Robin Hes is a global supply chain leader with 12 years' experience designing and running cold chain operations across EU and APAC for Johnson & Johnson, spanning biotherapeutics, vaccines, synthetics and MedTech. He builds resilient, scalable operating models—translating risk into disciplined governance, long-range capacity plans and innovative shipper technology deployments—while managing critical 3PL networks and complex NPI and tech-transfer programs. Known for servant leadership and customer-first decision making, he has strengthened forecasting, standardized processes and led high‑stakes disengagement and site lifecycle projects in highly regulated environments. Unusually for a senior operations manager, he also contributes to open-source software work on scalable Python clustering algorithms, bringing a pragmatic, data-driven mindset to performance benchmarking and parallelization improvements.
12 years of coding experience
3 years of employment as a software developer
HAVO (Senior General Secondary Education), HAVO (Senior General Secondary Education) at Dalton-Vatel
Master of Science (MSc) in Supply Chain Management, Master of Science (MSc) in Supply Chain Management at Rotterdam School of Management, Erasmus University
Bachelor of Business Administration (BBA), Logistics & Economics, Bachelor of Business Administration (BBA), Logistics & Economics at Rotterdam University of Applied Sciences
Leadership & Professional Development (Johnson & Johnson)
CPIM - Basics of Supply Chain Management, CPIM - Basics of Supply Chain Management at APICS Northern Colorado
Python implementations of the k-modes and k-prototypes clustering algorithms, for clustering categorical data
Role in this project:
Back-end Developer & QA Engineer
Contributions:8 commits, 2 PRs, 7 comments in 1 month
Contributions summary:Robin primarily contributed to improving the K-modes and K-prototypes clustering algorithms. They fixed a parallelization issue in the K-Modes implementation and added comprehensive testing of parallel execution for both algorithms. The user's work included the creation of a benchmarking script, demonstrating the performance benefits of parallel processing and improving the project's documentation.
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