By Paul R Salmon FCILT, FSCM
In today’s hyperconnected, contested world, supply chains must operate with speed, precision, and resilience. Defence and industry alike are investing millions in cutting-edge forecasting engines and AI-driven analytics to anticipate demand, optimise inventory, and keep goods moving in the face of disruption.
And yet… despite having access to the best data and world-class forecasting tools, human behaviour remains one of the biggest barriers to supply chain performance.
🧠 The Problem: “I Know Better” Syndrome
You’ve seen it before. A forecasting system recommends a procurement adjustment based on real-time demand signals and volumetric data. The system factors in historic patterns, supplier lead times, and even emerging risks. It’s faster, more accurate, and grounded in hard evidence.
But then someone – a planner, manager, or senior stakeholder – decides to override the recommendation.
“We’ve always done it this way.”
“My gut says we’ll need more than that.”
“The system doesn’t understand our situation.”
These biases might feel intuitive, even safe. In reality, they’re undermining the very systems we built to protect us.
🚨 When Bias Beats Data
Human bias creeps into supply chains in subtle but destructive ways:
✅ Overordering “just in case” – leading to bloated stockpiles, wasted capital, and fragility disguised as resilience.
✅ Ignoring forecasts – because they challenge assumptions or institutional habits.
✅ Anchoring on past crises – causing overcorrection and poor responsiveness to current conditions.
The result? Systems designed for agility and efficiency are bypassed. Instead of harnessing their power, organisations end up paying for tools they don’t fully trust or use.
🛠️ The Data Is Right (Most of the Time)
This is not to say forecasting systems are perfect – they depend on good inputs and require continuous refinement. But when properly configured and fed high-quality data, modern forecasting engines outperform even the most experienced human planners.
If we are serious about moving at pace in contested environments, trusting the data is not optional – it’s mission-critical.
🚀 Building a Culture of Trust in Data
To unlock the full potential of supply chain analytics, leaders must:
🔑 Educate teams on how forecasting tools work and why they are superior to manual methods.
🔑 Establish governance that limits overrides to exceptional cases – not preferences.
🔑 Monitor adoption rates and challenge teams where compliance is low.
🔑 Champion success stories where data-driven decisions outperformed “gut feel”.
💡 A Call to Action
We built these tools for a reason. They are faster, smarter, and better able to cope with the complexity of modern supply chains. But their value only materialises when we trust them.
In a world where the next disruption is always just over the horizon, we don’t have time for “I know better.”
📣 Let the data do its job. The cost of not doing so is too high.
