I am Jan-Philipp Grabowski \u2014 a supply chain leader with over 15 years of experience across global manufacturing, strategic sourcing, and operations management. What sets me apart is that I do not just manage supply chains \u2014 I build the data science tools to make them smarter.<\/p>\n
Throughout my career, I have worked at the intersection of procurement strategy and applied analytics. I write production-grade code in R<\/strong> and Python<\/strong> to solve real supply chain problems: forecasting demand, segmenting suppliers, mining process data from ERP systems, and visualizing cost drivers. This blog is where I share those methods \u2014 practical, reproducible, and designed for practitioners who work with real data.<\/p>\n My career spans five industries and some of the most demanding supply chain environments in European manufacturing:<\/p>\n Vice President Purchasing & Vendor Quality Management<\/strong> at corpuls<\/strong> (GS Elektromedizinische Ger\u00e4te G. Stemple GmbH) \u2014 leading procurement and supplier quality for a medical device manufacturer where compliance, traceability, and supply continuity are non-negotiable.<\/p>\n Head of Purchasing & Materials Management<\/strong> at Juzo<\/strong> (Julius Zorn GmbH) \u2014 responsible for end-to-end materials management in medical compression manufacturing. Drove the digitization of operational purchasing processes, led a major ERP rollout, and introduced data science methods to purchasing analytics.<\/p>\n Global Commodity Manager<\/strong> at Volvo Construction Equipment<\/strong> \u2014 managed the powertrain commodity group across all Volvo CE production sites globally, covering axles, brakes, prop-shafts, and engines. Operated within a European P&SM organization where sourcing decisions had direct impact on factory output across multiple continents.<\/p>\n Global Commodity Manager<\/strong> at GEA Group<\/strong> \u2014 strategic sourcing for one of the world’s largest industrial engineering companies, working across international supplier networks in process technology.<\/p>\n Earlier roles at seele GmbH<\/strong> (strategic purchasing) and KEBA Group<\/strong> (strategic sourcing) gave me a foundation in supplier development, contract negotiation, and cross-functional project leadership.<\/p>\n I do not treat data science as a theoretical exercise. Every technique I write about on this blog comes from a real operational need:<\/p>\n I program in R<\/strong> and Python<\/strong>, build dashboards and visualizations, and use tools ranging from ggplot2<\/strong> and tidyverse<\/strong> to scikit-learn<\/strong> and Claude API<\/strong> for LLM-powered supply chain analysis.<\/p>\n These are not resume decorations. CPIM and CSCP gave me the structured framework to connect shop-floor decisions to end-to-end supply chain strategy. The combination of formal supply chain education and hands-on data science capability is what makes the methods on this blog actionable for practitioners.<\/p>\n I have presented at AI conventions on the application of artificial intelligence and data science to supply chain operations. My focus is always the same: showing operations professionals how to extract value from the data their organizations already collect \u2014 ERP transaction logs, supplier scorecards, quality reports, and production schedules.<\/p>\n For companies looking for supply chain leadership:<\/strong> I bring a rare combination of strategic procurement experience and technical data science skills. I do not just identify problems \u2014 I build the analytical tools to solve them and the dashboards to monitor them.<\/p>\n For teams looking to modernize their supply chain analytics:<\/strong> I can help you move from Excel-based reporting to reproducible, automated analysis pipelines in R or Python. From supplier segmentation to demand forecasting to process mining \u2014 I have built and deployed these solutions in real manufacturing environments.<\/p>\n For readers of this blog:<\/strong> Every post includes working code, realistic datasets, and clear explanations. No theory without application. Take the code, adapt it to your data, and see the results for yourself.<\/p>\nProfessional Background<\/h2>\n
Where Data Science Meets the Shop Floor<\/h2>\n
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Certifications<\/h2>\n
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Speaking & Thought Leadership<\/h2>\n
What I Can Do For You<\/h2>\n
Get in Touch<\/h2>\n