Implementation of Business Intelligence for Enhanced Decision-Making: the case of a Pharmaceutical company

This thesis investigates the implementation of Business Intelligence (BI) systems and key performance indicators (KPIs) in a pharmaceutical manufacturing company, exploring both benefits and drawbacks. Emphasizing a process-based approach, critical success factors, and the utility of advanced IT systems in performance measurement, the study draws insights from diverse concepts. This aims to bridge the gap between performance measurement and BI implementation, offering practical recommendations for organizations. In alignment with prior literature, it examines the acceptance, routinization, and infusion of IT solutions, ensuring value creation and efficiency, more specifically in order to shift from excel-based to more intelligent solutions. The concept of data-driven insights is highlighted, emphasizing knowledge extraction from extensive datasets to enhance decision-making in complex environments. Additionally, a comprehensive overview of BI in a pharmaceutical manufacturing company is presented, addressing challenges and recommendations. The case study analysis of the company's performance metrics and KPI selection methodology contributes practical insights into cost reduction, increased ROI, and enhanced reliability. It integrates discussions on BI history, and characteristics analytics, particularly emphasizing the crucial role of KPIs in aligning organizational strategy. To sum up, this thesis explores the limitations of existing performance measurement systems and reporting and assesses the benefits of the proposed data-centric approach and identifies the obstacles and remedies during the implementation phase. Furthermore, it offers an in-depth case study of this company’s evolution, delivering valuable perspectives and framework for organizations aiming to harness data-driven methodologies.