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Could Digital DIY Break Manufacturing Hierarchies?

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Slides for the ABM workshop on "Organisational Plasticity" in Huddersfield, 25 - 26 January 2018 (https://sites.google.com/view/abmworkshops)

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Could Digital DIY Break Manufacturing Hierarchies?

  1. 1. Could  Digital  DIY  Break   Manufacturing  Hierarchies?   Ruth  Meyer,  Magnus  Josefsson     Centre  for  Policy  Modelling,  Manchester  Metropolitan  University       www.didiy.eu  
  2. 2. What  is  Digital  DIY?   •  Digital  DIY  =  the  set  of  all  manufacturing  activities  (and   mindsets)  that  are  made  possible  by  digital   technologies   –  enables  people  to  do  things  they  could  not  do  before     •  “sculpting”  with  3D  printer   –  gives  more  opportunities  to  do  things  together  by  freely   sharing  designs  and  know-­‐how   •  DiDIY  Project  investigated  impact  on  several  domains   –  Education  and  research   –  Work  and  organisation   –  Legal  systems   –  Creative  Society  
  3. 3. Digital  DIY  in  the  Workplace   •  How  will  the  work  of  a  worker  in  a  manufacturing   firm  be  reshaped  due  to  the  influence  of  DiDIY?   –  Direct  access  to  relevant  information  (e.g.  current   status  of  machines)  could  overcome  traditionally  strict   organisational  hierarchies   •  Applying  an  ABM  of  an  abstract  factory  with   supervisor,  workers,  machines  and  tasks  to   investigate  the  following  research  questions:   –  Allowing  workers  autonomy  in  deciding  which  task  to   do  next,  does  this  improve  the  effectiveness  of  the   production  process?   –  Do  supervisors  become  superfluous?  
  4. 4. StringWorld  Problem  Space   •  A  world  of  strings  (things)  made  up  of  a  sequence  of   letters  (elements),  e.g.    “A”,  “AA”,  “ABAC”,  “BBC”   •  Agents  (makers)  try  to  make  things  out  of  things  they   find  in  their  environment  (resources)  plus  things  they   might  get  from  other  agents,  using  a  limited  set  of   operations,  possibly  applying  specific  tools.   •  Agents  aim  to  produce  certain  things  (targets)   –   using  trial  and  error   –  following  a  plan   •  Problem  space  to  address  DiDIY  simulation  issues   –  Complex  enough  à  plans  are  worth  sharing   –  still  computationally  feasible   •  Prototype  implementation:  Model  of  Making  
  5. 5. Factory  Model  Overview   •  Workers  (agents)  realised  as   patches,  coloured  brown   •  Supervisor  marked  by  red   square   •  Other  patches  hold   •  Resources     •  Targets   •  Machines   •  Machines  are  tools  which   provide  a  particular  string   operation   •  “add-­‐B”   •  “join”   •  “envelope”              using  up  input  things  to              produce  a  new  output  thing   •  Factory  has  to  produce  a   certain  number  of  targets  
  6. 6. Specific  problems  to  solve   •  Need  to  ensure  that  it  is  always  possible  to   produce  the  targets  from  the  resources  with   the  available  machine  operations   •  Solution:  ‘Possible  products’  network  inspired   by  firm  skills  universe  (Taylor  &  Morone  2005)   –  build  network  of  nodes  (products)  and  links   (necessary  inputs),  starting  from  resources   –  total  number  of  nodes  defined  by  model   parameters  num-­‐resources,  num-­‐targets,  num-­‐ machine-­‐types  
  7. 7. Possible  Products  Network   Example:  3  resources,  5   machine  types,  3  targets   •  Max  18  nodes   •  Each  with  1,  2,  or  3   inputs   •  Random  distribution   based  on  pre-­‐defined   string  operations     Pick  3  of  the  5  potential   targets,  covering  all   resources   •  13,  15  and  17   Assign  operations  to   (bundles  of)  input  links     Assign  strings  to   resources   à  Derive  target  strings  
  8. 8. Model  Variants   •  With  supervisor   –  Supervisor  assigns  jobs  to  workers  based  on   •  which  target  is  the  most  outstanding   •  which  machines  are  free  (for  starting  on  the  job)   –  Workers  follow  the  plan  to  make  the  target   –  Once  finished,  they  ask  the  supervisor  for  their  next  job   •  Without  supervisor   –  Workers  know  the  current  status  of  all  machines  and  which   tasks  produce  what  from  which  inputs   –  Workers  decide  on  the  next  task  based  on   •  which  machines  are  free   •  what  things  they  have  (prefer  to  use  own  stuff  over  resources)   •  Pick  most  outstanding  target  if  nothing  else  possible  
  9. 9. First  Results  
  10. 10. Discussion   •  First  experiments  focused  on  overall  effectiveness  of   production  process  while  varying  number  of  agents   –  Production  time  (total  simulation  time  until  all  targets   achieved)   –  Average  time  workers  spent  waiting  for  a  free  machine   •  Introducing  simple  form  of  cooperation   –  When  deliberating  possible  next  tasks,  a  worker  may   consider  not  only  the  things  (s)he  has  themselves  but  also   things  other  agents  have   •  Subsequent  experiments  showed  that  results  are  very   dependent  on  the  factory  setup  (number  of  resources,   targets,  machine  types,  machines  per  type,  processing   times,  number  of  agents)    
  11. 11. Introducing  Garbage  Can  Measures   •  Wanted:  output  measures  to  gauge  impact  of   organisational  change   •  Garbage  Can  Model:  influential  model  of   organisational  behaviour   –  Problems,  participants,  opportunities,  solutions   •  Three  indicators   –  Problem  latency:  time  spent  by  problems  in  the  system   before  a  participant  attempts  to  solve  them   –  Unsolved  problems:  number  of  problems  left  at  the  end   of  the  simulation   –  Waiting  time:  time  opportunities  stay  in  the  system   waiting  to  be  used  
  12. 12. GC  Indicators  for  Factory   •  Translate  GC  terminology  to  factory  world   – Job  latency:  Time  spent  by  jobs  (‘problems’)  in   the  factory  before  a  worker  starts  working  on   them   – Unfinished  jobs:  number  of  unfinished  jobs  at   the  end  of  the  simulation   – Waiting  time:  Time  spent  by  free  machines  /   free  workers  waiting  to  be  used  /  start  working   on  a  new  job  
  13. 13. Model  AdaptaIons   •  GC  Model  assumes  streams  of  objects   •  Factory  Model   – Incoming  stream  of  jobs,  with  mean  arrival   time  and  different  probabilities  for  the   different  types   – Each  job  specifies  which  target  to  produce  (a   sequence  of  tasks)   – For  cooperation,  jobs  are  split  into  the  separate   tasks  
  14. 14. Preliminary  Results  (1)   Factory  1,  averages  of  20  runs  with  the  same  random  seeds,     mean  arrival  time  0.2,  simulation  stops  20  ticks  after  all  jobs  arrived  
  15. 15. Preliminary  Results  (2)   Factory  1,  averages  of  20  runs  with  the  same  random  seeds,     mean  arrival  time  0.3,  simulation  stops  20  ticks  after  all  jobs  arrived  
  16. 16. Preliminary  Results  (3)   Different  factory  setup  (more  complex  tasks)  with  higher  cooperation,     mean  arrival  time  0.2,  simulation  stops  20  ticks  after  all  jobs  arrived  
  17. 17. Conclusion  and  Outlook   •  Introduction  of  Garbage  Can  measures   helpful  in  assessing  the  factory  model   •  Cooperation  manages  to  outperform   supervision  when   – Jobs  are  fairly  complex  (e.g.  with  intermediate   products  used  in  several  tasks)   – Frequency  of  jobs  is  high   •  Investigation  will  continue…  

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