Entropy-Based Assessment of Nonfunctional Requirements in Axiomatic Design

The main information systems design techniques focus, almost exclusively, on the functional requirements of the system to be implemented. In its standard formulation, axiomatic design has such characteristics. However, in complex operational environments, this can lead to the identification of functionally valid solutions, but do not perfectly adhere to the system's nonfunctional requirements. In specific operational contexts, particularly in information systems design, neglecting nonfunctional requirements has been identified as a major threat to projects, which can prevent their proper utilization throughout the design process. In this paper, we focus on nonfunctional requirements in axiomatic design, whose impact assessments can only be performed according to a heuristic basis, i.e., by expert judgment. However, the value assignment by experts can lead to a decisional indeterminacy or cognitive bias. To overcome these limitations, we propose the adoption of a methodological approach based on a reinterpretation of the information axiom of axiomatic design in terms of a multi-criteria decision problem. This approach allows the formal inclusion of nonfunctional requirements in the design process, which can be accomplished by setting them as evaluation attributes to achieve the robust design solution. In this paper, we propose an algorithm to evaluate alternative design solutions based on the information theory of entropy, which then comply with the nonfunctional requirements. We illustrate our approach by a case study, which implements the process of managing patients in home care and compare it with the mathematical-based analytic hierarchy process method proposed in the literature. According to our method, the robust solution is computed in just a single step saving significant computational cost with respect to the iterative-based analytic hierarchy process method. In this perspective, the proposed approach can support information systems designers in decision making as it allows to select the most suitable solution for the context in which it must operate.