Computational Intelligence in the Financial Functions of Industrial Firms

Computational Intelligence in the Financial Functions of Industrial Firms

Petros Theodorou (Athens University of Economics and Business, Greece) and Dimitrios Karyampas (University of York, UK)
DOI: 10.4018/978-1-59904-582-5.ch003
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Information technology has been proved to be a strategic weapon in the business armory for the creation and sustention of competitive advantage, especially, when it is aligned with the needs of the internal and external environment. Solutions are provided from the operational level up to strategic planning and are capable to support every choice in the strategy portfolio, from cost to quality and flexibility. IT systems in the manufacturing and operational level were analyzed extensively in literature: ERP systems, computer aided design/computer aided manufacturing (CAD/CAM), and so forth. According to Wong, Bo, Bodnovich, and Selvi (1997), 53.5% of the reviewed literature in artificial intelligence refers to applications in production and operations management. Nevertheless, the second most important area for advanced IT applications is that of finance (25.4%). This research will be focused on the common set of the two previously mentioned areas: production management and the necessary financial tools. Production and operation management requires specific financial tools in order to accomplish the functions of production planning, costing, investment appraisal, and so forth. Computational intelligence in those financial functions is mostly needed for the production operation department and for the production operation strategy. Specifically, the weight will be put on information technology automation of financial functions adopted by production departments: forecasting production needs, production planning and control, profit volume analysis, cost analysis, investment appraisal analysis, and so forth. An attempt will be made to classify the various quantitative and qualitative techniques in relation to various financial aspects. Specifically, advances of neural networks, expert systems, advanced statistical analysis and operational research methods, and various hybrid techniques will be presented in relation to financial models applied in production. Financial applications will be analyzed according to their modules and their outputs in a strategic alignment concept. Finally, a strategic alignment model will be derived for the adoption of financial applications in businesses.

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