Resource Management

Abstract

Efficient resource management is crucial for maintaining operational efficiency and controlling costs in complex industrial environments. At Janssen Gurabo, project approval delays have become a substantial bottleneck, with 65% of projects beyond the designated 30-day period by an average of 30 extra days. The prolonged approval process leads to the misallocation of workers and equipment, scheduling conflicts, and inefficient resource utilization, resulting in increased costs and jeopardized project timelines. The Lean Six Sigma research identified significant inefficiencies arising from workflow bottlenecks, deficiencies in data collection, system silos, inadequate utilization of Lean technologies, and the absence of predictive analytics in resource forecasting. Moreover, approval delays create an inequity in job distribution among project managers, resulting in diminished productivity and reactive resource allocation. This project employs the DMAIC technique to systematically assess, optimize, and implement a resource management framework tailored for high-volume pharmaceutical production settings. Our strategy includes the creation of a tailored Quick Base solution, intended to facilitate real-time monitoring of resource allocation, predictive scheduling, and automated approval processes. Integrating Quick Base with ERP and scheduling systems enhances data accuracy, improves project visibility, and facilitates proactive decision-making. Moreover, Lean Six Sigma methodologies, including Swim Lane Diagrams, 8 Wastes Analysis, and Cause-and-Effect (Fishbone) Diagrams, were employed to identify inefficiencies and facilitate process improvements. The recommended solution is organized into a staged implementation plan that includes process redesign, technological integration, stakeholder training, and change management measures to guarantee sustainable acceptance. Anticipated results encompass a 30-50% decrease in approval delays, a 20% enhancement in resource utilization efficiency, and a quantifiable reduction in project cost overruns. This capstone project integrates data-driven decision-making, automation, and Lean approaches to provide a scalable and repeatable framework for enhancing project execution efficiency, optimizing resource allocation, and aligning industrial operations with the principles of continuous improvement.