Information Technology Project Benefit Realization in Military Enterprises of Sri Lanka Using Integrated Fuzzy Dempster - Shafer Algorithm

There are Information Technology (IT) projects in military organizations of Sri Lanka. However, these projects lack a scientific mechanism to measure and realize project benefits while quantifying qualitative project outcomes. This paper outlines a Fuzzy Inference System (FIS) for measuring the extent to which benefits could be realized. The objectives of the study are firstly, to formulate a fuzzy logic to measure the extent to which the project benefits are realized and secondly, to analyze its impact on benefit policy. The study mainly utilized the quantitative methodology of Dempster-Shafer algorithm to aggregate the selected experts’ opinions by filtering similarity of experts. Ninety-five IT project managers representing the Army, Navy and Air Force were selected based on their expertise. The study employed field-based tacit experts to find inputs for each level namely, project, program, portfolio, enterprise and hybrid. The findings of the study posited nine fuzzy rules and five benefit realization levels for organizational projects. Also, the approach pronounced an organizational project policy. The study recommended a strategic benefit approach with policy implications that can be used by managers to monitor the expected project outcomes both on short term and futuristically. The application of the study cannot  be generalized to all projects of the technology-domains thereby posing a limitation. Also the study is curtailed in its application to non-IT projects which singularly yield financial benefits. The study can be employed by policy makers to streamline benefit process emphasizing government IT infrastructure projects and private sector IT projects with a futuristic value. Keywords: Benefit Realization, Benefit Measurement, Fuzzy Inference Systems, Dempster-Shafer Algorithm, Benefit Polic

A stratified decision-making model for long-term planning: application in flood risk management in Scotland

In a standard decision-making model for a game of chance, the best strategy is chosen based on the current state of the system under various conditions. There is however a shortcoming of this standard model, in that it can be applicable only for short-term decision-making periods. This is primarily due to not evaluating the dynamic characteristics and changes in status of the system and the outcomes of nature towards an a priori target or ideal state, which can occur in longer periods. Thus, in this study, a decision-making model based on the concept of stratification (CST), game theory and shared socio-economic pathway (SSP) is developed and its applicability to disaster management is shown. The game of chance and CST have been integrated to incorporate the dynamic nature of the decision environment for long-term disaster risk planning, while accounting for various states of the system and an ideal state. Furthermore, an interactive web application with dynamic user interface is built based on the proposed model to enable decision makers to identify the best choices in their model by a predictive approach. The Monte Carlo simulation is applied to experimentally validate the proposed model. Then, it is demonstrated how this methodology can suitably be applied to obtain ad hoc models, solutions, and analysis in the strategic decision-making process of flooding risk strategy evaluation. The model's applicability is shown in an uncertain real-world decision-making context, considering dynamic nature of socio-economic situations and flooding hazards in the Highland and Argyll Local Plan District in Scotland. The empirical results show that flood forecasting and awareness raising are the two most beneficial mitigation strategies in the region followed by emergency plans/response, planning policies, maintenance, and self help

A stratified decision-making model for long-term planning: application in flood risk management in Scotland

In a standard decision-making model for a game of chance, the best strategy is chosen based on the current state of the system under various conditions. There is however a shortcoming of this standard model, in that it can be applicable only for short-term decision-making periods. This is primarily due to not evaluating the dynamic characteristics and changes in status of the system and the outcomes of nature towards an a priori target or ideal state, which can occur in longer periods. Thus, in this study, a decision-making model based on the concept of stratification (CST), game theory and shared socio-economic pathway (SSP) is developed and its applicability to disaster management is shown. The game of chance and CST have been integrated to incorporate the dynamic nature of the decision environment for long-term disaster risk planning, while accounting for various states of the system and an ideal state. Furthermore, an interactive web application with dynamic user interface is built based on the proposed model to enable decision makers to identify the best choices in their model by a predictive approach. The Monte Carlo simulation is applied to experimentally validate the proposed model. Then, it is demonstrated how this methodology can suitably be applied to obtain ad hoc models, solutions, and analysis in the strategic decision-making process of flooding risk strategy evaluation. The model's applicability is shown in an uncertain real-world decision-making context, considering dynamic nature of socio-economic situations and flooding hazards in the Highland and Argyll Local Plan District in Scotland. The empirical results show that flood forecasting and awareness raising are the two most beneficial mitigation strategies in the region followed by emergency plans/response, planning policies, maintenance, and self help

Decision analysis in the UK energy supply chain risk management: tools development and application.

The aims of this thesis are developing decision-making tools for risk identification, risk causal relationships analysis, risk prioritisation, and long-term risk mitigation strategy recommendations in the UK energy supply chain. The thesis is comprised of four study phases in eight chapters. In phase I, a framework is introduced including 12 risk dimensions, and 5 classification perspectives. Then, in phase II, the Neutrosophic Revised Decision-Making Trial and Evaluation Laboratory (NR-DEMATEL) method has been utilised in order to analyse the 12 identified risk dimensions based on the causal interrelationships between them. Additionally, a novel Hesitant Expert Selection Model (HESM) to systematically assist researchers with the expert selection process is proposed. In phase III, two extensions of the original Best-Worst Method (BWM) are proposed in order to contribute to the theoretical development and application of the BWM in energy supply chain risk prioritisation. The Neutrosophic Enhanced BWM (NE-BWM) and hybrid Spanning Trees Enumeration and BWM (STE-BWM) are introduced to enhance the efficiency of the original BWM in dealing with uncertainty in experts’ subjective judgements. In phase IV, a novel stratified decision-making model is introduced. It is based on Concept of Stratification (CST), game theory and Shared Socio-economic Pathway (SSP) to deal with long-term risk mitigation planning for the most critical identified risks. The model has been applied in the region of Highland and Argyll in Scotland based on the primary data obtained from experts to prioritise flooding risk mitigation strategies which were recommended by the Scottish Environment Protection Agency (SEPA). The stratified decision-making model is aimed at taking into account both UK socio-economic situations and flooding risk impacts for the long-term decision making

An Innovation-driven IT Governance Framework for Benefits Realisation and its Application to Public Sector

Information and communication technology (ICT or IT) provides benefits to an organisation. However, a large number of IT projects fail. The research literature shows extant governance frameworks are not adequately protecting against project failures and are not as effective on many of these IT projects as they should be. This thesis developed a new IT governance framework using an agile benefit management approach, aimed to achieve benefits realisation for any scale of IT projects, particularly for projects in the public sector such as Defence. The framework objectively targets digital transformation and technological agility, however, is shown in the thesis to assist in other enterprise challenges with IT acquisition and through-life management, such as cyber-resilience. The framework is based on many theories, principles, and practices, such as: Transaction Cost Economics Theory, Prospect Theory (Decision-Making Under Uncertainty), Reference Class Forecasting, Stratification and the Incremental Enlargement Principle and Fuzzy Logic. The framework is shown to be effective primarily through better informed decision-making. Benefits realisation is critical for information technology project success. The framework provides a systematic methodology on how to define economic benefit, technical benefit, and strategic benefit. It provides benefit realisation measures through fuzzy inference system, and it provides decision support based on the benefit performance measures and dis-benefit risk management. When compared to industry-based frameworks for IT acquisition and sustainment this new framework is unique because it includes all internal and external stakeholders such as users, providers, industry, and academia to continuously collaborate for innovating, iterating, and evaluating technology for realisation of benefits to achieve the organisational goals and objectives. The proof of concept has been conducted through a detailed case study in the Defence public sector, and several critiques of IT reform in other public sector applications where difficulties are occurring. Organisations will be able to use this framework for a more rapid and assured uptake of emerging technologies of the Fourth Industrial Revolution into their technology stack. Examples of some of these emerging technologies are AI, machine learning, geo-spatial, block chain, cognitive and brain computing, cloud computing, data echo system, and cybersecurity