Quantifying information technology´s generated services and incurred costs by applying empirical Artificial Neural Networks/Expert Systems modeling

In today´s expansive world of social media and collaborative systems [1], firms that invest immensely in Technology to enable their Businesses achieve goals effectively, competitively, and globally [2] expect their Technology Centers to deliver solutions and services at shortest turnaround time, lowest cost [3], with yet very rich functionality and sophistication. Additionally, such firms wish to be able to determine the relationship between the Technology investment [3] they make and the quantities of Technology Services generated as a result. To address such wishes, in this Paper empirical Artificial Neural Network - Expert System (ANN-ES) models are being introduced that utilize previous fiscal periods´ technology related investments, channeled through Information Technology´s (IT) classical services, for developing patterns, fine tuning and training the models [4]. The approach, illustrated via examples, is based on the distribution of Technology investment across IT service channels and converting them into more tangible services, called IT Generated Consumable Services in this Paper, that are directly utilized by Business Users in a firm. A limited number of Services have been chosen to demonstrate the Proof Of Concept, for each one of which the construction of the model and training method [4] follow a common methodology with different Inputs, Patterns and Outputs. Post convergence, it would be possible to feed a certain period´s relevant data as Input in to the model to determine quantities (Output) of a specific type service and its associated costs in relevance to the total cost of IT investment for a specific period in an organization [5]. The output of each model, which is the quantity of generated service of a particular type along with its associative cost is illustrated in one of the Tables 1 through 6 with summaries in Tables 7 & 8. In the following sections of this Paper details for developing ANN-ES models [6] relevant to quantification o- each service category are provided.