Smart Computing and Systems Engineering - 2023 (SCSE 2023)
Permanent URI for this collectionhttp://repository.kln.ac.lk/handle/123456789/27032
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Item Identifying the Factors for Influencing the Performance of the Virtual Teams in the Sri Lankan IT Industry(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Samarakoon, W.S.M.S.S.; Dharmawansa, A.D.The growth of virtual teams (VTs) in organizations can be attributed to the numerous technological advantages. However, virtual teams often face challenges when working on projects. Also, virtual teams are challenged not only to coordinate projects in virtual team environment but also to improve and build trust and, psychological safety within the culturally and geographically diverse team members. This research aims to identify the factors for influencing the performance of virtual teams in Sri Lanka, a topic that has received limited attention in developing countries. The study focuses on four independent variables; trust, knowledge sharing, psychological safety and team leader support with virtual team performance as the dependent variable. A 5 – point Likert-type online questionnaire was used to collect sample of 244 responses, representing 61 virtual teams from 22 private Information Technology (IT) companies in Sri Lanka. Structural Equation Modeling (SEM) was employed for data analysis. The findings indicate that trust and knowledge sharing significantly affect factors virtual team performance in the IT industry in Sri Lanka. However, knowledge sharing and team leader support were not found to have a significant impact on virtual team performance. Based on research findings, recommendations will be provided for IT industry employees and managers to ensure the trust and knowledge sharing between the team members, thereby improving overall team productivity.Item Minimising Last-Mile Delivery Cost and Vehicle Usage through an Optimised Delivery Network Considering Customer-Preferred Time Windows(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Abhilashani, G.Kasuri; Ranathunga, M.I.D.; Wijayanayake, A.N.In the dynamic and developing e-commerce era, last-mile delivery has emerged as one of the critical operations among all. The last-mile delivery in the e-commerce industry is facing high costs due to a going economic crisis which led to fuel and other operating cost increments. To overcome this situation, the e-commerce industry needs to optimise vehicle delivery routing based on time windows to minimize the overall cost. Despite numerous studies on last-mile delivery, there is a paucity of studies on last-mile delivery optimization considering the customer's anticipated time windows. Therefore, this study has been conducted with the objective of optimizing and minimizing transportation costs and vehicle usage in last-mile delivery operations while meeting some practical requirements such as a variety of package types, package compatibility on different types of vehicles, customer expected delivery time windows, and a heterogeneous fleet of vehicles. After a careful literature review, this paper introduces a mathematical model to optimize last-mile delivery. The proposed mathematical model was simulated in SupplyChainGuru® modelling and simulation software. The study concluded that the overall last- mile delivery cost is minimized by about 22% while reducing the number of vehicles on the route, failed delivery package count and utilising the maximum possible capacity of vehicles while also increasing customer satisfaction by giving consumers a chance to select customer preferred time windows for package delivery. This cluster-based delivery will improve the routing of the e-commerce logistic supply chain and will serve as a platform for extending the cluster-based delivery process to other industries as well.Item Process Improvement Framework for DevOps Adoption in Software Development(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Jayakody, J.A.V.M.K.; Wijayanayake, W. M. J. I.DevOps is welcomed by software development companies in recent years as a novel approach attached to the Agile software development methodology. Yet, they are in trouble with implementing DevOps because it doesn't just concentrate on technological changes. It alters the software development process more broadly. To assist this challenging process, DevOps maturity models have been established by a few scholars in recent years. Nevertheless, those models consist variety of drawbacks as; the majority of them have not been properly evaluated and published. This research aimed to provide a critical evaluation of the data available in existing studies on the DevOps maturity models and to propose a DevOps adoption process improvement framework that is validated by industry practitioners. To accomplish this target, a systematic literature review was applied and studied the available DevOps maturity models, weaknesses, and strengths of those models. A new framework for DevOps process improvement is developed by monitoring and contrasting the available data. Furthermore, it was assessed by an interview survey to strengthen the research's overall goal. The study presents a verified DevOps process improvement model which consists of four main DevOps success areas; DevOps practices, DevOps team, DevOps culture, and DevOps measurement. Each area follows five maturity levels starting with beginning to expert. This framework assists software development companies in obtaining benefits while reducing the difficulties associated with DevOps adoption.Item A Review of Recent Trends in Sri Lankan Social Media Analytics Research(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Sandaruwani, M.D.; Hewapathirana, I.U.Due to industry demands and massive applications, the social media landscape is rapidly expanding. However, in Sri Lanka, analyzing social media data is still considered a young research topic. This article examines the present status of social media analytics research in Sri Lanka, highlighting selected technologies and applications and discussing their proven and future benefits. The primary goal of this research is to provide information regarding social media analytics usage in Sri Lanka and to identify shortcomings in this area. We select 45 publications published between 2013 and 2022 from the most used web- based databases, including Google Scholar, IEEE Xplore, ScienceDirect, Springer, and ResearchGate. To identify eligible papers for thorough analysis, multi-phase searches and selections are accomplished. The study also includes extensive discussions on social media platforms and the technology, tools, and techniques used in analytics. The review discovered several methodologies and tools that were utilized with social media data. Descriptive analysis, regression analysis, and text analysis were the most commonly used analysis methods, while Facebook, Twitter, YouTube, Instagram, and Viber were the most popular social media networks. Current social media analytics research were noticed in a variety of domains, including marketing, education, politics, health, social, and business.Item Web-Based Data Hiding: A Hybrid Approach Using Steganography and Visual Cryptography(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Ediriweera, Seniru; Dilhara, B.A.S.; Disanayaka, ChamaraIn today's digital age, protecting sensitive data during transmission and storage is a critical concern. The rise of cyber threats has made it essential to develop secure communication channels to prevent unauthorized access and theft of confidential information. In this research, we propose a system that utilizes a combination of steganography and visual cryptography for secure data hiding. The main goal of this research is to address the issue of secure communication by concealing information in a digital image using steganography. After encoding the text in the image, the resulting steganographic image is divided into two shares using visual cryptography, ensuring that the data is protected from unauthorized access. This approach offers a practical and effective solution for secure data hiding, which can have potential applications in fields such as information security, privacy protection, and digital forensics. Overall, this research offers a viable solution to the problem of secure communication, which can help safeguard confidential information in today's digital world.Item Factors Influencing the Success of Software Startups in Sri Lanka: A Comparative Analysis using SmartPLS & SEMinR(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Attygalle, T.I.; Withanaarachchi, A.S.; Jayalal, S.IT industry is one of the fast-growing industries in Sri Lanka. In that industry the software development sector plays a massive role. Out of these software development firms, a considerable number of companies are startups. But compared to other countries, the contribution from software startups to the country’s economy is very low in Sri Lanka. Further with the current economic crisis Sri Lanka faces it is even harder for startups to continue their businesses and also it is challenging for an entrepreneur-minded person who wants to establish a software startup in Sri Lanka. This study focuses on the factors influencing the success of software startups in Sri Lanka and how those factors will be affected by the current economic crisis in Sri Lanka. The study has been conducted using a systematic literature review to discover and validate influential factors from past studies. Then the conceptual framework was formed to assess the variables. To validate the model, data was collected through an online questionnaire survey. Testing and validation of collected data were done using a comparative analysis between Smart PLS and SEMinR. The results of both studies show that the availability of finance is the only factor that has a significant relationship with the success of software startups in Sri Lanka. With that the study also recommends taking necessary actions to improve the availability of funds for software startup companies.Item An Efficient Deep Learning Model for Eye Disease Classification(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Saini, Archana; Guleria, Kalpna; Sharma, ShagunEarly detection of eye diseases is crucial, particularly for individuals with a family history of eye diseases, people over 60 years of age, individuals with diabetes, and those who have a history of eye injuries or surgeries, as they are at a higher risk of developing eye diseases. Early detection and timely treatment are crucial in treating eye diseases and preventing permanent vision loss. Detecting eye diseases early on is crucial in preventing or slowing down the progression of vision loss and blindness. Unfortunately, many eye diseases, including diabetic retinopathy, glaucoma, and cataracts, do not have early warning signs or symptoms. Therefore, regular eye checkups and early detection of these diseases can be essential in preventing vision loss and improving the quality of life for those affected. Retinal fundus image screening is a commonly used technique for diagnosing eye disorders, but manual detection is time-consuming and labour-intensive. To address this issue, various researchers have turned to deep learning methods for the automated detection of retinal eye diseases. In this work, a convolutional neural network model has been developed for classifying eye diseases, demonstrating an impressive accuracy rate of 99.85%. This suggests that the model can correctly classify eye diseases in nearly 4 out of 5 cases. These findings have the potential to significantly improve the accuracy and efficiency of diagnosing eye diseases using retinal fundus images.Item Prioritizing Warehouse Performance Measures in Sri Lankan 3PL Industry(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Gunathilaka, Madhavee; Kavirathna, Chathumi; Wijayanayake, Annista; Prabodhika, JinadariContinuous Integration and Continuous Delivery (CI/CD) is an Agile-based software development methodology becoming increasingly popular in the software development industry due to its ability to automate the software delivery process, reduce the time to market, and enhance software quality. However, despite the growing interest in CI/CD adoption, many organizations have not achieved full success in implementing and utilizing the CI/CD workflow. To address this gap, this study aimed to identify the factors that drive the behavioural intention of IT employees to use the CI/CD workflow: based on the Sri Lankan context. A systematic literature review using the PRISMA framework identified the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology models as the most widely used and accepted models for understanding technology adoption. Therefore, TAM, UTAUT and past literature were used to develop the conceptual framework. The variables in this research model were measured through questionnaires with nominal and five- point Likert scales and close-ended questions, which were completed by the IT employees in Sri Lanka. Data cleaning and demographic data analysis were conducted using IBM SPSS 21, and preliminary data analysis was performed using PLS-SEM (SmartPLS 4). The study found that Performance expectancy is the most significant factor determining IT employees' behavioural intention to use CI/CD workflow. Therefore, the study concluded that organizations and management should focus more on enhancing employees' performance expectancy to adopt CI/CD workflow successfully.Item Exploring Music Similarity through Siamese CNNs using Triplet Loss on Music Samples(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Kasif, Gibran; Thondilege, GaneshaIn the rapidly evolving digital music landscape, identifying similarities between musical pieces is essential to help musicians avoid unintended copyright infringement and maintain the originality of their work. However, detecting such similarities remains a complex and computationally challenging problem. A novel approach to address this issue is a song similarity detection system that utilizes a Siamese Convolutional Neural Network (CNN) with Triplet Loss for effective audio input comparison. The model is trained on a custom dataset from WhoSampled, an extensive database of information on sampled music, cover songs, and remixes. The dataset comprises pairs of audio samples and interpolations, making it suitable for the Siamese CNN approach. Incorporating Triplet Loss enhances the model’s performance by learning discriminative features for improved comparison. The performance of this system is assessed using a confidence interval-based metric, achieving a 96.86% accuracy at a 99.7% confidence level in determining the similarity between music samples. The solution provides a helpful tool for musicians to actively compare their creations with existing songs, helping to reduce the likelihood of unintentional plagiarism and possible legal issues.Item Success Factors for the Effective Usage of an ERP System in the Post Implementation Period; Case of Sri Lankan Firms(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) De Silva, H.S.M.; Withanaarachchi, A.S.ERP implementation failure can be one of the most expensive errors a business can make. It is critical to understand the success factors behind the effective usage of an ERP system in order to reduce the risk of failure. A large quantity of literature and research are available on factors behind a successful ERP initial implementation, but it lacks studies on post implementation. Moreover, there’s no such work has been done concerning the moderation implication of organizational culture and economic uncertainty over the association between the identified factors and the effective usage of ERP system making it a new addition to literature. Therefore, this research aims to study the success factors behind the effective usage of the ERP system in the post-implementation period and how the above moderators can moderate these factors on the effective usage of ERP system. A survey questionnaire was used to collect data from the users who are using an ERP system in a matured company in Sri Lanka. A preliminary data analysis was done using SPSS software and hypothesis were evaluated using Partial Least Squares Structural Equation Modelling approach as it enables researchers to estimate complex models with many constructs, indicator variables, and structural paths without imposing distributional assumptions on the data. The results of the study suggest that all the independent variables namely top management support, complexity of the ERP used, business IT infrastructure, hidden costs in ERP changes and training on ERP were influencing drivers for the effective usage of ERP system. In addition, concerning the moderator effect of organizational culture, top management support, ERP complexity and IT infrastructure showed a significant impact while the moderator effect of economic uncertainty, top management support, ERP complexity and training showed significant but negative relationship on the effective usage of the ERP system.Item Evaluating Business as Usual Activities between Agile and Information Technology Infrastructure Library (A-ITIL): Industry Practitioners’ Point of View(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Peliarachchi, Amalka; Wijayanayake, JanakaAlthough the Information Technology Infrastructure Library (ITIL) aims to manage the entire lifecycle of an IT service, the protocols of ITIL do not directly control the initiatives that lead to the formation or changes of IT services. However, researchers have explored combining the Waterfall lifecycle, a conventional software development methodology, with the extensive ITIL literature. Unfortunately, they have faced challenges in integrating the agile methodology with ITIL to accurately manage the project lifecycle. In order to address this issue and facilitate Business As Usual (BAU) activities in the IT sector; a proposed framework called A-ITIL (Agile-ITIL combined framework) has been introduced. This research primarily focuses on the requirement for combining the Agile approach with ITIL in BAU tasks within the IT industry. By utilizing shared questionnaires and related research articles, the critical success factors for integrating Agile with ITIL in BAU activities have been identified. The research process began by gathering sample data through an online questionnaire to assess the Agile-ITIL literacy of the respondents and the necessity of combining Agile and ITIL in BAU. A second questionnaire was then used to rate the transparency of transitioning BAU between Agile and ITIL, document the challenges faced during this transition, and propose mitigating strategies for these primary challenges, involving key stakeholders. Subsequently, a user story was created based on the results of the shared questionnaires to identify key points for the A-ITIL framework. Furthermore, the research articles were analyzed to identify key success factors for Agile and ITIL separately. Finally, an A-ITIL conceptual model was designed by mapping the existing research findings, incorporating key success factors from both Agile and ITIL, the key points identified in the user story, and insights from an interview with Agile/ITIL experts. These findings form the foundation for integrating Agile-ITIL and ensuring smooth BAU activities within IT organizations.Item Impact of Service Quality Factors of Courier/Parcel Delivery Industry on Online Shopping Customer Satisfaction with Reference to SERVQUAL Model(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Kodithuwakku, Supipi P. B.; Weerasekara, Dinusha S.In the recent decade there has been a significant increase in e-commerce platforms within the Sri-Lankan context and with the outbreak of COVID- 19 the e-commerce businesses truly started to flourish and expand. E- businesses mainly use courier/parcel providers to engage in the last-mile delivery of the goods to the end customers, hence the courier services in a way act as an extension of the online brands. This study aims to identify which courier/parcel delivery service quality factors has a relationship between online shopping customer satisfaction in Colombo District with reference to the SERVQUAL model. With the reference of SERVQUAL model, the service quality factors that was relevant to the scope of the study was determined. Based on the review of the literature in this regard and with the use of convenience sampling technique, an online self- administered questionnaire was distributed among a sample of 250 within the Colombo District. The dimension empathy out of the four dimensions studied, appeared to have the highest correlation and regression, hence it is recommended that the courier/parcel delivery service providers prioritize it as a key factor when providing the courier services to the end customer. Further research is needed to identify the other service quality factors within the courier industry that could further strengthen the relationship with online shopping customer satisfaction by referring to more current literature.Item Industry 4.0 Implementation in Sri Lankan Manufacturing Firms: A Lean Perspective(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Bandara, Lahiru; Withanaarachchi, Amila; Peter, SurenManufacturing industries require the highest quality and efficiency throughout their value chain, to compete with countries having a labor cost advantage. Today, manufacturing firms are in a fast- phased run to automate their processes and increase value chain integration through advanced technologies. Industry 4.0 has gained traction within this community, where its components like IoT, Big data, and Cloud computing are being used by manufacturing firms to optimize and increase the efficiency of their workplaces. Obtaining the proper outcomes from these advanced technologies has been an issue for most of its users. Very few studies were found in the literature, that propose ways to mitigate the issues faced by these companies in their Industry 4.0 journey. Lean concepts are a popular and proven methodology used by firms worldwide to decrease the complexity and increase the productivity of their processes. Based on a systematic literature review, the study identifies the current knowledge on mitigating the barriers faced by manufacturing firms in Industry 4.0 implementations. To address the knowledge gap identified in the literature review, the study proposes and statistically tests a framework, on how the manufacturing environment can be improved to obtain the expected outcomes of Industry 4.0 implementations, through a lean theoretical lens. Thus, the stakeholders of the company can contribute towards successful implementations of Industry 4.0 while organizational processes are being standardized and optimized to integrate these advanced technological shifts.Item Alapana Generation Using Finite State Machines and Generative Adversarial Networks(Jayatharan Vithushigan; Alwis Dileeka (2023), Alapana Generation Using Finite State Machines and Generative Adversarial Networks, International Research Conference on Smart Computing and Systems Engineering (SCSE 2023), Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka. Page 6, 2023) Jayatharan, Vithushigan; Alwis, DileekaThe recent advancements in deep learning techniques and computational power have promoted the development of novel approaches for music generation. In this study, generating alapana, an improvisational form of Carnatic music was proposed, by leveraging Generative Adversarial Networks (GANs) and Finite State Machines (FSM). The goal is to create melodious alapana sequences that follow a given input Raga, ensuring continuity and coherence throughout the generated musical piece. The proposed approach incorporates Carnatic music theory rules into the generation process to enhance the structural coherence of the generated alapana. Additionally, various hyperparameter settings were explored to achieve the best performance. The Fréchet Audio Distance, Percentage of Correct Pitches and the Subjective evaluation through human listeners are the evaluation metrics of this approach. The result of this study demonstrates the potential of using GANs and FSM for generating continuous and pleasing alapana sequences in Carnatic music, contributing to the growing body of research in computational music generation.Item Effectiveness of Using Deep Learning for Blister Blight Identification in Sri Lankan Tea(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Hewawitharana, G.H.A.U.; Nawarathne, U.M.L.A.; Hassan, A.S.F.; Wijerathna, L.M.; Sinniah, Ganga D; Vidhanaarachchi, Samitha P.; Wickramarathne, Jagath; Wijekoon, Janaka L.Ceylon tea industry faces a major challenge in the form of pathogen-induced crop loss, with Blister Blight (BB) caused by Exobasidium vexans posing the greatest threat, leading to harvest losses of over 30%. This fungus attacks the tender tea shoots, resulting in a direct negative impact on the tea harvest. This paper presents a system to identify the suspicious tea leaves and BB disease at its early stages along with an assessment of severity, offering a potential solution to this critical issue. By utilizing real-time object detection, the system filters out non-tea leaves from the captured initial image of a segment of a tea plant. The identified tea leaves are then subjected to BB identification and severity assessment based on differing visual symptoms of the BB stages. This approach enables the system to accurately identify BB in the initial stage and severity stage, allowing for timely and targeted intervention to minimize crop losses. The YOLOv8 model has been able to correctly identify 98% of the objects it has detected as relevant (precision), and it has been able to correctly identify 96% of all the relevant objects present in the scene (recall). The Residual Network 50 (Resnet50) convolutional neural network (CNN) model was selected as the final model, achieving an accuracy of 89.90% during the training phase and an accuracy of 88.26% during the testing phase.Item Impact of Green Supply Chain Practices on Organizational Performance of the Hotel Industry in Sri Lanka: A Systematic Literature Review(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Chamanthi, Dinusha; Thalpawila, Chamishka; Lakshani, Rashmi; Uththaman, Gowthaman; Nagendrakumar, Nagalingam; Karunarathna, NavodikaIn the modern world, the concept of the green supply chain is applied to introduce sustainable development and integrate it into production and operational management. Green standards and principles have sparked the interest of managers and professionals in selecting innovative practices for suppliers and organizations. Accordingly, this study aims to evaluate the impact of green supply chain management (GSCM) practices (reverse logistics, eco-design, green purchasing, internal management support, investment recovery, cooperation with customers, and green manufacturing) on the organizational performance of the Sri Lankan hotel industry. The empirical evidence verifies that adopting GSCM practices has a substantial positive impact on overall organizational performance. The study provided valuable insights into the types of GSCM practices that firms should adopt to enhance organizational performance. Moreover, this current study contributed to advancing the comprehension of the impact of GSCM practices on organizational performance. This review has the potential limitation of focusing only on the hotel industry within the service sector.Item TQM Practices on Supply Chain Performance of Third- Party Logistics Services in Sri Lanka: The Moderating Role of Green Supply Chain Practices(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Nawurunnage, K.; Prasadika, A.P.K.J.; Wijayanayake, A.N.The growing need to address the threat of global warming and greenhouse gas emissions has placed immense pressure on logistics companies to adopt sustainable practices. With logistics operations being a significant source of greenhouse gas emissions, incorporating green supply chain management practices (GSCM) has become crucial to achieving environmental sustainability within the third-party logistics (3PL) industry. Exploring the existing literature under the concepts of Total Quality Management and Green Supply Chain Management reveals the need for future investigations into how those practices might potentially improve the logistics firm’s performance to achieve sustainability. Therefore, the main objective of this study is to identify the interrelationships of TQM practices and supply chain performance third- party logistics industry in terms of overall performance and identify the suitable TQM practices that can be applied to enhance the overall performance of Sri Lankan 3PLs and assess moderating effect of GSCM practices on that TQM- performance relationships. An online survey instrument was used to collect the data from executives, senior executives, and managers of 3PL firms in Sri Lanka. The statistical data analysis was done using PLS-SEM. The results found that top management support, customer focus, statistical process control, and continuous improvements are the significant total quality management practice for overall performance in the Sri Lankan 3PL industry. The study's findings are useful for the top management of 3PLs, policymakers, and academia to identify the level of GSCM implementation within the industry, and results provide insights into further considerations regarding the implementation of GSCM practices and TQM practices to achieve the supply chain performance of the 3PLs while achieving sustainability.Item Integrating Weather Patterns into Machine Learning Models for Improved Electricity Demand Forecasting in Sri Lanka(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Abeywickrama, Shani; Asanka, P.P.G. DineshThe electricity demand in Sri Lanka is expected to increase steadily over time. Planning for future demand and ensuring an adequate electricity supply poses a significant challenge. It is crucial to accurately forecast the future demand in order to maintain an uninterrupted power supply. Previous studies have explored the correlation between weather factors and electricity demand with the aim of accurately predicting demand values. Thus, the objective of this study is to forecast the monthly electricity demand in Sri Lanka, by considering the influence of weather patterns. In this study, rainfall, humidity, and temperature weather parameters, along with historical monthly demand data, are taken into consideration. The identification of the most crucial weather variables is based on their correlation with electricity demand data. Various techniques have been employed for forecasting electricity demand over the past decade. However, the limitation of previous studies lies in their failure to incorporate past weather data alongside electricity demand data. This gap is addressed in the present study. This study used Vector Auto Regression (VAR) and Long Short-Term Memory (LSTM) models to forecast monthly electricity demand in each district of Sri Lanka. The VAR model demonstrated lower values by comparing the performance metrics, including Root Mean Square Error, Mean Square Error, Mean Absolute Error, and Mean Absolute Percentage Error. As a result, the VAR model was chosen as the most suitable model for forecasting monthly electricity demand by incorporating weather variables.Item The Impact of Social Media Usage During Office Hours on Employee Performance: Evidence from a Sri Lankan Apparel Manufacturing Firm(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Kothalawala, Charuka; Rathnayeka, ChamindaToday, social media usage is an essential tool for communication among individuals and organizations. However, evidence suggests that some industry sectors are striving to understand the relationship between social media usage during office hours and job performance. In the Sri Lankan context, the apparel sector is struggling to understand this relationship. Thus, this study investigated the impact of social media usage on employee performance with special reference to a leading apparel manufacturing company in Sri Lanka. A deductive approach was adopted to conduct the research. Individual social media usage (ISM) and work-related social media usage (WSM) are considered as independent variables and employee job performance is the dependent variable. Findings suggest that ISM and WSM enhance the job performance of apparel industry workers in Sri Lanka. Furthermore, findings indicate that the apparel industry must not discourage social media usage during office hours, instead, must find methods of utilizing social media usage for the betterment of the firm. Practical and theoretical implications, limitations, and suggestions for future research are mentioned in the Discussion. Concluding remarks are discussed in the Conclusion.Item Forecasting of Medium-Term Energy Output of On-Grid Rooftop Photovoltaic Arrays -Case Study for a Sri Lankan Solar Panel Installer(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Wickramasinghe, Bhagya; Asanka, P P G DineshThe world is shifting towards the higher utilization of renewable energy sources in the road to greener energy which conserves an environmentally friendly atmosphere. The generation of sustainable energy via adopting solar photovoltaic is common worldwide. The objectives of the research study are to identify the salient factors contributing to the energy generation of photovoltaic systems, to utilize a gamut of machine learning algorithms to build the predictive model and to identify the best machine learning algorithm to predict the energy generation based on accuracy and precision metrices. These objectives aid to achieve the aim of this study, which is to build a predictive model to determine the medium-term energy generated from on-grid rooftop solar systems. The study has unveiled a new piece of knowledge on how the photovoltaic system dynamics and location specific data has contributed to the prediction of the power output of the system. Further the findings are of paramount importance to the industry experts as well as the current and prospective solar panel users. The data of all solar panel sites of the installer was utilized and it was extracted from the source information systems. The necessary transformations and validations were applied and a detailed analysis was performed. The feature engineering, feature scaling, outlier-handling, multi-collinearity and feature selection was performed on data. The intended forecasting model based on fourteen supervised machine learning algorithms was built. The KNN Regression algorithm in the factor analysis of all features after principal component analysis has outperformed all other built models. Moreover, a strong positive co-relation was observed in the principal component analysis towards the solar panel energy output prediction. As part of future work, it’s imperative to build models utilizing a wider sample of on-grid roof top solar plants.
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