📦 Your Vendors Are Lying About Lead Times (And Your ERP Believes Them)

Welcome to The Ops Digest!

Each week, we drop no-BS insights + one AI prompt to cut wasted costs, tighten workflows, and eliminate manual grunt work.

Today: vendor lead times, why your ERP blindly believes them, and how that lie cascades into stockouts, excess inventory, and constant firefighting.

📦 Your Vendors Are Lying About Lead Times (And Your ERP Believes Them)

Your vendor says 14 days.

Your ERP says 14 days.

Your safety stock is calculated on 14 days.

But when was the last time you checked if they actually deliver in 14 days?

If you're like most distributors, the answer is never. And that gap between promised and actual lead times is quietly destroying your inventory planning, causing stockouts on critical items, and forcing your team into firefighting mode.

📉 The Lead Time Lie That's Costing You

Here's the reality: the average on-time delivery rate across businesses is only about 85%. That means roughly 1 in 6 orders arrives late - and that's just the average.

World-class suppliers maintain on-time delivery rates above 95%. Manufacturing benchmarks suggest that anything below 95% "needs improvement." Yet most companies never track this, so they don't know if their suppliers are world-class or bottom-tier.

The consequences ripple through your entire operation:

• Your safety stock is fiction. Safety stock formulas account for both demand variability and lead time variability. If you're using promised lead times instead of actual lead times, you're systematically understocking. Research shows that lead time variability is a primary driver of safety stock requirements - but you can't account for variability you don't measure.

• Your stockouts have a hidden cause. According to the Center for Retail Research, 60-70% of stockouts are caused by late deliveries from suppliers - not demand spikes or forecasting errors. You're blaming your planning team for supplier failures.

• Your customers pay the price. McKinsey research shows the U.S. food retail industry alone loses an estimated 2-3% of sales annually due to stockouts. When stockouts happen, studies indicate that 43% of consumers will go to a competitor. Some research puts this figure even higher - up to 70% of customers will buy elsewhere when faced with out-of-stock items.

🔍 Why Nobody Tracks This

Every ERP system has a lead time field. But almost nobody validates whether those numbers are accurate. Here's why:

• It's buried in transaction data: The information exists - PO date, promised date, receipt date - but it's scattered across purchase orders, receipts, and vendor records. Comparing them requires pulling reports that most ERPs don't make easy.

• Vendors set expectations, then miss them quietly: When a vendor quotes 14 days and delivers in 18, nobody updates the master data. The ERP still says 14 days. Your next safety stock calculation still uses 14 days.

• The variability matters more than the average: A vendor who delivers in exactly 14 days every time is very different from one who averages 14 days but swings between 8 and 25. The second vendor requires dramatically more safety stock - but both look identical in your ERP.

• Nobody owns the problem: Purchasing thinks it's a planning problem. Planning thinks it's a vendor problem. Vendors just keep quoting the same lead times they've always quoted.

🤖 How AI Uncovers the Truth

AI can analyze your entire purchase order history and tell you exactly which vendors deliver when they say they will - and which ones are consistently late.

Here's what the analysis reveals:

• Actual vs. quoted lead times: For each vendor and product, AI calculates the real average delivery time based on historical transactions. If your ERP says 14 days but the data shows 19, you've found a planning gap.

• Lead time variability: Beyond averages, AI measures the standard deviation - how much lead times fluctuate. High variability means you need more safety stock, regardless of the average.

• On-time delivery rates: What percentage of orders arrive by the promised date? Vendors below 90% are actively hurting your operations.

• Quantity accuracy: Late is one problem. Short-shipped is another. AI identifies vendors who consistently deliver less than ordered.

• Trend detection: Is a vendor getting better or worse over time? AI spots deterioration before it becomes a crisis.

Manual order entry makes lead time problems worse.

Y Meadows captures purchase orders directly from email and enters them accurately into your ERP - eliminating delays and data errors that distort lead times and inventory planning.

👉 See How Y Meadows Automates Order Entry (from Email to ERP)

📊 What Data to Provide With the Prompt

Before running the AI analysis, you'll need to export purchase order receipt data from your ERP. Here's exactly what to pull:

Required Fields (Export from ERP):

• PO Number – Links receipt back to original order

• Vendor ID or Name – Identifies the supplier

• SKU/Item Number – Product being ordered

• PO Date – When the order was placed

• Promised/Expected Delivery Date – The date vendor committed to

• Actual Receipt Date – When goods physically arrived

• Quantity Ordered – What you requested

• Quantity Received – What actually showed up

Highly Recommended Fields (if available):

• Product Category – Helps identify patterns by product type

• Vendor Quoted Lead Time – The "official" lead time in your ERP

• Unit Cost – Enables cost-weighted analysis

• Ship Method – Ground vs. expedited patterns

• Receiving Warehouse – For multi-location analysis

How to Get This Data:

• SAP: Transaction ME2M (Purchase Orders by Vendor) joined with MIGO receipts, or use tables EKKO/EKPO joined with MSEG

• Oracle: PO_HEADERS_ALL joined with RCV_TRANSACTIONS

• NetSuite: Purchase Order search with Receipt Date custom field or Transaction search filtering by type

• Microsoft Dynamics: Purchase Order Lines joined with Item Ledger Entries

• Sage/Epicor: Purchase Order History report with receipt details

Pro Tip: Pull at least 12 months of data to capture seasonal patterns. If a vendor only has a handful of orders, the statistics won't be reliable—flag those for manual review.

📋 Copy-Paste Prompt: Vendor Lead Time Audit

Once you have your data, upload it to Claude/ChatGPT and use this prompt to analyze vendor delivery performance:

Analyze our vendor delivery performance vs. quoted lead times:

For each vendor, calculate:

1. LEAD TIME ACCURACY
   - Average quoted/promised lead time (days)
   - Average actual lead time (days from PO to receipt)
   - Lead time gap (actual minus quoted—positive = consistently late)
   - Lead time accuracy rate (% of orders within +/- 2 days of promise)

2. DELIVERY RELIABILITY
   - On-time delivery rate (% delivered by promised date)
   - Early delivery rate (% delivered before promised date)
   - Average days late (for late orders only)
   - Lead time standard deviation (measures consistency)

3. QUANTITY ACCURACY
   - Fill rate (% of orders received in full)
   - Average quantity variance (% short-shipped)
   - Complete order rate (delivered on time AND in full)

4. TREND ANALYSIS
   - Is this vendor getting better or worse over time?
   - Any seasonal patterns in delivery performance?

Identify and flag:
- Vendors who OVER-PROMISE: Actual lead time consistently longer than quoted
- Vendors who UNDER-PROMISE: Actual lead time consistently shorter than quoted
- HIGH-VARIABILITY vendors: Standard deviation > 3 days
- UNRELIABLE vendors: On-time rate below 85%
- QUANTITY PROBLEMS: Fill rate below 95%

Output Format:
1. EXECUTIVE SUMMARY
   - Total POs analyzed and time period covered
   - Overall on-time rate across all vendors
   - Number of vendors in each reliability tier

2. VENDOR SCORECARD (ranked by reliability)
   - Table showing all metrics for each vendor
   - Color-coded: Green (>95% OTD), Yellow (85-95%), Red (<85%)

3. ERP CORRECTION RECOMMENDATIONS
   - For each vendor with >2 day lead time gap:
     * Current ERP lead time
     * Recommended lead time (based on actual average + 1 standard deviation)
     * Impact on safety stock if corrected

4. SAFETY STOCK IMPLICATIONS
   - SKUs that need increased safety stock due to lead time variability
   - Estimated additional inventory investment required
   - Estimated stockout reduction from accurate planning

5. ACTION ITEMS
   - Vendors requiring immediate performance discussions
   - Products to consider dual-sourcing due to unreliability
   - ERP lead time fields that need updating (prioritized list)

6. COST OF UNRELIABILITY (if cost data provided)
   - Estimated rush freight costs attributable to late deliveries
   - Estimated stockout costs from vendor delays

🎯 What the Analysis Will Reveal

When you run this analysis, expect to find some uncomfortable truths:

• Your "reliable" vendors aren't: The vendor you've used for 15 years may be coasting on reputation while their delivery performance has quietly declined. The data doesn't lie.

• Your ERP lead times are 20-40% off: Most companies find significant gaps between what's in the system and what actually happens. A vendor quoted at 10 days who actually averages 14 days throws off every downstream calculation.

• Some vendors are better than you think: You might also find vendors who consistently beat their quoted lead times. These are candidates for preferred supplier status - and you might be carrying excess safety stock for them.

• Product category patterns emerge: Certain product types may have systemic lead time issues regardless of vendor - indicating industry-wide supply constraints you need to plan around.

🔧 What To Do With the Results

• Update your ERP immediately: For every vendor where actual lead time exceeds quoted by more than 2 days, update the master data. Use actual average plus one standard deviation for a buffer.

• Have vendor conversations: Armed with data, approach underperforming vendors. "Your quoted lead time is 14 days, but you've averaged 19 days over the past year with a 76% on-time rate. What's happening?" You may get improvements - or confirmation that you need to find alternatives.

• Adjust safety stock: With accurate lead times and variability measures, recalculate safety stock. You'll likely increase some (for unreliable vendors) and decrease others (for vendors who consistently beat their commitments).

• Build ongoing monitoring: This shouldn't be a one-time analysis. Build a monthly or quarterly review cadence. Vendor performance changes - catch deterioration before it causes stockouts.

🚀 Stop Planning on Fiction

Your vendors quote lead times based on optimism, not reality.

Your ERP believes them because nobody checks.

Your safety stock is wrong because the inputs are wrong.

Your stockouts aren't a forecasting problem—they're a data accuracy problem.

Run the analysis. Find the gaps. Update the numbers.

The truth about your vendors is hiding in your transaction history. It's time to dig it out.

Want to automate vendor performance monitoring and catch lead time issues before they cause stockouts?

Y Meadows brings AI-powered automation to order management and supply chain operations. We help you process orders faster, identify operational issues before they escalate, and make data-driven decisions that improve delivery performance.

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See exactly where your vendor performance is costing you - and how automation can help you stay ahead of supply chain disruptions.