Picture the vast Amazon rainforest or the fertile Cerrado – these aren’t neatly mapped industrial parks. We’re talking about expansive agricultural landscapes, often involving smallholders who may lack digital record-keeping. How do you gather precise geolocation data when your supplier’s records are handwritten on scraps of paper? How do you ensure data accessibility when land-use information is scattered across disparate agencies? And how do you navigate the complexities of informal supply chains, where traceability can feel like chasing shadows?
Then, we journey to China, a land of intricate industrial supply chains. Here, the challenge isn’t just about farms; it’s about factories, processing plants, and a labyrinth of intermediaries. Tracing a wooden component back to its forest of origin requires navigating multi-tiered manufacturing processes, each with its own data systems and formats. Data standardization becomes a monumental task, and language barriers can further convolute the process. Add to that the understandable data sensitivity of some suppliers, and you’ve got a recipe for a data headache.
But it’s not just about geography. We’re battling data silos, where critical information is locked away in disparate systems, hindering our ability to get a holistic view. We’re grappling with data quality issues, where inaccuracies and inconsistencies can undermine the compliance efforts e.g. receiving geolocation data for wood that points to the North Sea.
