The real cost of “phantom” lead time
When promised lead times don’t match reality, two bad things happen: you either over-stock (cash trapped in shelves and write-downs) or under-stock (stockouts, expediting, missed revenue). The fix is measurable and operational: track actual lead time and its variability, then tune your min/max buffers and supplier expectations accordingly. With real data, replenishment shifts from firefighting to a steady, defensible cadence.
This article lays out a practical, professional approach you can run with a small team: how to capture true lead time, compute dynamic buffers, publish vendor scorecards buyers and suppliers both respect, and install a quarterly rhythm that eliminates surprises.
Step 1: Measure lead time the way operations actually experience it
Define the clock. For each PO line, measure from the commit point (PO release or supplier confirmation) to usable receipt (goods passed receiving checks and are available to pick). If QA or labeling adds a day, include it, planners need the operational number, not the wishful one.
Compute two numbers per item (and per vendor if item is dual-sourced):
🐦🔥 Average lead time (L̄): the simple mean in days across your last N receipts (ideally 6–12 months).
🐦🔥 Lead-time variability (σL): the standard deviation in days. This is what drives risk and your buffers.
Clean the data (light but essential):
🐦🔥 Remove clear outliers caused by PO holds you initiated (e.g., commercial dispute). Log these separately.
🐦🔥 Keep supplier-caused extremes (late ships, partials) in the dataset; they’re the very risk your buffer must absorb.
🐦🔥 Normalize calendar vs. working days: pick one convention and stick to it across the org.
Optional but powerful: also track receiving reliability (days from physical arrival to “available to pick”). If this “dock-to-stock” time is noisy, you’ll misdiagnose suppliers for a process problem inside your four walls.
Step 2: Make buffers dynamic (and defensible)
Static min/max settings decay the day you enter them. Instead, compute them from demand, lead time, and variability then refresh on a schedule so they keep pace with reality.
Core ideas for min/max:
🐦🔥 Demand rate (D): average daily demand for the item (ideally with a short moving window for seasonality).
🐦🔥 Service level (SL): your target probability of not stocking out during lead time (e.g., 95% for standard items; 99% for life-or-line-stoppers).
🐦🔥 Variability driver: either demand variability (σD) during lead time or lead-time variability (σL) multiplied by D. Use what your dataset supports today; improve over time.
Practical formulas (keep them simple):
🐦🔥 Cycle stock (CS): D × L̄
🐦🔥Safety stock (SS):
🐦🔥 If demand varies more than lead time: Z(SL) × σD × √L̄
🐦🔥 If lead time varies more than demand: Z(SL) × D × σL
(Use the larger driver, but don’t double count. Z(SL) is the z-score for your target service level.)
🐦🔥 Reorder point (ROP): CS + SS
🐦🔥 Min / Max: a practical pair is Min = ROP and Max = ROP + k × lot_size, where k is set to yield a reasonable order frequency and respect MOQs.
Segmentation and guardrails:
🐦🔥ABC risk tiering: Give critical A-items higher SL (and more frequent recalculation) than B/C items.
🐦🔥 MOQs & pack sizes: If MOQ dwarfs your buffer, show the days-of-cover jump in the buyer’s view so procurement can challenge MOQ with data.
🐦🔥 Shelf life: For perishable/expiry-risk items, cap Max by allowable days of cover instead of cost-optimal math.
Refresh rhythm:
🐦🔥 A-items: monthly recompute;
🐦🔥 B-items: bi-monthly;
🐦🔥 C-items: quarterly.
Automate the recompute—buyers should review exceptions, not wrangle spreadsheets.
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Step 3: Build vendor scorecards that actually change behavior
The goal is not a pretty dashboard; it’s shared facts that drive better outcomes. Keep the scorecard small, stable, and visible to both sides.
What belongs on the card
1. OTIF (On-Time, In-Full). Count an order on-time only when it arrives by the need date and in the confirmed quantity/quality. Partial shipments inflate handling and cloud reality—track them explicitly.
2.Lead-time variability. Publish both L̄ and σL. Suppliers with the same average but lower variability deserve better planning status.
3. Quality signals. Incoming defects, NCRs, and returns (% of lines or units). Tie these to rework time where relevant.
4. Responsiveness. PO acknowledgement lead time and issue resolution time (days from ticket to closure).
Keep a 12-month rolling view with month-by-month bars and a trailing average. That shape tells the story of improvement (or backsliding) without a war of anecdotes.
How to publish and use it:
🐦🔥 One page per supplier, one table per item family. Buyers can skim; suppliers can action.
🐦🔥 Traffic lights, not paragraphs. Green/yellow/red thresholds agreed upfront avoid debates.
🐦🔥Quarterly business reviews (QBRs). Use the scorecard as the agenda; end with 1–3 corrective actions per supplier, each with an owner and due date.
Step 4: Install a quarterly rhythm (ditch the 30-day template)
Calendars slip; rhythms stick. Use a simple Q1–Q4 cadence that compounds improvements without overwhelming teams:
🐦🔥 Q1 – Baseline & expose. Compute L̄ and σL for top items; publish the first supplier scorecards; stand up a days-of-cover dashboard by item and family.
🐦🔥 Q2 – Stabilize. Align min/max with reality; agree on lead-time targets and OTIF definitions with suppliers; activate exception alerts (e.g., OTIF < 90%, σL above threshold).
🐦🔥 Q3 – Optimize. Segment items by criticality; tighten buffers for predictable vendors, loosen for volatile ones; introduce incentives/penalties tied to OTIF and quality.
🐦🔥 Q4 – Extend. Add supplier feedback loops, co-review forecasts for A-items, and lock next year’s targets with quarterly check-ins already on the calendar.
This rhythm ensures you’re always either measuring, stabilizing, optimizing, or extending—never drifting.
Step 5: Give buyers a replenishment view that kills fire-drills
he buyer’s cockpit should prioritize exceptions, not exports. In a single view:
🐦🔥 Items at risk: days of cover vs. target, with heat-map coloring.
🐦🔥 Inbound vs. need: POs aligned to need dates; late risks highlighted with supplier name.
🐦🔥 Vendor performance cues: OTIF trend and σL next to each item so planners anticipate risk.
🐦🔥 Order suggestions: Min/Max-driven with MOQ/pack constraints, showing the post-order days of cover.
🐦🔥 Expedite calculator: cost of expedite vs. cost of stockout (simple assumptions are better than none).
With this visibility, escalations are faster, and meetings shift from “where’s the data?” to “what’s the decision?”
Step 6: Exception rules that prevent surprises
Define alerts that trigger review, not panic:
🐦🔥 OTIF slip: 2 consecutive months < target.
🐦🔥 Variability spike: σL up >25% vs. trailing average.
🐦🔥 Divergent reality: actual lead time > promised by N days for 3+ POs.
🐦🔥 Buffer breach: item sits < Min for M days or > Max for X weeks (aging risk).
Route each alert to an owner by role (planner, buyer, quality), with standard responses (expedite, reschedule, alternative supplier, temporary Max cap).
Step 7: Change management that sticks
Numbers are necessary; habits make them real:
🐦🔥 Definitions first. Publish a one-pager: how you time lead time, what counts as “on-time,” how partials are scored, and how Max interacts with MOQ and shelf life.
🐦🔥 Training in the flow. 10-minute, captioned videos: “How to acknowledge POs,” “How to date need vs. promise,” “How to log receiving exceptions.”
🐦🔥 Celebrate predictability. Don’t only praise speed; reward variance reduction, which unlocks lower safety stock without risk.
🐦🔥 Supplier transparency. Share the exact math behind OTIF and lead-time metrics; invite corrections to the data once, not arguments every cycle.
Common pitfalls (and how to avoid them)
🐦🔥 Chasing average lead time only. It’s variability that dictates how much buffer you need. Track both L̄ and σL.
🐦🔥 Counting “on-time” at the dock. If QA or labeling delays release to stock, you’re measuring the wrong outcome.
🐦🔥 Treating MOQ as immutable. Use days-of-cover math to challenge suppliers with objective over-stock evidence.
🐦🔥 Over-engineering the first pass. Start with a simple SS method and improve once the cadence is stable.
🐦🔥 Scorecards that change every quarter. Keep the metrics stable so trends mean something.
Quick start: 10 moves in 10 days (optional, but effective)
1 Agree the clock (commit → usable).
2 Pull 12 months of receipts for your top 100 items.
3 Calculate L̄ and σL per item (and per supplier if dual-sourced).
4 Draft initial Min/Max using the simple formulas above.
5 Publish a days-of-cover view for buyers.
6 Print a one-page definition sheet (OTIF, partials, QA holds).
7 Build v1 scorecards: OTIF, L̄, σL, quality, responsiveness.
8 Set alerts for OTIF and σL thresholds.
9 Run two supplier check-ins on the worst and best performers.
10 Book the Q2 stabilization workshop now.
What success looks like in a quarter
What success looks like in a quarter
🐦🔥 Stockouts drop and are explained by a small handful of known risks.
🐦🔥 Expedites shrink because lead times are no longer a surprise.
🐦🔥 Cash frees up as you right-size Max on predictable items.
🐦🔥 Suppliers engage: they see the same truth you do and adjust to hit targets.
🐦🔥 Buyers focus on exceptions and negotiations, not spreadsheet gymnastics.
Procurement becomes a stabilizer across the operation—and a partner suppliers prefer to improve with, not hide from.
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Schedule your FireFlight demo today and unlock a clearer path.