Manual processes are silently eroding Europe’s supply chains. Here’s why trust in technology collapsed, how to rebuild it, and what supply chain managers must do now to survive the AI-driven future.
Insightful and practical - thanks! On a related note, regarding AI, it is easy to feel overwhelmed by all the opportunities and the pressure to implement AI. A good tip for some will be to *enjoy* learning about AI and machine learning, rather than just seeing it as something that *must* be learned.
Thank you! You’re absolutely right, the mindset shift is everything.
In a space where AI is often framed as a race or a threat, simply choosing to enjoy the learning curve can be a strategic advantage in itself.
We don’t need every supply chain leader to become a machine learning expert overnight, but we do need them to stay curious, ask the right questions, and build the trust muscle again, first with the tech, then with their teams.
Appreciate you raising this perspective, it’s exactly the kind of mindset that will separate reactive adopters from thoughtful builders in this new AI-driven era.
I have worked on two major ERP rollouts for clinical supply (one at Genentech and the other at BeOne (formerly BeiGene), and the automation piece is always partly impacted by Quality Assurance. For biotech and pharma, QA groups might need to see a coordinated industry project that shows absolute transparency of the data, including Regulatory filings and appropriate shelf life data.
Great point, in biotech and pharma, automation success depends on full QA and Regulatory trust. Without transparent data (Reg filings, shelf life, audit trails), QA will always hesitate. The future lies in building systems with “trust by design”, where QA is a co-owner, not just a reviewer. Appreciate you highlighting this critical piece!
Great point on QA’s role in automation. Today, many large scale supply chain companies outsource teams just to clean and validate data, over 30% of their IT effort goes into this. I’ll be publishing a few articles soon diving deeper into this data bottleneck and its impact on automation and compliance.
Insightful and practical - thanks! On a related note, regarding AI, it is easy to feel overwhelmed by all the opportunities and the pressure to implement AI. A good tip for some will be to *enjoy* learning about AI and machine learning, rather than just seeing it as something that *must* be learned.
Thank you! You’re absolutely right, the mindset shift is everything.
In a space where AI is often framed as a race or a threat, simply choosing to enjoy the learning curve can be a strategic advantage in itself.
We don’t need every supply chain leader to become a machine learning expert overnight, but we do need them to stay curious, ask the right questions, and build the trust muscle again, first with the tech, then with their teams.
Appreciate you raising this perspective, it’s exactly the kind of mindset that will separate reactive adopters from thoughtful builders in this new AI-driven era.
I have worked on two major ERP rollouts for clinical supply (one at Genentech and the other at BeOne (formerly BeiGene), and the automation piece is always partly impacted by Quality Assurance. For biotech and pharma, QA groups might need to see a coordinated industry project that shows absolute transparency of the data, including Regulatory filings and appropriate shelf life data.
Great point, in biotech and pharma, automation success depends on full QA and Regulatory trust. Without transparent data (Reg filings, shelf life, audit trails), QA will always hesitate. The future lies in building systems with “trust by design”, where QA is a co-owner, not just a reviewer. Appreciate you highlighting this critical piece!
Totally agree. Too many think QA can come in later when they need to be part of the user requirements
Great point on QA’s role in automation. Today, many large scale supply chain companies outsource teams just to clean and validate data, over 30% of their IT effort goes into this. I’ll be publishing a few articles soon diving deeper into this data bottleneck and its impact on automation and compliance.