For years, lead time has been treated as a fixed number.
One field in the ERP. One assumption in the planning system. One average used across hundreds (or thousands) of SKUs.
But today’s supply chains don’t behave like averages.
Suppliers shift capacity. Ports slow down. Carriers reroute. Weather, labor, and demand all create constant friction. What used to be a predictable six-week lead time might now be four weeks one month and nine the next.
And yet, many planning systems still rely on a number that hasn’t been updated in a year, or longer.
That disconnect is quietly driving excess inventory, service failures, and costly expediting across manufacturing and distribution. The fix isn’t more buffers or better guesses. It’s a shift in mindset—from static averages to continuous lead time sensing.
When Lead Time Averages Stop Reflecting Reality
Lead time volatility isn’t the exception anymore—it’s just how supply chains work now.
Suppliers have good months and bad ones. Carriers reroute. Ports slow down. And many companies are sourcing from more regions and partners than ever before.
But the real issue isn’t just the variability. It’s the gap between what’s actually happening and what the planning system still believes.
In many ERPs, lead times get updated once a year—if that. And those updates are often based on what a supplier quoted, not what they’ve actually delivered.
Meanwhile, reality keeps moving.
That six-week lead time from eighteen months ago might now be four weeks because the supplier improved their process. Or it might be eight weeks because of capacity constraints.
If the system is still planning around six, decisions are being made on assumptions that no longer hold up.
That’s how teams end up with inventory arriving too early and tying up working capital, or running into stockouts because replenishment started too late. And in many cases, the real problem isn’t demand at all. It’s outdated lead time assumptions.
The Price of Planning With the Wrong Lead Time
When lead times are overstated, planners order earlier than necessary. Inventory arrives ahead of need and sits idle. Cash gets tied up in stock that isn’t generating revenue.
The math adds up quickly. Imagine a fast-moving item with a lead time overstated by two weeks. That can translate into two extra weeks of demand sitting on the shelf. Across hundreds or thousands of SKUs, that’s a significant amount of working capital locked in excess inventory.
Understated lead times are even more painful.
If the ERP assumes four weeks but the supplier consistently delivers in six, reorder points trigger too late. That leads to stockouts, rush orders, and expensive expedited freight. Customer relationships take the hit, and teams often respond by adding blanket safety stock.
That extra buffer may reduce stockouts, but it also masks the root cause (and inflates inventory investment even further).
What the Average Misses About Lead Time Performance
Most teams focus on the average lead time. But the average only tells part of the story.
Two suppliers may both average six weeks. One consistently delivers in five to seven weeks. The other swings from four to ten.
Those are very different planning risks.
To manage lead times effectively—and make better inventory and service decisions—companies need to measure two things for every supplier-item combination:
- Accuracy – How close the ERP lead time is to actual performance
- Variability – How much delivery timing fluctuates around that average
This distinction matters.
If a supplier consistently delivers in four weeks but the ERP says six, the problem is bias. The fix is straightforward: update the base lead time.
If a supplier averages six weeks but ranges from four to ten, the problem is variability. That requires adjusting safety stock to protect service levels.
Understanding which issue you’re dealing with helps avoid blanket buffers and unnecessary inventory.
Many organizations start by focusing on the highest-impact SKUs—the top 50 by demand value or the ones showing the largest deviations from planned lead times. These items often deliver the fastest and most visible improvements.
Treating Lead Time as a Governed Metric
In many companies, ERP lead time fields are set once and forgotten.
But lead time isn’t a static attribute. It’s a performance metric that should be measured and governed just like forecast accuracy or service levels.
That means:
- Assigning ownership for lead time maintenance
- Defining update frequency
- Establishing data sources and rules for changes
Procurement teams often own vendor lead times. Manufacturing teams manage internal production lead times. Planning teams help coordinate the updates.
For most items, monthly or quarterly reviews are enough. For high-velocity or high-volatility SKUs, weekly updates may be more appropriate.
It’s also important to measure the full lead time cycle—not just shipment to receipt. Order processing, production, transit, and internal handling all add time, and those internal steps are often overlooked. Many teams manage this with exception-based rules: set tolerance bands and only flag the items that fall outside them, so planners can focus where it matters most.
Supplier Collaboration Reduces Variability at the Source
Fixing the planning system is one part of the equation. The other is reducing variability at the source.
That starts with visibility.
Commit-versus-actual reporting gives suppliers a clear view of how their deliveries compare to quoted lead times. Monthly scorecards showing on-time performance, average deviation, and trends over time create a shared understanding of reliability.
In many cases, suppliers don’t realize how much their variability is affecting inventory costs downstream.
Structured exception reporting helps identify systemic issues. If a supplier’s lead times extend beyond commitments for multiple orders in a row, it may signal capacity constraints, process issues, or material shortages. Early visibility makes it easier to solve these problems collaboratively.
For critical SKUs, qualifying secondary suppliers can also provide flexibility. Even if alternate sources are slightly more expensive, the reduction in variability and safety stock often outweighs the cost difference.
Why Automation Is Essential for Modern Lead Time Management
Manual lead time updates can’t keep up with today’s pace of change.
By the time someone notices a pattern, validates the data, and updates the ERP, the conditions may have already shifted again.
That’s why many organizations are moving toward automated lead time sensing—where data-driven updates happen within defined guardrails, and planners step in when something falls outside the norm.
In this approach:
- Receipt data is collected continuously
- Rolling lead time metrics are calculated automatically
- Items with systematic bias or high variability are identified
- ERP lead time fields are updated within predefined tolerance ranges
Items that fall outside normal ranges can still be flagged for review. But routine maintenance happens automatically.
Automation also allows safety stock to adjust automatically as conditions change, rising when lead time variability increases and shrinking when reliability improves. The result is a planning system that reflects what’s happening now, not what was true a year ago.
Lead Time Sensing in Practice
We’ve seen this shift play out in real operations.
For example, in our work with Border States, a large electrical distributor, lead time variability was creating planning challenges across a massive SKU portfolio. By implementing AI-driven lead time prediction and continuously updating planning assumptions, they were able to:
- Achieve 976% ROI with payback in just 1.3 months—returning $13.50 for every $1 invested
- Reduce inventory by $21M within 6–8 months
- Cut lead time error by 31%
- Improve lead time accuracy by 65%
- Strengthen service performance across the network
Lead Time Prediction: The Next Step in Smarter Planning
No supply chain is perfectly predictable (and it never will be). The goal isn’t to chase perfect forecasts. It’s to stay in sync with what’s actually happening and adjust before small timing shifts turn into big inventory problems.
Smarter planning today means being able to:
- Spot changes in lead time behavior early
- Update planning assumptions automatically
- Align safety stock with real variability
- Free up working capital without sacrificing service
That shift requires moving beyond static averages and toward true lead time prediction.
At GAINS, we’ve built our lead time prediction capabilities around this idea—continuously measuring real-world performance, updating planning inputs, and recalibrating buffers as conditions change, so planning stays in step with the business.
If you’re looking for a more practical, data-driven way to manage lead time uncertainty, we’d love to show you what that can look like in your operation. Request a demo to get started!
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