission of loading forces throughout the device. 6. Time to 1st Failure: 1. Loads Elect. Chem. Thermal, Mech... Individual or combined, from environment & usage act on materials & structure. Knowledge of how/ which “Key Loads” act & interact is essential for “efficiently” developing good products, processes & evaluations. (Damage Accumulation verses Yield Strength A Function of: Σ [Stress Intensity, Material Properties, & Stress Exposure Cycles/Duration]. 7. Rate of Failure (Fall out) A function of variation in; Usage, Device Strength & Process Quality Control (i.e. latent defects). 5. Failure Site & Type: Typically due to a designed in: stress concentrator , design weakness, material/process variation or defect. 4. Damage Accumulation (or Stress Aging): Permanent change degradation retained after loads are removed. From small incremental damage, accumulated during periods/cycles of stress exposure. 39 © 2008 4.5) Generic Failure Categories - Wearout (Damage Accumulation) con’t 3) Wear out Over Time and Intensity of Stress Exposure STRESS INDUCED DAMAGE ACCUMULATION STRESS/ STRENGTH Design’s Strength Decay/Spreads Over Time / Usage Material Decay Increases UNRELIABILITY OVER TIME How well do you Understand & Design For Strengths & Stresses? © 2008 4 σ | 9 9 3 σ | 9 3 % t i l e % t i l e STRESS EXPOSURE TIME or USAGE CYC’S 2 σ | 6 9 % t i l e INITIAL UNRELIABILITY FREQUENCY OF OCCURRENCE 40 4.5) Generic Failure Categories Overstress - Examples of Wear Out Failure Mechanism Chemical / Contaminate Mechanical Moisture Penetration Fatigue Electro-Chemical-Migration Driven Creep Dendritic Growth. Wear Conductive Filament Format (CFF) Electrical Corrosion Electro-Migration Driven Radiation Damage Molecular Diffusion & Inter Diffusion Thermal Degradation When Over Stress Issue are Detected. Verify supplier’s are meeting material strength specs & purity expectation. Re-evaluate field loading / stress expectation used to design the part. Sort out stresses, Combined stress issues are often involved. Re-evaluate effectiveness of product durability testing 41 © 2008 4.6) Physics of Failure Examples - Circuit Board Related Vibration Durability Issues Board in Resonance Components. Shaken Off/Fatigued by Board Motion. By Flexing Attachment Features Bending Lead Wires Stressed Solder Joint Gull Wing I.C. Lead Motion - Flexed Down - Normal - Flexed up Displacement Components In Resonance. Components Shake/Fatigue themselves apart or off the Board. Especially Large, Tall Cantilever Devices 3 Med. Sized Alum CAPS 1 Small Long Leaded Snsr 1 Hall Effect Sensor. 1 Large Coil Assembly PC Board Time to Failure Determine by Intensity/Frequency of Stress Verses Strength of Material Solder Fatigue Life Log (Peak Strain) Steinberg’s Criterion: For a 10 million cycle life, Z < 0.0008995·B/(C·h·r (L1/2)). Ref: Vibration Analysis for Electronic Equipment, by David S. Steinberg Log (Number of Cycles to Failure) © 2008 42 4.6) PoF Example – E/E Module Vibration Analysis CAE Modal Simulation of Circuit Board Flexure Connector Provides Primary PCB Support Transformer A Large Mass, will drive a Large Vibration Modal Response Original Board Displacement (mils) Natural Frequency (Hz) Vib. Durability Calculation CAE Guided Redesign Adds Back Edge Support 1.15 489 > 50 Years 13.95 89 25 Days 43 © 2008 4.6) PoF Example Vibration Durability Calculations - For Alternative PCBA Support & Mass Locations DAYS TO FAILURE @ 2 Hrs Vib / Day TRANSFORMER RELOCATED ORIGINAL TRANSFORMER LOCATION || R101 + R102 || R825 1000M + R824 100 M 10 M 1M 100,000 3650 Days (10 Years) 10,000 1000 100 10 1 Edge1 Edge1 & (Connector) Corners © 2008 Edge1 & Middle Edge1, Edge1 & Edge2 Corners & Middle, All Edges Edge1 Edge1 & (Connector) Corners Edge1 & Middle Edge1, Corners & Middle, Edge1 & Edge2 All Edges || + || + || + || + || + || + || + || + || + || + || + || + || + || + || + || + || + || + || + || + || + || + || + || + 44 4.6) PoF Example - Thermal Stress Balance/Distribution & Stress Avoidance Alum. Caps Away From Heat Alum. Caps Infrared Thermal Imaging Reveals Hot Spots From Concentration of High Power Component Surrounding Heat Sensitive Alum. Electrolytic Cap. Another Design Uses an Array of Thermal Vias as a Heat Spreader to Lower Peak Temperatures. Alum. Caps Located Away From High Power Areas 45 © 2008 4.6) PoF Example - Moisture/Contaminate Failures - Electro-Chemical Metal Migration Shorts - a.k.a. Dendritic Growth Excessive Ionic Residue Contaminates on Circuit Board can interact with atmospheric humidity to form an electrolyte. When a voltage differential is present across a small distance copper ions can be excited to migrate from the anode to the cathode of the circuit ( + to -). A copper trail will be deposited along the way that will eventually support current leakage short circuits. 4 factors are required: © 2008 Ionic Chromatograph. Identifies Electro-Chemical Contaminates From Manuf. Processes 1) Excessive Ionic Residues 2) Humidity (typ.>65% R.H. varies with Temp. 3) Exposed Copper. 4) Voltage difference bias over a short distance 46 4.6) PoF Example - Moisture/Contaminate Failures - Detrimental Contaminates Chloride Residues One of the more detrimental residues found on PCB Typically related to flux residues. Chlorides will initiate and propagate electrochemical failure mechanisms, such as dendrite growth metal migration and electrolytic corrosion, when combined with water vapor and an electrical potential. Levels > 2 mg./sq. in. typically can not be tolerated. Bromide residues Generally related to bromide fire retardant in epoxy-glass laminates. Can also come from solder masks, marking inks, or fluxes with bromide activators Fire retardant, bromide is not typically degrading to long-term reliability of PCBs. Bromide from a flux residue, can be very corrosive Epoxy-glass laminate bromide levels typical range of 0 - 7 mg/sq. in. Bromide levels >12 mg./sq. in. can be detrimental on organic PCB Levels between 12-20 mg./sq. in. are borderline risks Levels above 20 mg/sq. in. are a significant risk especially if from flux residues. 47 © 2008 4.6) PoF Example - Moisture/Contaminate Failures - Detrimental Contaminates Sulfate Residue Sulfates can come from many sources, contact with sulfur-bearing paper or plastics, acid processes in fabrication, or from water used for rinsing & cleaning. Minimal Risk: Marginal Risk: High Risk: 0.0 – 1.0 mg./sq. in. 1.0 – 3.0 mg./sq. in. > 3.0 mg./sq. in. Sulfate levels above 3.0 mg./sq. in. are corrosive & detrimental to circuit reliability. With sulfate levels above 3.0 mg/ sq in, look for a sulfate-bearing chemical used in processing especially sodium/ammonium per-sulfate and sulfuric acid. Nitrate Residue © 2008 Nitrate has approximately the same electronegative corrosivetivity as sulfate. The mg./sq. in residue concentration risk levels for sulfate also apply to nitrate. 48 4.6) PoF Example - Moisture/Contaminate Failures - Detrimental Contaminates Wear Organic Acids (WOA) WOAs like adipic or succinic acid, are activators in many solder fuxes Residue levels vary greatly with the flux delivery system (foam, spray, paste) and the heating profile the determines the rate of consumption during soldering. Low solids solder paste: 0-20 mg./sq.in. Spray-applied, low-solids flux: 20-120 mg./sq.in. Foam-applied flux process: 120-150 mg./sq.in. Water soluble flux w/good cleaning: 0-15 mg./sq.in. Water-soluble fluxes generally have a much lower WOA content than low-solids (no-clean) fluxes. WOA levels are under 150 mg./sq. in. are generally not a risk. Excessive WOA amounts (>150 mg/in2) present a significant PCB reliability risk. Un-reacted WOA flux residues will readily absorb atmospheric moisture then support corrosion and the formation for current leakage dendritic growth failures. 49 © 2008 4.6) PoF Example - Capacitor Flex Cracking Examples Capacitor are fundamental, passive electric devices for energy/electron/charge storage. A cap is formed by two parallel conducting plates (electrodes) separated by a dielectric material. - Dielectrics are insulators, poor conductor of electricity that support electrostatic fields. Rather than passing an electric current, dielectrics absorb electronics into an electro-static field. For solid dielectrics such as Barium Titanate (BaTiO3) a hard, brittle ceramic, many small plates/dielectric sections are stacked in parallel to create a large capacitance in a very small package. The brittle fragile nature of the thin dielectric ceramics can result in fracture cracks in the capacitors if their circuit boards experience occasional bending or flexing. Cracked Chip Cap, Capacitor Ends Bend Up Tensile Stress (Crack Site) on Bottom Capacitor Ends Bend Down Tensile Stress (Crack Site) on Top © 2008 50 4.6) PoF Example - Thermal Stress & Thermal-Mech. Durability Thermal Analysis Identifies Internal Thermal Stress & Overstress “Hot Spots” From Power Dissipation & Environment Conditions. Infrared Thermal Imaging Of Thermal Stress & Overstress “Hot Spots” Thermal-Mechanic Durability Modeling to Identify Potential Intermittent Circuits Due to Themo-Mechanical Fatigue 1020 Resistor Fatigue Confirmed In Accelerated Life Test Durability Simulations Identifies Most Likely Parts to Fail Due To Thermo-Mechanical Fatigue Identified (Large Body 1020-S.M. Resistors) 51 © 2008 5) Manufacturing Issues Highly Reliable Products Need To Be Built Right As Well As Designed Right. © 2008 A Robust Well Balanced Design Can Be Rendered Un-Reliable by Fabrication and Assembly Errors or Excessive Variation Issues. A Consistent and Capable Manufacturing Process and Supply Chain is also Required 52 5) Manufacturing Issues The 5 Most Common E/E Device Manufacturing Issues Most Root Cause techniques are only call upon after a failure has happen to determine what when wrong. But the many of same methods can also be used to determine if new products are being built right 6 Sigma ASSEMBLY & SOLDERING PROCESS (Related to up to 60% of E/E Assembly Issues) In Process Board Flexure Cracked & Missing Components. (Related to up to 15% Of E/E Assembly Issues). RE-HEAT, REWORK & REPAIRS Electro Static Discharge (ESD) Ionic Contaminate (Component Damage) (Circuit Board Cleanliness to Prevent Humidity Related Short Circuit Growths) (% Varies Often Related To Spills) (Related to up to 20% Of E/E Assembly Issues). Rework & Repair Latent Rework & Handling Damage (% Varies) 53 © 2008 6) Identifying What the Problem Is - Root Causes Failure Analysis Techniques Return parts Root Cause Failure Analysis always starts with Non-Destructive Evaluation (NDE). Designed to obtain maximum information with minimal risk of damaging or destroying physical evidence Non Destructive Evaluation Methods © 2008 Visual Inspection Electrical Characterization Optical Microscopy Scanning Electron Microscopy Acoustic Microscopy Xray Microscopy Infrared Thermal Imaging SQUID Microscopy Spectral Material Analysis (Elemental Composition) Ion Chromatography-Chemical Analysis Destructive Evaluation Methods Decapsulation Microsectioning Metallographic Metallurgical Analysis Focused Ion Beam Milling Electrical Transient Probe Testing Material Property Characterization Thermo Mechanical Analysis (TMA) Differential Scanning Calorimetry (DSC) Polymer Thermal-Mechanical Properties 54 6.1 Visually Aided Inspection - Microscopy Optical & SEM Enables the visualization, inspection and evaluation of tiny objects and details. Light based optical Microscopes provides magnifications up to 1500x, resolution down the 0.2 micrometer. Electron beam based Scanning Electron Microscopes provides magnifications up to 2,000,000x. Modern professional grade microscopes are equipped with digital imaging capture for documentation and comparison purposes. © 2008 55 6.1) Microscopic Failure Analysis of Solder Separation in BGAs - Root Cause: Excessive Underfill Thermal Expansion © 2008 56 6.1) Microscopic Failure Analysis of Solder Joint Fracture - Root Cause: Failure Due to Gold Embitterment Cross section of failed solder joint revealed excessive Gold-Tin (AuSn4) intermetallics. SEM Energy Dispersive X-ray Spectroscopy (EDS) found solder’s gold content >8%. Embrittlement will occur if gold content exceeds 3.5% by weight. Excessive component gold plating allowed large amount gold to diffuse into the solder . Controlling Factors: Excessive Gold, Soldering Temperature and Time Above Liquidus 1200x SEM image reveals needle-like structures of AuSn4 intermetallics in the solder joint Cross section of component with thickness of gold plating layer. 57 © 2008 6.2) Thermal Imaging Microscope Thermography is the use of an infrared imaging and measurement camera to "see" and "measure " thermal energy emitted from an object. Important parameters include measurement temperature range, spectral range, accuracy, resolution and steady state vs. real-time Resolution, PCBA: 15 microns Resolution, on-die: 1 micron Use points © 2008 Provides precise non-contact temperature measurement capabilities. Spectral range can be broken into one of four ranges, near IR: 0.75-3 microns, middle IR: 3-6 microns, far IR: 6-15 microns and extreme IR: 1530 microns. Find Electrical shorts Power Components Identify Temperatures, Find Hot Spots Trace Heat Flow Paths 58 6.2) Infrared Thermal Imaging Q16 D11 Alum Caps Thermal Anomalies Detected - Q16 producing heat when is it suppose to be in an off state - Sneak circuit detected. - D11 detected a hot spot that exceeded thermal bogies. Resulting in over heating near by Alum Caps 59 © 2008 6.3) X-ray Microscope Enables internal inspection through the use of X-ray energy Latest innovations Digital Detector Laminography (‘virtual’ cross-sectioning) © 2008 3D reconstruction Nanofocus resolution Oblique viewing 60 6.4 Acoustic Microscopy Non destructive method for inspecting internal structures. By mapping the echo pattern of high frequency (>20 kHz) sound waves. Sonic energy excites loose or moveable structures. Requires immersion in water (acoustic signals reflected by air) Enable non-destructive detection/location of: structures, cracks, voids and delamination Transducer H2O Receive 61 © 2008 6.5) SQUID Microscopy Superconducting QUantum Interference Device Current flow in devices produce a magnetic field SQUID uses a highly sensitive magnetic detector (superconductor) to resolve these fields Magnetic field image is converted to a current density image, allowing for fault location Resolution down to 300 nm Critical technology for detecting the current path of electrical shorts through a package or material. © 2008 62 6.6) Micro Cross Sections - a Destructive Analysis technique for the internal evaluations of component's good for detecting manuf. defects - Metallographic Analysis involves X-Sections of metals (i.e. Leads & Solders) for material quality evaluations. Thru Hole Pins Text Book Perfect 63 © 2008 This Webinar is a based on a 2 day Short Course: “Understanding Failure & Root-Cause Analysis in Electronics” 1) Introduction and Objectives The” 5 Whys” Technique The Eight Disciplines (8D) Technique Shainin Red “X” Statistical Problem Solving Six Sigma Physics of Failure/ Reliability Physics Break 3) Generic Failure Categories © 2008 Design Quality & Errors Manufacturing Quality & Errors Environmental & Usage Considerations - Their Role in Over Stress & Accelerated Wear Out Failures Environment & Self Heat Temperature Issues Vibration, Shock & Drop Humidity Contaminates 4) Finding Failure Modes – Where Problems Are & How They Manifest Themselves. 2) Root Cause Approaches, Management & Reporting Methods The Need for Root Cause Analysis Difference Between Problem Solving, Failure Analysis & RCA Of Field Failures The Need for Data Collecting & Analyzing Data for Problem Solving Trending analysis results (plotting a timeline Pareto Analysis Other Data Sources Test Reports Warranty Data Fleet Maintenance Logs/Reports Customer Surveys Investigation Interviews Lunch 64 This Webinar is a based on a 2 day Short Course: “Understanding Failure & Root-Cause Analysis in Electronics” 5) Fault/Failure Investigation - Identifying What the Problems Is - Part I) Developing a Hypothesis 7) Identifying What the Problem Is - Part III) Root Causes Failure Analysis Customer & Service Technician Feedback & Interviews, Reference Product & Technology History/Lessons Learned Identifying Contributing Events Ishikawa (fishbone) diagrams Fault Tree Analysis Dealing with Multiple Problems – Event/Issue Charting 6) Identifying What the Problem Is - Part II) Return Parts Analysis Managing a Return Part Program Initial Issue Confirmation Functional Checks Electrical Fault Isolation Physical Component Failure Analysis Laboratory Methods Cross-Sectioning / Metallographic Analysis IC Decapsulation Optical Microscopy Electron Microscopy Ion Chromatography Surface Analysis (FTIR, EDS, XRF, etc.) Material Analysis (DSC, TMA, TGA, etc.) Mechanical Analysis Techniques (Micro-tester, Bend Testing, Pull Testing, etc.) End of Day One Break 65 © 2008 This Webinar is a based on a 2 day Short Course: “Understanding Failure & Root-Cause Analysis in Electronics” 8) Typical EE Failure Modes, Mechanism & Signatures Capacitors (Ceramic, Aluminum, Tantalum) Passive Components Electro-Mechanical Devices. Terminals and Contacts Wire Failures Relay Speakers & Audio Alarms PCB Assembly Solder Quality Issues 9)Using CAE Simulation in RCA Vibration & Shock Thermal Simulations 10) Developing/Implementing a Permanent Corrective Action Plan Developing the Corrective Action Plan Stakeholder Teamwork & Buy In. Fixing the Problem Rather Than Assigning Blame Fixing the Design, the Supply Chain or Assembly Process Building a Business Case/Getting Approval for the Plan Internal Failure Rev. Board/Management Rpts & Approval Customer Reports and Approval Regulatory Agency Review & Approval Break Break Printed Circuit Board Substrate Issues Manufacturing Defects Plated Through-Hole Via Issues Conductive Anodic Filaments Electrochemical Migration (Dendritic Growth) Integrated Circuit Packaging & Die Issues Wire Bond Failures IC Pop Corning Integrated Circuit Die Issues ESD/EOS Fluid Penetration Issues (new) Thermal Issues (new) Validating the Fix Implementation Verification Learning From Failure - Corrective Action to Prevention Documenting the Issues Document and Reusing Lessons Learned Implementing the Fix Engineering and Validation Issues Assembly Processes, Maunf. & Quality Issues Suppliers and Supplier Quality Issues Wrap-Up & Adjourn Lunch © 2008 66 Want to Know More, Contact Your Nearest DfR Solutions Location Bay Area Office John McNulty 415-806-7704 jmcnulty@dfrsolutions.com Sales Manager, Southwest Clayton Bonn cbonn@dfrsolutions.com © 2008 Midwest Office Jim McLeish 248-726-7600 jmcleish@dfrsolutions.com Corporate Headquarters College Park, MD 301-474-0607 askdfr@dfrsolutions.com 67 Root Cause Analysis Mechanical Components and Systems by Clifford Lange, PhD, PE, Ops A La Carte Copyright © 2008 Clifford H. Lange Proprietary Document Page 1 Root Cause Analysis – Mechanical Components Polling Questions Are you familiar with creep related problems or have direct experience with solving a creep issue? Don’t know what creep is Some familiarity with creep Direct experience with creep behavior Do you understand the application of structural reliability methods (e.g. FORM/SORM) for the understanding of failure mechanisms Don’t know what structural reliability methods are Some familiarity with structural reliability methods Direct experience with structural reliability methods Copyright © 2008 Clifford H. Lange Proprietary Document Page 2 Design for Reliability – Mechanical Components Conform to accepted industry design standards (ASTM, SAE, ANSI, etc.) Avoid the need to use high tolerances (e.g. < 0.010”) and be cognizant of tolerance stack up issues Ensure compliance with all recommended rating guidelines Anticipate unusual environmental effects Incorporate contract manufacturers early in the design process (they are the experts) Perform reliability assessment on primary wearout mechanisms Copyright © 2008 Clifford H. Lange Proprietary Document Page 3 Critical elements of mechanical systems Transmitting elements Constraining, confining, & containing elements Seals & gaskets Bearings & Shaft sealing devices Fixing elements Shafts, belt drives & flexible couplings Springs & gears Actuators, accumulators & reservoirs Brakes & clutches Motors, pumps & valves Bolted connections or threaded fasteners Weldments Elements supporting machinery functions Lubrication systems Copyright © 2008 Clifford H. Lange Proprietary Document Page 4 Typical failure mechanisms of mechanical systems Stress rupture or fracture Fatigue Insufficient design Changes in load history or component application Poor material characterization or load history Creep Wear and/or fretting Environmental effects Corrosion IGSCC Hydrogen embrittlement Copyright © 2008 Clifford H. Lange Proprietary Document Page 5 Reliability prediction for mechanical systems Bloch, H.P. and Geitner, F.K.; “An Introduction to Machinery Reliability Assessment;” Van Nostrand Reinhold, 1990. “Handbook of Reliability Prediction: Procedures for Mechanical Equipment;” Naval Surface Warfare Center – Carderock Division; CARDEROCKDIV, NSWC-94/L07, March 1994. Copyright © 2008 Clifford H. Lange Proprietary Document Page 6 Example: Creep Failure High temperature aluminum heater weldments Pre-stressed concrete (water) pipe failures Power plant steam pipe creep rupture Steam pipe ruptures lead to in depth inspections at all aging facilities Main steam piping at TVA Gallatin Units 3 & 4 showed excessive deformation (~ 10% radial strain – wall thinning) Average diametral strain is 5.3% (swelling) Initial “thin-wall” creep calculations indicated evidence of bending moments but results were inconsistent with data Thick wall “finite element” calculations improved predictions Results indicated that the ASTM creep rate law predicts approximately 2x service heater data Copyright © 2008 Clifford H. Lange Proprietary Document Page 7 8.6 Example: Creep Failure of Steam Piping Copyright © 2008 Clifford H. Lange Proprietary Document Page 8 8.6 Example: Creep Failure of Steam Piping Comparison of Wall Thinning Thick Wall creep results are more consistent with measured wall thinning Comparison of Diametral Swelling Thick Wall creep results are more consistent with measured diametral swelling Results reflect ASTM Creep Rate Law Copyright © 2008 Clifford H. Lange Proprietary Document Page 9 8.6 Example: Structural Reliability as a RCA Tool Wind Turbine design provides a good example of an ongoing RCA program Traditional fatigue analysis often focus on uncertainty with the material properties and/or the load (e.g. stress) spectrum New technology (e.g. Structural Reliability Methods) employed to improve the RCA In many cases uncertainty in the underlying load environment, the stress response and the computational techniques employed can be significant contributors to fatigue failures Problems involving many different sources of uncertainty are effectively addressed using Structural Reliability Techniques Copyright © 2008 Clifford H. Lange Proprietary Document Page 10 8.7 Example: Fatigue – Traditional Analysis Wind turbine blade application Typical S-N data for aluminum used for design Stress spectrum assumed to be determined experimentally – Monte Carlo simulation used to generate sample stress distribution Fatigue analysis considers both best fit and 95% CI on S-N properties as well as the measured stress histogram and a bounding load spectrum Results compared across all assumed input variables Copyright © 2008 Clifford H. Lange Proprietary Document Page 11 8.7 Example: Fatigue – Material Behavior 2.80 Least Sq. Fit, C=5.00E21 95% Lower CI, C=9.52E20 Teledyne Engr. Runout Specimen Southern Univ Stress (MPa) 2.70 500 2.60 2.50 2.40 250 2.30 2.20 2.10 100 2.00 1.90 1.80 50 1.70 N=Cσ-b b= 7.3 1.0E+04 1.0E+05 1.0E+06 1.0E+07 1.0E+08 1.0E+09 Cycles to Fail Δ= Fatigue data is for 6063 Extruded Aluminum Both a least squares best fit and a 95% confidence level used in fatigue analysis Miner’s Rule used to sum fatigue contributions over different stress amplitudes n n1 n + 2 + ⋅⋅⋅⋅ + i =1 N1 N 2 Nj Copyright © 2008 Clifford H. Lange Proprietary Document Page 12 8.7 Example: Fatigue – Applied Stresses Bin 1 Bin 2 Bin 3 Bin 4 Bin 5 0.14 0.12 0.1 fX(x) 0.08 0.06 0.04 0.02 0 0 5 10 15 20 25 Wind Speed (m/s) P[ X ≤ x ] αx ⎡ ⎤ x −⎢ β ⎥ =1− e ⎣ x⎦ βx = Applied stresses for wind turbine blade vary with wind speed A typical wind speed distribution representative of mid-west USA is assumed Distribution is Weibull with α = 2.0 & μ = 6.3 m/s 5 different stress amplitude distributions are assumed for 5 corresponding wind speed bins between 0 and 25 m/s. X (1 α )! Copyright © 2008 Clifford H. Lange Proprietary Document Page 13 8.7 Example: Fatigue – Applied Stresses 0.08 0.07 0.06 0.05 fS(s) Wind Speed Bin 0 - 5 m/s Wind Speed Bin 5 - 10 m/s Wind Speed Bin 10 - 15 m/s Wind Speed Bin 15 - 20 m/s Wind Speed Bin 20 - 25 m/s 0.04 0.03 0.02 0.01 0 0 10 20 30 40 50 60 Distribution of stress amplitudes stresses in each wind speed bin also assumed Weibull Assume αs = 2 with shape factor βs linearly dependent on wind speed, X Contribution potential for high stress amplitudes is evident Stress (Mpa) P[S | X ≤ s | x ] αs ⎡ ⎤ s | x −⎢ β s ⎥⎦ =1− e ⎣ β s = 1 .2 ⋅ x Copyright © 2008 Clifford H. Lange Proprietary Document Page 14 8.7 Example: Fatigue – load Spectrum 0.1 Relative Frequency Assumed Design Load Spectrum 0.01 0.001 0.0001 0.00001 1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 Monte Carlo simulation used to produce 10K stress amplitudes Assumed design load spectrum used to model anticipated long term loading conditions Both histogram and load spectrum used in analyses Stress (MPa) Copyright © 2008 Clifford H. Lange Proprietary Document Page 15 8.7 Example: Fatigue – Risk Level? S-N Material Loading Lifetime: years Cave Histogram Data 1232 Damage Δ .0162 Cave Design Spectrum 426 .0470 C.95 Histogram Data 216 .0925 C.95 Design Spectrum 81 .2465 All 4 combinations of C and Loading used to evaluate relative influence of each parameter & uncertainty level Both fatigue lifetime and damage results presented Results show satisfactory design against fatigue failure Copyright © 2008 Clifford H. Lange Proprietary Document Page 16 8.7 Example: Fatigue – Structural Reliability Used to evaluate designs probabilistically considering both the mean and standard deviation of design inputs Results are probabilities of failure and the relative importance of each input (random variable) For fatigue – rather than ask; “What is the actual fatigue life of the component?” the more appropriate question; “With what confidence will the component meet it’s target lifetime?” can now be answered. For RCA we can identify the leading contributors to failure Copyright © 2008 Clifford H. Lange Proprietary Document Page 17 8.7 Example: Fatigue – Structural Reliability σ x2 σ x1 μ x2 (L o a d ) μ x1 ( R e s is t a n c e ) Intuitively the risk or probability of failure can be inferred from the overlap of the region of the load and resistance random variables Both the relative values of the mean and variance of each random variable affect the failure probability Copyright © 2008 Clifford H. Lange Proprietary Document Page 18 8.7 Example: Fatigue – Structural Reliability U2 = x2 − μ x2 β= σ x2 μ x1 − μ x2 σ x21 − σ x22 p f = Φ −1 ( β ) G( X ) = X1 − X2 g(U) = 0 β θ Design Point U1 = x1 − μ x1 σ x1 Copyright © 2008 Limit state equation, G(X), defines the fail and non-fail conditions Failure probability determined by the μ and σ2 of X1 and X2 Calculations performed in standard “U-space” where the design point determines both the pf and the relative importance of X1 & X2 Clifford H. Lange Proprietary Document Page 19 8.7 Example: Fatigue – Structural Reliability Uj SORM g(U) < 0 β g(U) > 0 FORM Ui Copyright © 2008 In the general formulation the limit state equation is not linear and the random variables are not Normal Linear (FORM) and parabolic (SORM) approximations are used at the design point to calculate failure probabilities and importance factors Clifford H. Lange Proprietary Document Page 20 8.7 Example: Fatigue – Structural Reliability Limit State Equation defines failure conditions G ( X ) = T f − Tt Time to failure determined using Miners rule with an average damage per cycle Δ Tf = f0 D Average damage rate determined considers all possible stress amplitudes and their incremental damage ∞ D= ∞ ∫ ∫ x =0 s =0 f S | X (s | x ) f X (x ) N f (s ) Copyright © 2008 dsdx Clifford H. Lange Proprietary Document Page 21 8.7 Example: Fatigue – Structural Reliability Both the underlying environmental variable, X, and the stress amplitude, S, given the load environment, are Weibull distributions P[ X ≤ x ] αx ⎤ ⎡ x −⎢ β ⎥ =1− e ⎣ x⎦ P[S | X ≤ s | x ] αs ⎡ ⎤ s | x −⎢ β s ⎥⎦ =1− e ⎣ With the shape factor, bX, of the environment determined from the average, X, and the average of the stress response dependent upon the environment βx = X (1 α )! Copyright © 2008 Clifford H. Lange Proprietary Document Page 22 8.7 Example: Fatigue – Structural Reliability 3.5 4.37 p=1 2.5 σref = 1.75 2.0 3.74 p>1 3.12 p<1 2.50 1.5 1.87 1.0 1.25 Xref = 1.0 0.5 Average Stress (MPA) RMS Stress (MPa) 3.0 0.62 0.0 0.00 0.0 0.5 1.0 1.5 2.0 Load (e.g. WInd Speed) σ ( x) = Kσ ref ⎛ x ⎞ ⎜ xref ⎟ ⎝ ⎠ p β s = σ ( x)[2 /( 2 / α s )!]1 / 2 Copyright © 2008 The RMS of the stress process is a function of the underlying environment variable, X Random vibration theory is used to define the shape factor, βs, as a function of the RMS stress and shape factor, as The RMS exponent, p, used to identify increasing/decreasing stress processes Clifford H. Lange Proprietary Document Page 23 8.7 Example: Fatigue – Generalized Formulation Resulting expression for fatigue life a function of 12 random variables b bp ⎡⎛ ⎞ ⎛ ⎞ 2σ ref K ⎛ b X CΔ ⎢⎜ ⎟ ⎜ ⎟ ⎜ Tf = ⎢ ⎜ ⎟ ⎜ xref (1 / α x )! ⎟ ⎜⎝ α s f0 − ( 2 / )! ( 1 / ) KS S σ s m u ⎠ ⎝ ⎠ ⎢⎝ ⎣ ⎤ ⎞ ⎛ bp ⎞ ⎥ ⎟! ⎜ ⎟ ⎟ ⎜ α ⎟!⎥ ⎠ ⎝ x ⎠⎥ ⎦ Stress parameters K and σref are raised to the power, b, as a result of the S-N relationship Environmental parameters, X, are raised to the composite power, bp, reflecting the combined nonlinear effect of the RMS stress on the environmental variable, X Copyright © 2008 Clifford H. Lange Proprietary Document Page 24 8.7 Example: Fatigue – Traditional Approach Var Definition Dist Type Mean COV X αx xref σref p K αs C b fo Mean Wind Speed Wind Shape Factor Ref Wind Speed Reference Stress RMS exponent Stress Conc Factor Stress Shape Factor S-N Coefficient S-N Exponent Cycle Rate Miner’s Damage Constant Constant Constant Constant Constant Constant Normal Weibull Constant Constant Constant 6.3 2.0 1.0 1.75 1.0 1.0 2.0 5E21 7.3 1.2 1.0 .15 .613 - Δ Mean Lifetime: 467 years Failure Probability FORM SORM Importance Factors: Stress Shape Factor: S-N Coefficient, C: CYCLES computer program used to perform calculations Input values reproduce those used in the traditional fatigue analysis Results confirm previous results that fatigue design is not likely to fail Most significant input is the S-N coefficient .61 % .94 % 24.9 % 75.1 % Copyright © 2008 Clifford H. Lange Proprietary Document Page 25 8.7 Example: Fatigue – Generalized Approach Var Definition Dist Type Mean COV X αx xref σref p K αs C b fo Mean Wind Speed Wind Shape Factor Ref Wind Speed Reference Stress RMS exponent Stress Conc Factor Stress Shape Factor S-N Coefficient S-N Exponent Cycle Rate Miner’s Damage Normal Normal Constant Normal Normal Normal Normal Weibull Constant Normal Normal 6.3 2.0 1.0 1.75 1.0 1.0 2.0 5E21 7.3 1.2 1.0 .075 .15 .075 .05 .1 .15 .613 .2 .15 Δ Copyright © 2008 There exists uncertainty in design inputs other than the S-N law and loading spectrum in fatigue design X, ax, sref, p and K are all considered to be uncertain in the wind turbine example Uncertainty in Miners rule and the fluctuating cycle rate are also considered Clifford H. Lange Proprietary Document Page 26 8.7 Example: Fatigue – Results Mean Lifetime: 467 years Failure Probability FORM SORM Importance Factors: 5.67 % 7.38 % Mean Wind Speed, X : Wind Shape Factor, αx: Reference Stress, σref: RMS exponent, p: Stress Conc Factor, K: Stress Shape Factor, αs: S-N Coefficient, C Cycle Rate, fo: Miner’s Damage, Δ 6.7 % 25.2 % 6.2 % 24.0 % 10.6 % 9.3 % 16.6 % 0.8 % 0.5 % Copyright © 2008 Considering uncertainty contributions from all potential sources changes the conclusions from the original analysis Failure probabilities have increased to unacceptable levels (510%) while the mean lifetime remains unchanged Most significant inputs are mean wind speed and the RMS exponent, p Clifford H. Lange Proprietary Document Page 27 8.7 Example: Fatigue – Structural Reliability Structural Reliability methods provide risk levels (e.g. pf) as well as the relative importance of the design inputs (e.g. random variables) All 3 aspects of the fatigue problem; the loading environment, structural response and the local failure criterion may include uncertainty and can be included in the fatigue evaluation The methodology can employed through alternative limit state equations or extended to other fatigue problems (e.g. crack growth). The most critical design inputs are identified Copyright © 2008 Clifford H. Lange Proprietary Document Page 28 RCA Case Study The PEPCON Incident A Process Plant Accident & Guidelines for General Investigations T. Kim Parnell, PhD,PE Root Cause Analysis Webinar July 23, 2008 Why Review a Plant Accident? • Interesting and well-studied event • Provides general guidelines for RCA team organization • Insights for investigation and documentation • Contrast investigation of “unique” event like this with RCA of high-volume products July 23, 2008 RCA - Root Cause Analysis - T. Kim Parnell © 2 1 The PEPCON Incident • Fire and massive explosions at the PEPCON plant in Henderson, NV on May 4, 1988. • PEPCON produced Ammonium Perchlorate (AP) – an oxidizer • Combination of events: – Human error – cigarette likely started initial fire – Large quantity of AP on site due to Challenger disaster – 16” natural gas line running under the plant (with leaking stitch welds) July 23, 2008 RCA - Root Cause Analysis - T. Kim Parnell © 3 PEPCON Explosions • Two large explosions equivalent to 200 Tons and 500 Tons of TNT (3.0 and 3.5 on the Richter scale) • Over $70M property damage; windows broken up to 30 miles away • 16” Natural Gas Pipeline – Ruptured 40 foot section – Crushed more than 260 feet – Long-term leakage prior to blast from poor stitch welds July 23, 2008 RCA - Root Cause Analysis - T. Kim Parnell © 4 2 PEPCON Incident Investigation • Organization of Teams • Site documentation and evidence collection; develop timeline • Metallurgical analysis; Fracture mechanics • Fire cause & origin • Gas migration through soil • Blast effects & damage • Conditions for AP deflagration/detonation July 23, 2008 RCA - Root Cause Analysis - T. Kim Parnell © 5 Fire & Brimstone • Rapid spread of fire; catastrophic explosion • Most of event captured on video July 23, 2008 RCA - Root Cause Analysis - T. Kim Parnell © 6 3 Massive Explosion & Shockwave • Stills from video shot from Black Mountain – over 10 miles away July 23, 2008 RCA - Root Cause Analysis - T. Kim Parnell © 7 Aerial View - Before & After Before July 23, 2008 After RCA - Root Cause Analysis - T. Kim Parnell © 8 4 Near Ground Zero… • Rail cars overturned • Autos overturned July 23, 2008 RCA - Root Cause Analysis - T. Kim Parnell © 9 At the Plant July 23, 2008 RCA - Root Cause Analysis - T. Kim Parnell © 10 5 Plant Buildings July 23, 2008 RCA - Root Cause Analysis - T. Kim Parnell © 11 Production Equipment July 23, 2008 RCA - Root Cause Analysis - T. Kim Parnell © 12 6 Ruptured Gas Pipe – Initial View July 23, 2008 RCA - Root Cause Analysis - T. Kim Parnell © 13 Pipe After Some Digging July 23, 2008 RCA - Root Cause Analysis - T. Kim Parnell © 14 7 Pipe After Complete Excavation July 23, 2008 RCA - Root Cause Analysis - T. Kim Parnell © 15 Ruptured & Crushed Pipe Sections July 23, 2008 RCA - Root Cause Analysis - T. Kim Parnell © 16 8 Pipe Sections July 23, 2008 RCA - Root Cause Analysis - T. Kim Parnell © 17 16” Natural Gas Pipeline • Ruptured 40 foot section • Crushed more than 260 feet • Long-term leakage prior to blast from poor stitch welds • Big Question: Did the pipe rupture occur before or after the explosions?? July 23, 2008 RCA - Root Cause Analysis - T. Kim Parnell © 18 9 Pipeline Section Identification July 23, 2008 RCA - Root Cause Analysis - T. Kim Parnell © 19 Pipe/Soil Finite Element Model Reference: Parnell, T.K. and Caligiuri, R.D., “Analysis of the Dynamic Response of a Buried Pipeline due to a Surface Explosion,” Computational Aspects of Impact and Penetration, L. E. Schwer and R. F. Kulak, eds., Elme Press International, 1991. July 23, 2008 RCA - Root Cause Analysis - T. Kim Parnell © 20 10 Pipe Crushing Due to Blast Response Sequence #1 July 23, 2008 RCA - Root Cause Analysis - T. Kim Parnell © 21 Pipe Crushing Due to Blast Response Sequence #2 July 23, 2008 RCA - Root Cause Analysis - T. Kim Parnell © 22 11 Pipe Crushing Due to Blast Response Sequence #3 July 23, 2008 RCA - Root Cause Analysis - T. Kim Parnell © 23 Pipe Crushing Due to Blast Response Sequence #4 July 23, 2008 RCA - Root Cause Analysis - T. Kim Parnell © 24 12 Pipe Crushing Due to Blast Response Sequence #5 July 23, 2008 RCA - Root Cause Analysis - T. Kim Parnell © 25 Pipe Crushing Due to Blast Response Comparison Pressurized July 23, 2008 Depressurized RCA - Root Cause Analysis - T. Kim Parnell © 26 13 Summary • Document in detail – Inspection – Measurements • Get the right expertise on the team; update as needed • Develop the scenario • Test the hypotheses July 23, 2008 RCA - Root Cause Analysis - T. Kim Parnell © 27 PEPCON Explosion - References • Video links – History Channel, 8:00 minutes http://video.aol.com/video-detail/pepcon-explosion-may1988/1249549102 – Exponent – 2:00 minutes http://www.youtube.com/watch?v=HJVOUgCm5Jk – Z-Axis http://podcasts.zaxis.com/pac/pepcon-explosion – Summary article http://www.interfire.org/res_file/pdf/Tr-021.pdf http://www.reviewjournal.com/news/pepcon/ July 23, 2008 RCA - Root Cause Analysis - T. Kim Parnell © 28 14 Data Driven RCA Al Alaverdi SigmaQuest SigmaQuest Solutions for Data Driven Quality Management & RCA Focus on High Tech, Telecom, Consumer Electronics, Medical Devices Good Data = Shortest path to RCA RCA – A 360° Perspective Install Base Customer Complaints & Feedback Returns Engineering Manufacturing Component Suppliers Eliminate Data Fragmentation “Single View of Truth” Data Acquisition Challenges Political Data Quality Engineering, Ops, Service, Quality Component Suppliers, CMs, Repair Centers Are you collecting the right data ? Accuracy, Granularity, Latency Consistency (Part #, Serial #, Version, Revision) IT Data Storage, Analytics , Large volumes of data Typical Scenario - Data, Data Everywhere Call Home 40% of customers Legend RCA SAP Home Grown Repository (OEM) Pass/Fail Data 0.5% File Incident Ticket IFS / Stars / Tars XML (7C6) Test & Repair Supplier Repository 3PL-1 Customers ERP / CRM software (OEM) (Module) Oracle 99.5% RMA Receipt Detailed Test Data Part A 3PL-2 Repair Centers / sub-tiers Screening / Failure Analysis Centers (Module) Oracle Quality DW Other Reports 3PL-1 (System) Internal RMA (System) Test Data Part B Repair-1 (Component) Partial Data Repair/ FA data Repair-2 (Component) Engineering Feedback Test database Repair/ FA data Dept. Database Desktop Database Supplier Root Cause FA data Corporate Reliability Reports SQE FA Reports Typical Scenario - Data, Data Everywhere Call Home 40% of customers Legend RCA SAP Home Grown Repository (OEM) Pass/Fail Data 0.5% File Incident Ticket IFS / Stars / Tars XML (7C6) ERP / CRM software (OEM) Test & Repair Supplier Repository 3PL-1 Customers (Module) Oracle RMA Receipt 99.5% Fragmentation 3PL-2 Test Data Part B Repair Centers / sub-tiers Screening / Failure Analysis Centers (Module) Oracle Quality DW Other Reports 3PL-1 (System) > 15 Data Sources Internal RMA (System) Test Data Part B Repair-1 (Component) Repair-2 (Component) Engineering Feedback Test database Partial Data Repair/ FA data Repair/ FA data Dept. Database Desktop Database Supplier Root Cause FA data Corporate Reliability Reports SQE FA Reports Typical Scenario - Data, Data Everywhere Call Home 40% of customers Legend RCA SAP Home Grown Repository (OEM) Pass/Fail Data 0.5% File Incident Ticket IFS / Stars / Tars XML (7C6) ERP / CRM software (OEM) Test & Repair Supplier Repository 3PL-1 Customers (Module) Oracle RMA Receipt 99.5% 3PL-2 Repair Centers / sub-tiers Screening / Failure Analysis Centers (Module) 3PL-1 (System) Latency 30 days Test Data Part B RMA (System) Other Reports Engineering Feedback Test Data Part B Repair-1 Partial Data Repair/ FA data Repair-2 (Component) DW Test database Internal (Component) Oracle Quality Repair/ FA data Dept. Database Desktop Database Supplier Root Cause FA data Corporate Reliability Reports SQE FA Reports Building an Early Warning System To Expedite RCA Leading Risk Indicators What happened ? Why ? - What is the root cause - Is it a Design, Process or Supplier Issue? - How do I prevent it from happening again Demo Using Data To Accelerate RCA Cultivate holistic data strategy Invest in Early Warning to accelerate RCA Empower intellectual resources to make better decisions, sooner Contact Information For more information visit: www.sigmaquest.com Contact Information: Al Alaverdi 408-524-3181 al.alaverdi@sigmaquest.com Question & Answer
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