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  
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.  
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  
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  
5. Further  improvements  in  operaLonal  efficiency  will   o2en  have  to  come  from  measuring  and   understanding  machine  state.    
6. Further  producLvity  gains  in  farming  will  largely  have   to  come  from  the  opLmized  use  of  machines,   ferLlizer  and  pesLcides.  
7. With  every  product  available  everywhere  at  the  click   of  a  buRon,  customizaLon  of  products  and  services  to   ever  smaller  customer  groups  becomes  paramount.  
8. InnovaLons  in  logisLcs  such  as  “Same-­‐Day-­‐Delivery”   rest  on  real  Lme  planning  and  opLmizaLon.    
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.    
10. (Part  of)  CPS  und  3D  Prin8ng  Value  Proposi8on:   Lower  the  cost  of  flexibility  in  manufacturing    
11. Analy8cs  Value  Proposi8on:  New  ways  to  harness   paRerns  in  data  to  make  use  of  cheap  data  collecLon,   data  processing  and  flexible  manufacturing.    
12. AnalyLcs  Business  Value   Models   •  Data  Driven  Business   •  AnalyLcs  as  added  Value   •  Data  &  AnalyLcs  as  Business  
13. AnalyLcs  Business  Value   Models   •  Data  Driven  Business   – OperaLonal  Excellence   – Customer  InLmacy     – Product  Leadership   •  Analy8cs  as  added  Value   •  Data  &  AnalyLcs  as  Business  
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  
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%  
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  
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%  
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  
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%  
20. AnalyLcs  Business  Value   Models   •  Data  Driven  Business   •  Analy8cs  as  added  Value   – Capital  Goods   – Intermediate  Goods   – Consumer  Goods   •  Data  &  AnalyLcs  as  Business  
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  
22. No  unplanned  down8me:  Online  predicLve   maintenance  service  as  added  value  to  trucks   Navistar  
23. Asset  Op8miza8on:  OpLmize  use  and  uLlizaLon  of  an   Asset,  e.g.  reduce  accidents  by  detecLng  and   intervening  on  near  misses.     Lytx  Drive  Cam  
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  
25. AnalyLcs  Business  Value   Models   •  Data  Driven  Business   •  Analy8cs  as  added  Value   – Capital  Goods   – Intermediate  Goods   – Consumer  Goods   •  Data  &  AnalyLcs  as  Busines  
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  
27. AnalyLcs  Business  Value   Models   •  Data  Driven  Business   •  Analy8cs  as  added  Value   – Capital  Goods   – Intermediate  Goods   – Consumer  Goods   •  Data  &  AnalyLcs  as  Busines  
28. Consumer  Goods:  Product  offering  funcLonality   strongly  dependent  on  AnalyLcs,  e.g.  an  alarm  clock   that  wakes  based  on  sensor  analyLcs     Wiithings  Aura  
29. Or  heaLng  based  on  learned  paRerns.     Nest  /  Google  
30. AnalyLcs  Business  Value   Models   •  Data  Driven  Business   •  Analy8cs  as  added  Value   – Capital  Goods   – Intermediate  Goods   – Consumer  Goods   •  Data  &  AnalyLcs  as  Busines  
31. Strava   Data  as  Business:  Profit  from  harnessing  the  data   collecLng  through  other  services,  e.g.  data  for  urban   planning  from  fitness  devices     Strava  
32. Drilling  guys   Data  from  mobile  phone  use  for  retail  planning  ..       Drilling  Info   Telefonica  Smart  Steps  
33. Drilling  guys   or  collected  specifically  to  be  sold.     Drilling  Info  
34. connect  /  download  slides  at   www.vzach.de