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18 Nov - Forecasting and Bullwhip Effect

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BA 244: Supply Chain Management
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Page 1: 18 Nov - Forecasting and Bullwhip Effect

ì  BA  244:  Supply  Chain  Management  

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Internal  Supply  Chain  

Procurement   Produc,on   Storage   Distribu,on   Sales  and  Marke,ng  

Forecas,ng  

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Demand  Management  

ì  Balances  customer  requirements  with  the  capabili,es  of  the  supply  chain  (Lambert,  2008)  

ì  Process  within  an  organiza,on  to  “tailor  its  capacity,  to  meet  varia)ons  in  demand,  or  to  manage  the  level  of  demand  using  marke)ng  or  SCM  strategies”  (CIPS)  

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Successful  Application  

ì  Use  detailed  POS  data  to  match  the  rate  of  produc,on  to  demand:  forecast  

ì  Need  for  established  process  for  receiving,  storing,  and  using  POS  data  from  retailers  (Lawrie,  2007  b:2)  

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Successful  Application  

1.  Define  relevant  data  to  manage  demand,  followed  by  systema,c  and  accurate  recording  of  this  data  

2.  Synchronize  demand  with  supply  

3.  Long  term  planning  

4.  Strategically  assess  promo,onal  ac,vity  and  its  impact  on  demand  

Taylor  and  Fearne  (2006)    

 

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Hints  and  Tips  

ì  Collabora,ve  demand  forecas,ng  ì  firms  reach  a  consensus,  both  internally  and  with  

their  chain  partners  on  the  expected  level,  ,ming,  mix  and  loca,on  of  demand  (Lawrie,  2007b)  

ì  Use  pricing  and  promo,ons  to  s,mulate  demand  (Lawrie  et  al.,  2007b)  

ì  Monitor  sales  against  forecasts  

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Forecasting  

ì  Encompasses  techniques  employed  to  systema)cally  analyze  data  and  informa)on  in  an  aXempt  to  predict  future  paXerns,  trends  or  performance  (Lysons  and  Farrington,  2006)  

ì  Underlying  basis  or  all  business  decisions  ì  Produc,on  ì  Inventory  ì  Personnel  ì  Facili,es  ì  Budget  

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Steps  in  Forecasting  

1.  Select  the  items  to  be  forecast  

2.  Determine  the  ,me  horizon  of  the  forecast  

3.  Select  the  forecas,ng  model  

4.  Gather  data  

5.  Make  the  forecast  

6.  Validate  and  implement  results  

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Forecasting  Approaches  

Qualita,ve  Approach  

When  liXle  data  exist  

Involves  intui,on,  experience  

e.g.  new  technologies,  new  products  

Quan,ta,ve  Approach  

When  stable  and  historical  data  exist  

Involves  mathema,cal  techniques  

e.g.  current  technology,  exis,ng  products  

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Forecasting  Approaches  

ì  Which  approach  did  they  use  to  forecast  the  iPad?  

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Overview  of  Quantitative  Approaches  

Quan,ta,ve  Forecas,ng  

Time  Series  Models  

Moving  Average  

Exponen,al  Smoothing  (EWAM)  

Trend  Projec,on  

Associa,ve  Models  

Linear  Regression  

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What  is  a  Time  Series?  

ì  Evenly  spaced  numerical  data  ì  Regular  ,me  periods  

ì  Forecast  based  only  on  past  values  ì  Assumes  that  factors  influencing  past  and  present  

will  con,nue  to  influence  in  the  future  

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Overview  of  Quantitative  Approaches  

Quan,ta,ve  Forecas,ng  

Time  Series  Models  

Moving  Average  

Exponen,al  Smoothing  (EWAM)  

Trend  Projec,on  

Associa,ve  Models  

Linear  Regression  

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Forecasting  Demand  

ì  Moving  Average  ì  How  to  best  smooth  out  fluctua,ons  

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Moving  Average  

PAST   FUTURE  

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Moving  Average  

PAST   FUTURE  

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Moving  Average  

PAST   FUTURE  

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Observations?  

ì  What  is  the  trend?  ì  Upward  ì  Downward  

ì  Which  data  is  more  smooth?  ì  Demand  ì  3-­‐week  ì  6-­‐week  

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However  

ì  Which  data  is  more  relevant?  ì  Older?  ì  Most  Recent?  

ì  Example:  Philippine  Popula,on  for  2017  ì  2010:  150  M?  ì  2016:  200  M?  

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Forecasting  Demand  

ì  Exponen,ally  Weighted  Average  Method  (EWAM)  ì  Since  the  older  the  demand  data,  the  less  relevant  ì  Adds  weights,  with  more  weight  to  more  recent  

data  ì  Weights  must  add  to  1  or  100%  

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Exponentially  Weighted  Average  Method  (EWAM)  

Week   Demand,  pcs   Weight    Weighted  Qty      Weighted  FC    1   650   0.2    130    2   678   0.3    203    3   720   0.5    360    4   ?    693    

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Overview  of  Quantitative  Approaches  

Quan,ta,ve  Forecas,ng  

Time  Series  Models  

Moving  Average  

Exponen,al  Smoothing  (EWAM)  

Trend  Projec,on  

Associa,ve  Models  

Linear  Regression  

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Trend  Projection  

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Trend  Projection  

ì Y  =  a  +  bx  ì Y  is  the  forecast  ì a  is  the  intercept  ì b  is  the  slope  

ì Extrapola,on  

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Overview  of  Quantitative  Approaches  

Quan,ta,ve  Forecas,ng  

Time  Series  Models  

Moving  Average  

Exponen,al  Smoothing  (EWAM)  

Trend  Projec,on  

Associa,ve  Models  

Linear  Regression  

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Regression  

ì  Demand  is  expressed  as  a  func,on  of  an  independent  variable,  not  ,me  

ì  Demand  is  forecasted  by  plugging  values  of  the  independent  variable  

ì  e.g.  sokdrink  demand  as  a  func,on  of  temperature  

ì  e.g.  product  demand  as  a  func,on  of  promo  budget  

ì  Key  is  to  have  a  logical  rela,onship  between  variables  

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Regression    Demand  for  Burger  Steak  

Meal,  pc  Demand  for  Chickenjoy  

Meal,  pc  Marke,ng  Budget  for  Burger  Steak,  PHP  ,00  

1    37      11,190    2    53      15,930    10    83      25,020    9    64      19,200    11    48      14,400    13    58      17,490    17    64      19,140    16    66      19,740    20    53      15,900    18    80      23,970    19    77      23,100    24    71      21,150    21    75      22,410    28    60      18,030    26    79      23,730    30    97      29,130    36    73      22,020    41    81      24,420    40    70      21,120    42    62      18,450    48    90      27,060    

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Regression  

Variable    Demand  for  Burger  Steak  Meal        

Independent  Variables  

Marke,ng  Budget  for  Burger  Steak    29.948     **  

Demand  for  Chickenjoy  Meal   -­‐18.685     *  

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Forecasting  Time  Horizon  

ì  it  is  difficult  to  be  as  accurate  the  further  into  the  future  they  go;  there  are  poten,al  risks  associated  with  longer  horizons  

ì  technological  products  with  short  life  cycles  can  only  be  forecasted  a  few  months  into  the  future  ì  vs.  furniture  product  forecas,ng  that  can  be  done  

for  years  ahead,  since  furniture  products  have  a  longer  life  cycle  (Boyer  and  Verma,  2010)  

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Forecasting  Time  Horizon  

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Performance  Monitoring  

ì  forecast  accuracy  should  be  monitored  and  its  assump)ons,  techniques  and  validity  of  data  revisited  when  the  actual  outcomes  differ  considerably  from  those  predicted  (Lysons  and  Farrington,  2006)  

ì  Goodness-­‐of-­‐fit  tests  

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ì  Bullwhip  Effect  

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History  

ì  First  described  by  Forrester  in  1958  and  has  been  experienced  since  the  1960s  

ì  Term  was  first  used  in  the  management  circles  of  Proctor  &  Gamble,  when  in  the  1980s  the  company  experienced  extensive  demand  amplifica,ons  for  Pampers    

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Definition  

ì  Demand  distor)on  that  travels  upstream  in  the  supply  chain  due  to  the  variance  of  orders  which  may  be  larger  than  that  of  sales  (Lee  and  Billington,  1992)  

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Cause  

ì  Inventory  is  oken  a  subs)tute  for  informa)on,  as  any  kind  of  uncertainty  is  covered  by  inventory.  However,  adding  in  safety  stocks  can  send  out  false  signals  and  encourage  suppliers  to  also  compensate  for  uncertainty  by  similarly  building  in  safety  stocks  

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Effect  

ì  accumula)on  of  inventory  at  the  manufacturer's  end,  which  further  increases  supply  chain  costs  to  the  company  (Sucky,  2009)  

ì  stockholding  and  obsolescence  costs  

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Mitigations  

ì  Reduced  lead  ,mes  

ì  Shared  knowledge  with  suppliers  and  customers  to  beXer  gauge  demand;  Provide  each  stage  of  the  supply  chain  with  complete  access  to  customer  demand  informa)on  

ì  use  of  technology  to  speed  communica)ons  and  improve  response  ,me    

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Steps  to  Successful  Application  

ì  Improve  communica)on  and  informa,on  flow  along  the  supply  chain  

ì  Improve  data  forecas)ng  (e.g.  determining  product  demand  from  actual  data  entered  into  POS  computer  systems  will  improve  sales  forecast  accuracy)  

ì  Work  with  firms  upstream  and  downstream  in  the  supply  chain  

ì  Order  products  up  and  down  the  supply  chain  in  smaller  increments,  thus  reducing  the  )me  between  orders  and  allowing  for  )mely  informa)on  to  be  available  

Fransoo  and  Wouters  (2000)  

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Other  Tips  

ì  Eliminate  variability  of  demand  caused  by  unplanned  promo,onal  ac,vi,es  at  the  retailers'  end  (Towill  et  al.,1996)  

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Case  Studies  

ì  The  Barilla  S.p.A.  case  was  one  of  the  first  published  studies  to  empirically  support  and  provide  illustra,ons  of  the  issues  resul,ng  from  the  bullwhip  phenomena  

ì  One  of  the  major  pasta  producers  in  Italy,  offered  special  price  discounts  to  customers  

ì  ordered  full  truckload  quan,,es  

ì  Resulted  in  spiky  and  erra,c  customer  order  paXerns  

ì  As  a  result,  supply  chain  costs  outstripped  the  benefits  from  full  truckload  transporta,on  

(Barilla  S.p.A.,  HBS  Case  9-­‐694-­‐04)  

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Case  Studies  

ì  HewleX  Packard  printers  

ì  When  examining  the  actual  sales  at  a  major  reseller,  execu,ves  found  that  there  were  some  normal  fluctua,ons  over  ,me  

ì  However,  when  they  examined  the  orders  from  the  reseller,  they  observed  much  bigger  swings  

ì  Moreover,  the  orders  from  the  printer  division  to  the  company's  integrated  circuit  division  had  even  greater  fluctua,ons  

(Kuper  and  Branvold,  2000)  

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Internal  Supply  Chain  

Procurement   Produc,on   Storage   Distribu,on   Sales  and  Marke,ng  

Forecas,ng  

Corporate  Spend  Management  

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Internal  Supply  Chain  

ì  How  much  to  produce  or  purchase  ì  Demand  Planning  

ì  Which  one  to  produce  or  purchase  first?  ì  Corporate  Spend  Management  

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Pareto  Analysis  

ì  80/20  Rule  

ì  80%  of  spend  being  directed  towards  just  20%  of  the  suppliers  

ì  Cri,cal  Few  vs.  Trivial  Many  

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ì  Pareto  Analysis  

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HOMEWORK  

ì  READ!  

ì  Han,  K.,  et.  al.  2012.  Value  Cocrea,on  and  Wealth  Spillover  in  Open  Innova,on  Alliances.  MIS  Quarterly.  36:  291-­‐315.    

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ì  End  


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