By Paul R Salmon FCILT, FSCM
In today’s fast-moving supply chains, bad data is no longer just an inconvenience – it’s a critical threat. The old saying “garbage in, garbage out” implied poor inputs would result in equally poor outputs that could be spotted and corrected. But in the era of AI, automation, and end-to-end digital supply chains, that’s changed.
Now, flawed data doesn’t just produce bad insights – it produces decisions wrapped as facts, acted on instantly and at scale. If we don’t address this, entire supply chains risk becoming brittle, blind, and dangerously overconfident.
This is no longer a technical issue for IT teams. It’s a strategic leadership challenge that supply chain professionals cannot afford to ignore.
🛠 Why It Matters More Than Ever
Modern supply chains rely on data as their lifeblood.
In Defence logistics, volumetric data determines how assets are packed, shipped, and stored. Get it wrong, and critical equipment may never reach the front line on time. In consumer supply chains, corrupted sales data can send inventory to the wrong locations, leaving customers empty-handed. In pharma logistics, errors in cold-chain temperature readings can compromise entire batches of life-saving drugs.
When flawed data flows through automated systems, the issue isn’t just that decisions are slightly off. It’s that poor decisions are made confidently and at speed.
🚨 The Risks of ‘Facts Out’
Modern tools present outputs with clean dashboards and compelling visuals. If leaders assume these outputs are automatically correct, they fall into the trap of false certainty.
This can trigger:
✅ Cascading failures across partners and tiers
✅ Misplaced trust in systems
✅ An erosion of resilience in the face of disruption
In complex supply networks, one bad data point doesn’t stay in one place – it travels.
🧹 Fixing the Data First
To avoid this trap, supply chains must shift from focusing on outputs to managing inputs. That means:
Strong data governance – Assign ownership for critical data elements. Continuous quality checks – Validate and cleanse data at the source. Upskilling teams – Build data literacy so people question outputs, not just consume them. Partner collaboration – Demand quality from suppliers and share standards.
This isn’t just about cleaning up spreadsheets. It’s about protecting decision-making in the systems we’ve come to rely on.
🌐 A Shared Responsibility
In the words of a Defence logistician: “We don’t move parts anymore. We move data – and the parts follow.”
If the data’s wrong, everything else is too.
✅ The Bottom Line
In a world where decisions are automated, bad data doesn’t just mean bad insights anymore. It means flawed facts out – and dangerous consequences.
💡 Supply chain leaders must act now. Fix the data. Build trust in your systems. Protect your decisions.
📣 Join the Conversation
Are you confident in your supply chain’s data? Or are you unknowingly building decisions on flawed foundations?
👉 Share your experience in the Supply Chain Council community. Let’s work together to set the standards for data-driven, resilient, and future-ready supply chains.
