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Defect Analysis & Prevention, Data Mining & Visualization of Defect Matrix Document Transcript

  • 1. Defect Analysis & Prevention Data Mining & Visualization of Defect Matrix using MS Excel By Aniruddha Sahasrabudhe Consultant, Engineering Process Excellence & Integration http://in.linkedin.com/pub/aniruddha-sahasrabudhe/8/a19/414 E mail: a_sahasrabudhe@hotmail.com November - 2010
  • 2. Abstract For economic and operational reasons large engineering organizations outsource typical OVERVIEW OF SITUATION activities like conversion of existing drawings to 3d CAD models (using Pro Engineer or Catia or UG softwares), creation of manufacturing drawings, designing and detailing of BIW PROCESS MODEL: Fixtures, 3d models to Meshed models using Hypermesh or ANSA for FE Analysis, Reverse engineering of parts, CNC programming etc. to reputed Engineering Service Providers (ESPs). Raw Correlation Matrix with data of 10 types of Expectation from ESPs is that in a short period of time they should deliver Defect free Defects (D1..D10) for team of 10 Resources services, qualify as most favoured supplier of engineering services and become an integral (R1..R10) part of supply chain of outsourcing organization. A) Inputs from outsourcing engineering Generally the process begins with a pilot job i.e. a small part (5 to 10% of total work) is organization: -- outsourced to ESPs with the necessary technical details i.e. Statement of work (SOW), Quality Standards/ Work instructions/Reference models, drawings, Acceptance Quality Plan Statement of Work (SOW) and CD containing 77 (AQP), etc. Quality of pilot job delivered is one of the important criterion for shortlisting or data files for pilot job of 3d Modelling, Detailing, approving ESP as a supplier of engineering services. It is assessed on the basis of Number of Meshing, “Acceptance Quality Plan (AQP) “ and Defects found, Severity of defects (Minor or Major), First time yield (FTY) i.e. Number of some reference examples for each of these tasks Defect free jobs or files delivered / total number of jobs or files assigned, on time delivery was given to ESP. within agreed cost, turnaround time for rework if any, number of resources used , speed, clarity and preciseness of communication etc. B) Deliverables expected from ESP: Feedback of Quality of pilot job delivered (number of defects, first time yield etc.) is given to 3DModels, Manufacturing drgs, Meshed models the ESP for needful corrective action. In order to become an integral part of supply chain of & CNC programs as per “AQP“within agreed outsourcing organization it is essential for ESP’s team to have a shared vision of Customer’s delivery time. Critical Requirements, CCRs of Quality, Reliability, Manufacturing Process, Cost, Delivery period, Service etc. and a very clear understanding of definition of “Defect”. A very C) ESP’s estimation shown that to complete the systematic and disciplined study of defect data and associated processes on part of ESP is pilot job as per delivery time requirement of required to ensure that future jobs will be defect free. outsourcing organization, a team of 10 resources (R1...R10) will be required. ESP checked Defect Analysis and Prevention is a very systematic process and its application varies from competency inventory and selected 10 resources situation to situation. Every defect results into an expensive rework for the organization, who worked on the data and delivered the pilot adds to COPQ (Cost of Poor Quality) and directly impacts the bottom line – profits. It is job within agreed delivery time. therefore very essential for every organization to know and set KPI’s, Key Performance Indicators which reflect on effectiveness of organizational processes i.e. how they are D) Quality of pilot job: Outsourcing organization performing in terms of Output /Unit of Input. If issues related to process Defect are not checked quality of pilot job and found a total of addressed at the right time they can escalate into a very difficult situation for the 427 Defects which were informed to ESP. The organization. data of 427 defects has been co related with the resources and arranged into 10 types of unique Recent recalls of defective vehicles by Mercedes-Benz & Toyota are best examples of such “Defect categories (D1...D10) as shown in the situations matrix on page 2. ESP is required to correct the -- Mercedes-Benz is recalling 85,000 of its 2010 C-Class and the 2010-11 E-Class models defects and upload corrected data as soon as because a steering problem could make the vehicles difficult to control and possible. --Toyota is recalling 3.8 million vehicles worldwide, including 740,000 vehicles in the United States that could leak brake fluid and cause stopping power to “gradually decline.” Recalled Requirement of Engineering Service models are Avalon, Camry, ES 350, IS 250 and IS350, Tacoma, Tundra and Prius. Provider, ESP Complex problems of this magnitude require complex process modelling & FMEA (Failure 1) A detailed analysis of Defect data in the Mode and Effects Analysis) of Parts, Systems, Process, Service and Software and long-time to matrix on page 3 and get an idea of the fix till a reliable solution is found and approved by a competent Traffic safety authority. performance of each resource and the team using “Data Analysis” pack in MS Excel since this This simple case study is about a situation (see “Overview of Situation“ on left) where an is readily available with him. engineering organization found 427 defects in the pilot job of 3d modelling, detailing, meshing & CNC programming outsourced to an ESP. The analysis shows results of data 2) Graphic visualization of raw correlation mining of “Defect Matrix” using “Data Analysis pack “ and data visualization using column Matrix for better understanding of Defect data. charts & pie graph functionalities in MS Excel. Defects D1, D2, D3 pertain to 3d modelling, D4 & D5 to detailing D6, D7 & D8 to Meshing & D9 &D10 to CNC programming. The results 3) Process Capability & Gap Analysis: are useful for both the organizations. Outsourcing organization for comparing performance of ESPs & for ESP for doing “Gap Analysis” i.e. to know its current process capability (“As is “condition) and efforts required to comply with customer’s requirement -- Defect free An idea of current Process Capability & Sigma Level of the team & compare it with desired 6 service, “As should be” condition. Sigma level (3.4 Defects per Million Opportunities) and A Picture is worth a thousand words (or Numbers) – For a better understanding and visualization, Defect numbers have been transformed into graphics and displayed in various 4) CAPA -- Corrective and Preventive Action/s ways with appropriate comments / conclusions. Action that need to be taken to Correct detected Non conformity (427 Defects) and Preventive Based on results of analysis, corrective and preventive actions have been suggested to action to avoid recurrence of Defects for future minimise chances of recurrence of same defects for identical jobs in future. A detailed jobs. technical discussion of 427 Defects D1….D10 is beyond the scope of this article hence not covered. It is presumed that reader of this article is conversant with application of misc. formulas, “Data Analysis pack” and charting functionalities of MS Excel, hence only results have been shown and discussed. - Page 2 of 7
  • 3. Raw Correlation Matrix of Resources R1..R10 & Defects D1..D10 Team Resources → R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 Defect types ↓ D1 3 5 7 3 8 3 1 5 5 5 D2 5 2 3 7 4 4 3 3 7 2 D3 8 3 9 3 5 2 7 1 8 3 D4 4 1 6 3 6 5 3 6 2 3 D5 7 3 2 5 4 1 2 5 7 4 D6 3 7 3 6 2 3 6 5 4 7 D7 5 1 4 4 4 3 3 5 1 4 D8 2 2 7 2 3 2 5 6 4 4 D9 4 5 3 1 3 5 7 3 5 3 D10 10 8 5 4 7 3 4 6 5 6 Additional available Information Number of data files assigned 8 12 7 9 5 9 6 6 8 7 Opportunity for Defects / File 10 10 10 10 10 10 10 10 10 10 Maximum No of Defects possible 80 120 70 90 50 90 60 60 80 70 Data Mining from Raw Correlation Matrix showing meaningful information for each resource & the team Team Resources → R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 Total of % of Total Number Defect type ↓ Defects in Row of Defects D1 3 5 7 3 8 3 1 5 5 5 45 10.54 D2 5 2 3 7 4 4 3 3 7 2 40 9.37 D3 8 3 9 3 5 2 7 1 8 3 49 11.48 D4 4 1 6 3 6 5 3 6 2 3 39 9.13 D5 7 3 2 5 4 1 2 5 7 4 40 9.37 D6 3 7 3 6 2 3 6 5 4 7 46 10.77 D7 5 1 4 4 4 3 3 5 1 4 34 7.96 D8 2 2 7 2 3 2 5 6 4 4 37 8.67 D9 4 5 3 1 3 5 7 3 5 3 39 9.13 D10 10 8 5 4 7 3 4 6 5 6 58 13.58 Total number of Defects for Resource = SUM(D1:D10) 51 37 49 38 46 31 41 45 48 41 427 100.00 % of Total Defect by Resource = ((SUM(D1:D10)/427)*100 11.94 8.67 11.48 8.90 10.77 7.26 9.60 10.54 11.24 9.60 100.00 Colour indication Cell in this colour shows Minimum value of Defect in the column for that Resource Cell in this colour shows Maximum value of Defect in the column for that Resource Above matrix displays Maximum and Minimum number of type of Defect, Total number of Defects for each resource & the team. Performance of Team --Defect numbers in above table are transformed into graphics for better visualization & understanding 12 Pie graph 2 showing all Pie graph 1 showing % contribution Defects (D1..D10) & their of each resource to total of 427 10 % contribution to total of Defects 427 Defects. 8 6 4 2 0 R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 Conclusions from Data Mining table & Pie graphs Resource with Maximum Defects Maximum of total Defects = R1 (51 out of 427, 11.94%) = D10 (58 out of 427, 13.58 %) Resource with Minimum Defects Minimum of total Defects = R6 (31 out of 427, 7.26%) = D7 (34 out of 427, 7.96 %) Page 3 of 7
  • 4. Defect Matrix Transformed to Matrix of Statistically important numbers & information for all Resources Team Resources → R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 Total of % of Total Number Defect type ↓ Defects in Row of Defects D1 3 5 7 3 8 3 1 5 5 5 45 10.54 D2 5 2 3 7 4 4 3 3 7 2 40 9.37 D3 8 3 9 3 5 2 7 1 8 3 49 11.48 D4 4 1 6 3 6 5 3 6 2 3 39 9.13 D5 7 3 2 5 4 1 2 5 7 4 40 9.37 D6 3 7 3 6 2 3 6 5 4 7 46 10.77 D7 5 1 4 4 4 3 3 5 1 4 34 7.96 D8 2 2 7 2 3 2 5 6 4 4 37 8.67 D9 4 5 3 1 3 5 7 3 5 3 39 9.13 D10 10 8 5 4 7 3 4 6 5 6 58 13.58 Total number of Defects for Resource = SUM(D1:D10) 51 37 49 38 46 31 41 45 48 41 427 100.00 % of Total Defect by Resource = ((SUM(D1:D10)/427)*100 11.94 8.67 11.48 8.90 10.77 7.26 9.60 10.54 11.24 9.60 100.00 Colour indication Cell in this colour shows Minimum value of Defect in the column for that Resource Cell in this colour shows Maximum value of Defect in the column for that Resource RESULTS OF STATISTICAL ANALYSIS OF DATA OF DEFECTS Mean 5.100 3.700 4.900 3.800 4.600 3.100 4.100 4.500 4.800 4.300 Standard Error 0.795 0.775 0.722 0.573 0.600 0.407 0.657 0.522 0.696 0.517 Median 4.500 3.000 4.500 3.500 4.000 3.000 3.500 5.000 5.000 4.000 Mode 3.000 5.000 3.000 3.000 4.000 3.000 3.000 5.000 5.000 3.000 These results Standard Deviation 2.514 2.452 2.283 1.814 1.897 1.287 2.079 1.650 2.201 1.636 are obtained Sample Variance 6.322 6.011 5.211 3.289 3.600 1.656 4.322 2.722 4.844 2.678 by applying Kurtosis -0.001 -0.778 -0.856 -0.232 -0.468 -0.430 -1.169 0.833 -0.410 -1.093 “Data Analysis” Skewness 0.856 0.659 0.503 0.369 0.600 0.164 0.206 -1.206 -0.309 0.350 function in Range 8.000 7.000 7.000 6.000 6.000 4.000 6.000 5.000 7.000 5.000 MS Excel to Minimum 2.000 1.000 2.000 1.000 2.000 1.000 1.000 1.000 1.000 2.000 Raw Data Matrix Maximum 10.000 8.000 9.000 7.000 8.000 5.000 7.000 6.000 8.000 7.000 Sum 51.000 37.000 49.000 38.000 46.000 31.000 41.000 45.000 48.000 43.000 Count 10.000 10.000 10.000 10.000 10.000 10.000 10.000 10.000 10.000 10.000 Confidence Level(95.0%) 1.799 1.754 1.633 1.297 1.357 0.920 1.487 1.180 1.575 1.171 Calculation of Defects per File & Defects per Million Opportunities Number of data files assigned 8 12 7 9 5 9 6 6 8 7 Opportunity for Defects / File 10 10 10 10 10 10 10 10 10 10 Maximum No of Defects possible 80 120 70 90 50 90 60 60 80 70 % Yield 36.25 69.17 30.00 57.78 8.00 65.56 31.67 25.00 40.00 41.43 Defects per Million Opportunities, DPMO = 637500 308333.3 700000 422222.2 920000 344444.4 683333.3 750000 600000 585714.3 These results are obtained Finding Short term and Long term Sigma Level (Calculated using a normal DPMO, Sigma Level calculator) Short term Sigma Level of Resource 1.15 2 0.98 1.70 0.09 1.9 1.02 0.83 1.25 1.28 by using std. Long term Sigma Level of Resource -0.35 0.5 -0.52 0.2 -1.41 0.4 -0.48 -0.67 -0.25 -0.22 “Six Sigma Average Short term Sigma Level of Team 1.26 Calculator“ Average Long term Sigma Level of Team -0.24 Note: % Yield = (1-(Total number of defects of resource / Maximum number of defects possible))*100. Defect Data of numbers for Resources R1..R4 transformed to graphics 51 Defects for Resource R1 37 Defects for Resource R2 49 Defects for Resource R3 38 Defects for Resource R4 51 Defects for Resource R1 37 Defects for Resource R2 49 Defects for Resource R3 38 Defects for Resource R4 Page 4 of 7
  • 5. Defect Data of numbers for Resources R5..R10 transformed to graphics 46 Defects for Resource R5 31 Defects for Resource R6 41 Defects for Resource R7 45 Defects for Resource R8 46 Defects for Resource R5 31 Defects for Resource R6 41 Defects for Resource R7 45 Defects for Resource R8 48 Defects for Resource R9 48 Defects for Resource R9 41 Defects for Resource R10 41 Defects for Resource R10 Column chart showing all Defects D1..D10 for all Resources R1..R10 Defect D1 (51/427 =11.94%) Defect D2 (37/427 = 8.67%) Defect D3 (49 / 427 = 11.48%) Defect D4 (38 / 427=8.90 %) Defect D5 ( 46/427 = 10.77%) Defect D6 (31/427 = 7.26%) Defect D7 ( 41/427 = 9.61%) Defect D8 ( 45/ 427 = 10.54%) Defect D9 (48 / 427= 11.24%) Defect D10 ( 41/ 427 =9.6 %) Page 5 of 7
  • 6. Summary of Analysis 1. Number of resources used by ESP = 10 (R1...R10) 2. Number of Defects found by Customer = 427 (D1...D10) 3. Resource with Maximum number of Defects = R1 (51 Defects out of 427, i.e. 11.94%) 4. Resource with Minimum number of Defects = R6 (31 Defects out of 427, i.e. 7.26 %) 5. Resource with Maximum First time Yield, FTY = R5 (69.17%) 6. Resource with Minimum First time Yield, FTY = R2 (8.0 %) 7. Maximum Defect out of 10 types (D1...D10) = D10 (58 out of 427 i.e. 13.58%) 8. Minimum Defect out of 10 types (D1...D10) = D7 (34 out of 427 i.e. 7.96%) 9. Average short & long term Sigma Level of team = 1.26 & -0.24 10. Comparing short & long term Sigma Level of team with 6 Sigma level given in following table it can be seen that a very large effort will be required to reach 6 Sigma level i.e. 3.4 DPMO. Sigma level DPMO Percent defective Percentage yield Short-term C Long-term C pk pk 1 691,462 69% 31% 0.33 –0.17 2 308,538 31% 69% 0.67 0.17 3 66,807 6.7% 93.3% 1.00 0.5 4 6,210 0.62% 99.38% 1.33 0.83 5 233 0.023% 99.977% 1.67 1. 17 6 3.4 0.00034% 99.99966% 2.00 1.5 7 0.019 0.0000019% 99.9999981% 2.33 1.83 CAPA -- Suggested Corrective and Preventive actions Corrective actions: (What it was supposed to be? What actually was found? What needs to be corrected?) 1) Project Leader (PL)/Project Manager (PM) should present results of Defect Analysis done on pages 2 – 4 to the entire team. 2) PL/PM should conduct a Brainstorming session and RCA, Root cause Analysis (5Why’s &1H) for the defects in the meeting. Root cause analysis should address questions related to Quality check system in the team i.e. If all the resources have knowledge of Acceptance Quality Plan, AQP & customer’s definition of Defect? Why the defects were not noticed & corrected before data files were uploaded to customer? Human errors -- Forgetfulness, Incorrect understanding of customer’s requirement, Defects due to slowness and fatigue during the work. 3) After 2), concerned resources should check and correct all defects on their own in soft or hard copy and maintain a record. 4) PL/PM should ensure that all defects are put through a final toll gate quality check before uploading to customer. Inform details of final upload to customer. Preventive Actions: (Redefining execution processes to have in built detection and correction mechanism for Defects) 1) PL /PM should check and modify execution process by incorporating additional quality checks at appropriate stages. 2) Inform about modified execution processes to all members of the team. 3) Implement modified process for the next set of identical data from the customer. 4) Revisit competency inventory of resources. Explore possibility of various options of strengthening the team by imparting technical training in the areas where maximum number of defects occurred. Training can be imparted by in-house or external faculty or interaction with representative/s of customer through video conferencing or desktop sharing sessions. Conclusion: Process model of Raw Matrix containing data of Resources and defects has been analysed and information @ serial nos 1, 2, 3 & 4 on page 2 required by ESP has been provided using “Data Analysis” and column charting & pie graph functionalities in MS Excel. Page 6 of 7
  • 7. Glossary of few Quality related terms and their definitions for better understanding of concepts Defect: Six Sigma: DFSS: Any characteristic of a Product or Service that A data-driven method for achieving near (Design for Six Sigma) is a systematic does not conform to its intended implicit or perfect Quality. Sigma is the Greek letter methodology utilizing tools, training and explicit specification / requirement. Defect used to denote standard deviation, or the measurements to enable us to design could be biggest irritant for a Product / Service measure of variation from the mean, which products and processes that meet for not achieving desired quality. in production terms is used to imply a customer expectations of Quality , Cost, Defect. Higher value of sigma indicates and can be produced at Six Sigma quality Defective: fewer defects. In true Six Sigma levels. Any Product or Service that contains at least environments, companies operate at a DMAIC: one defect and does not conform to or fails to quality level of six standard deviations from (Define, Measure, Analyze, Improve and meet Customer’s implicit or explicit the mean or at 3.4 defect opportunities per Control) is a process for continued specification / requirement. million operations (DPMO). Six Sigma improvement. It is systematic, scientific methodologies can be applied to Design, and fact based. This closed-loop process Typically combination of Defects results into a Production or Service and Customer- eliminates unproductive steps, often Defective Product. Functionality of a Product / oriented activities. It is based on Statistical focuses on new measurements, and Service may be affected depending on the Tools and Techniques of Quality applies technology. impact of the defect. For example any fitment Management. Process Mapping: defect in doors, windows, seats, headlamps Illustrated description of how things get etc. may not affect movement of the car Poka-yoke: done, which enables participants to during daytime but will cause discomfort to A Japanese term that means mistake visualize an entire process and identify passengers and pose a risk to the safety of car proofing. A poka-yoke device is one that areas of strength and weaknesses. It helps and passengers during night and the car will prevents incorrect parts from being made or reduce cycle time and defects while be termed as a defective car. assembled or easily identifies a flaw or error recognizing the value of individual contributions. DPU: Defects Per Unit is the average number Baka-yoke: A Japanese term for a Root Cause Analysis: of defects observed when sampling a manufacturing technique for preventing Study of original reason for non- population. mistakes by designing the manufacturing conformance with a process. When the process, equipment and tools so an root cause is removed or corrected, the Defects per million opportunities (DPMO): operation literally cannot be performed non-conformance will be eliminated. Is the average number of defects per unit incorrectly. In addition for preventing observed during an average production run incorrect operation, the technique usually Kurtosis: divided by the number of opportunities to provides a warning signal of some sort for A statistical measure used to describe the make a defect on the product under study incorrect performance distribution of observed data around the during that run normalized to one million. FMEA: mean. Failure Mode and Effects Analysis: A Cost Of Poor Quality (COPQ): systematized group of activities to recognize Skewness: The costs incurred by organization for and evaluate the potential failure of a Describes asymmetry from the normal correcting poor quality products or services. product , service or process and its effects, distribution in a set of statistical data. These are incurred due to four factors: identify actions that could eliminate or Confidence Level: Extent to which an reduce the occurrence of the potential assumption or number is likely to be true. Internal Failure Costs (Cost of rework incurred failure and document the process. Standard deviation: It is a measure of for correcting non conformities / Defects variability or diversity and shows how found before the customer receives the much variation or 'dispersion' there is from DFMEA (Design Failure Mode and Effect Product or Service) the 'average' (Mean or expected/budgeted Analysis) is the application of the Failure External failure costs (Costs incurred for value) Mode and Effects Analysis (FMEA) method correcting non conformities / Defects found specifically to product/service design after the customer receives the Product or Variation: Service) Error Modes and Effects Analysis (EMEA) What the customer sees and feels. Appraisal costs (Costs incurred to determine the degree of conformance to Quality A procedure in which each potential error Stable Operations: Ensuring consistent, requirements) and made in every sub-process of a process is predictable processes to improve what the Prevention Costs (Costs incurred to keep analyzed to determine its effect on other customer sees and feels. Failure and Appraisal costs to a minimum) sub-processes and on the required accuracy Mode: The value or item occurring most of the process. frequently in a series of observations or CTQ: Critical to Quality (Critical "Y") – An EMEA is also used to prioritize & rank statistical data. Attributes most important to the customer potential causes of process or human The most often occurring value in the data which will directly impact Quality of Product / failures as well as create, launch and set. A data set may contain more than one Service. evaluate preventative actions. mode, e.g., if there are exactly 2 values or Customer Needs, Expectations – Needs, as Process Capability: items that appear in the data the same defined by customers, which meet their basic Product/Service that your process can number of times, we say the data set is bi- requirements and standards. produce/deliver. modal Quality Function Deployment (QFD) Voice of the Business (VOB): Represents the Control – The state of stability, normal needs of the business and the key variation and predictability. Process of A systematic process used to integrate stakeholders of the business. It is usually items regulating and guiding operations and customer requirements into every aspect such as profitability, revenue, growth, market processes using quantitative data. of the design and delivery of products and share, etc. services. Process Audit: Range Voice of the Customer (VOC): Represents the A timely planned process or system of A measure of the variability in a data set. It expressed and non-expressed needs, wants inspection to ensure that Product / Service is the difference between the largest and and desires of the recipient of a process specifications conform to documented smallest values in a data set. output, a product or a service. It’s is usually Quality standards. An Audit also brings out expressed as specifications, requirements or discrepancies between the documented Corrective & Preventive Actions: expectations. standards and the standards followed and Corrective actions are actions undertaken also might show how well or how badly the to correct the observed non conformity / Voice of the Process (VOP): Represents the documented standards support the Defect in a Product / Service. performance and capability of a process to processes currently followed. Preventive Actions are the actions achieve both business and customer needs. It Variance – A change in a process or business undertaken to avoid the recurrence of non is usually expressed in some form of an practice that may alter its expected – conformity / Defect in the Product / efficiency and/or effectiveness metric. outcome. Service in future. Page 7 of 7