In today’s globalized world, cost pressure, the demand for higher production efficiency, and growing product diversity lead to increasing complexity of manufacturing and business processes. Insufficiently understood human and social factors further increase this complexity. Game-based learning environments and business simulation games can reduce this complexity by identifying and understanding the contributing human factor. Business simulation games can be used (1) to identify and quantify human as well as social factors influencing the effectivity and efficiency, (2) to assess the aptitude of prospective employees and identify suitable training interventions, (3) to improve management and production software, and (4) to present effective, efficient, and entertaining training environments. By allowing employees to investigate cause-and-effect relationships of simulated manufacturing and business environments, they can test and understand the consequences of their actions in safe environments. In this paper, we report a practical case of a business simulation game for conveying quality management strategies. The development of the game is presented along the definition of learning objectives, the underlying System Dynamics model, and the design of the user interface. The evaluation of the game reveals that human factors relate to the simulation’s metrics. Finally, we give guidelines to design and develop game-based simulation and training environments.

1. (digital) Game-Based Learning in
Manufacturing and Business
The many faces of
Philipp Brauner, Martina Ziefle
Human-Computer Interaction Center
RWTH Aachen University, Germany
6th International Conference on Competitive Manufacturing 2016 (COMA), Stellenbosch, South Africa
Brauner P, Ziefle M. How to train employees, identify task-relevant human factors, and improve software
systems with Business Simulation Games. Procedings of the 6th International Conference on Competitive
Manufacturing 2016, COMA ’16. Stellenbosch, SA; 2016. p. 541–546.

2. RWTH Aachen University, Human-Computer Interaction Center
2016-01-29 Game-based Learning in Manufacturing and Business & the Quality Intelligence Game
Philipp Brauner (brauner@comm.rwth-aachen.de), Martina Ziefle
page 2
n Human-Human & Human-Technology communication
n Design, development, and evaluation of interactive systems
– Requirements analysis
– Usability & User Experience
– Technology Acceptance and risk perception
– Human Factors, User Diversity & Aging population
Human-Computer Interaction Center at
RWTH Aachen University, Germany

3. RWTH Aachen University, Human-Computer Interaction Center
Context: Part of the Cluster of Excellence
“Integrative Production Technology for High-Wage Countries”
n Goal: Strengthen competitiveness of high wage countries
n Engineering of future production systems
– New materials and processes
– Improved and smarter machinery
– Optimize assembly cells, shop floor, cross-company cooperation
n > 25 Institutes, > 100 researchers
n Funded by German Research Foundation (DFG)
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2016-01-29 Game-based Learning in Manufacturing and Business & the Quality Intelligence Game
Philipp Brauner (brauner@comm.rwth-aachen.de), Martina Ziefle

4. RWTH Aachen University, Human-Computer Interaction Center
Our research objective:
Optimize cross-company cooperation
n Optimize cross-company supply chains (SC)
– Technical factors influencing performance of SCs
– Human Factors influencing performance of SCs
– Influence of Interface Factors on SCs
– Interrelationship of technical, interface, and human factors
n Why are humans considered?
– Humans make final decision
– Overview over not explicitly modelled relationships
(e.g., closed-world assumption)
– Complexity and uncertainty increases,
less time for making decisions
Information flow
flow of goods
Supply Chain
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2016-01-29 Game-based Learning in Manufacturing and Business & the Quality Intelligence Game
Philipp Brauner (brauner@comm.rwth-aachen.de), Martina Ziefle
Goal: Understand system and user factors that influence efficiency, effectivity,
and user satisfaction in Enterprise Resource Planning Systems, Supply Chains
and Quality Management.

5. RWTH Aachen University, Human-Computer Interaction Center
How can human decision making be investigated?
n Convergence between field and laboratory study
n Simplified & controllable (game-based) environment
n Experimentally manipulate complexity and interface
n Empirical methodology to quantify human influence
on decision making and performance
– Identify and measure influencing personality factors
– Build a formal model that explains performance
n Side-effect:
Usable for game-based learning (GBL) in
professional trainings etc.
Test in the field
(ecological validity)
Controlled
experiment in
laboratory
(internal validity)
We are here
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2016-01-29 Game-based Learning in Manufacturing and Business & the Quality Intelligence Game
Philipp Brauner (brauner@comm.rwth-aachen.de), Martina Ziefle

6. RWTH Aachen University, Human-Computer Interaction Center
Short definition of
(digital) Game-Based Learning (GBL)
n Use motivational potential of (computer)
games to convey learning [Prensky 2001]
n Benefits
– Increased motivation
– Understanding in context
– Increases Network Thinking abilities
– Explore and understand
cause-and effect relationships
– Safe and inexpensive
n Very successful in medical contexts
– Physiotherapy and rehabilitation
– Knowledge dissemination
– Change attitudes and behavior
“A game is a system in which players engage
in an artificial conflict, defined by rules, that
result in a quantifiable outcome.”
[Salen & Zimmerman, 2003]
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2016-01-29 Game-based Learning in Manufacturing and Business & the Quality Intelligence Game
Philipp Brauner (brauner@comm.rwth-aachen.de), Martina Ziefle

7. RWTH Aachen University, Human-Computer Interaction Center
Our hypothesis:
Game-based learnings in manufacturing and business can be used to…
n Understand task relevant human factors
n Measure aptitude of potential employees
n Train current or prospective employees*
n Benchmark User Interfaces*
Human
Factors
Personnel
selection
Training
intervention
Usability
studies
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2016-01-29 Game-based Learning in Manufacturing and Business & the Quality Intelligence Game
Philipp Brauner (brauner@comm.rwth-aachen.de), Martina Ziefle

8. RWTH Aachen University, Human-Computer Interaction Center
Practical Example 1:
The Beer Distribution Game
Objective of the game: Train prospective employees
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2016-01-29 Game-based Learning in Manufacturing and Business & the Quality Intelligence Game
Philipp Brauner (brauner@comm.rwth-aachen.de), Martina Ziefle

9. RWTH Aachen University, Human-Computer Interaction Center
Game-Based Learning in Production Engineering:
The „Bullwhip effect“ and the Beer Distribution Game
n “Bullwhip Effect” described by [Forrester] in the 1960’s
– Increasing customer demand leads to
exaggerated demand along supply chains
– Resulting stock level graphs look like a bullwhip
n Beer Distribution Game:
– 4 players / 4 positions in 4-tier supply chain
– Costs for out-of-stock and stock keeping
– Time lag between order and delivery
– Goal: Keep costs minimal
– Small variance in customer order function
– Learning objective: Sensitize and train
FactoryDistributor
purchase order
forwarded
immediatly
order delivered
immediately
1 week 1 week 1 week
2 weeks 2 weeks 2 weeks
WholesaleRetailer
purchase orders
shipments
Imagesource:ft.com
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2016-01-29 Game-based Learning in Manufacturing and Business & the Quality Intelligence Game
Philipp Brauner (brauner@comm.rwth-aachen.de), Martina Ziefle

10. RWTH Aachen University, Human-Computer Interaction Center
Our study on the Beer Distribution Game:
Train current or prospective employees
n Results:
– Bullwhip effect clearly present
– Delayed with position in chain
– Increases with position in chain
[F3,117=7,941,p<.001**]
– Strong correlation between multiple rounds
[r2,112-2=.628, p<.001**]
– Players get sig. better
[F1,111=4.204, p<.05*]
n Conclusion:
– Replication of similar studies ⇒ concept works
– Stables results ⇒ underlying factors
– Performance increases ⇒ learnability
-20
-10
0
10
20
30
40
1 5 10 15 20
Average Stock Level
Week
Retailer Wholesale Distributor Factory
Remember:
Small variance in
customer’s orders
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2016-01-29 Game-based Learning in Manufacturing and Business & the Quality Intelligence Game
Philipp Brauner (brauner@comm.rwth-aachen.de), Martina Ziefle
Empirical study, n=112 players, 1 of 4 positions
randomly assigned, 2 rounds played
Week
Order
5 10 15 20
48
Customer order function
order(round)
⇒

11. RWTH Aachen University, Human-Computer Interaction Center
Practical Example 2:
A supply chain game with quality management aspects – “QI-Game”
Objective of the game: Benchmark for user interfaces
(e.g., usability, efficacy of decision support systems, business intelligence)
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2016-01-29 Game-based Learning in Manufacturing and Business & the Quality Intelligence Game
Philipp Brauner (brauner@comm.rwth-aachen.de), Martina Ziefle

12. RWTH Aachen University, Human-Computer Interaction Center
Development of business simulation game
n Extension of „Beer Distribution Game“
n Based on System Dynamics model
n Includes product quality
– Product intact or broken
– Supplier's quality varies
– Internal production quality varies
n Increased complexity (tradeoff 3 parallel tasks)
– Management of stocks
– Investment in incoming goods inspection
– Investment in internal quality management
n Over 20 variables in the user interface
n Players must infer state of the production
Part of the game’s simulation model:
Stock level at a given time t:
S(t) = S(t-1) + O(t-1) – D(t)
Net profit:
P(t) = R(t) – cStock×S(t) – Iigi(t) –
Iipq(t) – C(t-1) – …
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2016-01-29 Game-based Learning in Manufacturing and Business & the Quality Intelligence Game
Philipp Brauner (brauner@comm.rwth-aachen.de), Martina Ziefle

13. RWTH Aachen University, Human-Computer Interaction Center
n Continuous discussions with experts and users
n 1st prototype
– Based on SD model
– Paper based
– Layout arrangement
n 2nd prototype
– Spreadsheet-based
– Definition of game model, indicators, …
– Quick optimization of simulation parameters
– Simplified discrete event simulation
n 3rd prototype
– Web-based interface
Agile design and development process
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2016-01-29 Game-based Learning in Manufacturing and Business & the Quality Intelligence Game
Philipp Brauner (brauner@comm.rwth-aachen.de), Martina Ziefle

14. RWTH Aachen University, Human-Computer Interaction Center
The Quality Management Game’s interface
n Web-based game environment
– Accessible across the world (scale)
– Laboratory studies (scope)
n Captures all metrics and interactions
n Controllable conditions
– Varying supplier’s quality, own production quality,
customer’s demand, user interface, …
n Map game performance and other metrics to
Human-Factors
– Identification of attitude, aptitude & motivation
– Evaluation of the game, interface, …
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2016-01-29 Game-based Learning in Manufacturing and Business & the Quality Intelligence Game
Philipp Brauner (brauner@comm.rwth-aachen.de), Martina Ziefle

15. RWTH Aachen University, Human-Computer Interaction Center
Example – Interface evaluation through exchangeable user interfaces:
Model-View-Controller pattern (MVC)
n Separation of simulation model, data, and user
interface
n User Interface interchangeable
P(t), S(t), …
QA
PR
MODEL
CONTROLLER
VIEW
Interface ➝ Profit(t)
Profitt > Profitt
?
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2016-01-29 Game-based Learning in Manufacturing and Business & the Quality Intelligence Game
Philipp Brauner (brauner@comm.rwth-aachen.de), Martina Ziefle

16. RWTH Aachen University, Human-Computer Interaction Center
Evaluation of user interfaces:
n Research question:
– Do interfaces influence player’s performance?
n Interface optimizations based on user feedback
– Better spatial layout
– Key Performance Indicators (e.g., stock level)
n Method
– Study (N=40) with old vs. new interface, surveys
n Results
– Users preferred revised user interface
– Higher profits and higher product quality w. new interface
n Conclusion:
– Good interfaces crucial for performance
(V = 0.263, F1, 38 = 13.548, p = .001 < .05*)
revised interface
first interface
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2016-01-29 Game-based Learning in Manufacturing and Business & the Quality Intelligence Game
Philipp Brauner (brauner@comm.rwth-aachen.de), Martina Ziefle

17. RWTH Aachen University, Human-Computer Interaction Center
Discussion and Takeaway
n Game-Based Learnings
– Address Millennials / Generation Y
– Training for Network Thinking, complexity, and uncertainty
– Assessment of personnel, Identify task relevant Human Factors
– Evaluate User Interfaces
n Studies on Beer Distribution Game & QI-Game
– Replication of known effects ⇒ concept works
– Good players & bad players ⇒ underlying human factors
– Players learn ⇒ training intervention
– Good interface lead to higher performance
⇒ Usability competitive advantage
n Our next steps
– Influence of Business Intelligence & Decision Support on performance
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2016-01-29 Game-based Learning in Manufacturing and Business & the Quality Intelligence Game
Philipp Brauner (brauner@comm.rwth-aachen.de), Martina Ziefle

18. RWTH Aachen University, Human-Computer Interaction Center
n Brauner P, Runge S, Groten M, et al (2013) Human Factors in Supply Chain Management – Decision making in complex logistic scenarios.
In: Yamamoto S (ed) Proceedings of the 15th HCI International 2013, Part III, LNCS 8018. Springer-Verlag Berlin Heidelberg, Las Vegas,
Nevada, USA, pp 423–432
n Brauner P (2014) Understanding Human Factors in Supply Chains and Quality Management by Using Business Simulations. In: Brecher C,
Wesch-Potente C (eds) Proceedings of the Conference of the Cluster Of Excellence “Integrative Production Technology For High Wage
Countries” 2014/1, 1st edn. Apprimus Verlag, Aachen, Germany, Aachen, Germany, pp 387–396
n Hering N, Meißner J, Runge S, Brauner P (2014) Exzellenzcluster: Was bestimmt die Performance meiner Supply-Chain? – Eine
Untersuchung technischer und menschlicher Einflussfaktoren im Hinblick auf die Effizienz von Lieferketten. Unternehmen der Zukunft -
Zeitschrift für Betriebsorganisation und Unternehmensentwicklung 27–28.
n Philipsen R, Brauner P, Stiller S, et al (2014a) The role of Human Factors in Production Networks and Quality Management. – How can
modern ERP system support decision makers? First International Conference, HCIB 2014, Held as Part of HCI International 2014,
Heraklion, Crete, Greece, June 22-27, 2014. Proceedings, LNCS 8527. Springer Berlin Heidelberg, pp 80–91
n Philipsen R, Brauner P, Stiller S, et al (2014b) Understanding and Supporting Decision Makers in Quality Management of Production
Networks. Advances in the Ergonomics in Manufacturing. Managing the Enterprise of the Future 2014 : Proceedings of the 5th International
Conference on Applied Human Factors and Ergonomics, AHFE 2014. CRC Press, Boca Raton, pp 94–105
n Stiller S, Falk B, Philipsen R, et al (2014) A Game-based Approach to Understand Human Factors in Supply Chains and Quality
Management. Procedia CIRP 20:67–73. doi: 10.1016/j.procir.2014.05.033
n Brauner P, Ziefle M (2015) Human Factors in Production Systems – Motives, Methods and Beyond. In: Brecher C (ed) Advances in
Production Technology. Springer International Publishing, pp 187–199
n Mittelstädt V, Brauner P, Blum M, Ziefle M (2015) On the visual design of ERP systems – The role of information complexity, presentation
and human factors. 6th International Conference on Applied Human Factors and Ergonomics (AHFE 2015) and the Affiliated Conferences,
AHFE 2015. pp 270–277
n Calero Valdez A, Brauner P, Schaar AK, et al (2015) Reducing Complexity with simplicity - Usability Methods for Industry 4.0. 19thTriennial
Congress of the International Ergonomics Association (IEA 2015).
n Ziefle M, Brauner P, Speicher F (2015) Effects of data presentation and perceptual speed on speed and accuracy in table reading for
inventory control. Occupational Ergonomics 12:119–129. doi: 10.3233/OER-150229
n Brauner P, Ziefle M. How to train employees, identify task-relevant human factors, and improve software systems with Business Simulation
Games. Procedings of the International Conference on Competitive Manufacturing 2016, COMA ’16. Stellenbosch, SA; 2016. p. 541–6.
Publications
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2016-01-29 Game-based Learning in Manufacturing and Business & the Quality Intelligence Game
Philipp Brauner (brauner@comm.rwth-aachen.de), Martina Ziefle

19. RWTH Aachen University, Human-Computer Interaction Center
Thank you four your attention!
Summary
n Human Factors Perspective increasingly important
n Business Simulation Games
– Address Millennials
– Training environment for onboarding / production ramp-ups
– Assessment of personnel & Identification of crucial factors
– Identification and quantification of interface costs
Dipl.-Inform. Philipp Brauner
Human-Computer Interaction Center
Chair for Communication Science
RWTHAachen University, Germany
eMail: brauner@comm.rwth-aachen.de
Funded by the German Research Foundation (DFG) within the Cluster of Excellence
“Integrated Production Technology for High Wage Countries” (EXC 128).
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2016-01-29 Game-based Learning in Manufacturing and Business & the Quality Intelligence Game
Philipp Brauner (brauner@comm.rwth-aachen.de), Martina Ziefle