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Then, if the corresponding offers were expensive they declined to buy them or if they
were cheap they took up the offers. However, in the meantime, since the suppliers have
limited capacity they scheduled other Day 0 orders for much later in the game. Thus
when this happened our Day 0 bidding was severely effected (sometimes up to Day 70)
and we received severely delayed delivery dates for our orders. In such cases, we were
simply unable to obtain the components we needed through our Day-0 procurement
policy and so we made very few sales.
4.2
Competition Game Analysis
To complement and better understand the competition result and to evaluate the effec-
tiveness of our pricing model we conducted a post hoc analysis. However it is hard to
see how the pricing works from only the game results since the competition entrants
contain a variety of interrelated strategies (for the different facets of their operation).
Thus we decided to compare for the RFQs that the agents responded to, how the price
varies among different agents. 5 To do this, we analysed competition games and we were
especially interested in those cases where there were strong agents. Here we take a ran-
domly chosen representative game in the semi-final (game 1136) and analyse it in more
detail. 6
In this game, we compare our agent with FreeAgent and Mr.UMBC which were the
first and second placed agents in the final. Thus, in each such competition, we extracted
from the game data, details of the RFQs that were received by the competing agents,
the offers that they sent to the customers in response and the orders that resulted. 7 This
data enabled us to compare the orders that the agents were winning with the prices that
they offered. Specifically, Figure 2 shows for each simulation day, the daily price (per
production cycle, see Figure 2(a)) offered by each agent and the cumulative order quantity
that each agent won (expressed as factory production cycles averaged over all products see
Figure 2 (b)). Since the ultimate profitability of the agents depends on both these factors,
we also calculate the average cumulative revenue (i.e. the number of PC orders multiplied
by their prices, see Figure 2 (c)).
Throughout the game, SouthamptonSCM adaptively adjusts the price offered to the
customer to ensure that the factory maintains as close to full production as possible (the
factory utilisation for our agent, FreeAgent and Mr.UMBC are 76%, 58%,and61%).
Generally, having a high factory utilisation means the agent can produce more PCs
and thus win more customer orders. For example, in this game, the number of orders
for these three agents are 5405, 4011,and4300. In this example, all three agents have
sufficient components to allow them to compete for the same orders. However, our
5 We aim to compare the pricing model and the revenue made by responding to the customer
RFQs. Thus the price paid for the components and any late penalties need not be considered
here.
6 We did not choose a game from the final because of the skewing introduced by the Day 0
bidding strategies used by some of the agents. Also it is impossible to compare the pricing
of multiple games in one figure, thus we only show one representative game. However, the
following discussion also applies to the other games we analysed.
7 For clarity, we omit from this plot the other three agents, and just show data for Southampton-
SCM, FreeAgent and Mr.UMBC. The plots of the other agents show they were less effective.
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