Turning Supply Chain Data Into Decisions

I am Jan-Philipp Grabowski — 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 — I build the data science tools to make them smarter.

Throughout my career, I have worked at the intersection of procurement strategy and applied analytics. I write production-grade code in R and Python 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 — practical, reproducible, and designed for practitioners who work with real data.

Professional Background

My career spans five industries and some of the most demanding supply chain environments in European manufacturing:

Vice President Purchasing & Vendor Quality Management at corpuls (GS Elektromedizinische Geräte G. Stemple GmbH) — leading procurement and supplier quality for a medical device manufacturer where compliance, traceability, and supply continuity are non-negotiable.

Head of Purchasing & Materials Management at Juzo (Julius Zorn GmbH) — 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.

Global Commodity Manager at Volvo Construction Equipment — 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.

Global Commodity Manager at GEA Group — strategic sourcing for one of the world’s largest industrial engineering companies, working across international supplier networks in process technology.

Earlier roles at seele GmbH (strategic purchasing) and KEBA Group (strategic sourcing) gave me a foundation in supplier development, contract negotiation, and cross-functional project leadership.

Where Data Science Meets the Shop Floor

I do not treat data science as a theoretical exercise. Every technique I write about on this blog comes from a real operational need:

• Supplier segmentation using Self-Organizing Maps and hierarchical clustering — because ABC analysis alone does not capture supplier risk, quality, and delivery performance in a single view.
• Process mining with bupaR — reconstructing actual process flows from ERP timestamps to find bottlenecks, rework loops, and resource conflicts that process documentation misses.
• Correlation analysis for supply chain KPIs — quantifying the trade-offs between lead time, cost, quality, and delivery reliability so that sourcing decisions are based on evidence, not gut feeling.
• Cost driver analysis with ternary plots — visualizing how material, labor, and overhead costs interact across supplier portfolios.
• Demand-Driven MRP (DDMRP) — implementing pull-based replenishment strategies that reduce inventory while improving service levels.

I program in R and Python, build dashboards and visualizations, and use tools ranging from ggplot2 and tidyverse to scikit-learn and Claude API for LLM-powered supply chain analysis.

Certifications

• CPIM — Certified in Planning and Inventory Management (ASCM/APICS)
• CSCP — Certified Supply Chain Professional (ASCM/APICS)
• CQM — Certified Quality Manager

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.

Speaking & Thought Leadership

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 — ERP transaction logs, supplier scorecards, quality reports, and production schedules.

What I Can Do For You

For companies looking for supply chain leadership: I bring a rare combination of strategic procurement experience and technical data science skills. I do not just identify problems — I build the analytical tools to solve them and the dashboards to monitor them.

For teams looking to modernize their supply chain analytics: 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 — I have built and deployed these solutions in real manufacturing environments.

For readers of this blog: 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.

Get in Touch

Connect with me on LinkedIn or explore the articles on this blog to see my work in action.