A presentation given at ISAGA 2015

1. 1 © Hajime Mizuyama1
ColPMan: A Serious Game for Practicing
Collaborative Production Management
Hajime Mizuyama, Tomomi Nonaka,
Yuko Yoshikawa, and Kentaro Miki
Aoyama Gakuin University
mizuyama@ise.aoyama.ac.jp
ISAGA 2015 @ Kyoto 18/July/2015

2. 2 © Hajime Mizuyama2
• A large-scale MTO company is composed of several sites,
and planning and control of their operations is a huge problem.
• Production and delivery operations in those sites are affected
by stationary and non-stationary disturbances.
• The information on the changing environment is dispersed
among the sites, and it is difficult to collect all the relevant
information in one place in a timely manner.
• Operational planning and control in the in-house supply chain
of such a company is divided into several sub-problems
and handled by multiple decision makers in those sites.
In-house SC of a large-scale MTO company

3. 3 © Hajime Mizuyama3
• The inter-related sub-problems should be repeatedly solved
reflecting the changing environment.
• None of the decision makers hold the entire picture of the
environment.
• It is important for the decision makers
– not only to appropriately solve the respective sub-problems
– but also to effectively communicate and coordinate with
one another in the dynamic environment.
In-house SC of a large-scale MTO company

4. 4 © Hajime Mizuyama4
• Such dynamic decision-making skills are not easy to be
trained in lectures alone.
• Experiential learning is potentially effective supplemental
approach and serious games are a suitable medium for it.
• The objective of this research is
– to develop an original serious game suitable for training
the dynamic organizational decision-making skills, and
– to test how the developed game named ColPMan works.
Research Objective

5. 5 © Hajime Mizuyama5
• Research background and objective
• Game design
• Game implementation
• Application case
• Conclusions
Agenda

6. 6 © Hajime Mizuyama6
Hierarchical
The relation between a site, e.g. HQ, deciding an abstract plan
and the other, e.g. a factory, deciding a detailed schedule under
the constraint of the abstract plan.
Serial
The relations between a pair of factories, where one’s output is
used as the input of the other.
Parallel
The relations between a pair of factories, which are in charge of
a same production function and are substitutable to each other.
Typical relations among sites

7. 7 © Hajime Mizuyama7
Downstream
factory
(DSF)
Downstream
factory
(DSF)
Parallel
Headquarters
(HQ)
Downstream
factory
(DSF)
Overall topology of in-house SC
Hierarchical
Upstream
factory
(USF)
Serial
DSF1 player
DSF2 player
DSF3 player
USF player
HQ player

8. 8 © Hajime Mizuyama8
Order assignment
Upstream
factory
(USF)
Make-to-stock
Make-to-order
Custo-
mers
Materials
inventory
Materials
inventory
Orders
Products
inventory
Delivery
Information
Material
Downstream
factory 1
(DSF1)
Headquarters
(HQ)
Downstream
factory 2
(DSF2)
Downstream
factory 3
(DSF3)
Overall topology of in-house SC
Five material types
×
Five product sizes
Five material types
×
Five product sizes

9. 9 © Hajime Mizuyama9
Upstream
factory
(USF)
Make-to-stock
Make-to-order
Custo-
mers
Materials
inventory
Materials
inventory
Orders
Products
inventory
Delivery
Information
Material
Downstream
factory 1
(DSF1)
Headquarters
(HQ)
Downstream
factory 2
(DSF2)
Downstream
factory 3
(DSF3)
How SC is operated
Order assignment
Five material types
×
Five product sizes
Five material types
×
Five product sizes

10. 10 © Hajime Mizuyama10
66
55
44
33
22
11
0
• Customer’s location
• Customer’s importance
• Material type
• Product size
• Number of products
• Remaining time to due date
0
• Customer’s location
• Customer’s importance
• Material type
• Product size
• Number of products
• Remaining time to due date
Order arrivals from customers
Random
arrival

11. 11 © Hajime Mizuyama11
Order assignment
Upstream
factory
(USF)
Make-to-stock
Make-to-order
Custo-
mers
Materials
inventory
Materials
inventory
Products
inventory
Delivery
Information
Material
Downstream
factory 1
(DSF1)
Headquarters
(HQ)
Downstream
factory 2
(DSF2)
Downstream
factory 3
(DSF3)
How SC is operated
Orders
Five material types
×
Five product sizes
Five material types
×
Five product sizes

12. 12 © Hajime Mizuyama12
This term Next term
Term after
the next
DSF1
DSF2
DSF3
Decisions made by HQ player
List of
orders
List of
orders

13. 13 © Hajime Mizuyama13
Order assignment
Upstream
factory
(USF)
Make-to-stock
Make-to-order
Custo-
mers
Materials
inventory
Materials
inventory
Orders
Products
inventory
Delivery
Information
Material
Downstream
factory 1
(DSF1)
Headquarters
(HQ)
Downstream
factory 2
(DSF2)
Downstream
factory 3
(DSF3)
How SC is operated
Five material types
×
Five product sizes
Five material types
×
Five product sizes

14. 14 © Hajime Mizuyama14
Production schedule
• Each DSF is modeled as a single machine with sequence-
dependent setup times (and costs).
• Which orders among those assigned to the factory are to be
processed in this term, and their sequence should be
determined.
Materials order
• The materials inventory in each DSF is controlled by the
respective DSF player.
• How many materials of each type are ordered should be
determined.
Decisions made by DSF players

15. 15 © Hajime Mizuyama15
Order assignment
Upstream
factory
(USF)
Make-to-stock
Make-to-order
Custo-
mers
Materials
inventory
Materials
inventory
Orders
Products
inventory
Delivery
Information
Material
Downstream
factory 1
(DSF1)
Headquarters
(HQ)
Downstream
factory 2
(DSF2)
Downstream
factory 3
(DSF3)
How SC is operated
Five material types
×
Five product sizes
Five material types
×
Five product sizes

16. 16 © Hajime Mizuyama16
Production schedule
• USF is modeled as a single machine of fixed-size lot
production with sequence-dependent setup times (and costs).
• The materials inventory in USF is controlled by the USF player.
• How many lots of each type are to be produced in this term,
and their sequence should be determined.
Decisions made by USF player

17. 17 © Hajime Mizuyama17
Discrete event simulation representing SC operations
according to given plans under uncertainties
Game flow
Table discussionTable discussion
DSF1
player
DSF2
player
DSF3
player
USF
player
HQ
player
USF
DSF3DSF1 DSF2
HQ
Planning information
Progress information

18. 18 © Hajime Mizuyama18
Environmental disturbances incorporated into the game
– Orders and their arrival times
– Production lead-time in DSF
– Defectives and machine failures in DSF
– Material delivery lead-time
– Production lead-time in USF
– Defectives and machine failures in USF
Uncertainties in simulation

19. 19 © Hajime Mizuyama19
Terms and periods
Time
Term 1 Term 2 Term 3 ...
Period 1-5 Period 1-5 Period 1-5 ...

20. 20 © Hajime Mizuyama20
P mode
Time
Term 1 Term 2 Term 3 ...
Period 1-5 Period 1-5 Period 1-5 ...
A team of playersA team of players
SimulationSimulation SimulationSimulation SimulationSimulation SimulationSimulation
Planning
information
Progress
information

21. 21 © Hajime Mizuyama21
PDCA mode
Time
Term 1 Term 2 Term 3 ...
Period 1-5 Period 1-5 Period 1-5 ...
A team of playersA team of players

22. 22 © Hajime Mizuyama22
Game score
Profit = Revenue - Costs
Revenue
∝ The number of products delivered to customers
Costs
– Materials inventory cost at both USF and DSF
– Setup cost in both USF and DSF
– Material delivery cost
– Product inventory cost
– Product delivery cost
– Late delivery penalty cost
Game score

23. 23 © Hajime Mizuyama23
• Research background and objective
• Game design
• Game implementation
• Application case
• Conclusions
Agenda

24. 24 © Hajime Mizuyama24
• The computer simulation part and its graphical interfaces with
human players are implemented with Processing, a Java-
based programming language suitable for interactive graphics.
• A screen is provided to each site and basic information on the
progress directly observable from the site is visually
displayed on it.
• More detailed progress information is given in CSV files.
• The simulator incorporates the decisions made by the players
also from CSV files.
Implementation outline

25. 25 © Hajime Mizuyama25
A short demoA short demo
Resultant game system

26. 26 © Hajime Mizuyama26
• Research background and objective
• Game design
• Game implementation
• Application case
• Conclusions
Agenda

27. 27 © Hajime Mizuyama27
• Participants are 107 junior students in the dept. of industrial
and systems engineering, Aoyama Gakuin University, Japan.
• The class is open every Thursday and is composed of two 90-
minute time slots with 15-minute break in between.
• The whole class lasts 15 weeks, but only five weeks are
instructed by the authors.
• The objective of the class is (1) to understand how
optimization techniques work in practical situation, and (2) to
brush up programming skills by related exercises.
• Thus, two weeks are devoted to programming exercises, and
only three time slots are given to playing ColPMan.
Class outline

28. 28 © Hajime Mizuyama28
1st time slot (90 min.) 2nd time slot (90 min.)
1st week Introduction to ColPMan Game play #1
2nd week
Lecture on production
management techniques
Game play #2
3rd week
Introduction to
programming exercises
Programming #1
4th week Programming #2 Programming #3
5th week Game play #3 Presentation
Class schedule

29. 29 © Hajime Mizuyama29
• 107 students are randomly grouped into 12 teams; each is
composed of nine or eight students.
• One of them is assigned to a role called facilitator, who
operates the simulation software.
• The others are assigned to one of the five sites. This means
that some sites are controlled by a sub-team of two players.
• The role assignments are determined by the students
themselves.
• After each game play session, all the students are requested
to hand in a report discussing how to get high score.
Team formation and role assignment

30. 30 © Hajime Mizuyama30
• All the reports submitted by the students are read through
and individual items describing a key point are carefully
picked up.
• The obtained items are classified into different principles.
• They are also categorized into overall, HQ-related, USF-
related, and DSF-related principles.
• It results in nine overall, seven HQ-related, eight USF-
related, 17 DSF-related principles.
Indirect evaluation of learning effects

31. 31 © Hajime Mizuyama31
Number of principles learned
0123456
1st report
2nd report
3rd report
Overall HQ
-related
USF
-related
DSF
-related
Facilitator players
0123456
1st report
2nd report
3rd report
Overall HQ
-related
USF
-related
DSF
-related
Upstream factory players
0123456
1st report
2nd report
3rd report
Overall HQ
-related
USF
-related
DSF
-related
Downstream factory players
0123456
1st report
2nd report
3rd report
Overall HQ
-related
USF
-related
DSF
-related
Headquarters players

32. 32 © Hajime Mizuyama32
Q1: Did you enjoy playing ColPMan?
Q2: Did your tactics change as you repeat playing ColPMan?
Q3: Was it possible to apply your strategy prepared beforehand?
Q4: Was your motivation encouraged by the game score?
Q5: If you have a chance, do you want to play ColPMan again?
Q6: Was it difficult for you to play ColPMan?
Q7: Is the ColPMan software easy to operate?
Subjective evaluation questions #1

33. 33 © Hajime Mizuyama33
Yes
(Lecture)
Slightly
yes
Neutral
Slightly
no
No
(Game)
Q1 47 42 11 2 0
Q2 45 48 8 1 0
Q3 32 57 5 6 2
Q4 55 33 10 4 0
Q5 36 40 16 7 3
Q6 22 55 21 4 1
Q7 15 34 13 33 7
Q8 72 26 2 1 1
Q9 35 58 6 2 1
Q10 7 10 10 27 48
Q11 54 37 9 1 1
Subjective evaluation results

34. 34 © Hajime Mizuyama34
Q8: Did ColPMan facilitate communication among the team
members?
Q9: Did ColPMan deepen your understanding on production
management?
Q10: Which do you think more helpful for deepen your
understanding lectures or games like ColPMan?
Q11: Do you want to use a simulation game like ColPMan for
other purposes?
Subjective evaluation questions #2

35. 35 © Hajime Mizuyama35
• Research background and objective
• Game design
• Game implementation
• Application case
• Conclusions
Agenda

36. 36 © Hajime Mizuyama36
• A serious game called ColPMan is developed as a medium for
experiential learning of dynamic decision-making skills for
collaborative production management.
• The developed game is actually tested as an undergraduate
classroom exercise.
• The learning effects provided by ColPMan game are
indirectly observed, and the game obtained positive
response from the students.
• The future directions include simplification of the game
structure so as to level the workload of different roles.
Conclusions

37. 37
Thank you for your kind attention!
Questions and comments are welcome.
Thank you for your kind attention!
Questions and comments are welcome.