A Management Simulator to Support Group Decision Making
in a Corporate Gaming Environment
Professor Dr. Peter M. Milling
Industrieseminar der Universitat Mannheim
D-68131 Mannheim, Germany
Phone: (+49 621) 292-55 25/27 + Fax: (+49 621) 292-5259
e-mail: pmilling@is. bwl.uni-mannheim.de
CORPORATE PLANNING GAMES IN MANAGEMENT EDUCATION
Successful corporate management requires specialization, i.e. the separation of tasks. In a
historic perspective this lead to the manufacturing philosophy of “Taylorism” and the delega-
tion of decision making — concepts that have proved highly successful in the past. But the
same developments bear the risk of failure through uncoordinated activities. Management
becomes futile without coherent action. Especially in a dynamic environment, as it is found
e.g. in innovation management, this (potential) gap between isolated operations and coherent
strategy has to be closed. Team or Cooperative Learning is necessary to define and to achieve
the overall corporate objectives (Senge 1990; Argyris 1990).
Management games work as catalysts in such a process of group decision making. They
counteract narrow specialization, lead to improved communication between different corpo-
rate functions, and encourage the identification and the pursuit of shared values and overall
objectives.
| Board of Directors | | Board of Directors |
| Company | | :
pany Actions 1 Company2
! H
} }
|
Results | Market Results
! Board of Directors i
| Board of Directors
i
! | Company 4 |
l
Company
i i
Figure 1: Modules of the Corporate Planning Game
Management games play an important role as training and teaching tools. Most of them
belong either to the class of Management Flight Simulators or to the class of multi-person
Corporate Planning Games. In the first case a single person plays against a computer model.
In the second case, groups of players emulate the board of directors of a company and com-
pete against other groups and a computer model clears the market by adjusting supply and
3G4
demand. Both types of games have their specific advantages. Planning Games catch many of
the behavioral aspects of real world decision making. Flight Simulators help to achieve a bet-
ter understanding of the system under investigation by providing the opportunity to go rapidly
through repeated learning cycles.
Since more than a decade we use a Corporate Planning Game, now called LOBSTER (an
acronym from Learning Organization By Simulating The Economic Reality), in academic edu-
cation and corporate management training (Figure 1).
At the beginning of the game, all corporations have a product with the same level of tech-
nological sophistication and with the same market share. The simulator deals with the proc-
esses of Research and Development, the time-to-market, and the time-to-volume for new
products. It focuses on the substitution between different generations of innovative products.
It requires decision inputs for all classical fields of corporate management, i.e. budgeting and
resource allocation for R&D and advertisement, investment in production capacity and the
way to finance it, personnel recruitment, etc. The timing of market introduction, investment
and production planning, cost management and pricing policies, product quality and delivery
delays are key control variables in these processes.
Figure 2 provides an overview over the coarse structure of the market module. Its
behavior is dominated by the diffusion processes of the innovators and imitators purchasing
decisions (Milling 1994).
supply
| capability |
—> strong _
= —— influence
> average
--> weak
Figure 2: Coarse Structure of the Market Module
During the game, each group of players is confronted with interrelated decision making
requirements. It is difficult to understand intuitively, how the decisions interact with each
other, how the competitors and the whole system will react. To improve their market perform-
ance, the group must identify and collect relevant information. The team members must derive
alternative courses of action and evaluate their expected consequences. A feeling for complex
3To
system behavior should be gained (Vennix 1990) and our experience supports this expecta-
tion.
A MANAGEMENT SIMULATOR IN A GAMING ENVIRONMENT
In the context of a management game, the typical chain between observed state of the system,
decision and action is intersected. The players interfere with the model. They receive and ana-
lyze the model output, discuss different courses of action, decide on one and implement it.
Then the model takes over again and continues the circle. Figure 3 shows this relationship.
Since the players are not restricted to a particular and predefined set of actions — as it is the
case in Figure 3a — they can adopt a new theory-in-use and change ,,the rules of the game“
(Figure 3b).
decision
ew
‘Computer model
ie model H
Py |
‘ | decision, |
Poy | oS |
! {
|
van |
: |
i
i { action } |
: i
system system
tC
Fig. 3: The feedback process of decision making in
a) a Standard Simulation Model b) the Management Simulator
Over the last years several hundred master students played the game. Although most of
them were familiar with System Dynamics concepts, hardly anybody tried to use tools like
causal loop diagrams or even simulation to support their decision making. In spite of this
experience, it has become kind of a System Dynamics paradigm, that the effectiveness of
decision making can be substantially improved by the use of management flight simulators.
To test this hypothesis, we made a microworld, that contains a replicate of the computer
model used in our Corporate Planning Game available to the players.
The simulator LEARN! (for Learning Environment in an Artificial Reality Network) is
used to investigate the particular market dynamics. We apply it to support decision making in
the artificial reality of our corporate gaming environment. It allows in a man-machine dialog
to test the market response of different courses of actions. Figure 4 illustrates a typical deci-
sion situation. The difference to the LOBSTER-Game — which in the context of the flight
simulator is interpreted as the ,,reality“ — are the missing direct actions and reactions of the
competitors. They are included as endogenous model variables.
311
[Sammon Cockpit Paaumieeteds
ey Coo Tad Pana Oy
S wpe ‘ae pe pens
vn 70 | emma Bia] [Tneenmeges i | Sota
poser 2s | sence pa so
Forschungsautwaed eS eimmeane: Nong «srt
Veniedaautand cd Rosas att eet Md EJ
ett _ vo | =
= 4 sn
aon = aig
eens Logt Kredte: om
ont Hl ie 2 ae fi
we oe = oa
Entscnekdangon aingeba:
Boch und Anayson, ‘Stevenng
“ann Sa gsc Option
Figure 4: View of the LEARN! Simulator
In the Corporate Planning Game, as a test design, two groups (out of four) can use the
simulator to investigate the expected consequences of their actions. The other two rely for
their analyses only on conventional tools like spread sheets. Different behavior modes and
different performance of the groups with and without simulator should be expected. Up to
now, our research provides no clear and definite answer, whether the use of LEARN! really
causes significantly different modes of behavior or profit performance.
REFERENCES
Argyris, C. 1990. Overcoming Organizational Defenses. Facilitating Organizational Learning.
Boston: Allyn and Bacon.
Milling, P. M. 1994. Management Games for Group Decision Making in a Dynamic Envi-
ronment. System Dynamics: Exploring the Boundaries. Microworlds, pp. 83-92.
Milling, P. M. 1990. The Design of Strategy Support Systems, Advances in Support Systems
Research. ed. G. E. Lasker and R. R. Hough, Windsor: I.1.A.S. pp. 227-231.
Milling, P. M. 1986. Decision Support for Marketing New Products. ed. J. Aracil, J. A. D.
Machuca, and M. Karsky: System Dynamics: On the Move, Seville, pp. 787-793.
Scheper, W. J. 1991. Group Decision Support Systems. An Inquiry into Theoretical and
Philosophical Issues. Diss. University of Brabant, Tilburg.
Senge, P. M. 1990. The Fifth Discipline: The Art and Practice of the Learning Organization.
New York: Doubleday.
Vennix, J. A. C. 1990. Mental Models and Computer Models. Design and Evaluation of a
Computer-Based Learning Environment for Policy-Making. Diss. University of Nijmegen.
31>
