A smart AI that always only wants the next best thing
A start-up is developing an AI that is supposed to help with supply chain planning by always making the best short-term decisions.
The AI continuously analyzes current data on transport times, inventory levels, and demand to quickly suggest optimal routes and order quantities.
But after some time, it becomes apparent that the entire supply chain becomes unstable and frequent small changes lead to delays and higher costs.
The team wonders: Why does an AI that always seeks the best short-term solution lead to worse results for the overall system?
Question: What problems can arise when an AI in complex systems like supply chains relies only on short-term optimization, and why is it difficult to adequately consider long-term effects and system dynamics?
Solution follows tomorrow.
Solution
An AI that only pursues short-term optimizations rarely takes into account the long-term consequences and the complex interactions within the system.
In supply chains, short-term adjustments such as frequent order changes or route switches can lead to instabilities, increased costs, and delays – an effect known as the "bullwhip effect."
The AI optimizes locally and promptly without sufficiently modeling the overall strategy or future states.
Long-term planning requires balancing risks, buffer times, and stability, which short-term optimizations often ignore.
Additionally, many system dynamics are nonlinear and difficult to predict, which complicates simple optimization approaches.
Without explicitly modeling long-term consequences and system feedbacks, the AI can unintentionally worsen the overall system.
Result: An AI that only makes the best short-term decisions can destabilize complex systems because it does not adequately consider long-term effects and system dynamics. For sustainable and robust solutions, integrated models with a long-term perspective and feedback mechanisms are therefore necessary.