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Business Value from Big Data for Manufacturing

Business Value from Big Data for Manufacturing

A structured overview (with examples form market leader) on the way Big Data and Analytics add value to manufacturing companies.

Valentin

June 05, 2014
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  1. Business  Value  from  
    BigData  for  Industry  4.0  
    Valen8n  Zacharias,  codecentric.de  
    Stu>gart,  5.6.2014  

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  2. 220  consultants,  enthusiasts,  engineers,  cra2smen,  experts,  nerds  
    •  build  systems  to  create  value  from  big  data  
    •  help  businesses  scale  so2ware  
    •  realize  agile  so2ware  development  –  with  our  customer‘s  in  house  
    development  and  in  custom  so2ware  development  
    We  are  codecentric.  

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  3. My  Goal  today:    
    •  Present  a  structured  overview  (with  examples  from  market  
    leaders)  on  the  ways  BigData  &  AnalyLcs  can  add  value  in  the  
    context  of  Industry  4.0  

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  4. n=All  &  t=now  
    It  is  no  longer  enough  to  plan  and  produce  for  
    averages  (Lme,  space,  customers),  but  
    necessary  to  opLmize  for  the  individual,  precise  
    locaLons  and  now  
    Big  Data  +  Industry  4.0  
    Shared  Challenge  

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  5. Further  improvements  in  operaLonal  efficiency  will  
    o2en  have  to  come  from  measuring  and  
    understanding  machine  state.    

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  6. Further  producLvity  gains  in  farming  will  largely  have  
    to  come  from  the  opLmized  use  of  machines,  
    ferLlizer  and  pesLcides.  

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  7. With  every  product  available  everywhere  at  the  click  
    of  a  buRon,  customizaLon  of  products  and  services  to  
    ever  smaller  customer  groups  becomes  paramount.  

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  8. InnovaLons  in  logisLcs  such  as  “Same-­‐Day-­‐Delivery”  
    rest  on  real  Lme  planning  and  opLmizaLon.    

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  9. Big  Data  Technology  Value  Proposi8on:  lower  the  cost  to  
    build  systems  that  do  more  complex  processing  with  
    more  data  faster.  Most  common,  but  not  topic  today.    

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  10. (Part  of)  CPS  und  3D  Prin8ng  Value  Proposi8on:  
    Lower  the  cost  of  flexibility  in  manufacturing    

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  11. Analy8cs  Value  Proposi8on:  New  ways  to  harness  
    paRerns  in  data  to  make  use  of  cheap  data  collecLon,  
    data  processing  and  flexible  manufacturing.    

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  12. AnalyLcs  Business  Value  
    Models  
    •  Data  Driven  Business  
    •  AnalyLcs  as  added  Value  
    •  Data  &  AnalyLcs  as  Business  

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  13. AnalyLcs  Business  Value  
    Models  
    •  Data  Driven  Business  
    – OperaLonal  Excellence  
    – Customer  InLmacy    
    – Product  Leadership  
    •  Analy8cs  as  added  Value  
    •  Data  &  AnalyLcs  as  Business  

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  14. Opera8onal  Excellence:  Use  of  data  to  increase  the  efficiency  in  
    the  creaLon  of  products  and  service,  e.g.  through  proacLve  
    maintenance  based  on  data  from  track  mounted  sensors.        
    Metropolitan  Transporta8on  
    Union  Pacific  

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  15. Opera8onal  Excellence:  Most  prominent  concrete  use  cases,  
    subjecLve  selecLon  from  “Big  Data  AnalyLcs  –  auf  dem  Weg  zur  
    Datengetriebenen  Wirtscha2”,  BARC  Research    
    Use  Case   Department   Use  BDA   Plan  BDA  
    Log  File  Analysis  and  Performance  
    OpLmizaLon  
    IT   27%   59%  
    SimulaLon  and  Scenario  based  
    Risk  Analysis  
    Controlling   20%   69%  
    Inventory  OpLmizaLon   LogisLcs   18%   47%  
    ProacLve  Maintenance   ProducLon  /  
    Customer  
    Service  
    9/10
    %  
    40/  
    41%  

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  16. Customer  In8macy:  Use  of  data  to  beRer  tailor  products  and  
    services  to  customers,  e.g.  instantly  display  a  prospecLve  
    customers  value  in  order  to  tailor  offers  to  that.    
    Meena  Kadri  @  Flickr  

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  17. Customer  In8macy:  Most  prominent  concrete  use  cases,  
    subjecLve  selecLon  from  “Big  Data  AnalyLcs  –  auf  dem  
    Weg  zur  Datengetriebenen  Wirtscha2”,  BARC  Research    
    Use  Case   Department   Use  BDA   Plan  BDA  
    Customer  LifeLme  Value  Analysis  
    and  PredicLon  
    MarkeLng   26%   60%  
    Analysis  of  Customer  Behaviour   Customer  
    Service  
    20%   69%  
    Customer  SegmentaLon   MarkeLng   19%   54%  
    Trend  /  Market  Analysis   R  &  D   17%   63%  

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  18. Product  Leadership:  Use  of  data  to  create  products  of  
    unmatched  quality,  e.g.  through  the  systemaLc  collecLon  
    of  DRO  data  for  all  cars  throughout  their  lifecycle.      
    Volvo  

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  19. Product  Leadership:  Most  prominent  concrete  use  cases,  
    subjecLve  selecLon  from  “Big  Data  AnalyLcs  –  auf  dem  
    Weg  zur  Datengetriebenen  Wirtscha2”,  BARC  Research    
    Use  Case   Department   Use  BDA   Plan  BDA  
    Test  Data  Analysis   R&D   20%   52%  
    Root  Cause  Analysis   ProducLon   13%   59%  
    PaRern  DetecLon  in  Customer  
    Complaints  
    Customer  
    Service  
    7%   61%  
    Warranty  Analysis   Customer  
    Service  
    6%   39%  

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  20. AnalyLcs  Business  Value  
    Models  
    •  Data  Driven  Business  
    •  Analy8cs  as  added  Value  
    – Capital  Goods  
    – Intermediate  Goods  
    – Consumer  Goods  
    •  Data  &  AnalyLcs  as  Business  

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  21. AnalyLcs  Business  Value  
    Models  
    •  Data  Driven  Business  
    •  Analy8cs  as  added  Value  
    –  Capital  Goods  
    •  Vision  “No  unplanned  downLme”*  
    •  Asset  OpLmizaLon  
    •  Enterprise  OpLmizaLon      
    –  Intermediate  Goods  
    –  Consumer  Goods  
    •  Data  &  AnalyLcs  as  Busines  
    *:  credits  to  Jeff  Immelt,  GE  

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  22. No  unplanned  down8me:  Online  predicLve  
    maintenance  service  as  added  value  to  trucks  
    Navistar  

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  23. Asset  Op8miza8on:  OpLmize  use  and  uLlizaLon  of  an  
    Asset,  e.g.  reduce  accidents  by  detecLng  and  
    intervening  on  near  misses.    
    Lytx  Drive  Cam  

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  24. Enterprise  Op8miza8on:  Find  an  opLmal  course  of  acLon  
    in  the  deployment  of  asset,  e.g.  an  opLmal  sequence  for  
    re-­‐starLng  flights  a2er  a  large  disturbance  
    Taleris  

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  25. AnalyLcs  Business  Value  
    Models  
    •  Data  Driven  Business  
    •  Analy8cs  as  added  Value  
    – Capital  Goods  
    – Intermediate  Goods  
    – Consumer  Goods  
    •  Data  &  AnalyLcs  as  Busines  

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  26. Analy8cs  as  added  value  for  intermediate  goods:  E.g.
    machines  and  services  to  opLmize  agricultural  yield  
    down  to  the  square  foot.    
    Monsanto  integrated  farming  systems  

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  27. AnalyLcs  Business  Value  
    Models  
    •  Data  Driven  Business  
    •  Analy8cs  as  added  Value  
    – Capital  Goods  
    – Intermediate  Goods  
    – Consumer  Goods  
    •  Data  &  AnalyLcs  as  Busines  

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  28. Consumer  Goods:  Product  offering  funcLonality  
    strongly  dependent  on  AnalyLcs,  e.g.  an  alarm  clock  
    that  wakes  based  on  sensor  analyLcs    
    Wiithings  Aura  

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  29. Or  heaLng  based  on  learned  paRerns.    
    Nest  /  Google  

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  30. AnalyLcs  Business  Value  
    Models  
    •  Data  Driven  Business  
    •  Analy8cs  as  added  Value  
    – Capital  Goods  
    – Intermediate  Goods  
    – Consumer  Goods  
    •  Data  &  AnalyLcs  as  Busines  

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  31. Strava  
    Data  as  Business:  Profit  from  harnessing  the  data  
    collecLng  through  other  services,  e.g.  data  for  urban  
    planning  from  fitness  devices    
    Strava  

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  32. Drilling  guys  
    Data  from  mobile  phone  use  for  retail  planning  ..      
    Drilling  Info  
    Telefonica  Smart  Steps  

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  33. Drilling  guys  
    or  collected  specifically  to  be  sold.    
    Drilling  Info  

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  34. connect  /  download  slides  at  
    www.vzach.de  

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