The example below shows how assumptions can mislead.
This National Geographic article compares the value of an average pound of earth to the average pound of asteroid by amount of precious metals found. And yes, there are definitely more precious metals found in an asteroid than your backyard. And although the average total value of 100 tons of asteroid is worth $12,844 while dirt is $85 - as professor Jonathan Caulkins put it: "At $13,000 per 100 tons, comes to about 0.06 cents per pound. A carrot at the store is worth more than that." Despite asteroid's value over dirt, this depends on the assumption that precious metals hold value and that average dirt is comparable.
Although this example is not directly related to supply chains it shows how data and technology can be implemented for useless analysis. Models can be made with complex algorithms and flawless logic, yet if they are not understandable by decision makers - how useful can they be? Managers and executive decision makers must be able to understand how the models apply and ask the following question. Does this help me answer the right question?
Here's a fun strip showing how perfect information can be perfectly wrong:
With advances towards automation, simulation, un-head-wrappable data, and demand for guaranteed forecasting - I wonder: What is the future position of the supply chain manager? How will they balance and sift through mountains of data and be sure the data answers the right question and they don't become guilty of Type III error?