Faculty of Engineering
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Publication Open Access Integration of industry 4.0 technologies to overcome lean manufacturing barriers in Sri Lanka’s apparel sector(Emerald Publishing, 2026-12-14) Silva, Niranga; Hettiarachchi, D. I; Perera, P; Perera, Curpose – This study aims to examine how Industry 4.0 (I4.0) technologies can enable Lean Manufacturing (LM) practices in Sri Lanka’s apparel industry. Although LM has been widely adopted to improve efficiency and reduce waste, persistent barriers such as frequent product changes, limited real-time visibility and infrastructural constraints have restricted its full potential. The purpose of this research is to explore how advanced digital solutions, including Internet of Things (IoT), real-time analytics and augmented/virtual reality (AR/VR), can address these barriers and enhance the competitiveness and sustainability of apparel manufacturing in a dynamic global market. Design/methodology/approach – A qualitative single-case study design was used to provide an in-depth understanding of digital–lean integration. The research was conducted in collaboration with a leading Sri Lankan apparel manufacturer. Data were collected through on-site factory observations, semi-structured interviews with managers and employees and examination of company records. Using Yin’s (2018) case study methodology as a guiding framework, the study analyzed how selected I4.0 technologies were implemented alongside lean tools and how these interventions addressed identified operational inefficiencies. Findings – The study found that I4.0-enabled solutions significantly enhanced lean practices by improving production workflow transparency, defect detection and downtime reduction. Tools such as IoT-linked dashboards, electronic Kanban systems and automated performance monitoring minimized non-value-adding activities and reduced bottlenecks. AR/VR applications demonstrated potential for training and machine setup, while predictive maintenance improved equipment reliability. However, the research also identified persistent shortcomings, including data confidentiality issues, workforce adaptability challenges and high capital investment requirements. The findings highlight both the opportunities and practical limitations of integrating digital technologies into lean environments. Research limitations/implications – The research was limited to a single case study of a large apparel manufacturer in Sri Lanka, which constrains the generalizability of findings. Data confidentiality policies restricted access to detailed financial information, preventing quantitative analysis of productivity gains and return on investment. Future studies could extend this research by including multiple firms across varying scales and geographies, enabling comparative insights. Broader empirical studies that quantify the financial outcomes of digital–lean integration would provide further validation and support for industry-wide adoption. Practical implications – For practitioners, the study offers a roadmap for integrating I4.0 technologies with lean practices in apparel manufacturing. The evidence suggests that digital lean tools can enhance transparency, improve workflow efficiency and support more accurate decision-making. Managers should prioritize investments in IoT-enabled monitoring, predictive maintenance and digital visual management systems while addressing workforce readiness through training programs. Attention must also be given to cybersecurity and change management to ensure sustainable implementation. These findings are particularly relevant for resource-constrained firms seeking to maximize operational efficiency while navigating global competitive pressures. Social implications – The integration of I4.0 and LM in Sri Lanka’s apparel sector holds broader social benefits by safeguarding employment in a critical export industry that provides livelihoods for over 300, 000 workers. Enhanced productivity and competitiveness contribute to economic stability and foreign exchange earnings. Moreover, digital lean practices can reduce waste, contributing to environmental sustainability and aligning with global sustainable development goals. By strengthening the resilience of the apparel sector, these advancements can help sustain jobs and improve working conditions, particularly in developing country contexts where apparel remains a cornerstone of industrial growth. Originality/value – This study provides one of the first in-depth examinations of how I4.0 technologies can act as enablers of LM in the Sri Lankan apparel industry. Unlike prior studies that treat lean and digital transformation as separate trajectories, this research highlights their synergies and tradeoffs in practice. By capturing both the benefits and shortcomings of digital lean tools, the paper contributes to theory by extending understanding of lean–I4.0 integration in emerging economy contexts. It also offers practical value by providing industry-specific insights that can inform managers’ strategic decisions on digital transformation.Publication Open Access Integration of industry 4.0 technologies to overcome lean manufacturing barriers in Sri Lanka’s apparel sector(Emerald Publishing, 2026-02-09) Silva, N; Hettiarachchi, D. I; Perera, P; Perera, CPurpose – This study aims to examine how Industry 4.0 (I4.0) technologies can enable Lean Manufacturing (LM) practices in Sri Lanka’s apparel industry. Although LM has been widely adopted to improve efficiency and reduce waste, persistent barriers such as frequent product changes, limited real-time visibility and infrastructural constraints have restricted its full potential. The purpose of this research is to explore how advanced digital solutions, including Internet of Things (IoT), real-time analytics and augmented/virtual reality (AR/VR), can address these barriers and enhance the competitiveness and sustainability of apparel manufacturing in a dynamic global market. Design/methodology/approach – A qualitative single-case study design was used to provide an in-depth understanding of digital–lean integration. The research was conducted in collaboration with a leading Sri Lankan apparel manufacturer. Data were collected through on-site factory observations, semi-structured interviews with managers and employees and examination of company records. Using Yin’s (2018) case study methodology as a guiding framework, the study analyzed how selected I4.0 technologies were implemented alongside lean tools and how these interventions addressed identified operational inefficiencies. Findings – The study found that I4.0-enabled solutions significantly enhanced lean practices by improving production workflow transparency, defect detection and downtime reduction. Tools such as IoT-linked dashboards, electronic Kanban systems and automated performance monitoring minimized non-value-adding activities and reduced bottlenecks. AR/VR applications demonstrated potential for training and machine setup, while predictive maintenance improved equipment reliability. However, the research also identified persistent shortcomings, including data confidentiality issues, workforce adaptability challenges and high capital investment requirements. The findings highlight both the opportunities and practical limitations of integrating digital technologies into lean environments. Research limitations/implications – The research was limited to a single case study of a large apparel manufacturer in Sri Lanka, which constrains the generalizability of findings. Data confidentiality policies restricted access to detailed financial information, preventing quantitative analysis of productivity gains and return on investment. Future studies could extend this research by including multiple firms across varying scales and geographies, enabling comparative insights. Broader empirical studies that quantify the financial outcomes of digital–lean integration would provide further validation and support for industry-wide adoption. Practical implications – For practitioners, the study offers a roadmap for integrating I4.0 technologies with lean practices in apparel manufacturing. The evidence suggests that digital lean tools can enhance transparency, improve workflow efficiency and support more accurate decision-making. Managers should prioritize investments in IoT-enabled monitoring, predictive maintenance and digital visual management systems while addressing workforce readiness through training programs. Attention must also be given to cybersecurity and change management to ensure sustainable implementation. These findings are particularly relevant for resource-constrained firms seeking to maximize operational efficiency while navigating global competitive pressures. Social implications – The integration of I4.0 and LM in Sri Lanka’s apparel sector holds broader social benefits by safeguarding employment in a critical export industry that provides livelihoods for over 300, 000 workers. Enhanced productivity and competitiveness contribute to economic stability and foreign exchange earnings. Moreover, digital lean practices can reduce waste, contributing to environmental sustainability and aligning with global sustainable development goals. By strengthening the resilience of the apparel sector, these advancements can help sustain jobs and improve working conditions, particularly in developing country contexts where apparel remains a cornerstone of industrial growth. Originality/value – This study provides one of the first in-depth examinations of how I4.0 technologies can act as enablers of LM in the Sri Lankan apparel industry. Unlike prior studies that treat lean and digital transformation as separate trajectories, this research highlights their synergies and tradeoffs in practice. By capturing both the benefits and shortcomings of digital lean tools, the paper contributes to theory by extending understanding of lean–I4.0 integration in emerging economy contexts. It also offers practical value by providing industry-specific insights that can inform managers’ strategic decisions on digital transformation.Publication Embargo Long-term recovery from the 2004 Indian ocean tsunami in two Sri Lankan east coast municipalities(Elsevier Ltd, 2026-01) Thamboo, J; Josiah, R; Saja, A; Salah, P; Rossetto, T; Dias, PSri Lanka was the second most affected country after Indonesia, in the 2004 Boxing Day Indian Ocean tsunami (IOT). A study mission was therefore carried out twenty years after the 2004 IOT to assess the recovery of the affected regions, especially in the Eastern region of Sri Lanka, focusing on two of the most affected municipalities, i.e. Kalmunai and Batticaloa. The social and infrastructure characteristics of resettlements/relocations/new settlements in the affected regions, presence of critical infrastructure, preparedness and early warning systems installed have been assessed. It was observed that similar approaches have been adopted to plan the community relocation in both of these municipalities, while the significant reemergence of residential and commercial developments in the coastal stretches of Kalmunai municipality have been noted. Exposure analyses have revealed that there are still some critical infrastructure situated in the tsunami hazard zones. It can be construed that these municipalities have recovered from the physical losses incurred, and spatial planning is in place for future developments considering the tsunami risk. Challenges and opportunities from their differing geographical contexts appear to have been judiciously handled. However, shortcomings are noted in actual implementation due to various reasons, such as limited resources, availability of funding and preference of communities to live close to their original lands. Improving the resilience of infrastructure by designing against the expected tsunami hazard and multi-hazards, regular verification of the early warning systems and evacuation procedures are emphasized to mitigate the impacts from future tsunami.Publication Open Access Strategic implementation of PPP for small-scale infrastructure in Sri Lanka: a comparative analysis of alternative PPP models(Ceylon Institute of Builders - Sri Lanka, 2023-07-21) Guruge, K; Coomasaru, P; Weeraman, CThis study aims to provide a thorough comparison of Private Finance Initiative (PFI), Build-Operate-Transfer (BOT), and Joint Venture (JV) models for Small-Scale Infrastructure Development (SSID) in Sri Lanka and devise innovative, tailored recommendations to maximise efficiency, effectiveness, and compatibility. Employing a mixed-methods approach, the research systematically examines the key features, benefits, and limitations of PFI, BOT, and JV models in the context of SSID. A compatibility assessment is conducted, focusing on financing approaches, stakeholder engagement, and other critical success factors. The findings reveal that the BOT and JV models are superior to the PFI model due to their balanced stakeholder engagement, risk sharing, and alignment with critical success factors. Based on these insights, the study formulates novel, customised recommendations for optimising the selected model's efficiency, effectiveness, and compatibility with SSID in Sri Lanka, with the aim of informing policy and practice. Furthermore, the study highlights the need for future research exploring alternative financing models and emerging technologies in SSID, opening new avenues for innovative approaches to infrastructure development in Sri Lanka. In conclusion, this comprehensive comparison offers valuable guidance for academics, industry professionals, and policymakers seeking to enhance small-scale infrastructure development in Sri Lanka, emphasising the importance of selecting the most suitable financing model.Publication Open Access POTENTIAL IMPACTS OF BLOCKCHAIN TECHNOLOGY IMPLEMENTATION ON CONSTRUCTION CONTRACT MANAGEMENT IN SRI LANKA(Construction Contract Management, 2023-07-21) Karunaratne, B.C.T.M.; Abeynayake, D.N.The construction contract is the mainstay for the ascendancy of the construction project requiring proper contract management. The Sri Lankan construction industry has many complications associated with contract management. Blockchain, as a decentralised transaction and data management technology, can potentially address the issues related to contract management amidst the impediments to effective implementation. However, blockchain technology adaptation in the Sri Lankan construction industry lacks evidence, even though other sectors, for example, banking and agriculture, are with the initial implementation. Hence, this research aimed to identify the potential impacts of implementing blockchain technology in construction contract management in Sri Lanka. A literature review was conducted to identify the concept of blockchain technology, its applications and its benefits. A qualitative survey strategy was adopted, and data were collected via semi-structured interviews in two phases; Phase I with ICT and finance industry experts and Phase II with construction contract experts. Samples were selected purposively through snowball sampling. The data analysis revealed that the awareness and use of blockchain technology in Sri Lanka are relatively low. However, Sri Lanka has the potential to adopt Blockchain in different fields, depending on their capabilities. Furthermore, the study found associated positive impacts of Blockchain, e.g., avoiding complex procedures, providing transparency, no ambiguities, no human errors and reducing political influence to mitigate contract management issues. Besides, Blockchain may negatively impact due to, e.g., high initial and maintenance costs, lack of knowledge and expertise, unavailability of rules and regulations, and reluctance to change those need mitigations.Publication Open Access A STUDY ON THE PHYSICAL AND MENTAL HEALTH ISSUES TO THE NEIGHBOURING RESIDENCES DUE TO THE CONSTRUCTION PROJECTS IN SRI LANKA(Ceylon Institute of Builders - Sri Lanka, 2023-07-21) Arjuna, M.P.; Edirisinghe, V.; Manoharan, K.; Herath, S.S.This study investigates the physical and mental health issues experienced by neighbouring residences as a result of construction projects in Sri Lanka. Specifically, it examines the impact of these projects on respiratory distress, hearing impairments, traffic congestion, lack of landscape, and flooding conditions. Additionally, the study explores the psychological effects on residents and emphasises the importance of health and safety measures in project management. Data collection involved conducting interviews with project managers, site safety officers, and a male nurse from three selected construction sites, followed by a questionnaire survey administered to 30 neighbouring residents. The study provides recommendations to mitigate adverse impacts, raise community awareness, and promote environmentally friendly practices in the construction industry. The findings enhance understanding of the health challenges faced by neighbouring residents and offer insights to policymakers and project managers to improve the well-being of affected communities.Publication Open Access Profiling Microplastic Pollution in Surface Water Bodies in the Most Urbanized City of Sri Lanka and Its Suburbs to Understand the Underlying Factors(Springer, Cham, 2023-02-23) Bandara, R. M. L. S.; Perera, M. D. D.; Gomes, Pattiyage I. A.; Yan, Xu-FengThis study investigated the microplastic pollution of surface waters in and around the most populated and urbanized city in Sri Lanka from 2019 to 2022. The sampling regime was designed to cover the rainfall-driven hydrology and varying levels of urbanization approximated by the built area fraction. Mass and particle concentrations of microplastics ranged from undetected to 0.01 g/L (average ± standard deviation: 0.00464 ± 0.00528 g/L) and from 2 to 36 particles/L (5.3 ± 6.9), respectively. The highest microplastic pollution was observed in the lake; however, in many cases it was without a statistically significant (P < 0.05) difference with canals. Concentrations in the dry state (i.e., at least 30 days after no rainfall) were about 1.5 times more than the wet state (i.e., at least 50 mm/day rainfall for 10 days) in the lake and in the semi-urban canal, but again, the differences were not significant; however, in urban canals, the concentrations were similar in both states. Over 80% of the microplastics were fibre and fragments. Mass concentrations of microplastics showed moderately positive (Pearson’s r > 0.6) correlations with the built area fraction of the contributing catchment in both states but was significant (P < 0.1) only in the dry state. In the case of particle concentrations, none showed even a weak correlation. The independence of microplastic content against built area fraction and rainfall, as well as twice the concentrations found in point source inputs against the surface waters, gave the following insights. Microplastic content in our study area was governed mostly by the modified catchment hydrology spearheaded by stormwater drainages (some cases trans-catchment) and diffusion factors such as non-residential population.Publication Open Access Projected Water Levels and Identified Future Floods: A Comparative Analysis for Mahaweli River, Sri Lanka(IEEE, 2023-01) Rathnayake, N; Rathnayake, U; Chathuranika, I; Dang, T. L; Hoshino, YThe Rainfall-Runoff (R-R) relationship is essential to the hydrological cycle. Sophisticated hydrological models can accurately investigate R-R relationships; however, they require many data. Therefore, machine learning and soft computing techniques have taken the attention in the environment of limited hydrological, meteorological, and geological data. The accuracy of such models depends on the various parameters, including the quality of inputs and outputs and the used algorithms. However, identifying a perfect algorithm is still challenging. This study develops a fuzzy logic-based algorithm called Cascaded-ANFIS to accurately predict runoff based on rainfall. The model was compared against three regression algorithms: Long Short-Term Memory, Grated Recurrent Unit, and Recurrent Neural Networks. These algorithms have been selected due to their outstanding performances in similar studies. The models were tested on the Mahaweli River, the longest in Sri Lanka. The results showcase that the Cascaded-ANFIS-based model outperforms the other algorithms. The correlation coefficient of each algorithm’s predictions was 0.9330, 0.9120, 0.9133, 0.8915, 0.6811, 0.6811, and 0.6734 for the Cascaded-ANFIS, LSTM, GRU, RNN, Linear, Ridge, and Lasso regression models respectively. Hence, this study concludes that the proposed algorithm is 21% more accurate than the second-best LSTM algorithm. In addition, Shared Socio-economic Pathways (SSP2-4.5 and SSP5-8.5 scenarios) were used to generate future rainfalls, forecast the near-future and mid-future water levels, and identify potential flood events. The future forecasting results indicate a decrease in flood events and magnitudes in both SSP2-4.5 and SSP5-8.5 scenarios. Furthermore, the SSP5-8.5 scenario shows drought weather from May to August yearly. The results of this study can effectively be used to manage and control water resources and mitigate flood damages.Publication Open Access Statistical Evaluation and Trend Analysis of ANN Based Satellite Products (PERSIANN) for the Kelani River Basin, Sri Lanka(Hindawi, 2022-08-31) Perera, H; Gunathilake, M. B; Panditharathne, R; Al-mahbashi, N; Rathnayake, USatellite-based precipitation products, (SbPPs) have piqued the interest of a number of researchers as a reliable replacement for observed rainfall data which often have limited time spans and missing days. The SbPPs possess certain uncertainties, thus, they cannot be directly used without comparing against observed rainfall data prior to use. The Kelani river basin is Sri Lanka’s fourth longest river and the main source of water for almost 5 million people. Therefore, this research study aims to identify the potential of using SbPPs as a different method to measure rain besides using a rain gauge. Furthermore, the aim of the work is to examine the trends in precipitation products in the Kelani river basin. Three SbPPs, precipitation estimation using remotely sensed information using artificial neural networks (PERSIANN), PERSIANN-cloud classification system (CCS), and PERSIANN-climate data record (CDR) and ground observed rain gauge daily rainfall data at nine locations were used for the analysis. Four continuous evaluation indices, namely, root mean square error (RMSE), (percent bias) PBias, correlation coefficient (CC), and Nash‒Sutcliffe efficiency (NSE) were used to determine the accuracy by comparing against observed rainfall data. Four categorical indices including probability of detection (POD), false alarm ratio (FAR), critical success index (CSI), and proportional constant (PC) were used to evaluate the rainfall detection capability of SbPPs. Mann‒Kendall test and Sen’s slope estimator were used to identifying whether a trend was present while the magnitudes of these were calculated by Sen’s slope. PERSIANN-CDR performed well by showing better performance in both POD and CSI. When compared to observed rainfall data, the PERSIANN product had the lowest RMSE value, while all products indicated underestimations. The CC and NSE of all three products with observed rainfall data were also low. Mixed results were obtained for the trend analysis as well. The overall results showed that all three products are not a better choice for the chosen study area.Publication Open Access Spatio-Temporal Rainfall Variability and Concentration over Sri Lanka(Hindawi, 2022-09-28) Pawar, U; Karunathilaka, P; Rathnayake, UChanges in precipitation patterns significantly affect flood and drought hazard management and water resources at local to regional scales. Therefore, the main motivation behind this paper is to examine the spatial and temporal rainfall variability over Sri Lanka by Standardized Rainfall Anomaly Index (SRAI) and Precipitation Concentration Index (PCI) from 1990 to 2019. The Mann–Kendall (MK) trend test and Sen’s slope (SS) were utilized to assess the trend in the precipitation concentration based on PCI. The Inverse Distance Weighting (IDW) interpolation method was incorporated to measure spatial distribution. Precipitation variability analysis showed that seasonal variations are more than those of annual variations. In addition, wet, normal, and dry years were identified over Sri Lanka using SRAI. The maximum SRAI (2.27) was observed for the year 2014 for the last 30 years (1990–2019), which shows the extremely wet year of Sri Lanka. The annual and seasonal PCI analysis showed moderate to irregular rainfall distribution except for the Jaffna and Ratnapura areas (annual scale-positive changes in Katugastota for 21.39% and Wellawaya for 17.6%; seasonal scale-Vavuniya for 33.64%, Trincomalee for 31.26%, and Batticaloa for 18.79% in SWMS). The MK test, SS-test, and percent change analyses reveal that rainfall distribution and concentration change do not show a significant positive or negative change in rainfall pattern in Sri Lanka, despite a few areas which experienced significant positive changes. Therefore, this study suggests that the rainfall in Sri Lanka follows the normal trend of precipitation with variations observed both annually and seasonally.