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q-Maxim’s approach to problem solving & optimization – typical manufacturing project 
A high impact project leading to dra...
BY READING ONE CAN GET SOME INTUITION ABOUT THE APPROACH AND HOW ONE CAN APPLY IT IN THEIR OWN WORK 
THIS PRESENTATION GIV...
3 
•Background, Business case 
•Get insights 
•Prepare for DOE 
•Design and run experimentation 
•Analyze results 
•Confir...
Background, Business case 
•An export focussed foundry was getting high level of rework during first stage inspection 
•Ma...
Why sand casting chosen? 
Sand casting is very noisy process with many variables, uses highly variable natural substances ...
Background, Business case 
•An export focussed foundry was getting high level of rework during first stage inspection 
•Ma...
Background, Business case 
•Complicated production process (3 box, cored), low throughput 
•Project relates to 1st stage i...
Background, Business case 
•Severe cost / time implications : 
–Non value adding activities : Inspection, marking defectiv...
Background, Business case Schematic process flow 
10 
1st stage inspection, defect detection & repair
Background ,Business case 
11 
Sand mould under preparation, core in place
Background ,Business case 
12 
Metal being poured into sand moulds
Background ,Business case 
13 
Sand moulds filled
Business case – background, goal of project 
•Top 3 defects in the first stage visual inspection: 
–Sand mark type 
–Slag ...
Business case – background, goal of project 
•Defect levels in the industry are somewhat similar 
•Traditional problem sol...
Background, Business case 
16 
Sand mark type defects appearance (circled)
17 
•Background, Business case 
•Get insights 
•Prepare for DOE 
•Design and run experimentation 
•Analyze results 
•Confi...
Get insights- Possible sources of variation 
18 
Metallurgical literature 
Historical data 
Company documents, procedures ...
Gating design- e.g. metal velocity 
Ladle temperature 
Sand production parameters 
Ambient humidity, temperature 
Pour hei...
Get insights- typical data analysis output 
20 
In 40 % casts no defects 
In 20 % casts all castings defective 
Variation ...
Get insights- typical output 
21 
Output of Data mining – cluster analysis Which variables have possible linkages?
Get insights- conclusions 
•Large number (>50) of variables (called factors, noise) & their interactions could have effect...
Get insights- conclusions 
•Defects could be interrelated. (i.e. increase one type of defect could lead to drop of others ...
Get insights- conclusions 
•Traditional experimentation methods – ”experiement by changing one variable at a time” 
or 
te...
Get insights- conclusions 
•Statistical methdologies called Design of experiments (DOE) are suitable for solving such comp...
26 
•Background, Business case 
•Get insights 
•Prepare for DOE (design of experiments) 
•Design and run experimentation 
...
27 
Prepare DOE (design of experiments) – DOE some background info. 
•A statistical methodology 
•Not a theoretical exerci...
28 
Prepare DOE (design of experiments) – DOE some background info. 
•Highlights - steps 
1.Select some factors & interact...
29 
Prepare DOE – DOE some background info. -advantages 
•less expensive –fewer tests 
•More sure of results, results have...
Prepare for DOE- DOE some background info. 
•Before doing DOE, checked / ensured adequecy of measurement system ( MSA done...
Prepare for DOE- Who is Taguchi? 
•Dr. in Engineering and Statistics, renowned Quality Guru – Deming award 3 times, Blue R...
Prepare for DOE- Where is Taguchi DOE used? 
•Anywhere- manufacturing, transaction process, non-manufacturing, software de...
Prepare for DOE- Where is Taguchi DOE used? 
•Some application examples: 
–Improving yield in Aluminium pressure die casti...
Prepare for DOE- Taguchi design goals 
•Primary Goal : 
1. Find the effect of chosen factors & interactions on responses. ...
Prepare for DOE – Objectives of this project 
•Reduce number of factors for testing & number of interactions to a managebl...
Prepare for DOE – Objectives of this project (cont.) 
•Responses: 
1.Count number of defects of each type in all castings ...
37 
•Background, Business case 
•Get insights 
•Prepare for DOE 
•Design and run experimentation 
•Analyze results 
•Confi...
Design and run experiments – DOE flow chart- schematic representation 
38 
Gating design 
Pouring height 
Melt temp 
Delay...
Design and run experiments – choose an appropriate experimental design 
•A prefixed Taguchi experimental design [Taguchi a...
Design and run experiments – choose an appropriate experimental design (cont.) 
•Levels of Four control factors (two level...
Design and run experiments - Experimental plan 
41 
DOE_Run_ NoA mould designB ladle_tempC pour_ htD dealy_ cst-mld1oldpro...
Design and run experiments – Do experimentation 
•Production done as usual but controlling factor levels as specified in t...
Design and run experiments – Do experimentation 
•Responses and tabulation 
1.During first stage of inspection count the t...
44 
•Background, Business case 
•Get insights 
•Prepare for DOE 
•Design and run experimentation 
•Analyze results 
•Confi...
Analyze results – data analysis 
•Response data for each run fed into specilaised Statistical program- Minitab 
•Program o...
Analyze results – data analysis 
•By studying the output we can see: 
1.To what extent each factor has influence 
2.which ...
Analyze results – program output – response table 
Sand inclusion defect analysis: 
•Response Table for Signal to Noise Ra...
Analyze results – 
program output- sand mark type of defects 
old new 
4.0 
3.5 
3.0 
2.5 
2.0 
proc low 
present low 
4.0...
DOE runs findings 
old new 
-8 
-10 
-12 
proc low 
present low 
-8 
-10 
-12 
low high 
A gating 
Mean of SN ratios 
B la...
Analyze results – Conclusions - sand mark type defects 
Influential factors: 
1.Gating design has the highest effect 
2.De...
Analyze results – Conclusions - sand mark type defects 
•Most desirable Level of influential factors 
1.Gating design: New...
Analyze results – Findings were confirmed by using machine learning 
•A statistical model was built using machine learning...
DOE results 3d simulations Number of defects Vs design + delay 
54 
This confirms previous finding from DOE means charts 
...
DOE results 3d simulation Number of defects Vs design Vs ladle temp 
55 
This confirms previous finding from DOE means cha...
56 
•Background, Business case 
•Get insights 
•Prepare for DOE 
•Design and run experimentation 
•Analyze results 
•Confi...
Confirmatory runs, conclusions- Confirmatory runs 
•It is necessary to confirm findings by repeating the runs under most d...
Confirmatory runs, conclusion 
Sand mark type defect results / comparison with base line 
Sand_after sand_before 
20 
15 
...
Two-sample T for Ele_consumed_after 
vs Ele_cons_before 
N Mean StDev 
SE Mean 
Ele_consumed_after 28 22.8 24.1 
4.6 
Ele_...
Box plot comparison of sand defects before project and after project for 1 year period significant benefits seen in 1 year...
•Reponses (means) comparison: 
–Sand mark type defect : using optimum operating conditions as determined by this project l...
•Reponses (means) comparison: 
–Welding rod consumed for repair : using optimum operating conditions as determined by this...
•Findings are statistically valid 
•Implementation lead to significant cost savings – rework effort, time, money 
•Meets p...
CONTACT US FOR DETAILS (DETAILS NEXT SLIDE) 
QUESTIONS? 
DOUBTS? 
WHAT NEXT? 
WOULD YOU LIKE TO DISCUSS FURTHER TO DO SIMI...
THANKS! QUESTIONS? 
66 
Jagadish C.A. (Rao) jagadish.chandra@qmaxim.com qmaxim001@gmail.com +91 9900 606620 +91 9538 32870...
Jagadish C.A. ‘s Profile 
67 
LinkedIn profile: http://goo.gl/Lp3lWv 
•B.Tech (Metallurgical Engg., NIT-K,Surathkal, India...
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q-Maxim’s approach to waste reduction in foundry application using Taguchi method and Machine learning

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Description of a high impact project for waste reduction in a foundry using best of the breed methodologies such as Taguchi method, Machine learning

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q-Maxim’s approach to waste reduction in foundry application using Taguchi method and Machine learning

  1. 1. q-Maxim’s approach to problem solving & optimization – typical manufacturing project A high impact project leading to dramatic reduction in defects in a Foundry application 1 By Jagadish C.A. (Rao) , Founder of q-Maxim V 1.3F 17-10-2014
  2. 2. BY READING ONE CAN GET SOME INTUITION ABOUT THE APPROACH AND HOW ONE CAN APPLY IT IN THEIR OWN WORK THIS PRESENTATION GIVES OVERVIEW OF WASTE REDUCTION APPROACH OF Q-MAXIM FOR RAPID OPTIMIZATION WITHOUT COMPROMISING PRODUCTIVITY. IT USES COMBINATION OF TAGUCHI METHOD AND & MACHINE LEARNING. THIS APPROACH IS ILLUSTRATED IN A SAND CASTING CASE STUDY 2
  3. 3. 3 •Background, Business case •Get insights •Prepare for DOE •Design and run experimentation •Analyze results •Confirmatory runs, conclusions, contacts, background of founder q-Maxim
  4. 4. Background, Business case •An export focussed foundry was getting high level of rework during first stage inspection •Manufacturing Process : Sand casting, highly manual, induction melting furnace (see the next few slides for overview of the mfg. process) •Design : Casing (valve case), Aus. Stainless Steel (CF8M) 4
  5. 5. Why sand casting chosen? Sand casting is very noisy process with many variables, uses highly variable natural substances & is manual process – if it can work in sand casting it can work in any manufacturing situation 5
  6. 6. Background, Business case •An export focussed foundry was getting high level of rework during first stage inspection •Manufacturing Process : Sand casting, highly manual, induction melting furnace (see the next few slides for overview of the mfg. process) •Design : Casing (valve case), Aus. Stainless Steel (CF8M) 6
  7. 7. Background, Business case •Complicated production process (3 box, cored), low throughput •Project relates to 1st stage inspection & repair, steps being: –marking defectives, grinding, notching, repair welding, grinding, re-inspection, X- Ray, etc 7
  8. 8. Background, Business case •Severe cost / time implications : –Non value adding activities : Inspection, marking defectives, notching, welding, grinding, re- inspection –Additional cost: welding consumables, salaries of welders, grinders, inspectors –Capital cost: welding equipment, grinders , ovens, etc –Time lost: inspection, marking, notching, welding, grinding, re-inspection (all these activities are non-value adding & complete waste) 8
  9. 9. Background, Business case Schematic process flow 10 1st stage inspection, defect detection & repair
  10. 10. Background ,Business case 11 Sand mould under preparation, core in place
  11. 11. Background ,Business case 12 Metal being poured into sand moulds
  12. 12. Background ,Business case 13 Sand moulds filled
  13. 13. Business case – background, goal of project •Top 3 defects in the first stage visual inspection: –Sand mark type –Slag type –Cold metal •Sand mark type defect largest •Several similar casing designs produced by company, similar defects 14
  14. 14. Business case – background, goal of project •Defect levels in the industry are somewhat similar •Traditional problem solving approaches unsuccessful •Target : 1.Reduce top 3 defects by 50% 2.Reduce cost of repair by welding by 50% [See picture of sand mark type defect in the next slide] 15
  15. 15. Background, Business case 16 Sand mark type defects appearance (circled)
  16. 16. 17 •Background, Business case •Get insights •Prepare for DOE •Design and run experimentation •Analyze results •Confirmatory runs, conclusions, contacts, background of founder q-Maxim
  17. 17. Get insights- Possible sources of variation 18 Metallurgical literature Historical data Company documents, procedures Casting literature Theoretical / computer simulation Analyse data Excel – VB programming, Pivot tables Data mining Monte Carlo simulation Discussions Brainstorming Technological information INSIGHTS
  18. 18. Gating design- e.g. metal velocity Ladle temperature Sand production parameters Ambient humidity, temperature Pour height Mould fill time Wait time after mould preparation …………….. …………….. Sand mark defect Slag inclusion defect Cold metal defect Factors Effects Responses ++ ? --? No effect? Strong / weak? interactions? responses interrelated? Range dependant? By brainstorming it is concluded that > 50 variables & their interactions could have influence on defects generation. Casting temperature
  19. 19. Get insights- typical data analysis output 20 In 40 % casts no defects In 20 % casts all castings defective Variation in Sand mark type defects
  20. 20. Get insights- typical output 21 Output of Data mining – cluster analysis Which variables have possible linkages?
  21. 21. Get insights- conclusions •Large number (>50) of variables (called factors, noise) & their interactions could have effect on defect(s) generation •Noise – no control : e.g. humidity, ambient temperature 22
  22. 22. Get insights- conclusions •Defects could be interrelated. (i.e. increase one type of defect could lead to drop of others and vice versa. For example, increasing melt temp. may result in reduction of cold metal type defects which may lead to increase in slag type defects) •Thus it is a complex problem not easy to solve 23
  23. 23. Get insights- conclusions •Traditional experimentation methods – ”experiement by changing one variable at a time” or technological / metallurgical solutions are of limited use due to complexity as the company found out 24
  24. 24. Get insights- conclusions •Statistical methdologies called Design of experiments (DOE) are suitable for solving such complex problems & have been used extensively in the industry 25
  25. 25. 26 •Background, Business case •Get insights •Prepare for DOE (design of experiments) •Design and run experimentation •Analyze results •Confirmatory runs, conclusions, contacts, background of founder q-Maxim
  26. 26. 27 Prepare DOE (design of experiments) – DOE some background info. •A statistical methodology •Not a theoretical exercise, experiments are run on the shop floor like any production run
  27. 27. 28 Prepare DOE (design of experiments) – DOE some background info. •Highlights - steps 1.Select some factors & interactions for which knowledge has to be gained 2.Run experiments by varying all chosen factors at simultaneously unlike traditional experimentation methods in which experimentation is run by varying one factor at a time keeping others constant. 3.Run experiments as per prefixed experimental plans 4.Measure responses 5.Analyze results using specialized computer program like Minitab 6.Confirm findings
  28. 28. 29 Prepare DOE – DOE some background info. -advantages •less expensive –fewer tests •More sure of results, results have statistical validity •Can estimate interactions •Forms one of the main constituents of Six Sigma •Number of variables (factors) to test has to be reduced to a manageble number considering insights gained during earlier stage. (Not reducing the number of factors results in unacceptably large number of experiements)
  29. 29. Prepare for DOE- DOE some background info. •Before doing DOE, checked / ensured adequecy of measurement system ( MSA done ) •There are several types of DOE. •Taguchi DOE chosen, which is one the most popular. 30
  30. 30. Prepare for DOE- Who is Taguchi? •Dr. in Engineering and Statistics, renowned Quality Guru – Deming award 3 times, Blue Ribbon Award from the Emperor of Japan ,Stewart Medal from the ASQC • Concepts: Quality Engineering , Robust design, parameter design, Taguchi DOE • Developed a earlier method for doing DOE ( first developed by R.Fisher 1935) •Used extensively in Japan since 1960s, became popular in US since 1980 and in India 31
  31. 31. Prepare for DOE- Where is Taguchi DOE used? •Anywhere- manufacturing, transaction process, non-manufacturing, software development, website optimization, food production, health care……. •Applications: production, R+D, process optimization, improving yield, product design 32
  32. 32. Prepare for DOE- Where is Taguchi DOE used? •Some application examples: –Improving yield in Aluminium pressure die casting –Improving yield in sand casting foundry –Reducing the defects in welding process & choosing optimum parameters –Reducing wait time in banking –Improving the hospital discharge-process –Software testing – improving coding errors –Website design -deciding on optimum design 33
  33. 33. Prepare for DOE- Taguchi design goals •Primary Goal : 1. Find the effect of chosen factors & interactions on responses. 2.Find settings of factors that minimize responses while adjusting or keeping the process on target. •Secondary goal 1. Find setting of factors to maximize the S/N (signal to noise ) ratio. A product designed with this goal will deliver more consistent performance regardless of the environment in which it is used. 34
  34. 34. Prepare for DOE – Objectives of this project •Reduce number of factors for testing & number of interactions to a manageble number - 4 chosen •Four factors (called control factors) & some interactions chosen for experimentation: 1.Gating design 2.Pouring height 3.Melt temperature 4.Delay between mould prepartion and casting (other variables held constant in a certain range, or simply monitored) 35
  35. 35. Prepare for DOE – Objectives of this project (cont.) •Responses: 1.Count number of defects of each type in all castings 2.Measure total weight of Welding electrode consumed for repair •Objective is to minimise following response variables by 50% : 1.Numbers of defects of each type 2.Overall weight of Welding electrodes consumed for repair 36
  36. 36. 37 •Background, Business case •Get insights •Prepare for DOE •Design and run experimentation •Analyze results •Confirmatory runs, conclusions, contacts, background of founder q-Maxim
  37. 37. Design and run experiments – DOE flow chart- schematic representation 38 Gating design Pouring height Melt temp Delay between prep. & casting Factors chosen Other factors held constant/ monitored: e.g. mould preparation method, mould coatings, furnace content, furnace treatment, ambient temp. , relative humidity Design chosen Experimental design Experiments run Response & other data collected Data analysed Program output: response table and charts (mean & S/N) Conclusions 1.Factors of significance 2.Interactions of significance 3.Optimum factor levels
  38. 38. Design and run experiments – choose an appropriate experimental design •A prefixed Taguchi experimental design [Taguchi array] chosen, choice of design depends on: –Number of control factors –Number of levels of each control factor –Number of Interactions to be known between control factors –Number of replicates –Other considerations – such as confoundings •Responses are measured at selected combinations of the control factor levels 39
  39. 39. Design and run experiments – choose an appropriate experimental design (cont.) •Levels of Four control factors (two levels) chosen for experimentation 1.Gating design (old, new) 2.Pouring height (normal-present, low) 3.Melt temperature (procedure, low) 4.Delay between mould prepartion and casting (low, high) Others variables held constant in a certain range [see next slide for the experiemental design] 40
  40. 40. Design and run experiments - Experimental plan 41 DOE_Run_ NoA mould designB ladle_tempC pour_ htD dealy_ cst-mld1oldprocedurepresent low 2oldprocedurelow high 3newlow present low 4newlow low high 5oldlow present high 6oldlow low low 7newprocedurepresent high 8newprocedurelow low
  41. 41. Design and run experiments – Do experimentation •Production done as usual but controlling factor levels as specified in the experiemental plan- each combination called Run •Each run repeated twice, operating conditions noted down in a specially designed form 42
  42. 42. Design and run experiments – Do experimentation •Responses and tabulation 1.During first stage of inspection count the total number of each type of defect in all castings 2.Weight of Welding electrode consumed for repair of all the castings for each run [Schematic picture of all the steps in DOE is later in the presentation] 43
  43. 43. 44 •Background, Business case •Get insights •Prepare for DOE •Design and run experimentation •Analyze results •Confirmatory runs, conclusions, contacts, background of founder q-Maxim
  44. 44. Analyze results – data analysis •Response data for each run fed into specilaised Statistical program- Minitab •Program output in the form of response table and charts (Response of means and S/N ratio) 45
  45. 45. Analyze results – data analysis •By studying the output we can see: 1.To what extent each factor has influence 2.which levels of the factors are better •Typical outputs in the next 3 slides [only information on for sand mark type of defect is shown in this presentation] 46
  46. 46. Analyze results – program output – response table Sand inclusion defect analysis: •Response Table for Signal to Noise Ratios Level A gating B ladle_temp C pour_ ht Delay cst-mld 1 -13.088 -9.505 -10.193 -12.112 2 -7.185 -10.768 -10.080 -8.161 Delta 5.903 1.263 0.114 3.952 Rank 1 3 4 2 •Response Table for Means Level A gating B ladle_temp C pour_ ht D Delay cst-mld 1 3.792 2.875 3.042 3.292 2 2.000 2.917 2.750 2.500 Delta 1.792 0.042 0.292 0.792 Rank 1 4 3 2 Conclusion: Gating design has the most influence & pouring height has least influence on generation of sand mark type defects
  47. 47. Analyze results – program output- sand mark type of defects old new 4.0 3.5 3.0 2.5 2.0 proc low present low 4.0 3.5 3.0 2.5 2.0 low high A gating Mean of Means B ladle_temp C pour_ ht D dealy_ cst-mld Main Effects Plot for Means Data Means This level is better This level is better
  48. 48. DOE runs findings old new -8 -10 -12 proc low present low -8 -10 -12 low high A gating Mean of SN ratios B ladle_temp C pour_ ht D dealy_ cst-mld Main Effects Plot for SN ratios Data Means Signal-to-noise: Smaller is better This level is better This level is better Analyze results – typical program output- sand type of defects
  49. 49. Analyze results – Conclusions - sand mark type defects Influential factors: 1.Gating design has the highest effect 2.Delay between mould preparation and casting has the 2nd largest influence. 3.Others factors have minimal effect
  50. 50. Analyze results – Conclusions - sand mark type defects •Most desirable Level of influential factors 1.Gating design: New design is better 2.Delay between mould preparation and casting : longer delay is better 3.Levels of other two factors are not important
  51. 51. Analyze results – Findings were confirmed by using machine learning •A statistical model was built using machine learning algorithm of Artificial neural networks •Simulation was done, data generated, and 3d plot was made with response (i.e. number of sand type defects ) in z axis & important features in x & y axis [see next 2 slides]
  52. 52. DOE results 3d simulations Number of defects Vs design + delay 54 This confirms previous finding from DOE means charts Best operating range
  53. 53. DOE results 3d simulation Number of defects Vs design Vs ladle temp 55 This confirms previous finding from DOE means charts
  54. 54. 56 •Background, Business case •Get insights •Prepare for DOE •Design and run experimentation •Analyze results •Confirmatory runs, conclusions, contacts, background of founder q-Maxim
  55. 55. Confirmatory runs, conclusions- Confirmatory runs •It is necessary to confirm findings by repeating the runs under most desirable levels of influential factors & compare the responses with baseline levels •Responses are compared by 1. Box plot and 2. statistical tests [Comparison shown in next two slides]
  56. 56. Confirmatory runs, conclusion Sand mark type defect results / comparison with base line Sand_after sand_before 20 15 10 5 0 count of defects Boxplot of Sand_after (confirmatory), Sand_before Two-sample T for Sand_after vs sand_before N Mean StDev SE Mean Sand_after 28 3.07 2.68 0.51 sand_before 17 7.47 4.08 0.99 Difference = mu (Sand_after) - mu (sand_before) Estimate for difference: - 4.40 95% upper bound for difference: -2.50 T-Test of difference = 0 (vs <): T-Value = -3.96 P-Value = 0.000 DF = 24 Mean Defect level is about 60% better than baseline levels
  57. 57. Two-sample T for Ele_consumed_after vs Ele_cons_before N Mean StDev SE Mean Ele_consumed_after 28 22.8 24.1 4.6 Ele_cons_before 6 74.9 56.6 23 Difference = mu (Ele_consumed_after) - mu (Ele_cons_before) Estimate for difference: -52.1 95% upper bound for difference: - 4.6 T-Test of difference = 0 (vs <): T-Value = -2.21 P-Value = 0.039 DF = 5 Ele_consumed_after Ele_cons_before 160 140 120 100 80 60 40 20 0 Net Wt of electrode used (gm) Boxplot of Electrode consumed_after (confirmatory runs), Ele_cons_before Confirmatory runs, conclusion Electrode consumed for repair -comparison with baseline Mean Electrode consumed for repair is about 70 % less than baseline
  58. 58. Box plot comparison of sand defects before project and after project for 1 year period significant benefits seen in 1 year results also 61 before after
  59. 59. •Reponses (means) comparison: –Sand mark type defect : using optimum operating conditions as determined by this project lead to 59% lower level of defects as compared to baseline operating conditions –Similar benefits seen in 1 year operating results also Confirmatory runs, conclusion overall conclusion, benefits
  60. 60. •Reponses (means) comparison: –Welding rod consumed for repair : using optimum operating conditions as determined by this project lead to about 70% less welding electrode consumption for defect repair as compared to baseline operating conditions Confirmatory runs, conclusion overall conclusion, benefits
  61. 61. •Findings are statistically valid •Implementation lead to significant cost savings – rework effort, time, money •Meets project objectives consistently •Knowledge gained will be extended to the other designs of same family of products Confirmatory runs, conclusion overall conclusion, benefits
  62. 62. CONTACT US FOR DETAILS (DETAILS NEXT SLIDE) QUESTIONS? DOUBTS? WHAT NEXT? WOULD YOU LIKE TO DISCUSS FURTHER TO DO SIMILAR TRANSFORMATIONAL PROJECT? 65
  63. 63. THANKS! QUESTIONS? 66 Jagadish C.A. (Rao) jagadish.chandra@qmaxim.com qmaxim001@gmail.com +91 9900 606620 +91 9538 328704 blog: qmaxim.wordpress.com Twitter : @JagSpeak q-Maxim
  64. 64. Jagadish C.A. ‘s Profile 67 LinkedIn profile: http://goo.gl/Lp3lWv •B.Tech (Metallurgical Engg., NIT-K,Surathkal, India) •Done many graded online courses (4-15 weeks) from leading US universities on operations management, advanced data analysis, marketing, finance, accounting, strategy, advanced competitive strategy, data scientist (5 courses), credit risk management, game theory • ASQ (American society for quality) certified Six sigma Black belt (since 2002) • ASQ certified Manager of Org. Excellence/ Quality (since 1999) •Certified EFQM assessor extensive experience assessing companies, fashioning transformational roadmap & implementing EFQM model •Juran QI facilitator, Cert. adv. Industrial experimentation, Analytics • ISO 9001:2008,14001,QS-9000 (TS 16949) lead auditor •~32 years experience in Manufacturing and transactional fields, 7.5 years as management consultant •Rich experience in Quality, process management, R +D, Technology, Cost reduction, Management consultancy. Extensive experience in using advanced methodologies for problem solving and optimization •Extensive exposure/ knowledge of heavy process oriented manufacturing – Aluminium cast house, Steel, Welding, casting, foundry, Metallurgy, etc •Widely travelled – India, north America, Europe, M.E.

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