Browsing by Author "Fathima, Fazaal"

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    Drivers of Actual Usage of Building Information Modelling Tools by Civil Engineering Professionals in Construction Industry of Sri Lanka
    (Institute of Electrical and Electronics Engineers (IEEE), 2024) Fathima, Fazaal; Jayasinghe, Shan; Prasadika, Jinendri; Wijerathna, Sujith
    The construction industry in Sri Lanka is a key driver of the country’s economy, contributing significantly to GDP, employment, and infrastructure development. However, the industry faces challenges due to outdated design methods and antiquated technology, hindering efficient stakeholder communication and collaboration, particularly during crucial stages like design. Cloud-based Building Information Modeling (BIM) emerges as a solution, providing a centralized platform for real-time collaboration. BIM is widely recognized as an industry standard worldwide, but its implementation in Sri Lanka’s construction industry is still in its early stages. This research, guided by the Unified Theory of Acceptance Model and Use of Technology (UTAUT) and Technology Acceptance Model (TAM), explores BIM adoption factors. A systematic literature review was conducted to identify key drivers through a meticulous analysis of 50 studies: Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Social Influence (SI), Facilitating Conditions (FC), and Behavioral Intention (BI). The conceptual framework, based on TAM and UTAUT, was developed. Analyzing data from 131 respondents via PLS-SEM, the study found positive impacts of SI on BI, as well as impacts of BI and FC on Actual Usage (AU). Moreover, the impact of SI, PU, and PEOU on AU was fully mediated by BI. Results of this research underscore BIM’s significance, offering insights for effective adoption in Sri Lanka’s construction projects.
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    Impact of AI-based predictive analytics on demand forecasting in ERP systems: A Systematic Literature Review
    (Institute of Electrical and Electronics Engineers (IEEE), 2024) Fathima, Fazaal; Inparaj, Rishani; Thuvarakan, Dushyanthan; Wickramarachchi, Ruwan; Fernando, Ishenka
    Artificial intelligence (AI) has revolutionized demand forecasting within Enterprise Resource Planning (ERP) systems, offering a powerful tool to enhance accuracy and efficiency in predicting future demand patterns. This literature review explores the impact of AI-based predictive analytics on demand forecasting in ERP systems by synthesizing and analyzing existing research. This paper provides a comprehensive examination of the transformative effects of AI-driven demand forecasting across diverse industries, including fashion retail, biopharmaceuticals, energy management, and transportation. We highlight the unique benefits and applications of AI-driven demand forecasting, such as anticipating customer needs, optimizing inventory levels, and making data-driven decisions, ultimately leading to a competitive edge in the marketplace. Our study emphasizes the importance of AI integration into ERP systems for businesses seeking to enhance decision-making and achieve organizational success in today's dynamic and competitive business landscape. By providing valuable insights and showcasing significant improvements in forecasting accuracy, real-time insights, supply chain efficiency, and risk management facilitated by AI-based predictive analytics, this research contributes to advancing knowledge in the field and offers practical guidance for businesses and researchers alike.