6. DISCUSSION ON RESULTS
6.3. Implications for other uses: Simulation of results in a non-Company X
The results of this thesis can, of course, be applied to other environments as well. As a means to test the distribution structures proposed in this thesis, the author simulated a similar situation for McDonald’s both in Sweden and in Finland. McDonald’s was cho-sen as it has an extensive network of restaurants in both countries – and their coordi-nates are publically available online in GPS files.
The figures used to approximate demand were the GPS coordinates of McDonald’s res-taurants and the populations of Finnish and Swedish municipalities. Different centers of gravity were calculated for Finland and Sweden using the methods applied in this thesis.
These were then mapped to a fictional situation that was made to be similar to Company X’s, and it is described in table 6.1. Also, distribution model alternatives that are
identi-cal to the ones used in this thesis were created based on the centers of gravity. For this use, the unweighted “distributors”, meaning restaurants, are a far better fit for demo-graphic data than are the actual sales numbers of Company X, as their number is larger and volumes similar; it is safe to assume that three largest restaurants do not constitute over 50% of the sales in each country as is the case with Company X’s distributors in Russia.
Table 6.1. The principles of fitting McDonald's in Finland and Russia to simulate Com-pany X in Russia. The cities in italics are imaginary production and import locations.
Country Russia Finland Sweden
Company Company X McDonald’s McDonald’s
Major port city
- 100 % of volume flow now - 20 % of vol. in the future
Saint Petersburg Turku Malmö
New inland manufacturing - near center of population - 80 % of vol. in the future
Tatarstan Tampere Uppsala
West/south center of demand Moscow cl. Hämeenlinna Boxholm East/north center of demand Novosibirsk cl. Siikajoki Härnosand
This thought experiment includes Finland and Sweden being divided both by a moun-tain range like the Urals. In Finland the imaginary mounmoun-tain range runs west-to-east along the 63rd parallel and in Sweden it is the 60th. This combined with the improvised production and importing structure makes the situation remarkably similar to Company X’s – especially since the population in Sweden and Finland is divided much in a simi-lar way as it is in Russia. The main difference between these countries is that the popu-lation of Russia is mainly in the west leaving the east largely uninhabited, whereas Sweden and Finland hold most of their population in the south, and the north is sparsely inhabited. In a way, Russia is like Sweden and Finland rotated 90 degrees to the right.
The results of this thought experiment can be seen in figure 6.1., where the darkest hues represent Company X and Russia, followed by McDonald’s in Finland and Sweden, re-spectively. Actual sales data from Company X is left out and the locations of the distri-butions centers are based on demographic data (meaning Novosibirsk instead of Chel-yabinsk for Russia in alternative 2) to show what can be derived from external sources.
Figure 6.1. Indexed comparisons of "total TKMs" for Company X in Russia and McDonald’s in Finland and Sweden
As the figure indicates, the data from the McDonald’s examples in Finland and Sweden shown in paler colors is barely distinguishable from Company X’s situation in Russia.
Sweden does, however, seem to differ more in the proportions of inbound and outbound transportation.
Other than similarities between three completely different markets and two different industries, the similarities between population and McDonald’s data in Finland and in Sweden are also remarkably level. This means that weighted population numbers seem to correlate well with the distribution of McDonald’s restaurants (or vice versa), and when detailed “end-node” data is lacking, data such as the distribution of population in a given market may be effective in approximating it – provided that the market is devel-oped and somewhat saturated, which is a safe assumption for McDonald’s in the pros-perous and stable Nordic countries.
What does this thought experiment suggest? The main conclusion of such a brief and superficial application of centers of gravity to “homogenized” yet completely independ-ent situations seems to suggest that the method would direct the alternatives to similar relative levels of transportation regardless of the geographic location. As mentioned above, it also suggests that demand can be approximated based on data that is not direct sales figures of a company to be used in planning distribution networks.
The extent to which the results are applicable is questionable. In the McDonald’s exam-ple, an imaginary supply chain was created for existing restaurant locations, and this yielded results similar to Company X’s situation. This could indicate that similar condi-tions in different markets yield similar results. The question is: Would a different kind of supply chain construction (with numerous in-country production facilities, for exam-ple) yield mutually consistent results in markets that are independent of each other yet similar like the ones examined here? This is something that cannot be answered within the scope of this thesis, and other research in the field of using centers of gravity as lo-cation optimization tools may give the answer for this – or call for new research.