Simulation Approach to Decision-Making
This article describes using simulation programs for making IT decisions, but similar simulations are made to determine geopolitical and business outcomes based on specific conditions. These are different from scenarios as they are usually computer-based. In contrast, scenarios are typically role-played, even when they are "table-top exercises", meaning that the entire scenario environment has not been replicated but imagined in a symposium-like setting. Have you ever designed or participated in a simulation? Did it provide full information to inform the resulting decision-making?
5. Conclusions
The first research question asked whether simulation modeling is used to support decision making in the IT service strategy context. This paper explores the use of simulation modeling in this scope and the works founded show that different simulation approaches have been used. According to their application fields, these works have been associated with the most suitable process of ITIL Service Strategy module.
The second research question asked whether it is adequate to use System Dynamics to solve IT service strategy problems. The main contribution of this work is a System Dynamics model in the scope of ITIL strategy of IT services process that helps service providers in the decision-making process to define their strategic goals. The simulation model allows analyzing the outcomes of changes in the service capacity, which a service provider assigns to a specific customer, and in the tendency of customer's service requests on the fulfillment of one of the business rules associated with the strategic goal customer satisfaction. This business rule is reflected in the SLA that the service provider and the customer sign and determines the maximum percentage of service requests that are permitted to be rejected because they have exceeded the response time established.
To do that, different simulation scenarios were configured by varying the service capacity and the tendency of service requests received. The main objectives of the experiments described in this study are as follows: (a) to evaluate the fulfillment of the business rule with the service capacity contracted by the customer, (b) to determine the lowest service capacity that ensures the fulfillment of the business rule, and (c) to analyze the service behavior by varying the service capacity assigned to the customer and the tendency of service requests received. The dynamic feature of the simulation model helps analyze the fulfillment of the business rule and the service behavior through time and determine the adequate moment to change the service capacity to guarantee the business rule fulfillment.
On the other hand, several optimization experiments were performed that allow determining the best values of decision variables that meet the following optimization objectives: (a) to minimize the service requests rejected, (b) to maximize the service requests validated, and (c) to minimize the nonfulfillment of the business rule.
The main objectives of our further works are as follows:
(i) extend the functionality of the simulation model presented in this work to solve more complex problems in the context of the IT service strategy.
(ii) develop simulation models to help in decision making in different domains of IT service management processes. For this, different simulation approaches can be applied;
(iii) apply the simulation models built in real companies to help calibrate and validate them. The usage of these models will provide important benefits for the companies.