With managers facing increasing amounts of information to process daily, the need for intelligent tools to perform these operations is likely to increase in the future. This situation is accentuated by the exponentially increasing amount of information available through the Internet. We simply cannot cope with this information overload any longer without using intelligent tools. Therefore, the use of data-mining tools, such as self-organizing maps, is likely to increase dramatically in the future.
In this study, the self-organizing map has been shown to be a feasible tool for financial benchmarking. The results are easy to visualize and interpret, provide a very practical way to compare the financial performance of different companies, and could be used as a complement to traditional net sales comparisons ( Rhiannon, Jewitt, Galasso, & Fortemps, 2001).
Using this method, an interesting pattern emerges. It is interesting to note that most of the largest pulp and paper-producing companies in the world, with the exception of Kimberly-Clark, belong to below-average groups. The ranking shows that the largest companies according to net sales are not necessarily the best-performing companies. In fact, the smaller companies appear to utilize their resources much more effectively than their larger competitors.
As has been shown in several studies (Neural Networks Research Centre, 2001), the application range for self-organizing maps is virtually limitless, and is certainly not restricted to use in financial benchmarking. One potentially huge application for self-organizing maps in the future is within Web mining. Web mining is a data-mining technique for comparing the contents of Web pages in order to provide more accurate search engines. The possibility of applying neural network technology, called WEBSOM, to solve this problem is very interesting, and preliminary results are encouraging (Honkela, Kaski, Lagus, & Kohonen, 1997; Lagus, 2000).