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Food Insecurity in America: A Macro and Micro-Level

Analysis

Virag Mody, Marielle Lenowitz, Aneesha Chowdhary, Eboni Freeman, Jasmyn Mackell and Ben Gross

April 12, 2017

 

   

   

 

   

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TABLE  OF  CONTENTS      

1.   Executive  Summary……………………………………………………………………………………..3    2.   Project  Goal…………………………………………………………………………………………………4  

 3.   Part  I:  A  Macro  Perspective  on  Food  Insecurity…………………………………………….4  

 3.1  Food  Insecurity  in  the  United  States……………………………………………4  

 3.2  Selection  of  Exponential  Smoothing  and  Alpha  Coefficient………….4  

   3.3  Exponential  Smoothing  Data  Analysis………………………………………….5  

 3.4  Forecasting  Food  Insecurity  with  Regression  Analysis………………….6  

 3.5  Comparing  Forecasts  –  Exponential  Soothing  vs.  Regression………..7    3.6  Note  on  Regression  Analysis………………………………………………………..7  

 4.  Part  II:  Quality  of  Inventory  at  a  Local  Food  Pantry…………………………………………7  

 4.1  Background  on  Toco  Hills  Community  Alliance………………………......7    4.2  Data  Collection……………………………………………………………………………8    4.3  P-­‐Bar  Chart  Construction  and  Analysis…………………………………….….8  

 4.5  R-­‐Chart  Construction  and  Analysis  …………………………………………….10  

 5.   Recommendation………………………………………………………………………………………..11  

 6.    Future  Considerations………………………………………………………………………………..11  

 7.   Appendix  A  (for  Part  II  data  and  graphs)……………………………………………………..13  

 8.   Appendix  B  (for  Part  I  data  and  graphs)………………………………………………………14  

 9.   Sources……………………………………………………………………………………………………….19  

     

 

   

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1.  Executive  Summary  There  is  a  food  insecurity  epidemic  in  America.  In  2016  over  17  million  American  households  were-­‐  at  some  point-­‐  food  insecure  (USDA  Food  Security  Study).  The  taxpayer  burden  of  this  insecurity  is  massive;  in  fiscal  year  2015,  the  federal  government  spent  over  75  billion  dollars  on  supplemental  food  programs  (Center  for  Budget  and  Policy  Priorities).        While  some  of  the  hunger  burden  is  relieved  through  specific  federal  programs,  such  as  the  free  and  reduced  lunch  program  for  students,  SNAP,  and  WIC,  a  significant  amount  of  food  is  distributed  through  non-­‐profit  entities  such  as  food  banks  and  food  pantries.  In  fact,  1  of  out  every  7  US  families  at  least  partially  relied  on  a  food  bank  or  food  pantry  in  the  last  year  to  meet  their  needs  (Feeding  America  Study).    For  our  study,  we  first  wanted  to  focus  on  a  local  food  pantry  where  we  could  offer  a  recommendation  that  could  benefit  the  thousands  of  families  whom  it  serves  each  month.  We  chose  Toco  Hills  Community  Alliance  in  Druid  Hills  to  conduct  our  survey,  due  to  its  proximity  to  Emory  and  the  wide  range  of  food  products  it  receives  each  month.  By  visiting  the  food  pantry,  we  were  able  to  collect  both  qualitative  and  quantitative  observations  about  its  inventory  management  system  and  the  clientele  it  serves.      We  took  16  samples  of  Toco  Hill  Community  Alliance’s  inventory  in  an  attempt  to  calculate  the  approximate  number  of  goods  that  are  defective  (expired).  Using  this  data,  we  were  then  able  to  calculate  that  number  of  defective  goods  per  million,  as  well  as  construct  a  P-­‐Bar  Chart  for  the  number  of  expired  goods  present  in  each  sample.  Additionally,  we  created  an  R-­‐Chart  of  the  sample  ranges  to  better  understand  the  extent  of  quality  management  situation.  From  both  of  these  charts,  we  found  that  the  number  of  expired  goods,  as  well  as  the  range  in  expiration  for  expired  goods,  varied  widely.  Such  variation  showed  that  the  food  bank  most  likely  does  not  have  a  system  in  place  to  ensure  that  goods  expiring  soonest  are  distributed  first.    To  better  understand  our  data,  we  also  examined  broader  food  insecurity  trends  in  the  US.  Using  data  from  USDA  studies  on  food  insecurity  in  the  US  from  1998-­‐2015,  we  conducted  exponential  smoothing  forecasts  of  total  US  households  and  US  households  with  general  food  insecurity  (as  well  as  for  subsections  with  low  food  security  and  very  low  food  security).  These  forecasts  turned  out  to  be  consistent  with  actual  data  from  the  period.  We  also  ran  regressions  for  these  four  categories  as  well,  which  had  a  noticeably  higher  error  when  compared  with  real  data  from  the  period.  We  then  used  the  regression  models  to  forecast  the  the  number  of  food  insecure  households  the  next  5  years.      Based  on  our  analysis  of  the  Toco  Hills  Community  Alliance  data,  as  well  as  the  P-­‐Bar  and  R-­‐Charts  we  constructed,  we  recommend  that  the  food  pantry  create  a  system  that  organizes  goods  by  expiration  date.  Goods  that  are  expiring  sooner  should  be  placed  in  the  front  of  the  room  and  on  the  outermost  edge  of  the  shelves,  as  a  means  of  encouraging  shoppers  to  pick  those  goods.  Meanwhile,  goods  that  are  received  and  have  several  years  before  their  expiration  should  be  placed  towards  the  back,  because  they  have  a  significantly  longer  “use-­‐by”  date.  This  organizational  solution  will  not  completely  eliminate  the  food  pantry’s  problem  with  expired  goods;  indeed,  some  of  the  goods  the  food  pantry  receives  are  already  close  to,  if  not  past,  their  expiration  date.  The  implementation  of  

 

   

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such  a  system  could  make  an  impact  in  reducing  the  total  number  of  expired  goods  that  the  pantry  keeps  in  its  inventory.      2.  Project  Goal  This  project  aims  to  analyze  the  quality  of  inventory  at  the  Toco  Hills  Community  Alliance,  a  local  food  pantry  in  Atlanta,  Georgia.  Additionally,  to  understand  the  large  number  of  food  insecure  households  that  frequent  these  types  of  food  pantries,  this  project  also  sought  to  forecast  macro  level  data  about  food  insecurity,  such  as  the  total  number  of  food  insecure  households  each  year,  from  1998-­‐2015  (as  well  as  subsections  of  this  data,  such  as  those  with  very  low  food  security).  3.  Part  I:  A  Macro  Perspective  on  Food  Insecurity  3.1  Food  Insecurity  in  the  United  States  The  United  States  Department  of  Agriculture  (USDA)  defines  food  insecurity  as  a  state  in  which  “consistent  access  to  adequate  food  is  limited  by  a  lack  of  money  and  other  resources  at  times  during  the  year.”  Food  insecurity  exists  whenever  the  availability  of  healthy,  nutritionally  adequate,  and  safe  foods  is  limited,  or  the  ability  to  obtain  sufficient  foods  in  a  legitimate  and  socially  acceptable  way  is  uncertain.  An  estimated  1  in  7  Americans  struggles  with  food  insecurity.      We  were  interested  in  the  relationship  between  food  pantries  and  food  insecurity,  but  before  we  could  focus  specifically  on  our  local  food  pantry,  the  Toco  Hills  Community  Alliance,  we  wanted  to  understand  the  larger  food  insecurity  problem  in  the  United  States.  Using  data  from  the  USDA  report  entitled  “Household  Food  Security  in  the  United  States  in  2015,”  we  chose  to  forecast  Total  Food  Insecurity  as  a  function  of  Total  Households,  which  could  then  further  be  broken  down  into  Low  Food  Security  and  Very  Low  Food  Security  (Exhibit  8,  Appendix  B).  By  analyzing  this  data,  we  would  be  able  to  get  a  macro-­‐level  perspective  on  a  topic  that  affects  people  both  locally  within  the  Atlanta  area,  as  well  as  nationally.    3.2  Selection  of  Exponential  Smoothing  and  Alpha  Coefficient  To  properly  forecast,  the  first  step  is  to  identify  which  method  of  forecasting  is  most  appropriate  to  use.  The  five  methods  available  are  Naïve,  Moving  Average,  Weighted  Moving  Average,  Exponential  Smoothing,  and  Regression.  The  following  shows  our  analysis  and  applicability  of  each  method,  except  for  regression  analysis,  which  is  mentioned  later:  

•        Naïve  Forecasting  –  This  method  does  not  appropriately  account  for  historical  data,  with  the  exception  of  the  previous  period.  At  a  minimum,  the  population  tends  to  grow  positively,  so  using  the  prior  period’s  data  point  would  be  empirically  wrong,  thus  eliminating  this  method  as  a  viable  option.  •        Moving  Average  –  This  method  weights  each  data  point  equally,  meaning  that  data  from  1998  is  just  as  relevant  as  data  from  2014.  Weighting  older  data  equally  to  recent  data  would  be  problematic  for  this  project  because  numerous  factors  influence  levels  of  food  security  over  time,  such  as  economic  trends,  immigration,  population  changes,  and  health.  These  factors  cause  food  insecurity  to  evolve  over  time,  meaning  that  more  current  factors  are  more  relevant  to  present  food  insecurity  trends.  Therefore,  the  historical  data  from  1998  should  not  have  as  much  weight  as  recent  years,  removing  the  Moving  Average  as  an  option.  

 

   

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•        Weighted  Moving  Average  –  WMA  could  have  some  applicability,  but  without  knowing  how  to  weight  historical  data,  doing  so  would  be  arbitrary.  This  eliminates  WMA.  •        Exponential  Smoothing  –  This  forecasting  method  assigns  exponentially  decreasing  weights  as  the  observations  get  older,  allowing  us  to  put  more  weight  on  more  recent  and  more  relevant  data,  which  was  the  concern  pointed  out  in  the  Moving  Average  model.  This  means  that  Exponential  Smoothing  is  a  viable  method  for  forecasting  our  data.  

 Given  that  there  are  macro  factors  for  variability  in  food  insecurity,  including  immigration,  population  changes,  health,  and  economic  factors,  we  cannot  solely  rely  on  historical  data,  as  there  is  most  likely  not  a  consistent,  holistic  trend.  However,  we  cannot  assume  an  alpha  of  1  because  it  will  become  naive  forecasting.  Additionally,  immigration,  population  changes,  and  the  economy  often  follow  trends  and  cycles,  so  to  some  extent,  historical  data  is  useful.  Thus,  to  use  only  the  previous  years  would  be  inaccurate  and  naïve,  while  discounting  historical  data  altogether  would  make  for  a  poor  forecast.  In  order  to  appease  both  sides  of  this  narrative,  we  selected  an  alpha  value  of  0.5  as  a  median  between  discounting  historical  data  and  accounting  for  historical  information.    3.3  Data  Analysis  –  Exponential  Smoothing  After  forecasting  using  exponential  smoothing,  the  following  graphs  show  noteworthy  information.  The  raw  data  can  be  found  in  Exhibit  1  and  2  under  Appendix  B.  Exhibit  2  also  shows  the  MAPE  to  calculate  the  error.  

•        Total  Households  –  Our  forecast  for  this  metric  is  fairly  accurate  in  tracking  Historical  Data,  with  a  MAPE  of  1.99%.  However,  except  for  1998,  forecasted  Total  Households  is  consistently  below  the  actual  data.  This  is  most  likely  because  there  were  variable  jumps  in  the  number  of  real  total  households,  which  could  not  be  accurately  accounted  for,  due  to  the  fact  that  our  exponential  smoothing  model  weights  the  previous  year’s  forecast  as  heavily  as  the  actual  data.  Thus,  any  lag  in  the  forecast  would  permanently  influence  future  predictions.  

   

 

   

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•        Total  Food  Insecurity  –  Analysis  of  Total  Food  Insecurity  be  can  be  broken  up  into  “Pre  2007”  and  “Post  2007.”    •   Pre  2007  –  The  exponential  smoothing  forecasts  had  a  low  forecast  error  because  they  

normalized  the  variability  in  total  food  insecurity.  The  dip  from  1998  to  2000  is  offset  by  the  increase  in  food  insecurity  from  2000  to  2004.  Because  the  model  accounts  for  historical  data  at  an  exponentially  decaying  rate,  the  variability  over  time  will  be  smoothed  in  our  forecasted  graph.    

•   Post-­‐2007  –  The  massive  jump  in  Total  Food  Insecurity  likely  resulted  from  the  housing  market  collapse  and  subsequent  recession.  Our  forecast  model  didn’t  intersect  the  actual  data  from  2007  to  2013  due  to  our  use  of  a  0.5  alpha.  An  alpha  of  1  would  have  better  accounted  for  the  spike.    

 •        Low  Food  Security  and  Very  Low  Food  Security  –  These  graphs,  found  under  Exhibits  3  and  4  in  Appendix  B,  provide  a  very  similar  analysis  to  that  of  the  Total  Food  Insecurity  graph.  A  notable  difference  can  be  seen  in  the  Very  Low  Food  Security  Graph,  whose  forecast  lags  from  2000  to  2014.  This  lag  is  due  to  the  same  reason  cited  as  Total  Households;  Very  Low  Food  Security  has  been  steadily  increasing  for  years,  and  our  exponential  smoothing  model  has  lagged  as  it  continually  accounted  for  historical  data  at  an  exponentially  decreasing  rate.    Exponential  smoothing  limited  our  ability  to  forecast  into  the  future  to  only  one  year  ahead,  2016.  If  we  wanted  to  forecast  further  into  the  future,  we  would  have  to  use  a  regression  analysis.    

 3.4  Forecasting  Food  Insecurity  with  Regression  Analysis  We  used  regression  analysis  because  this  method  allows  for  forecasting  beyond  a  single  year,  unlike  Exponential  Smoothing.  Additionally,  regression  analysis  predicts  linear  trends  more  accurately  than  exponential  smoothing.  The  regression  model  used  the  same  data  as  exponential  smoothing  (data  which  can  be  found  in  Exhibit  1,  Appendix  B).  In  analyzing  the  regression  results,  P-­‐values  for  all  different  regressions  are  less  than  0.05,  which  indicates  significance.  We  thus  felt  comfortable  using  the  regression  analysis  to  forecast.  Additionally,  looking  at  the  R2  values:  

 

   

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•        The  high  R-­‐Square  value  of  98  percent  for  the  “Total  Households”  regression  indicates  that  the  regression  is  representative,  though  there  may  be  concerns  of  overfitting  data,  which  may  account  for  noise  that  could  impede  future  projections.  •        The  R-­‐Square  values  of  the  regressions  for  Total  Food  Insecurity,  Low  Food  Security,  and  Very  Low  Food  Security  ranged  between  64  percent  and  84  percent,  which  indicates  that  there  is  a  higher  amount  of  variability  in  the  actual  data  relative  to  that  of  our  regression.  (Raw  numbers  for  p-­‐values  and  R-­‐squared  are  shown  in  Exhibit  5,  Appendix  B)  

 3.5  Comparing  Forecasts  –  Exponential  Soothing  vs.  Regression  (Graphs  of  regression  analysis  can  be  found  in  Exhibit  6,  Appendix  B)  Exponential  smoothing  is  limited  in  how  far  into  the  future  we  can  forecast  data,  but  it  excels  at  its  ability  to  fit  actual  data  closely.  This  is  shown  by  the  differences  in  MAPE  for  the  comparative  models.  MAPE  for  the  regression  models  is  higher  for  nonlinear  trends  than  it  is  for  linear  trends.  Indeed,  the  only  linear  trend  that  we  found  was  for  the  regression  for  total  households.  The  MAPE  calculations  can  be  seen  in  Exhibit  7,  Appendix  B.  MAPE  for  total  households  is  much  lower  when  the  regression  model  is  used  than  when  the  exponential  smoothing  model  is.  For  total  households,  there  are  more  predictable  causal  reasons  for  a  linear  trend.  Ultimately,  it  is  the  least  squares  component  of  regression  that  does  a  better  job  of  accounting  for  causal  factors  of  change  in  the  number  of  total  households.      3.6  Note  on  Regression  Analysis  The  regression  model,  while  applicable  for  periods  in  which  there  is  historical  data  following  a  linear  trend,  has  future  forecasts  for  years  2016-­‐2020  that  are  likely  inaccurate  (forecasts  for  those  years  can  be  found  in  Exhibit  7,  Appendix  B).  This  is  due  to  the  fact  that  the  regression  model  only  looks  at  aggregate  numbers  and  doesn’t  account  for  causal  factors.  A  multivariable,  non-­‐linear  regression  model  would  have  been  a  more  appropriate  way  to  forecast,  but  we  didn’t  have  the  capability  to  do  that  for  this  analysis.    Now  that  we  have  analyzed  overall  food  insecurity  in  the  United  States,  we  can  address  the  issues  faced  by  our  one  of  Atlanta’s  own  food  pantries,  Toco  Hills  Community  Alliance.      4.  Part  II:  Quality  of  Inventory  at  a  Local  Food  Pantry  4.1  Background  on  Toco  Hills  Community  Alliance    A  food  pantry  is  defined  as  a  charitable  organization  that  provides  those  in  need  with  food  and  grocery  products  for  use  and  consumption  at  home.  The  food  pantry  we  analyzed,  Toco  Hills  Community  Alliance,  is  a  food  pantry  that  serves  DeKalb  County  and  several  of  the  zip  codes  in  the  surrounding  area.  According  to  its  website,  Toco  Hills  Community  Alliance’s  chief  goal  is  “to  provide  assistance  and  support  for  individuals  and  families…  who  face  the  possibility  of  the  loss  of  housing  and/or  who  are  without  sufficient  food  for  themselves  of  their  families”  (Toco  Hills  Community  Alliance  Website).  The  pantry  receives  a  wide  variety  of  food  donations  from  both  local  grocery  stores  and  individuals  in  the  community.  These  goods  are  then  organized  into  different  rooms,  based  on  the  type  of  food  item,  by  the  employees  at  the  food  pantry.  For  example,  one  room  consists  of  mainly  canned  goods  and  breads,  while  another  room  contains  mostly  snacks.    

 

   

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The  food  pantry  follows  a  specific  routine  when  serving  its  patrons.  Individuals  enter  the  building  that  houses  the  pantry  and  must  prove  that  they  qualify  for  assistance.  Next,  they  are  placed  on  a  waiting  list  and  provided  with  forms  to  complete.  One  by  one,  Toco  Hills  Community  Alliance  workers  guide  these  individuals  through  the  different  food  storage  rooms.  Qualifying  individuals  are  allowed  to  select  the  types  of  items  they  want,  but  only  workers  can  physically  collect  the  groceries.  At  the  end  of  the  shopping  period,  the  workers  weigh  the  selected  groceries  and  record  the  amount.        Following  our  initial  visit  to  the  Toco  Hills  Community  Alliance,  we  decided  to  focus  on  the  “quality”  of  the  inventory.  For  our  purposes,  a  poor  quality  food  item  is  one  that  is  past  its  expiration  date.  We  chose  this  aspect  for  analysis  because  the  pantry’s  primary  goal  is  providing  food  to  those  in  need,  and  thus  it  is  important  that  it  is  serving  quality  food  that  won’t  make  people  sick.      Since  Toco  Hills  Community  Alliance  does  not  collect  information  on  the  donations  they  receive,  we  had  to  use  a  heuristic  that  would  represent  the  quality  of  inventory.  We  ultimately  decided  on  the  expiration  date  heuristic.  By  collecting  expiration  date  data,  we  hoped  to  determine  whether  a  quality  issue  existed  and  to  give  a  possible  recommendation  to  address  this  problem,  if  this  turned  out  to  be  the  case.    4.2  Data  Collection    To  analyze  the  quality  of  the  inventory  and  tracking  system  at  the  Toco  Hills  Community  Alliance,  we  visited  the  food  pantry  to  collect  samples.  We  took  three  samples  from  each  of  the  food  bank’s  five  storage  rooms,  for  a  total  of  15  samples.  Each  sample  was  obtained  randomly  and  contained  a  mix  of  10  perishable  and  non-­‐perishable  items.    For  every  sample,  we  recorded  the  number  of  defective  (expired)  goods  found  amongst  the  ten  items  surveyed.  The  expiration  date  of  an  item  was  recorded  if  the  item  was  found  to  be  defective.  See  Exhibit  1  in  Appendix  A  for  the  raw  sample  data.  By  taking  an  average  of  the  15  samples,  we  found  that  34%  of  the  sample  goods  were  defective.  This  finding  indicates  that,  on  average,  3.4  out  of  every  10  goods  at  the  Toco  Hills  Community  Alliance  should  be  expired.  Converting  this  number  to  defective  goods  per  million,  we  can  expect  that  340,000  out  of  every  million  goods  donated  to  Toco  Hills  Community  Alliance  will  be  defective.    4.3  P-­‐Bar  Chart  Construction  and  Analysis    After  collecting  our  data  and  calculating  the  average  number  of  defective  goods  per  million  at  the  food  bank,  we  constructed  a  P-­‐Bar  Chart.  We  created  a  P-­‐Bar  Chart  because  it  can  be  an  efficient  tool  to  analyze  the  number  of  defective  goods  relative  to  the  UCL  and  LCL,  as  well  as  show  whether  a  process  is  out  of  control  or  not.  In  our  case,  we  wanted  to  see  the  variation  in  defective  goods  among  the  five  sample  rooms  and  determine  whether  any  specific  rooms  fell  significantly  outside  of  the  average.        To  begin  the  construction  of  the  P-­‐Bar  Chart,  we  used  P-­‐Bar,  previously  found  to  be  0.34,  and  the  parameters  of  three  sigmas,  to  calculate  the  Upper  Control  Limit  (UCL)  and  the  Lower  Control  Limit  (LCL)  of  the  data.  The  UCL  and  LCL  were  found  to  be  0.45603  and  0.22396,  respectively.  It  is  important  to  note  that  we  are  not  analyzing  a  machine  or  production  process;  rather,  in  our  case,  the  UCL  and  LCL  serve  as  lower  and  upper  bounds  to  assess  if  our  data  goes  beyond  these  numbers  when  

 

   

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analyzing  the  quality  of  the  inventory.  After  the  calculation  of  these  values,  we  were  then  able  to  construct  the  P-­‐Bar  Chart.  See  Exhibit  2,  Appendix  B  for  the  full  P-­‐Bar  Chart  calculations.    Looking  at  our  P-­‐Bar  Chart,  represented  below,  we  can  see  that  the  data  varies  widely  in  respect  to  P-­‐Bar,  UCL,  and  LCL.  There  are  two  key  reasons  for  this  vast  amount  of  variation.  First,  each  sample  corresponds  to  a  particular  room,  and  some  rooms  contained  significantly  more  expired  goods  due  to  the  types  of  items  that  they  stored.  For  example,  Room  4  (samples  7,  8,  and  9)  stores  goods  that  have  a  relatively  short  shelf  life  like  bread.  In  comparison,  Room  3  (samples  4,  5,  and  6)  mostly  stores  items  with  extended  shelf-­‐lives  such  as  canned  soups.  Second,  it  was  not  uncommon  to  find  a  group  of  cans  several  years  expired  sitting  next  to  a  loaf  of  bread  that  was  set  to  expire  in  a  few  days,  when  we  conducted  our  survey.  These  two  factors  created  significant  variation  in  the  data.  

     While  the  data  fluctuates  significantly,  it  is  important  to  point  out  samples  that  fall  either  considerably  below  the  LCL  or  considerably  above  the  UCL.  One  sample  that  fell  significantly  below  the  LCL  was  sample  4,  which  had  no  defects.  Two  samples  that  significantly  exceeded  the  UCL  were  samples  12  and  15,  each  of  which  had  six  defects.  Such  outliers  may  be  due  to  random  sampling  chance,  given  the  fact  that  on  average,  about  3.4  out  of  every  10  goods  at  Toco  Hills  Community  are  expected  to  be  defective.  It  is  also  possible  that  these  values  are  partially  due  to  the  rooms  where  the  sample  was  taken,  as  discussed  earlier.  For  instance,  when  compared  with  the  other  two  samples  from  the  refrigeration  room,  samples  13  and  14,  sample  15  does  not  stand  out  as  an  outlier.    4.4  R-­‐Chart  Construction  and  Analysis    In  addition  to  making  a  P-­‐Bar  Chart,  we  also  created  an  R-­‐Chart.  We  decided  to  make  an  R-­‐Chart  because  we  wanted  to  analyze  the  range  of  the  defective  goods-­‐–how  long  the  goods  in  each  sample  

 

   

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had  been  expired,  relative  to  the  day  that  we  took  the  sample  (April  5,  2017).  Ideally,  the  range  would  be  more  accurate  if  we  had  information  on  when  the  item  was  donated  to  the  pantry;  after  all,  some  goods  may  have  already  been  expired  when  donated.  However,  since  Toco  Hills  did  not  collect  this  information,  we  decided  that  we  could  best  estimate  this  figure  by  comparing  expiration  dates  to  the  date  we  took  the  samples.  To  calculate  the  range  for  each  sample,  we  found  the  good  with  the  most  recent  expiration  date,  and  subtracted  it  from  the  good  with  the  oldest  expiration  date.  Next,  we  found  the  average  of  the  15  sample  ranges,  or  R-­‐Bar,  which  we  calculated  to  be  10.357  months.  As  with  the  P-­‐Bar  Chart,  we  found  the  UCL  and  LCL,  which  were  18.3457  months  and  2.2785  months,  respectively.  These  control  limits  were  determined  using  the  D4  and  D3  values  on  page  185  of  the  Bus351  Textbook.  Calculations  for  the  R-­‐Chart  can  be  seen  in  Exhibit  3  of  Appendix  A.    The  R-­‐Chart,  shown  below  for  the  Toco  Hills  Community  Alliance,  shows  data  that  appears  to  have  no  distinct  pattern,  except  a  few  samples  (samples  13,  14,  and  15).  Some  samples  had  a  range  of  0  months  (significantly  below  the  LCL),  which  would  indicate  that  all  of  the  defective  goods  in  the  sample  had  the  same  expiration  date.  Such  an  R  value  makes  sense  for  samples  13,  14,  and  15  because  these  samples  were  from  the  refrigeration  room,  where  items  are  likely  to  have  a  short-­‐term  shelf  life,  and  are  consequently  likely  to  have  expiration  dates  close  to  one  another.  Meanwhile,  some  samples  had  an  enormous  range,  such  as  samples  7  and  9,  which  were  significantly  above  the  UCL  and  had  ranges  of  42  and  41  months,  respectively.  The  significant  variation  among  R  values,  as  well  as  the  presence  of  some  incredibly  high  values  (R=41,  R=42),  indicates  that  the  food  bank  does  not  have  a  way  to  monitor  the  expiration  of  goods,  therefore  the  data  suggests  the  need  for  some  type  of  organizational  system  to  ensure  that  the  food  pantry  serves  customers  items  that  have  not  yet  expired.      

                       

 

   

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4.   Recommendation  Our  analysis  using  the  P-­‐Bar  Chart  and  R-­‐Chart  demonstrates  that  the  Toco  Hills  Community  Alliance  needs  an  organization  schedule  by  expiration  date.  To  address  this  issue,  we  suggest  that  the  pantry  implement  a  First-­‐In  First-­‐Out  (FIFO)  system  to  prevent  donated  items  from  reaching  their  expiration  date  while  in  storage.  Under  our  proposed  system,  goods  would  continue  to  be  organized  by  food  type,  but  they  would  also  be  arranged  by  expiration  date.  For  example,  if  a  bag  of  apples  is  donated,  the  item  would  not  only  be  placed  in  a  room  with  similar  items,  but  would  also  be  placed  near  items  which  had  a  similar  expiration  date.  Items  that  are  close  to  their  expiration  date  would  in  the  front  of  the  room,  while  items  that  have  a  longer  time  before  expiration  would  be  placed  towards  the  back  of  the  room.  This  layout  would  encourage  shoppers  to  choose  items  that  are  close  to  their  expiration  date  because  those  items  would  be  in  their  direct  line  of  sight  when  entering  the  room.  This  model  mimicks  how  grocery  stores  stock  their  shelves.  We  believe  the  total  percentage  of  expired  goods  at  the  Toco  Hills  Community  Alliance  would  decrease  under  this  proposal,  as  goods  that  are  close  to  expiration  will  exit  the  pantry  sooner.      6.  Future  Considerations  While  we  believe  that  our  recommendation  will  reduce  the  amount  of  expired  goods  at  the  Toco  Hills  Community  Alliance  at  a  given  time,  we  do  not  believe  that  the  inventory  quality  problem  can  be  completely  resolved  by  implementing  this  recommendation.  This  is  due  to  the  complicated  reasons  why  the  food  pantry  has  expired  goods  in  the  first  place.  For  example,  a  large  portion  of  the  pantry’s  food  donations  come  from  major  grocery  stores  in  the  surrounding  area.  These  stores,  however,  primarily  donate  items  that  are  either  close  to  their  expiration  date  or  are  already  past  it.  This  raises  the  issue  of  whether  Toco  Hills  Community  Alliance  and  other  similar  institutions  should  dispose  of  items  once  they  expire.  Such  a  policy  would  eliminate  the  pantry’s  food  quality  problem  –  goods  simply  would  not  remain  in  storage  past  their  expiration  date.    Many  might  find  this  solution  to  be  wasteful  and  impractical.  The  disposal  of  expired  items  might  be  a  net  negative,  as  it  would  reduce  the  amount  of  food  available.  Also,  some  opponents  of  the  disposal  method  might  argue  that  food  products  are  often  “good”  well  past  their  expiration  date,  and  that  eating  them  would  not  cause  serious  illness.  For  these  reasons,  we  ultimately  refrained  from  implementing  a  disposal  policy  for  expired  goods.      Before  reaching  our  current  recommendation,  we  considered  the  idea  of  implementing  a  spreadsheet  system  to  record  every  item  the  pantry  received  as  a  donation.  The  proposed  spreadsheet  would  record  an  item’s  date  of  arrival,  food  type,  storage  room,  location  in  the  storage  room,  expiration  date,  and  date  of  exit.  After  further  consideration,  however,  we  felt  that  this  suggestion  was  impractical  given  the  human  capital  available  of  the  Toco  Hills  Community  Alliance.  Indeed,  the  pantry  is  relatively  small  and  run  by  a  few  volunteers,  and  such  a  solution  might  prove  to  be  too  time-­‐consuming.  Thus,  instead  of  recommending  a  spreadsheet,  we  decided  to  recommend  a  new  organization  system,  a  relatively  simple  change  that  we  feel  is  better-­‐suited  to  the  Toco  Hills  Community  Alliance’s  current  capabilities.  However,  if  the  pantry  increases  in  size  or  gains  more  full-­‐time  volunteers  we  suggest  that  the  pantry  consider  the  idea  of  a  spreadsheet  system.      

 

   

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Lastly,  it  is  important  to  mention  that  we  had  previously  planned  to  analyze  the  effect  that  President  Trump’s  proposed  budget  cuts  might  have  on  increasing  food  insecurity  in  the  United  States.  However,  after  research,  it  became  apparent  that  government  programs  such  as  the  Supplemental  Nutrition  Assistance  Program  (SNAP),  which  helps  millions  of  low-­‐income  individuals  in  the  US  afford  groceries,  would  likely  be  unaffected  by  these  broad  budget  cuts.    

                                                                 

 

   

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7.  Appendix  A  Exhibit  1  

   Exhibit  2  

   Exhibit  3  

     

 

   

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8.  Appendix  B  Exhibit  1  –  Actual  Historical  Data  (Household  Food  Security  in  the  United  States  in  2015)  

   Exhibit  2  –  Forecasting  Historical  Data  using  Exponential  Smoothing  

               

 

   

15  

Exhibit  3  

   Exhibit  4  

       

 

   

16  

Exhibit  5  

   Exhibit  6  

   

 

 

   

17  

 

                                 

 

   

18  

Exhibit  7  

   Exhibit  8  

 

 

   

19  

9.  Works  Cited  

Coleman-­‐Johnson,  Alisha,  Matthew  P.  Rabbitt,  Christian  A.  Gregory,  and  Anita  Singh.  Household  Food  

Security  in  the  United  States  in  2015.  Rep.  United  States  Department  of  Agriculture,  Sept.  

2016.  Web.  25  Mar.  2017.  

Echevarria,  Samuel,  Robert  Santos,  Emily  Engelhard,  Elaine  Waxman,  and  Theresa  Del  Vecchio.  Food  

Banks:  Hunger's  New  Staple.  Rep.  Feeding  America,  2011.  Web.  20  Mar.  2017.  

"Our  Organization."  Toco  Hills  Community  Alliance.  Web.  25  Mar.  2017.  

"Policy  Basics:  Introduction  to  the  Supplemental  Nutrition  Assistance  Program  (SNAP)."  Center  on  

Budget  and  Policy  Priorities,  18  Aug.  2016.  Web.  1  Apr.  2017.  

"Supplemental  Nutrition  Assistance  Program  (SNAP)."  Food  and  Nutrition  Service.  United  States  

Department  of  Agriculture,  30  Jan.  2017.  Web.  25  Mar.  2017.  

Wunderlich,  Gooloo  S.,  and  Janet  L.  Norwood.  "Chapter  3."  Food  Insecurity  and  Hunger  in  the  United  

States:  An  Assessment  of  the  Measure.  Washington,  D.C.:  National  Academies,  2006.  41-­‐54.  

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