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2015-11-24-pepite-data-analytics

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how data analytics can provide interesting new possibilities for the food industry

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2015-11-24-pepite-data-analytics

  1. 1. Slide | 1Slide | 1 HOWDATAANALYTICSCANPROVIDEINTERESTING NEWPOSSIBILITIESFORTHEFOODINDUSTRY PhilippeMack
  2. 2. Slide | 2Slide | 2 Industrial internet of things Cloud computing Factory 4.0 Predictive Analytics Big Data In Memory Hadoop DATA Lake
  3. 3. Slide | 3Slide | 3
  4. 4. Slide | 4Slide | 4
  5. 5. Slide | 5Slide | 5 MB 19555
  6. 6. Slide | 6Slide | 6 2015 5.000MB
  7. 7. Slide | 7Slide | 7 Connection Transmission Storage Analytics … as a service Focus on your business… not on complex infrastructure Quick starts… quick wins!
  8. 8. Slide | 8Slide | 8 ABOUT US Specialised in predictive analytics solutions for industrial applications (Yield, productivity, quality, energy optimisation, predictive maintenance) – More than 10 years of experience Our distinctiveness !  Business minded consultants !  Software technology •  DATAmaestro: cloud based data mining software •  DATAserver: automatic and systematic data extraction, preparation and merging platform !  Dedicated vertical applications •  ENERGYmaestro: energy management solution based on data analytics and operator participation •  Wintell : performance tracking and predictive maintenance for wind turbines •  FINDIT : batch tracking and performance management for aquaculture
  9. 9. Slide | 9Slide | 9 WHERE HAS IT BEEN APPLIED ? Type of project Impact Increase yield and reduce scrap by 5% Predict in real-time the quality of the steel to increase yield and reduce scrap Analyze drilling operation data to increase ROP Faster drilling and less downtimes due to reduced well head failure E&P drilling operations Predict and understand root causes of breaks in paper sheets Paper making Reduce shutdowns and increases OEE by 5% Chemicals Optimize use of energy in exothermic processes Reduce energy costs by 15% Industry Steel Analyse the quality of the end products using advanced analytics Improve quality and find the root causes Carbon technology Electrical networks Forecast dynamic security of transmission grid Avoid costly curtailment of loads or generations
  10. 10. Slide | 10Slide | 10 BUT WHAT ABOUT ADVANCED DATA ANALYTICS for the! FOOD INDUSTRY?!
  11. 11. Slide | 11Slide | 11 for windturbines Precision farming
  12. 12. Slide | 12Slide | 12 predictive maintenance for windturbines WINTELL energy management system based on analytics, management & people! BIG DATA ANALYTICS ! in utilities to improve operations forthe FindIT continuous improvement platform fish production industry
  13. 13. Slide | 13Slide | 13
  14. 14. Slide | 14Slide | 14 a brief history!
  15. 15. Slide | 15Slide | 15 •  FineFish project •  Data initially collected in XLS sheet (a lot of error, many inconsistencies, difficult to process to compute basic statistics •  Development of a web app prototype to gather data in order to monitor malformation in fish •  Evangelization on big data and advanced analytics capabilities •  Interest was raised in the market •  A real need was identified to collect and centralize data from farm operation to •  Increase fish quality •  Increase farm performance in general A LITTLE HISTORY ON THE PROJECT
  16. 16. Slide | 16Slide | 16 THE PARTNERS
  17. 17. Slide | 17Slide | 17 •  Enter and store production data in the cloud (web based) •  Benchmark own data, and against the other producers (anonymised data) •  Use data mining to analyse big data and extract new knowledge, validate hypothesis KPI Benchmark Data entry Advanced Analytics User hatchery A User hatchery B User hatchery C Webinterface Webinterface Webinterface THE TOOL
  18. 18. Slide | 18Slide | 18
  19. 19. Slide | 19Slide | 19 RECORDS AND TRACK OPERATION CHANGES AND ACTIVITIES: MEASUREMENTS, FEEDS, OBSERVATIONS
  20. 20. Slide | 20Slide | 20 TRACK MOVEMENTS OF FISHES : IMPORTANT TO LINK MONITORING WITH BATCH TRACKING
  21. 21. Slide | 21Slide | 21 BROWSE THE BATCHES OF PRODUCTION
  22. 22. Slide | 22Slide | 22 KEY PERFORMANCE CALCULATION, REPORTING AND DASHBOARDING
  23. 23. Slide | 23Slide | 23 ANALYTICS FOR QUANTITATIVE ROOT CAUSE ANALYSIS, PREDICTION, OPTIMIZATION…
  24. 24. Slide | 24Slide | 24 •  Average SGR from start feeding to smolt : –  Higher than 2.29 is good (green) –  Lower than 2.29 is bad (red) Decision tree analysis of Water parameters vs. KPI_SGR THE SALMON DEMON FARM
  25. 25. Slide | 25Slide | 25 •  Automatic KPI calculation and reporting •  Advanced KPI management to predict, diagnose, optimise productivity and reduce mortality and increase quality •  State of the art information technology: big data, statistics, predictive analytics with machine learning •  Based on successful experiences in other industries •  KISS principle •  Secured software as a service for your tablet, your PC FEATURES
  26. 26. Slide | 26Slide | 26 168.000€ energy savings in UHT milk production process optimization
  27. 27. Slide | 27Slide | 27 SAVING THROUGH OPERATIONAL MANAGEMENT Procurement Investments Operations
  28. 28. Slide | 28Slide | 28 A PEOPLE MINDED APPROACH Analytics People •  Gap analysis •  Cost driver diagnostic •  Root cause analysis •  Optimised targets •  KPI •  Workshops •  Training •  Monitoring •  Culture A continuous improvement system based on:
  29. 29. Slide | 29Slide | 29 APPROACH Process & business understanding Workshops Training & reviewAdvanced analytics & implementation ENERGYmaestro 1 - 2 weeks 4 - 6 weeks 4 - 6 weeks 2 - 3 weeks Steam extraction < maximum Capacity of steam extraction Steam extraction down Maintenance – waiting for spare parts Sulfine plant is not in operation SO2 alarm Lack of sulfur There is no steam for the turbine Capacity of LP steam network Low pressure boilers at P2 HP vers LP Boiler BERI Boiler SO2 Demand/losses Consumers Start-up valves Overall management Training Communication & coordination Gap analysis 1 – 2 weeks 2012 11 Taux de soutirage - moyenne du mois 95 % Ouverture vannes démarrage 11 h Cible = 2 meilleurs mois + 5t/h 90% HP - vanne SO2 # heures 1 h Gain (pertes) par rapport à la cible du mois 1.490 t BP - vanne HRS # heures 10 h Débit moyen soutirage 32,3 t/h Uptime 96 % Tonnes excès BERI 5,7 t/h Taux de soutirage durant uptime 97 % Tonnes excès chaudières P2 0,9 t/h Gains vs taux de 75% - 2011 5.074 tPEPITE - RAPPORT MENSUEL Année Mois 0 10 20 30 40 50 1/nov. 2/nov. 3/nov. 5/nov. 6/nov. 7/nov. 9/nov. 10/nov. 11/nov. 13/nov. 14/nov. 15/nov. 17/nov. 18/nov. 19/nov. 21/nov. 22/nov. 23/nov. 25/nov. 26/nov. 27/nov. 29/nov. 30/nov. 0 10 20 30 40 0 5 10 15 20 25 30 35 40 45 0 200 400 600 800 1000 1200 1400 1600 Somme cumulative du gain / perte par rapport à la cible 2 4 6 8 10 12 14 16 soufre sulfine soufre sulfine Commentaires : ............................ ............................ ............................ ............................ ............................ ............................ ............................ ............................ ............. :…………………… …………………… …………………… …………………… …………………… …………………… …………………… …………………… …………………… …………………… …………………… …………………… … …………………… …………………… …………………… …………………… …………………… …………………... :…………………… …………………… …………………… …………………… …………………… …………………… … …………………… …………………… …………………… …………………… …………………… …………………… …………………… …………………… …………………… …………………… …………………… …………………... :… …………………… …………………… …………………… …………………… …………………… …………………… …………………… …………………… …………………… …………………… …………………… …………………… … …………………… …………………… …………………… …………………… …………………… ………………...:… …………………… …………………… …………………… …………………… …………………… …………………… … …………………… …………………… …………………… …………………… …………………… …………………… …………………… …………………… …………………… …………………… …………………… ………………...... ...... .. FLASH ANALYSIS
  30. 30. Slide | 30Slide | 30 ENERGY SPECIFIC CONS. (KWH/T MILK) UHT
  31. 31. Slide | 31Slide | 31 MOST IMPORTANT PARAMETERS TO EXPLAIN GLOBAL CONSUMPTION
  32. 32. Slide | 32Slide | 32 SPECIFIC STEAM CONSUMPTION AT UHT Target 15T/m3 milk
  33. 33. Slide | 33Slide | 33 STEAM SAVINGS ESTIMATES @UHT 6000 t in 6 months
  34. 34. Slide | 34Slide | 34 •  Process improvements •  12 000 T in 1 year •  168.000 EUR savings •  Operator involvement •  Sustainable energy culture •  No capital investment BENEFITS
  35. 35. Slide | 35Slide | 35 advanced imaging for automated quality control
  36. 36. Slide | 36Slide | 36 OK OK OK Ciabatta tomate KO KO “DEFECTS” ON BREAD DIFFICULT TO DETECTS

  37. 37. Slide | 37Slide | 37 IMAGE COLLECTION •  Collect Images
  38. 38. Slide | 38Slide | 38 CREATE A LIBRARY OF DEFECTS
  39. 39. Slide | 39Slide | 39 TRAIN COMPUTER TO AUTOMATE ANNOTATION & CLASSIFICATION

  40. 40. Slide | 40Slide | 40 KEY BENEFITS
 •  Faster and more reliable defect detection •  React faster to correct problems •  Identify parameters that impact quality crisis •  Mix image information with other sensors data to improve performance of process operations •  Update easily annotators with newer images
  41. 41. Slide | 41Slide | 41 advanced imaging for automated quality control $ 27.500 ANNUAL ENERGY SAVINGS for a brewery packaging line
  42. 42. Slide | 42Slide | 42 CORRELATION MATRIX GIVES RELATIONSHIP BETWEEN PROCESS VARIABLES
  43. 43. Slide | 43Slide | 43 MAIN VARIABILITIES: SEASON AND PRODUCTION LEVEL
  44. 44. Slide | 44Slide | 44 SIGNIFICANT VARIABILITY OF STEAM USE PER DAY CLEAR IMPACT OF PRODUCTION SHUTDOWN
  45. 45. Slide | 45Slide | 45 DECISION TREE EXPLAINS THE DIFFERENCE BETWEEN LEVELS OF STEAM USE (LOW-MED-HIGH) levels are defined based on distribution CO2 filler > 3180 kg leads to high use
  46. 46. Slide | 46Slide | 46 •  Process improvements •  146 MWh energy: - 7% •  2145 T steam: - 11% •  28 200 m3 water: - 15% •  EUR 27.500 direct savings annually •  No CAPEX BENEFITS
  47. 47. Slide | 47Slide | 47 Thank you ! ph.mack@pepite.be phone: 0477 380 005 www.pepite.be

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