Program Manager
Current- Saved ~$17,400,000 per year in total supply chain cost by developing and implementing a new scalable machine learning model in R to forecast demand more accurately, reducing WAPE from 20.7% in 2022 to 6.2% in 2023o Met with key team members and stakeholders to define the problem statement and scope of the project, set the goals and objectives, prioritize deliverables, and develop a project plan to be executed upono The new model is both more accurate and clearer to understand than the previous process, allowing stakeholders to more easily access and monitor metric deviations, and identify corrective actions- Reduced time needed to analyze weekly forecast misses by 83%, improving organization efficiencyo Streamlined the communications process between stakeholders by removing duplicate reports that could be combined, improving the efficiency of data flow between teams and opening up more time to work on other projectso Built a dashboard that compares week-over-week metrics related to customer buying behavior to highlight trends and identify root causes for forecast misses which can then be implemented back into the machine learning model- Coordinated the implementation of the operations planning process into the Japan region of the Amazon exports supply chaino Collaborated with stakeholder teams to integrate their metrics and data into our systems and tools, establish program goals, and prioritize deliverables based on their impact to the program’s key metrics- Led weekly business reviews with executive leadership to report key program metrics and align on business goals and objectives
