Research Publications Authored by SLIIT Staff
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This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.
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Publication Open Access Optimizing Asset Transfer Process in ERP Using Business Process Management Technique(Science and Information Organization, 2025-10-30) Yasarathne, R; Ranatunga, N; Herath, V; Chalinda, L; Kahandawaarachchi, C; Perera, S; Randula, CEnterprise Resource Planning (ERP) systems are critical for managing enterprise-wide business processes, including asset management. Yet, many ERP platforms lack efficient mechanisms for bulk asset transfers, leading to high manual effort, increased costs, and data inconsistencies. This study applies Business Process Reengineering (BPR) techniques as the methodology to optimize ERP asset management, focusing on workflow optimization and automation, contributing both practical and methodological insights. A mixed-method approach was adopted, analyzing a financial organization with 256 branches and over 450 Oracle ERP users. Data from 51 representative branches identified inefficiencies such as manual transfer delays, approval bottlenecks, and synchronization issues. The proposed solution introduces automated bulk asset transfers, optimized approval workflows, and real-time data synchronization, along with new metrics for evaluating efficiency, compliance, risk, and asset utilization. Compared to the As-Is system, the reengineered framework achieved a 100% reduction in operational costs per user ($7,500 annual saving), an 80% reduction in compliance incidents, a 67% reduction in asset transaction errors, and a 20% improvement in asset utilization. These results demonstrate a scalable, adaptable, and effective framework that enhances ERP operational efficiency, strengthens data integrity, and advances both academic understanding and industrial practice of asset management process reengineering.Publication Open Access Achieving zero hunger: A global policy lens on food security drivers and income group disparities(Elsevier B.V., 2026-01-19) Pulle, N; Sampath, P; Perera, S; Wijayaweera, D; Jayathilaka, RMany countries struggle to meet their daily dietary requirements despite numerous attempts to address the existing demand. Consequently, this study collectively analyses the impact of urbanisation, renewable energy, greenhouse gas emissions, population growth, gross domestic product per capita and agricultural land on food production relying on Sen’s Entitlement Theory, thus providing insights to resolve the long-standing issue of food insecurity, and support the achievement of the Sustainable Development Goals. The study utilises a stepwise panel ordered Probit model on 146 countries, for the years 1993 to 2023. It further categorises the food production index into three categories of food security as; low, moderate and high, thereby enabling discussion of the likelihood of a country falling into one of the aforementioned food security categories over the years. Urbanisation, agricultural land, and the dummy variables introduced to represent the income groups have been identified to have a significant and favourable relationship with the food production index. In contrast, the greenhouse gas emissions and renewable energy variables have a significantly inverse impact on the food production index. This makes a unique contribution to the existing body of literature, especially by comparing odds over the years, across different food secure categories, countries, and their specific income levels. This study enables policymakers to gain a comprehensive historical perspective on each case. This study further promotes the Sustainable Development Goals, highlighting areas where these goals have been negatively impacted. Additionally, the study discusses optimised investment allocations, agricultural research and development, agricultural technology, climate resilient farming, and sustainable urbanisation planning as solutions for extreme casesPublication Embargo Supply and Demand Planning of Electricity Power: A Comprehensive Solution(IEEE, 2019-12-06) Perera, S; Dissanayake, S; Fernando, D; De Silva, S; Rankothge, WElectrical energy is one of the fastest growing energy demands in the world. Uncertainty in supplying the demand can threaten the social economic aspects of a country. The biggest driver of electrical demand is weather. Climatic changes not only affect the demand but also renewable energy supply. Wind and Solar are two alternative energy sources with less pollution. We have proposed a platform which helps energy providers, energy traders with services related to electricity supply and demand planning, with following modules. (1) Forecasting electricity consumption patterns (2) Forecasting wind power generation (3) Optimizing Load Shedding. Our platform has been implemented using statistical and machine learning techniques: Multi-Linear Regression for consumption prediction, Random forest regression for wind power forecast, and genetic algorithm to optimize load shedding. Our results show that, using our proposed module, we can minimize the imbalance between the supply and demand of electricity by predicting the consumption patterns of consumers, predicting the wind power generation and by selecting the best feeder to be selected for load shedding under given constraints.Publication Open Access A Conceptual Framework for the Association between Relationship Conflicts and Employees’ Intention to Leave(Canadian Center of Science and Education, 2018) Weerarathna, R. S; Perera, SThe main objective of this research paper to conceptualize a model to test the association between relationship conflicts and employees’ intention to leave with the support of past theories and research findings. This research paper contains the definitions for variables related to relationship conflicts and employees’ intention to leave from the organization and also it explains models which explain the interconnections among two variables. The conceptualization of this research includes a logically developed model that leads to identify the relationships between the independent variable and dependent variable. The proposed model suggested that there is an association between relationship conflicts and employees’ intention to leave.Publication Embargo A Real-Time Cardiac Arrhythmia Classifier(IEEE, 2019-10-08) Abayaratne, H; Perera, S; De Silva, E; Atapattu, P; Wijesundara, MCardiovascular diseases (CVD) have increased drastically among Non-Communicable diseases, which have peaked over the past recent years. In 2018, around 17.9 million which is an estimated 31% of the people have died worldwide due to CVDs. A novel machine learning algorithm for continuous monitoring, identification and classification of cardiac arrhythmias from Electrocardiogram (ECG) data is presented here. The proposed solution has two stages where the first stage is a rule based cardiac abnormality identification which has an individual 97.55% ± 0.3% of accuracy (Acc) for a dataset of 705,000 and the second stage is a Neural Network (NN) based classification model which is trained and tested to identify 15 different classes recommended by ANSI/AAMI standard [1], and has 97.1% of individual accuracy for MIT-BIH Arrhythmia dataset [2] of 96265 beat samples. The combined real-time cardiac arrhythmia classifier is parallelized with CUDA in order to utilize the GPU and increase the execution speed by 4.86 times.Publication Open Access Influence of Supply Chain Network Topology on the Evolution of Firm Strategies(Institute of Transport and Logistic Studies (ITLS), 2020-01-01) Perera, S; Kasthurirathna, D; Bell, MThis study investigates the influence of the topological structure of a supply chain network (SCN) on the evolution of cooperative and defective strategies adopted by the individual firms. First, a range of topologies representative of SCNs was generated using a fitness-based network growth model, which enabled cross comparisons by parameterising the network topologies with the power law exponent of their respective degree distributions. Then, the inter-firm links in each SCN were considered as repeated strategic interactions and were modelled by the Prisoner’s Dilemma game to represent the self-interested nature of the individual firms. This model is considered an agent-based model, where the agents are bound to their local neighbourhood by the network topology. A novel strategy update rule was then introduced to mimic the behaviour of firms. In particular, the heterogeneously distributed nature of the firm rationality was considered when they update their strategies at the end of each game round. Additionally, the payoff comparison against the neighbours was modelled to be strategy specific as opposed to accumulated payoff comparison analysis adopted in past work. It was found that the SCN topology, the level of rationality of firms and the relative strategy payoff differences are all essential elements in the evolution of cooperation. In summary, a tipping point was found in terms of the power law exponent of the SCN degree distribution, for achieving the highest number of cooperators. When the connection distribution of an SCN is highly unbalanced (such as in hub and spoke topologies) or well balanced (such as in random topologies), more difficult it is to achieve higher levels of co-operation among the firms. It was concluded that the scale-free topologies provide the best balance of hubs firms and lesser connected firms. Therefore, scale-free topologies are capable of achieving the highest proportion of cooperators in the firm population compared to other network topologies.Publication Open Access Consumer Surplus based Method for Quantifying and Improving the Material Flow Supply Chain Network Robustness(2018-06-01) Perera, S; Bell, M. G. H; Kurauchi, F; Bliemer, M. C. J; Kasthurirathna, DRecent advances in network science has encouraged researchers to adopt a topological view when characterising the robustness of supply chain networks (SCNs). However, topology based characterisations, without considering the heterogeneity among the supply chains which form the SCN, can only provide a partial understanding of robustness. Hitherto, focus of robustness studies have been on cyclic SCNs, with unweighted and undirected links representing general inter-firm interactions. Here, we consider the specific case of a material flow SCN with multi-sourcing, which is characterised by a tiered structure with directed and weighted links. The proposed method uses the multinomial logit model to estimate the utility levels of supply chains within the SCN, as perceived by a focal firm which is indicative of the SCN consumers. The robustness of the SCN is characterised by considering the degree to which supply chains overlap with each other as a cost in the logit formulation. Finally, using a randomisation scheme to generate ensembles of SCN configurations which preserve the number of connections at each firm, the configuration which maximises the consumer surplus for the focal firm is identified. The proposed method is implemented on a real world SCN to identify the optimal configuration in terms of robustness.Publication Embargo Absorbing Markov Chain Approach to Modelling Disruptions in Supply Chain Networks(IEEE, 2019-08) Perera, S; Bell, M; Kurauchi, F; Kasthurirathna, DRecent developments in the area of network science has encouraged researchers to adopt a topological perspective in modelling Supply Chain Networks (SCNs). While topological models can provide macro level insights into the properties of SCN systems, the lack of specificity due to high level of abstraction in these models limit their real-world applicability, especially in relation to assessing the impact on SCNs arising due to individual firm or supply channel level disruptions. In particular, beyond the topological structure, a more comprehensive method should also incorporate the heterogeneity of various components (i.e. firms and inter-firm links) which together form the SCN. To fill the above gap, this work proposes using the idea of absorbing Markov chains to model disruption impacts on SCNs. Since this method does not require path enumeration to identify the number of supply chains which form the SCN, it is deemed more efficient compared to the other traditional methods.Publication Embargo Topological rationality of supply chain networks(Taylor & Francis, 2020-05-18) Kasthurirathna, D; Perera, S; Bell, MIn this study, we apply a topologically distributed bounded rationality model to quantify the level of rationality in supply chain networks. We use the averaged Jensen-Shannon divergence values between Nash and Quantal Response equilibria for all inter-firm strategic interactions, which are represented as Prisoner’s Dilemma games, to characterise the average level of rationality in a given supply chain network. This is based on the game theoretic assumption that as the rationality of a particular interaction increases, it converges towards Nash equilibrium, in a certain strategic decision making scenario. Using this model, we demonstrate that hub-and-spoke topologies are collectively more rational compared to scale-free and random network topologies. Finally, we compare our theoretical results against the empirical findings reported for networked systems in various domains. In particular, it is shown that network topologies comprising higher average rationality levels emerge under increasingly competitive environments.Publication Embargo Value chain approach for modelling resilience of tiered supply chain networks(IEEE, 2017-05-29) Perera, S; Perera, H. N; Kasthurirathna, DRecent advances in network theory has encouraged the supply chain researchers to investigate the resilience of various supply chain network (SCN) topologies. In a typical SCN model, nodes and links represent the firms and their exchange relationships, respectively. A key requirement in such SCN models is accounting for node and link level heterogeneity, which can be used to more realistically represent the intricacies of real world SCNs. However, this requirement remains largely unaddressed in the contemporary literature. Accordingly, this work attempts to customize the standard network theoretic growth models and resilience metrics, to more closely represent the real world SCN characteristics. In particular, the model proposed in this paper accounts for: (1) the evolution of SCNs through fitness based attachment, (2) the tiered nature observed in real world SCNs, (3) the value added process, from upstream to the downstream of a typical supply chain, which captures heterogeneity of nodes at each tier, (4) the heterogeneity in link weights, and (5) the partial functionality, instead of complete omission, of nodes when simulating failures. The simulation results presented indicate that the proposed model, which closely represents the specific heterogeneous features between the individual network constituents, can be effectively utilized to gain valuable insights on resilience characteristics of real world SCNs.