Prioritizing and Optimizing Disaster Recovery Solution using Analytic Network Process and Multi Attribute Utility Theory

Abstract

Selection of a suitable disaster recovery solution is an essential activity performed in an enterprise to facilitate recovery of critical business functions and Information Technology (IT) systems within a tolerable time limit known as disaster recovery time (DRT). The estimation of optimal DRT plays a significant role in IT as it influences overall costs required to ensure business continuity. The estimation of optimal DRT depends upon the capabilities of a chosen disaster recovery solution and multiple conflicting attributes. This paper presents an integrated approach to selecting the best disaster recovery solution using analytic network process (ANP) and estimating optimal DRT using Multi-Attribute Utility Theory (MAUT). ANP is applied to determine the best disaster recovery solution using seven criteria: people, recovery objectives, security, technology, cost, site infrastructure, and regulatory. MAUT estimates the optimal DRT for the best disaster recovery solution based on three conflicting attributes: cost, reliability, and processed backlog transactions. The proposed approach applies to an enterprise application in the banking sector and this paper tests its effectiveness by comparing the results from four different enterprises. This study offers valuable insights to the disaster recovery practitioner to select the best disaster recovery solution and to estimate optimal DRT.