DRAWING CONCLUSIONS
DIVERSIFIED RESOURCING
64 outside of the lorry , the speed it is travelling at , and if there are any roadworks which will slow the delivery down . Every detail is pinpointed with time and date stamps .
Now think of thousands of sensors everywhere ; the autonomous supply chains need to capture and process every detail in real time . To do this , organisations need to build a platform that can manage and interpret all the signals and insights with a processor capable of managing and integrating data from AI , ML and IoT .
DRAWING CONCLUSIONS
The capacity to both store and manage every piece of information is necessary , but without translating these billions of nuggets of information , every detail is rendered powerless . A standard approach of applying a rule under specific conditions could miss dozens of other factors . The autonomous supply chain uses AI and ML to connect all the seemingly unrelated pieces for a clear picture of supply and demand . Leaving it to dashboards that report conditions or status systems , an approach commonly used across the supply chain , won ’ t fuel the autonomy
required to respond to the growing pressures across industries .
The anticipatory shipping technology that Amazon patented in 2013 , which calculates the expected demand for items in certain locations , is the perfect example . The ability to predict supply and demand in advance enables Amazon to move products with efficiency and precision so they can ship to a customer sooner and at the lowest possible cost . Calculating demand and pre-positioning supply can offer a formidable edge over competitors , something Amazon has
MARCH 2019