Measuring business effectiveness of information technology investment by using empirical artificial neural networks and expert system

As Information Technology (IT) solutions and services play a significant role in enabling firms to achieve their strategic business goals, it is imperative to clearly understand the IT´s contribution, impact and overall investment effectiveness in relevance to the firm´s business objectives achievement. Although there are many methods and tools available on the market for measuring the Return On Investment (ROI), Net Present Values (NPV), etc., there does not seem to exist a mechanism for measuring quantitatively the effectiveness of incurred technology investment in an organization, leveraging concepts such as Neural Nets or Fuzzy Logic. While Neural Networks has been providing possibilities for solving problems in various fields such as Medicine, Engineering, Finance, Economics, etc., its capabilities do not appear to have been explored adequately in the field of Information Technology, which is an indispensible part of the Business in every Organization. The purpose of this research is to develop a mathematical model based on artificial neural networks and expert systems that can identify within a firm the correlation between IT cost factors, and IT services, usage ratio of services by Business Functions and the percentage of contributions made by Functions toward achieving Business Objectives. The model will be able to calculate the incurred cost per unit of each Business Objective, compare it with the optimized unit cost and determine the level of impact that the technology investment has caused on the achievement of the Objectives within a given fiscal period. Artificial Neural Network is used in this model to enable the quantification of correlations between various layers based on the past experience. This model would also enable a firm more accurately to project the technology investment for a specific fiscal period.