Assignment output decision analytics

Case study: San Siro
DECISION ANALYTICS




Joël Jame, 0553376 Stijn Damoiseaux, 0553713
13/01/2023 | PROF. DR. LIESELOT VANHAVERBEKEMR. DENNIS VERBIST

,1 EXECUTIVE SUMMARY............................................................................................................................... 2
2 INTRODUCTION.......................................................................................................................................... 4
3 THE MODEL................................................................................................................................................ 6
4 PRICE-DEMAND RELATIONSHIP.................................................................................................................. 7
5 SENSITIVITY ANALYSIS................................................................................................................................ 8
6 MONTE CARLO......................................................................................................................................... 10
7 MANAGERIAL QUESTIONS........................................................................................................................ 12
7.1 WILL WE LOSE OUT ON EXTRA PROFITS BECAUSE OF A LOWER CAPACITY IN THE NEW STADIUM?...................................12
7.2 BY HOW MUCH WILL WE BE ABLE TO INCREASE TICKET PRICES WHEN THE OUTDATED SAN SIRO IS NO LONGER IN USE?.....12
7.3 GIVEN THAT THE AVERAGE ATTENDANCE IN THE 22-23 SEASON IS HIGHER THAN THE MAXIMUM CAPACITY FOR THE NEW
STADIUM, DOES THIS CAPACITY HAVE TO BE RECONSIDERED?........................................................................................12
7.4 HOW WILL CAPACITY INFLUENCE TICKET PRICES?..................................................................................................13
7.5 IS THE PROJECT'S SUCCESS DEPENDENT ON THE CLUBS' SUCCESS?............................................................................13
8 CONCLUSION............................................................................................................................................ 14
9 APPENDIX................................................................................................................................................ 15




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, 1 Executive summary
San Siro, the home stadium of football clubs AC Milan and Inter Milan, is going to be rebuilt
in 2024. The cost for the new investment is estimated to be around € 1.2 billion and will
result in a yearly estimated revenue of €120 million. The future stadium will host only a
maximum of 65.000 fans, in contrast to the current stadium that can host almost 76.000 fans.
The consideration between a capacity of 65.000 or a higher, together with other managerial
questions will be analysed and answered throughout this assignment. Furthermore, we
developed a model, which includes all expenses and income related to the stadium. The
model will be examined by making use of a sensitivity analysis, a price-demand relationship,
and a Monte Carlo analysis. Additionally, the managerial questions will be investigated by
making use of an array of techniques. A list of the assumptions we had to make can be found
in the appendix.
First, we constructed a default situation for the model, which resulted in a net present value of
€ 1.455.795.178,91. Next up, the price-demand relationship was analysed to investigate and
compare the difference in sensitivity between domestic games and European games, as this
information has been useful in further analyses that we have carried out. The result for the
price-demand relationship suggested that the demand for domestic games is less sensitive to
price changes in comparison with demand for European games.
A sensitivity analysis is necessary in this model to witness the effect of multiple variables on
the result of the project, the robustness of the results in case these variables change as well as
to check for errors in the model. The sensitivity analysis contained the average ticket price,
capacity and the average number of games played in European competitions per season. The
results show that for an average domestic ticket price of € 180 and an average EU price of €
120, the highest NPV is reached which is € 3.224.271.060,20. Unfortunately, for these prices
the seat demand would be too low. For the capacity, the two scenarios are 65.000 and 75.000
seats and resulted in fairly the same NPV, IRR and payback period. For the number of games
played in European competitions per season, a worst-case scenario and a best-case scenario
were constructed. Since both scenarios are improbable, the thing to keep in mind is the NPV
that is still reasonably high in the worst-case scenario, showing the potential of the
investment.
Furthermore, by using a Monte Carlo simulation, we included uncertainty regarding some of
the variables into our model. This technique calculates both the optimistic and the pessimistic
view of the uncertain variables and gives you the most favourable result on average. The
variables, which are each assigned with a different distribution, that are included are the
following: the seasonal average number of visitors per game, the number of games played in
European competitions per season, and the yearly inflation. Two scenarios were created, one
for a capacity of 75.000 and one for 65.000 seats. The results when comparing the NPV
showed that the MAXIMIN chooses 65.000 as the best option, whereas the MAXIMAX
prefers the 75.000 seats. Additionally, when taking the Hurwicz and percentile approach into
account, we can see that risk-averse investors prefer the 65.000 seats, in contrary to risk
seeking investors who would go for the 75.000 seats.
After taking all these insights into consideration, we can finalise by stating that the 65.000
seats is the best option for achieving the highest NPV if both the current ticket prices are
maintained. Nevertheless, a higher NPV could be realized while still maintaining the current

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