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.

Transcript

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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