Browsing by Author "Perera, S"

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Absorbing Markov Chain Approach to Modelling Disruptions in Supply Chain Networks
(IEEE, 2019-08) Perera, S; Bell, M; Kurauchi, F; Kasthurirathna, D
Recent 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.
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, R
Many 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 cases
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, S
The 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.
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, D
Recent 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.
Open Access
Event Detection and Latency Analysis in High Frequency Trading Dashboards
(Department of Mathematics and Statistics, Faculty of Humanities and Sciences, SLIIT, 2025-10-10) de Silva, U; Perera, S; Liyanage, U.P; Erandi, H
High frequency trading relies on millisecond-level decisions, where profitability is strongly influenced by both market responsiveness and system latency. Traditional dashboards offer real-time visualizations but fall short in detecting abrupt regime shifts or quantifying latency. This study presents an AI-aided Market Pulse and Latency Panel that integrates candlestick pattern recognition, change point detection and latency measurement into a unified dashboard. The system detects technical patterns, identifies structural market shifts, and quantifies infrastructural bottlenecks. Experimental results demonstrate that the panel enhances situational awareness by combining event detection with latency analytics, providing traders with actionable insights for strategy adjustment and infrastructural optimization.
Open Access
Impact to the Quantity Surveyors Due to the Current Economic Crisis in Sri Lanka
(Sri Lanka Institute of Information Technology, 2023-03-25) Manawasinghe, S.I; Gunarathna, N; Perera, S
This research focuses on the impact to the Quantity Surveyors due to the current economic crisis in Sri Lanka. The Easter bomb attack, covid 19 pandemic, and political instability can be seen as the proximate causes of the current economic crisis, while there were many structural issues of the local economy which had paved way for the same. As a developing country, the crisis had a stronger impact on the construction industry than other industries of the economy. The main reasons for the collapse of the construction industry are the suspension of construction projects by the government, the increase in the price of construction materials, the lack of investors to invest in new projects, and bottlenecks in terms of wrong policy directives. The professionals in the construction industry were severely impacted by the downfall. Among the professionals in the construction industry, this study focusses on QSs- (Quantity Surveyors). Thirty (30) semi-structured interviews were carried out in terms of data gathering. The survey findings demonstrated the type of organization and working experience of QSs. The collected data were analyzed using techniques of thematic analysis. Moreover, the findings identified factors which were the challenges due to current economic crisis and proposed strategies to help overcome those challenges
Open Access
Influence of IoT on Warehouse Management Performance in the Global Context: A Critical Literature Review
(SLIIT Business School, 2023-12-14) Perera, S; Pinto, A; Sewmini, H; Ulugalathenne, A; Thelijjagoda,S; Karunarathna,N
This systematic research paper explores the impact of Industry 4.0 technology, specifically the Internet of Things (IoT), on Warehouse Management Performance (WMP) in the worldwide logistics sector. IoT serves as a pivotal component in the paradigm shift towards Industry 4.0, facilitating the seamless storage, packaging, and distribution of commodities. By harnessing interconnected devices and data-driven insights, warehouses have undergone significant transformations since the inception of IoT technology. This study reviews state-of-theart literature on the influence of IoT on WMP, emphasizing the vital role warehouse operations play in ensuring the smooth functioning of the supply chain management process. Warehouse operations encompass aspects ranging from planning and layout to receiving, order picking, shipping, and distribution. To optimize warehouse operations, we conduct a comprehensive literature assessment using Scopus as our primary database. The findings from this systematic review indicate that the integration of IoT technologies such as RFID, QR codes, scanners, and Warehouse Management Systems (WMS) can substantially enhance warehouse management performance. These technologies provide real-time data, predictive maintenance, and improved inventory accuracy, resulting in increased operational efficiency, reduced operational expenses, and improved customer satisfaction. Additionally, the IoT framework combines big data, blockchain, cloud computing, and wireless sensor networks to tackle key challenges related to data storage, retrieval, and utilization. This integration significantly improves the effectiveness of the warehouse management process and facilitates data-driven decision-making. Furthermore, IoT's communication capabilities enhance efficiency and reduce costs in warehouse operations. In conclusion, this systematic review underscores the transformative potential of IoT technology in the global logistics sector. It highlights the importance of technological integration, data-driven decision-making, and smart packaging solutions. Recognizing the disruptive effects of IoT on the logistics industry, it emphasizes the need for companies to adopt and leverage IoT technologies to remain competitive and agile in the evolving global supply chain landscape.
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, M
This 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.
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Intelligent Wheelchair with Emotion Analysis and Voice Recognition
(IEEE, 2022-12-26) Perera, S; Gamage, S; Weerasinghe, C; Jayawardena, C; Pathinayake, K; Rajapaksha, S
Intelligent wheelchairs are becoming more and more prevalent in contemporary life, and the peaceful interaction of humans with wheelchairs is one of the most popular research topics. The development of a voice recognition and emotion recognition based intelligent wheelchair framework is being addressed here for truly impaired/disabled people who are unable to operate the wheelchair by hand. The patient can operate the wheelchair using voice commands, and the wheelchair’s Emotion Analysis module recognizes the patient’s face and records the patient’s emotions before sending the information to a cell phone application. A portion of the intelligent wheelchair is made to gather crucial information given by other units and send out emergency calls or notifications to the caregivers. Face recognition technology uses image processing to identify facial expressions by detecting the patient’s face and facial expressions. This helps the other components collect and send data via Internet of Things technologies. Speech – to –Text and Text – to-Speech Methodology is used in the voice recognition module and it captures the voice command data set and extracts the features of the commands.The model is already built and trained to recognize the commands and to send action request to the relevant unit.The Responsive AI auto starts the timer when the patient moves away from the wheelchair, recognizes time and responses back. This unit auto also sends the alert and calls to the guardian when the user has no response.
Open Access
Investigating the strategies for supply chain agility and competitiveness
(2019-06-27) Perera, S; Soosay, C; Sandhu, S
Research aims: This study explores the strategies that enable firms to establish supply chain agility and competitiveness in Australian manufacturing firms. Design/Methodology/Approach: Using a case study approach, interviews are conducted with eight Australian manufacturing firms. Underpinned by the dynamic capability perspective, data are then thematically analysed to derive the findings. Research findings: Agility strategies are based on collaborative efforts, requiring information sharing among firms in the supply chain and diversifying core competencies in a strategic manner to remain competitive. Theoretical contribution/Originality: This study shows how dynamic capabilities foster competitive advantage. It identifies both the strategic and operational agility which enable firms to respond to market changes and to remain competitive. Practitioner/Policy implication: The manufacturing industry in Australia is undergoing a transition where Australian manufacturers need to transform and be more agile by leveraging their competencies and supply chains collectively. Research limitation/implications: The results are based on a crosssectional study of firms identified from the Australian manufacturing industry.
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, C
Enterprise 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.
Open Access
Performance measurement system for a lean manufacturing setting
(Emerald Publishing Limited, 2019-10-22) Perera, S; Perera, H. S. C
Purpose The purpose of this paper is to propose a performance measurement system for a lean manufacturing environment, which assesses the multi-dimensional performance of lean manufacturing. Design/methodology/approach Following a case study approach, structured interviews were conducted to identify the parameters to measure the performance of a lean manufacturing apparel company. A model was developed with the analytical hierarchical process to assess the performance. Findings The proposed model consists of three levels: first level (overall manufacturing performance), second level (criteria that represent the stakeholders’ view of manufacturing performance) and third level (sub-criteria for the criteria which represent the areas affected by lean manufacturing). The model connects indicators that measure manufacturing performance with the areas required improvements, according to their relative importance to stakeholders. Research limitations/implications The interviewers’ perspectives were used to determine the importance of each manufacturing area for stakeholders. Key performance measures can vary from company to company. Practical implications Managers can use this model to identify important areas for manufacturing performance and the performance improvements driven by different types of lean practices. The results revealed that identifying stakeholders’ requirements was an important aspect of evaluating manufacturing performance. Social implications The model embeds a stakeholder approach in performance measurement, thereby providing a comprehensive model to assess performance. Originality/value This study applies the stakeholder view to identify the multi-dimensional nature of performance in a lean manufacturing setting. It also defines the key performance measures using lean practices.
Open Access
Pneumonia Detection and Lung Disease Assessment from Chest X-rays: Developing A Diagnostic Support System
(SLIIT, Faculty of Engineering, 2025-01) Jayawardena, C.A; Wedasingha, N; Kolambage, N; Perera, S
This research, dedicated to developing an accurate and efficient pneumonia detection system from Chest X-Ray images, highlights the significance of automated tools in enhancing healthcare diagnostics. Its significance lies in the fact that pneumonia is a prevalent respiratory condition that requires timely and accurate diagnosis for effective medical intervention. The project's objective was to make use of convolutional neural networks and image analyses to create an automated diagnostic tool that could assist healthcare professionals in identifying pneumonia with precision and efficiency. To achieve this, the system initially made use of two custom deep learning architectures but ultimately used a pretrained CheXNet-based model, developed by using transfer learning. This choice was made by considering CheXNet’s proven performance in identifying pneumonia and other pulmonary conditions. The project's results proved promising, with the CheXNet-based model achieving high diagnostic accuracy and providing valuable insights into the presence of pneumonia. The system's architecture, using deep learning and the use of DICOM images, demonstrated its effectiveness in improving the accuracy and efficiency of pneumonia diagnosis. Based on the results, this paper further demonstrates a web-based application for interaction with the system. Additionally, it provides information on the work that could be done in the future. Thus, this research contributes to the growing field of medical image analysis and highlights the significance of automated tools in enhancing healthcare diagnostics. The project's outcomes are meant to pave the way for more efficient and accessible methods for pneumonia detection, ultimately benefiting both healthcare providers and patients.
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A Real-Time Cardiac Arrhythmia Classifier
(IEEE, 2019-10-08) Abayaratne, H; Perera, S; De Silva, E; Atapattu, P; Wijesundara, M
Cardiovascular 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.
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Structural characteristics of complex supply chain networks
(IEEE, 2017-05-29) Perera, S; Perera, H. N; Kasthurirathna, D
There is a scarcity of analyses of topological properties of real world supply chain networks (SCNs), mainly due to lack of data. Most theoretical research has used the popular Barabasi-Albert (BA) growth model, in which new nodes (firms) entering the network attach preferentially to the existing nodes with more links (contractual relationships), to generate network topologies supposedly representative of real world SCNs. This paper looks at real world SCN topologies using data presented in Willems (2008) and establishes a general set of topological characteristics for the manufacturing sector. Based on observations from twenty six SCNs, it is found that, although manufacturing SCNs exhibit power law node degree distributions, they tend to be hub and spoke like, with hubs that are significantly larger than those predicted by the BA model. Also, the SCNs indicated the presence of closely knit, vertically integrated communities of firms, while highly connected firms were found to avoid connecting with each other. Since the above structural properties are not features of the networks generated by the BA model, it is concluded that a new mechanism is required to model the growth of SCNs.
Open Access
Study on the Impact of Cost Controlling Techniques in Mini Hydropower Projects in Sri Lanka
(Sri Lanka Institute of Information Technology, 2023-03-25) Hirunika, S; Gunarathna, N; Perera, S
Due to the worst economic crisis, Sri Lanka is currently facing sporadic power failures. There is a lack of dollar reserves to pay for the fuel suppliers and the existing national grid network has failed to power the whole nation. The prevailing situation is becoming quite challengeable since there is a huge power crisis due to the shortage of fuel oil which causes to fail whole operations of oil power plants. Therefore, Sri Lanka has relied on hydropower for a majority of its electricity needs. The share of mini hydropower power plants is performing a significant role in contemporary electricity generation considering national policy targets in order to move with sustainable green energy. Consequently, the necessity of the establishment of MHP plants has become higher but there are significant cost overrun factors identified in the construction phase of MHP projects. This research was carried out to identify the cost overrun factors in MHP projects in Sri Lanka. As a result, efficient cost-control measures will be required to address the above matter. Therefore, it was expected to explore the current practices of cost control techniques used in infrastructure projects based on the findings. Various cost control approaches have been created from time to time throughout the last few years. These may include Earn Value Management (EVM), performance reviews, variance analysis, value engineering, site meetings, work programmes, daily material and labor controlling, etc. The data collection process was conducted through questionnaire surveys which are distributed between industry professionals who have enough knowledge and experience regarding the MHP projects. The set of data was converted into quantitative values and collected data from the questionnaire surveys were evaluated by using percentage analysis and weighted score analysis. The results indicates the challenges in current cost control practices and identify the mechanisms to overcome those challenges and determine their effectiveness in mini hydropower project delivery in Sri Lanka.
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Supply and Demand Planning of Electricity Power: A Comprehensive Solution
(IEEE, 2019-12-06) Perera, S; Dissanayake, S; Fernando, D; De Silva, S; Rankothge, W
Electrical 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.
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Topological rationality of supply chain networks
(Taylor & Francis, 2020-05-18) Kasthurirathna, D; Perera, S; Bell, M
In 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.
Open Access
Unpacking how the Living Arrangements of Undergraduates Influence Quality of Life
(School of Psychology. Faculty of Humanities and Sciences, SLIIT, 2025-10-10) Bandara, K; Abeysekara, M; Vihangi, S; Perera, S; Jayasekara, S; Samaratunge, T; Goonetilleke, N
This study examined the connection between undergraduate students' living arrangements (private vs rented accommodation) and their Quality of Life (QoL) at the Sri Lanka Institute of Information Technology (SLIIT). All four domains of quality of life, psychological well-being, physical health, environmental factors, and social relationships were measured using an adapted version of the World Health Organisation Quality of Life: Brief Version (WHOQOL-BREF). The cross-sectional studyincluded a sample of 64 individuals obtained from the campus premises between the ages of 18-25. Multivariate Analysis of Variance (MANOVA) revealed no statistically significant differences in QoL dimensions based on accommodation type. However, the effect sizes indicate living arrangements to be a better predictor of the environmental factors as opposed to other domains of QoL. Furthermore, a chi-square test yielded a strong association between the year of study and living arrangements among students, suggesting that the year of study may have an impact on students’ choice of accommodation. These results further demonstrate the diversity of QoL and imply that, although environmental influences are worthy of consideration, living arrangements might not be a strong factor to explain students’ well-being. While the nature of the sample (i.e., small and convenient) may have hindered the statistical significance of the study, the present findings highlight the necessity for subsequent studies to accurately uncover the impact of student life, accommodation, and other related factors onthe quality of life.
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Value chain approach for modelling resilience of tiered supply chain networks
(IEEE, 2017-05-29) Perera, S; Perera, H. N; Kasthurirathna, D
Recent 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.