Using AI And ML To Reduce Supply Chain Risk: Know What You’re Looking For

Existing technology – specifically artificial intelligence (AI) and machine learning (ML) – can in theory be used to reduce inefficiencies in supply chains, but you have to be clear on what you are looking for, in order to avoid “just getting stupider faster.” Examples in this guidance from the online trade publication Supply & Demand Chain Executive include using video to monitor truck drivers and the conditions they encounter, or to ascertain how much cargo is stuck in warehouses because there are no trucks to move them, or even how much product is being lost to pilfering. The generic term for this kind of management is “supply chain orchestration,” and according to this post it’s a term we are going to be hearing more of in the future. To be successful, the writers cite two essential ingredients. One,  is trust between companies and suppliers. which can be a challenge because much of this data will be inherently sensitive. Second, and perhaps most important, is having a clear idea of what you are trying to find out before you unleash the technology.