Project manager’s decisions are analysed by studying the chosen options and project performance in the business game SimulTrain®. A simulation provides an excellent opportunity for data mining and analysis because all participants manage similar projects and face the same challenges. The results of project simulations carried out by more than 8000 project managers and students of business schools around Europe show the differences between participants’ choices and experts’ opinions. The analysis highlights the major shortcomings of project delivery, decision making, and risk reaction and lists the common mistakes made by managers. Further, it demonstrates that managers’ proactive behaviour and risk prevention attitudes as well as investing into communication and team development result in better project performance.

1. The Shift from Teaching
to Learning:
Individual, Collective and
Organizational Learning
Through Gaming Simulation
Proceedings of the 45th Conference
of the International Simulation and
Gaming Association
Dornbirn, 2014
Willy C. Kriz, Tanja Eiselen, Werner Manahl

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Using Business Simulation to Analyse
Project Management Decision Making
Igor Kokcharov
Abstract
Project manager’s decisions are analysed by studying the chosen options and
project performance in the business game SimulTrain®. A simulation provides an
excellent opportunity for data mining and analysis because all participants mana-
ge similar projects and face the same challenges. The results of project simula-
tions carried out by more than 8000 project managers and students of business
schools around Europe show the differences between participants’ choices and
experts’ opinions. The analysis highlights the major shortcomings of project deli-
very, decision making, and risk reaction and lists the common mistakes made by
managers. Further, it demonstrates that managers’ proactive behaviour and risk
prevention attitudes as well as investing into communication and team develop-
ment result in better project performance.
Keywords
business game, data mining, decision making, manager, performance, project ma-
nagement, risk, serious game, simulation, SimulTrain
1 Introduction
The difficulty of scientific analysis in project management is linked to the diversi-
ty that exists in the project management profession. Most defining characteristics
of complexity in projects are multiple stakeholders (57%), ambiguity of project
features (48%), significant authority influence (35%), unknown project features
(33%) and others [1]. Project managers use different methods like risk manage-
ment, project performance indexes, change management practice, agile project
management to turn complexity into success.
While each project is unique, the common aspects, phases, and steps of
project management are summarised in professional standards [2–4]. Only
practice makes perfect. Managers only find situations, projects, and problems to
be similar when performing a business game [5]. For example, while pilots impro-
ve their reaction skills in critical situations by using flight simulators, project ma-

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nagers employ project management simulators. The advantage of a simulation is
the possibility to repeat critical situations quickly and without incurring financial
losses and develop skills via collaboration [6]. Modern project simulations Alba-
sim, Cayenne, Cesim, Fissure, PM Simulation, Prendo, Sharkworld, Simventure,
Simultrain®, SMG, Topsim and others [7] are widely used for corporate professio-
nal training.
The business game SimulTrain® (Fig. 1), a simulation of a three-month
project carried out by a group of four people in 6–8 hours, is used for the analysis.
The simulation is based on the results of two scientific surveys [8]. The simulator
includes an expert system that analyses participants’ decisions and provides
them with a conclusion that highlights the strong and weak aspects of their mana-
gerial behaviour.
Figure 1 SimulTrain® project office and project performance indexes.
Because participants face similar situations, their decisions can be compa-
red. The main objective of the analysis of the results of more than 2000 simula-
tions is establishing a list of the common mistakes made by managers. Making
recommendations for improving the simulation model was an additional objective
of the research.
2 Data Mining
Participants’ input includes different information: they allocate people to activities,
plan meetings and social events, react to risk issues, and make multiple-choice

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decisions on project issues. The output of the simulations is a set of project perfor-
mance indexes: the Schedule Performance Index, Cost Performance Index, Quali-
ty Index, and Risk Management Index as well as their average value. Optimal
values are 100% or higher. In other words, the greater the indexes, the better it is
for the players.
Log files of 2054 simulations were used for data mining. The average num-
ber of decisions during a simulation is 483. On the average, attendees make one
decision per minute during the simulation. Thirty-one percent of the games were
carried out in French, 27% in German, 25% in English, 6% in Portuguese, 5% in
Spanish, 4% in Russian, and the remainder in the other 15 languages available.
The average score for all simulations was 95%.
3 Project Performance
The analysis in Fig. 2 shows weak or no dependence between project performance
(the average score) and the number of decisions taken during the simulation.
Figure 2 Integral performance score versus the number of decisions taken during the simulation project.
Figure 3 Risk Management Index versus the number of risk reaction decisions.

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Decisions about risk prevention and risk mitigation show two trends: 1)
there is a strong increase of the Risk Management Index when the number of de-
cisions is small and 2) a further increase in the number of decisions has a small
effect on the indicator.
4 Decision Analysis
Participants resolve project issues by making decisions. There are 68 decisions
with three options (Fig. 4). After a discussion, a team of four participants chooses
one of three options. The team may refuse to make a decision, but this action re-
sults in an unfavourable decision being taken by top management.
Figure 4 A typical situation that participants meet in SimulTrain®
Participants’ choices affect many parameters in the simulation, such as
budget, schedule, motivation, quality, and risk. These choices then influence sta-
keholder relationships [6]. A numerical measure of stakeholder relationships has
been suggested (i.e., 0% to 100%). Fig. 5 shows two examples of when there is
large and small differences between experts and participants’ opinions. The text
of several decisions has been modified as a result of this analysis.
a) b)
Figure 5 Two situations where the choices of participants and experts are different (a) and similar (b).

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The analysis also allows us to establish a list of the most common mistakes made
during the simulation. This is a list of the themes where the difference in opinions
was maximised:
1. Proactive use of a large range of communication tools;
2. Adapting to different stakeholder requests;
3. Honestly informing the team about all project situations;
4. Spending money to prevent risks;
5. Involving the team in decision making;
6. Investing into team development;
7. Avoiding micro-management;
8. Fighting for the organisational resources assigned by top management.
These major points discovered by the analysis are important themes for
follow-up discussions during project management training courses.
5 Risk Management Analysis
Participants react to risks by planning actions in a Risk Register. Fifteen risk situ-
ations concerning scope, schedule, quality, resource, finance, and operation are
under consideration. Participants can choose up to five reactions to a risk. They
can even choose all the suggested measures; however, this action affects the pro-
ject budget. Therefore, they have to balance risk prevention and budget spending.
Fig. 6 shows the differences between experts’ and participants’ choices for one of
the situations.
Figure 6 A situation when the opinions of experts and participants about risk reaction options are different.

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The analysis shows the risk prevention situations where the opinions of
experts and participants are sufficiently different. In particular, participants
1. Don’t involve external experts during project planning;
2. Don’t consider regular project plan updates;
3. Don’t consider training to be an important measure against missing qualifi-
cations;
4. Don’t plan/prepare alternative technical solutions in case the initially planned
solution does not work.
6 Discussion
Business simulation provides an opportunity for data mining and analysis becau-
se all participants manage similar projects. The analysis recommends modifica-
tions to the business game model and highlights some of the major shortcomings
in project delivery, decision making, and risk reaction, such as:
1. Proactive use of a large range of communication tools;
2. Adapting to different stakeholder requests;
3. Investing into team development;
4. Using external expertise during project planning;
5. Investing into alternative technical solutions.
The analysis shows that managers’ proactive behaviour and risk preventi-
on attitudes result in better project performance.
References
PMI’s Pulse of the Profession in-Depth Report: (2013). Navigating Complexity.
A Guide to the Project Management Body of Knowledge, (PMBOK® Guide) (2013). Project Management Institute.
ICB – IPMA Competence Baseline (2006), Version 3.0, International Project Management Association.
Managing Successful Projects with PRINCE2. OGC (Office of Government Commerce) (2009). TSO (The Stationery Office).
Salas, E., Wildman, J. L., & Piccolo, R. F. (2009). Using simulation-based training to enhance management Education,
Academy of Management Learning & Education, 8, 559–573.
Kokcharov I., Strehmel S., & Burov A. (2013). Case Study-based Collaborative eLearning. International Conference on
Interactive Collaborative Learning, ICL, IEEE Proceeding, 2.
List of Project Management Simulations http://pmgames.tuxfamily.org/
Sauter, R. (1996). La modélisation des facteurs humains dans la gestion de projects. Thèse # 1494, EPFL, Lausanne.
Author/Contact
Igor Kokcharov
Sauter Training & Simulation SA, Switzerland
igor.kokcharov@sts.ch