Melbourne, Victoria, Australia
As a seasoned Supply Chain Analyst and Demand Planner with over 5 years of cumulative experience, I bring expertise in optimizing supply chain operations, managing stakeholder relationships, and driving efficient demand planning. My proficiency spans cross-functional collaboration, data analytics, and forecasting using Machine Learning, contributing to my successful track record. My accomplishments include improving product availability, streamlining order processes, and implementing effective inventory control measures, resulting in reduced inaccuracies and enhanced stock management. I've played a pivotal role in developing a comprehensive master data system and resolving lead time discrepancies, leading to cost savings and operational efficiency. With a strong passion for data analytics and planning, I consistently leverage my analytical skills to drive data-driven decisions. Additionally, my proactive and adaptable nature, coupled with exceptional problem-solving and innovative thinking, allows me to excel in fast-paced and dynamic supply chain environments. My dedication centres on achieving exceptional results while focusing strategically on customer satisfaction and broader business objectives.
* Own demand forecasting across ANZ, supporting S&OP, budgeting, and supply planning decisions. * Developed forecasting models and automated planning processes using Python, Power Query, and Power BI, improving forecast accuracy and reducing planning cycle time by 40%. * Partner with commercial, supply, finance, and regulatory teams to support product launches, improve service levels, and optimize inventory performance.
* Supported inventory replenishment and inbound supply planning to ensure product availability and service performance. * Automated reporting and enhanced supply visibility using Power BI and Python. * Collaborated across planning, commercial, and regulatory teams to manage supply risks and support new product launches.
* Managed demand and supply planning for a D2C refill business, supporting inventory availability and operational performance. * Built forecasting and reporting solutions using Excel, Python, and Power BI to improve planning visibility and decision-making. * Improved service levels (DIFOT 74% → 91%) and reduced excess inventory by 27% through data-driven replenishment and inventory optimization.