Scheduling Laboratory Work Through Power BI

This project addresses the critical need for efficient laboratory scheduling in research and clinical environments, where managing personnel, equipment, and test workflows is essential for productivity and operational excellence. Traditional methods, including spreadsheets, paper logs, and static dashboards, often fail to handle complex, multi-use laboratory environments. These outdated approaches can lead to scheduling conflicts, workflow bottlenecks, underutilized equipment, and delayed test processing, ultimately impacting both operational efficiency and staff satisfaction. To address these challenges, this project proposes a Power BI-based scheduling platform that integrates multiple data sources, including Laboratory Information Management Systems (LIMS), technician shift schedules, equipment logs, and historical test volumes. The platform provides interactive dashboards that visualize real-time workloads, predict periods of high demand, and allow managers to allocate resources effectively. The system is designed to improve decision-making, increase equipment utilization, reduce scheduling conflicts, and enhance transparency across laboratory operations. The literature supports the use of business intelligence dashboards to optimize laboratory workflows. Prior studies have shown that dashboards can improve throughput, reduce turnaround times, and enable data-driven decision-making (Mansoor & Dar, 2024; Petrides et al., 2022; Maury et al., 2021). This project extends these findings by focusing specifically on dynamic scheduling, predictive workload allocation, and multi-resource optimization in multi-use laboratory environments, which have been less explored in previous research. The results of the project demonstrate that the Power BI platform successfully improved scheduling efficiency and resource visibility in the test environment. Simulations and dashboard analyses showed reduced scheduling conflicts, more balanced technician workloads, and higher utilization of critical laboratory equipment. Predictive visualizations allowed managers to anticipate peak workload periods, enabling proactive resource allocation. Feedback from trial users indicated that the dashboards were intuitive and provided actionable insights, confirming the platform’s potential to enhance laboratory operations and support evidence-based decision-making. Based on the project findings, it is recommended that laboratories adopt a semi-automated, Power BI-driven scheduling platform to improve efficiency, transparency, and staff satisfaction. This approach allows managers to make evidence-based decisions, allocate resources more effectively, reduce workflow disruptions, and prepare laboratories for future growth and technological integration. Overall, implementing this platform represents a strategic investment in modernizing laboratory operations and enhancing productivity, providing measurable operational benefits while supporting data-driven decision-making in research and clinical settings.