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Could	
  Digital	
  DIY	
  Break	
  
Manufacturing	
  Hierarchies?	
  
Ruth	
  Meyer,	
  Magnus	
  Josefsson	
  
	
  
Centre	
  for	
  Policy	
  Modelling,	
  Manchester	
  Metropolitan	
  University	
  
	
  	
  
www.didiy.eu	
  
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	
  
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?	
  
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	
  
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	
  
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	
  
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	
  
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	
  
First	
  Results	
  
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)	
  
	
  
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	
  
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	
  
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	
  
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	
  
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	
  
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	
  
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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Could Digital DIY Break Manufacturing Hierarchies?

  • 1. Could  Digital  DIY  Break   Manufacturing  Hierarchies?   Ruth  Meyer,  Magnus  Josefsson     Centre  for  Policy  Modelling,  Manchester  Metropolitan  University       www.didiy.eu  
  • 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. 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. 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. 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. 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. 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. 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  
  • 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. 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. 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. 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. 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. 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. 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. 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…