Want deeper insights? Most remarks on use cases are exclusive to our subscribers. Here's a sample—subscribe now for full access!
Risk assessment for Product Contamination is, unsurprisingly, a core focus. Typically, this involves ongoing monitoring of contamination indicators and a periodic Monte Carlo simulation that correlates contamination risk with compliance to good manufacturing practices (GMP) indicators.
This is an interesting case: contamination is difficult to detect unless chemical tests clearly expose it. Inspection is both costly and often frustrating. As a result, the best approach is to correlate risk exposure with the level of effort invested in contamination control—there is no good, cost-effective alternative at the moment. Do you agree?
One significant challenge is that data pipelines in the food industry are highly fragmented and disorganized. I believe Agentic AI could dramatically improve this situation by streamlining data collection, integration, and analysis.
On Materiality
A key question: How would Verdenost report this risk in a materiality assessment for sustainability disclosure? Since this is an inside-out materiality issue, it would likely be limited in scope. However, using Monte Carlo simulation outcomes as disclosure parameters could add meaningful depth to the assessment.
Comentarios