IBP Team Lead for one of the fastest and most comprehensive IBP implementations working with the Demand Planning and Integrated Planning teams in requirements gathering, design, testing, training and documentation, data maintenance, OMP configuration, and user support during hyper-care leading to $15M EBIT and $65M cash impact in 2023. Heavily involved in data maintenance during initial go-live to ensure stability of planning environment. Leveraged coding experience in SQL and Python to impute optimal values for missing data which included the use of machine learning (Random Forest).

Analyzed data and processes within production and supply chain in addition to demand planning responsibilities. During this time, I have used of advanced analytical tools and solutions such as simulation models, linear and mixed integer linear programming, data management and visualization tools, distribution fitting, and time series analysis. Projects have included supply chain network optimization, supply planning and asset optimization, product portfolio analysis, inventory management, continuous time production capacity, and supply chain dashboard development. Over five years of experience in demand planning role providing valuable data insights and collaborating with cross-functional teams to create monthly demand plan.

Performed quality control research in collaboration with Rolls Royce to monitor broaching process using advanced analytical techniques developed in MATLAB including time-series analysis, profile monitoring, and regression trees. Developed a process monitoring algorithm along with a graphical user interface for Rolls Royce using a time series model to predict force signals and monitor process changes. Extensive literature review performed on the topic of process monitoring through sensory signals.

Worked in a team of four other people on global safety stock reduction project for performance films. Developed a simulation model using @Risk to determine appropriate safety stock considering variability in lead time and uncertainty in forecasted demand. Created a database using MS Access to sort forecasted demand, demand uncertainty, expected lead time, and lead time variability for all products at each distribution center for each month.

Led a group of three students to develop pull system for Mounds and Almond Joy processes that could potentially save $136,000 and reduce average wait time from 7.8 hours to 8 minutes. Created a scheduling program using MS Excel to determine when to start a new batch of coconut based on a pull system. Helped develop a simulation model using Simio and computed results along with sensitivity analysis.