An Integrated Model for Risk Assessment of using RFID Technology in Baggage Management

Abstract

The digital transformation through Internet of Things taking place in the businesses today is proving to be of paramount significance both in terms of competitive advantage and profitability. Radio frequency identification detection technology is one such internet of things application that is vastly being adopted on a broad scale by industries. It is a surveillance technology being incorporated into various industries for visibility and tracking purposes. The technology uses radio frequency to detect digital-tagged objects, items and humans across a supply chain. One of the many industries using radio frequency identification detection technology effectively in its operations is the aviation industry. Airports, as a result, improve their infrastructure intelligence and progress as smart facilities to promote growth by providing a pleasant travel experience. The volume of logistics flow at airports is huge and for real time monitoring of these flows radio frequency identification detection technology is being used. Although, radio frequency identification detection has its benefits yet there are risks associated with it that can disrupt the operational flow at airports and pose privacy and security issues. Extensive research is available on studying the optimality of using radio frequency identification detection technology in aviation industry; however, the risks analysis is almost nonexistent in literature. This research aims to assess and minimize the risks involved in using the radio frequency identification detection technology in the logistics operations of an airport. One of the major high value logistics flow through an airport is baggage management. Radio frequency identification detection is used here to detect items, objects, and luggage across the supply chain from point of origin to point of delivery. It although has immense benefits, yet research shows that radio frequency identification detection devices can be targeted easily and are thus can be exposed to security and privacy risks. This research proposes a new risk assessment framework which uses a fuzzy based hybrid multicriteria decision making technique for risk prioritization followed by a minimization of risk by selecting optimum mitigation strategies that minimize the risk under risk reduction and cost minimization constraints.