| dc.description.abstract | Small businesses are critical drivers of
economic growth and innovation globally. However, in Sri
Lanka, these businesses face significant challenges related
to resource management, including inefficient allocation of
labor, materials, and equipment. This study addresses
these challenges by developing a data-driven approach to
optimize resource allocation, focusing specifically on
small-scale aqua plant businesses. Capitalizing on Sri Lanka's rich biodiversity and the growing global demand for ornamental plants, this research integrates an
ontology-based framework with machine learning
techniques to enhance operational efficiency and
sustainability of resource allocation processes in smallscale aqua plant businesses. The methodology employed a
mixed-methods approach, combining qualitative insights
from interviews with business owners, managers, and
workers, alongside quantitative analysis of historical
business data. An ontology was created using Protégé to
categorize essential resources such as labor, materials,
and equipment, and to map their interdependencies.
Building on this, a machine learning model was developed
in Python to dynamically adjust resource allocation based
on real-time inputs, minimizing waste and reducing costs.
The findings demonstrate that this integrated model
significantly improves resource management practices,
leading to increased efficiency and sustainability in
operations. By tailoring solutions to the specific context of
small-scale aqua plant businesses in Sri Lanka, this
research provides actionable insights that can help small
businesses overcome resource-related obstacles and thrive
in competitive markets. This study highlights the practical
implications of adopting data-driven optimization
strategies and offers a framework that can be replicated
across similar industries facing resource management
challenges. | en_US |
