Browsing by Author "Babu, Jacinta Akinyi"

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    An Interoperable Data-Informed Procurement And Distribution Information System For Enhanced Stock Control
    (NM-AIST, 2025-01) Babu, Jacinta Akinyi
    This report presents a comprehensive study on developing and implementing an Interoperable Data-Informed Procurement and Distribution Information System designed to enhance stock control across multiple branches of Transchem Pharmaceuticals. The pharmaceutical industry relies heavily on efficient supply chain management, and the research addresses the challenges of poor visibility and coordination between procurement and distribution functions, which result in operational inefficiencies and increased costs. The study employs qualitative research methods, including focus group discussions, semi-structured observations, survey questionnaires and a benchmark test to gather data and analyse Transchem Pharmaceuticals' operations. The Agile Approach was used for system development, specifically Extreme Programming. The developed system exhibited a modestly superior overall multi-core performance compared to the baseline system. The baseline system performed better in file compression by a slight margin, surpassing the developed system's navigation efficiency. The developed system showed a marginally better performance in HTML5 handling and the baseline system demonstrated a slight advantage in PDF rendering. Key findings include the system's unforeseen benefit of providing valuable insights into consumer preferences and demand patterns, supporting more informed decision-making and strategic planning. The project's implications in supply chain management and information systems underscore the importance of leveraging real-time data for decision-making in contemporary business practices. This research contributes to the advancement of enhanced stock control practices across company branches and demonstrates the effectiveness of data- driven insights in supply chain management. Future research directions include exploring machine learning-driven predictive demand forecasting, emerging technologies integration such as blockchain technology for transparency, and adoption of robotic process automation to accelerate routine processes.