• Big data-driven fuzzy cognitive map for prioritising IT service procurement in the public sector

      Choi, Y.; Lee, Habin; Irani, Zahir (2018-11)
      The prevalence of big data is starting to spread across the public and private sectors however, an impediment to its widespread adoption orientates around a lack of appropriate big data analytics (BDA) and resulting skills to exploit the full potential of big data availability. In this paper, we propose a novel BDA to contribute towards this void, using a fuzzy cognitive map (FCM) approach that will enhance decision-making thus prioritising IT service procurement in the public sector. This is achieved through the development of decision models that capture the strengths of both data analytics and the established intuitive qualitative approach. By taking advantages of both data analytics and FCM, the proposed approach captures the strength of data-driven decision-making and intuitive model-driven decision modelling. This approach is then validated through a decision-making case regarding IT service procurement in public sector, which is the fundamental step of IT infrastructure supply for publics in a regional government in the Russia federation. The analysis result for the given decision-making problem is then evaluated by decision makers and e-government expertise to confirm the applicability of the proposed BDA. In doing so, demonstrating the value of this approach in contributing towards robust public decision-making regarding IT service procurement.
    • A decision support system for vessel speed decision in maritime logistics using weather archive big data

      Lee, Habin; Aydin, N.; Choi, Y.; Lekhavat, S.; Irani, Zahir (2018-10)
      Speed optimization of liner vessels has significant economic and environmental impact for reducing fuel cost and Green House Gas (GHG) emission as the shipping over maritime logistics takes more than 70% of world transportation. While slow steaming is widely used as best practices for liner shipping companies, they are also under the pressure to maintain service level agreement (SLA) with their cargo clients. Thus, deciding optimal speed that minimizes fuel consumption while maintaining SLA is managerial decision problem. Studies in the literature use theoretical fuel consumption functions in their speed optimization models but these functions have limitations due to weather conditions in voyages. This paper uses weather archive data to estimate the real fuel consumption function for speed optimization problems. In particular, Copernicus data set is used as the source of big data and data mining technique is applied to identify the impact of weather conditions based on a given voyage route. Particle swarm optimization, a metaheuristic optimization method, is applied to find Pareto optimal solutions that minimize fuel consumption and maximize SLA. The usefulness of the proposed approach is verified through the real data obtained from a liner company and real world implications are discussed.
    • Optimizing enterprise risk management: a literature review and critical analysis of the work of Wu and Olson

      Choi, Y.; Ye, Xiaoxia; Zhao, L.; Luo, A.C. (2016-02)
      Risks exist in all aspects of our lives. Using data in both Scopus and ISI Web of Science, this review paper identifies pioneer work and pioneer scholars in enterprise risk management (ERM). Being ranked the first based on the review data, Desheng Wu has been active in this area by serving as a good academic network manager on the global research network, His global efforts with diverse networking have enabled him to publish outstanding papers in the field of ERM. Therefore, this paper also conducts a literature review of his papers and critical analysis of the work of Wu and Olson, from the perspective of the ERM, to glean implications and suggestions for the optimization and customization of the ERM.
    • The role of e-participation and open data in evidence-based policy decision making in local government

      Sivarajah, Uthayasankar; Weerakkody, Vishanth J.P.; Waller, P.; Lee, Habin; Irani, Zahir; Choi, Y.; Morgan, R.; Glikman, Y. (2016)
      The relationships between policies, their values, and outcomes are often difficult for citizens and policymakers to assess due to the complex nature of the policy lifecycle. With the opening of data by public administrations, there is now a greater opportunity for transparency, accountability, and evidence-based decision making in the policymaking process. In representative democracies, citizens rely on their elected representatives and local administrations to take policy decisions that address societal challenges and add value to their local communities. Citizens now have the opportunity to assess the impact and values of the policies introduced by their elected representatives and hold them accountable by utilizing historical open data that is publicly available. Using a qualitative case study in a UK Local Government Authority, this article examines how e-participation platforms and the use of open data can facilitate more factual, evidence-based, and transparent policy decision making and evaluation. From a theoretical stance, this article contributes to the policy lifecycle and e-participation literature. The article also offers valuable insights to public administrations on how open data can be utilized for evidence-based policy decision making and evaluation.