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Providing Managers with a Visual Cross-Level Analysis Method

Tomas Eklund, Mikael Collan, Aapo Länsiluoto, Barbro Back, Providing Managers with a Visual Cross-Level Analysis Method . In: P. R. Fullér Walden, J. Carlsson (Eds.), Expanding the Limits of the Possible, 220-240, Painotalo Gillot, 2006.

Abstract:

In global markets the information available to managers plays an important role in the competitiveness and efficiency of companies. Profitability and margins depend not only on firms' own actions, but also on the industry and the overall (macro)economic situation. This would indicate that including industry and macro level data in corporate analyses may bring considerable benefits with regards to information completeness and reliability. At the same time, while benefits can be reaped from including more than one level of data, problems of increased complexity, tractability, and understandability of large amounts of data also become apparent. This paper proposes the use of the self-organizing map (SOM) in the integrated (simultaneous) analysis of firm, industry, and macro level financial data for the production of understandable visual managerial information from large and multidimensional datasets. The paper illustrates the proposed techniques by presenting and combining results from four cases by using the SOM. Further, the development of two multinational companies is followed and issues affecting their financial situation analyzed by using the proposed techniques.

BibTeX entry:

@INBOOK{cEkCoLaBa06a,
  title = {Providing Managers with a Visual Cross-Level Analysis Method },
  booktitle = {Expanding the Limits of the Possible},
  author = {Eklund, Tomas and Collan, Mikael and Länsiluoto, Aapo and Back, Barbro},
  editor = {Walden, P. R. Fullér and J. Carlsson},
  publisher = {Painotalo Gillot},
  pages = {220-240},
  year = {2006},
  keywords = {Self-organizing map; Visualization; Multidimensional comparison; Multi-level comparison; Financial benchmarking; Industry-level comparison; Socio-economic development},
}

Belongs to TUCS Research Unit(s): Data Mining and Knowledge Management Laboratory

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