How to Reduce Stockouts and Excess Inventory Simultaneously

TL;DR

Stockouts and excess inventory coexist when a company holds the wrong products in the wrong places based on outdated planning assumptions. You reduce both stockouts and excess inventory simultaneously not by cutting stock everywhere, but by correcting the demand signal, segmenting SKUs, recalculating safety stock from real variability, repositioning inventory across locations, and governing trade-offs through S&OP. The goal is better inventory, not simply less inventory.

How to Reduce Both Stockouts and Excess Inventory Simultaneously

Here is the uncomfortable truth most companies discover eventually: the warehouse is full, working capital is stretched, and customers still cannot get what they ordered on time.

Finance wants to cut inventory by 15%. Operations knows that will make service worse. Sales blames the forecast. The planner blames the supplier. Everyone is partially right and completely stuck.

The reason stockouts and excess inventory coexist is simple. The excess is usually not protecting the stockout risk. It is sitting in the wrong SKU, the wrong location, the wrong time bucket, or the wrong planning assumption. ToolsGroup frames this as a common pattern: companies carry surplus inventory and still experience stockouts because they hold too much of the wrong products or hold stock in the wrong places while critical items remain unavailable elsewhere. Source

Reducing stockouts and excess inventory simultaneously means improving inventory precision at the SKU-location level. It means replacing blanket buffers with segmented, data-driven policies that put the right stock where demand and business risk justify it. IBM defines inventory optimization as maintaining the right amount of goods to meet demand while minimizing cost and maximizing profitability, using data, analytics, and algorithms to set optimal inventory levels across the supply chain. Source

This is not about holding less inventory everywhere. It is about holding the right inventory where it matters.

See how BetterDemand connects forecasting to inventory decisions

Why Companies Have Stockouts and Excess Inventory at the Same Time

Most companies do not have an inventory quantity problem first. They have an inventory allocation and policy problem. They carry too much where risk is low and too little where risk is high.

The root causes are predictable:

• Forecasts are biased or stale. The demand plan does not reflect what customers actually want, where, and when.
• Safety stock is set by rule of thumb. A flat “keep 30 days of supply” creates excess on stable items and shortages on volatile ones.
• Service levels are identical for every SKU. A 95% target applied uniformly wastes working capital on slow movers while under-protecting bestsellers.
• Lead times are assumed fixed. Supplier variability is ignored even when delivery windows fluctuate by weeks.
• MOQs force overbuying. Minimum order quantities push the company to buy more than it needs on items where demand does not justify the batch size.
• Inventory records are wrong. The system says stock exists, but the warehouse cannot find it. That creates phantom availability.
• Sales history misses lost demand. When a product is out of stock, recorded sales drop to zero, and the next forecast learns the wrong lesson.
• S&OP does not produce one executable plan. Demand, supply, finance, and commercial teams operate from different assumptions.

AGR makes a similar observation: traditional approaches average demand, treat lead times as fixed, and adjust safety stock infrequently, which becomes fragile as SKU counts, locations, and volatility increase. Source

If your team is seeing these patterns, the diagnostic guide for hidden causes of excess inventory walks through each root cause in detail.

The Wrong Fixes

Before getting to what works, here is what makes the problem worse.

Cutting inventory by the same percentage everywhere. A blanket 15% reduction trims protection on critical A items just as much as it trims dead stock. Service collapses on the SKUs that matter most.

Raising safety stock everywhere. This hides the problem behind more cash. Inventory carrying costs typically run 15% to 40% per year when warehousing is included. Source Excess inventory is not free insurance.

Chasing forecast accuracy without changing replenishment. A forecast is an input. Inventory policy is the decision. Replenishment is the execution. If the forecast improves but reorder points, safety stock, MOQs, and service targets remain stale, the business will still stock out and overbuy. AGR explicitly states that forecasts alone do not prevent stock issues; the value comes when forecasting connects to inventory policies and replenishment decisions. Source

Trusting ERP inventory blindly. Practitioners on Reddit report that retailers commonly struggle with inaccurate on-hand quantities due to misplaced items, theft, bad manifests, and manual errors. One commenter in a retail data science thread estimated that about 30% of system stock numbers were wrong in their environment. Source If your inventory record is wrong, your replenishment policy is optimizing fiction.

Letting every sales override become a purchase order. Without accountability, overrides introduce bias that compounds over time. Understanding common forecasting mistakes helps teams avoid this trap.

The Four Loops That Reduce Stockouts and Excess Inventory Together

Here is a practical framework. Reducing both stockouts and excess inventory simultaneously requires four connected loops working together, not just one.

Loop 1: Fix the Demand Signal

The forecast needs to represent true demand, not distorted sales history.

This is the gap most competitor guides ignore. When a product is out of stock, historical sales understate demand. If the planning system uses sales as the only signal, it may conclude demand fell, reduce the forecast, and order too little again. This creates a stockout feedback loop.

Practitioners on Reddit stress this point forcefully. In a parts-counter discussion, one commenter argued that lost sales should be posted in the dealer management system because actual sales plus lost sales equals true demand. Source

What to do:

• Mark stockout periods in sales history.
• Estimate lost sales during out-of-stock windows.
• Separate baseline, promotional, seasonal, and new-product demand.
• Track forecast bias, not just forecast accuracy. Aggregate accuracy can hide SKU-level failures. The forecast bias and aggregate trap explains why this distinction matters.
• Flag zero-sales periods where inventory was unavailable. Do not train the forecast on shortage-constrained history without adjustment.

Loop 2: Set Smart Inventory Policy

The goal here is setting the right service target, safety stock, reorder point, and order quantity for each SKU-location, not a single rule applied everywhere.

A flat “15 days of average sales” safety stock rule ignores demand variability and lead-time deviation. LinkedIn practitioners make this critique directly: static days-of-supply buffers can be too high for stable high-volume products while too low for erratic or long-lead products. Source

Bad Rule	Better Rule
Same days of supply for every SKU	Safety stock based on forecast error, lead-time variability, and service target
Same service level for every SKU	Differentiated service by margin, velocity, customer importance, and substitutability
Same review cycle for every supplier	Review cycle based on lead-time reliability, MOQ, and supply risk
Same forecast method for all demand	Method matched to demand pattern: stable, seasonal, intermittent, promotional

Service levels should be financial decisions. Set them higher where a stockout loses key accounts, margin, or production continuity. Lower them where customers can wait, substitute, or where margins do not justify high working capital. If your safety stock formula may be misleading your team, it is worth recalculating from real variability inputs.

Practitioners on Reddit frame safety stock the same way: if the lost-sale cost is higher than holding cost, hold more. If not, hold less. The goal is not zero safety stock. The goal is right-sized safety stock. Source

Loop 3: Position and Replenish Smarter

If each warehouse, DC, or branch protects itself independently, the company duplicates buffers. Total inventory rises while stockouts still appear wherever demand actually shows up.

Multi-echelon thinking changes this. If five branches each hold their own emergency buffer, total inventory rises. If some risk can be pooled upstream at a central DC and replenished quickly, the network may need less total safety stock while improving downstream availability.

Before issuing new purchase orders, ask: Is this SKU excess somewhere else in the network? Can inventory be transferred instead of bought? Is stock stuck in QC, transit, returns, or an overstock bin?

For companies managing imported goods with long lead times, buffer sizing for overseas POs is especially relevant to this loop.

Loop 4: Govern Through S&OP

The best analytics in the world fail without a decision cadence. S&OP should decide which demand plan is official, which customers get priority during constrained supply, where service targets should change, and whether finance’s inventory reduction target is feasible without service damage.

StockIQ argues that AI supports planning, but humans must make strategic trade-offs: how much cash to tie up, what service risk to accept, and which exceptions matter. Source

Explore how BetterDemand supports S&OP decisions

Diagnostic Matrix: What You See vs. What to Fix

This table maps common symptoms to their likely root causes and better fixes.

What the Business Sees	Likely Root Cause	Better Fix
High total inventory, stockouts on bestsellers	Inventory mix problem	Reallocate capital from slow movers to critical fast movers
Excess in one warehouse, stockouts in another	Placement problem	Rebalance stock, review multi-echelon targets
Stockouts during promotions, excess after	Event planning problem	Separate baseline and uplift; plan ramp-down and exit inventory
System shows inventory, customer cannot get product	Inventory accuracy problem	Fix cycle counts, bin discipline, receiving, and putaway
Sales history says low demand, customers complain unavailable	Censored demand	Estimate lost sales, exclude OOS periods from forecast training
Expedited freight rising alongside excess inventory	Lead-time and policy drift	Recalculate lead times, safety stock, and supplier scorecards
Every SKU has the same safety stock days	Static rule problem	Use variability-based safety stock with differentiated service levels
New products stock out, then become obsolete	Lifecycle planning problem	Use launch scenarios, early demand sensing, controlled replenishment
Slow movers fill the warehouse	SKU proliferation	SKU rationalization and lower service for weak long-tail items
Forecast looks good in aggregate, service is poor	Aggregation trap	Measure accuracy at the SKU-location-week level where replenishment happens

Where AI Actually Helps

The SERP for how to reduce both stockouts and excess inventory simultaneously is heavy on AI promises. The honest answer is more nuanced.

AI can help by updating forecasts as new signals arrive, detecting bias and demand pattern changes, recalculating safety stock and reorder points dynamically, simulating demand surges and supplier delays, prioritizing exceptions, and pushing recommendations into purchasing workflows. eTurns describes predictive inventory systems that collect data from ERP, WMS, POS, and supplier portals, then use forecasting, scenario modeling, automated replenishment, and continuous monitoring. Source

But AI will not fix bad master data, inaccurate on-hand balances, unclear service targets, or a company culture that overrides forecasts without accountability.

One practitioner in a Reddit supply chain thread reported using inventory optimization software in a complex make-to-order/make-to-stock environment and claimed inventory fell 40% while stockouts fell 90% in under 90 days. But another commenter in the same thread warned that explaining service level, safety stock, lead time, and forecast error to sales and marketing stakeholders is a major change-management challenge. Source The math must be operationalized and explained, not just computed.

Forecasts should guide targets and scenarios. Replenishment should respond to inventory position, demand, lead time, and risk. A forecast that sits in a dashboard but never changes a purchase order is decoration.

A Concrete Example

A distributor has $5 million in inventory and still misses orders on 40 top SKUs. Finance wants to cut inventory 15%. A blanket cut would crush service on the items that drive revenue.

The team digs into the data. They find that 35% of inventory value is tied up in slow-moving C items with low margin and easy substitutes. Meanwhile, A items have stale lead-time assumptions from two years ago, under-calculated safety stock, and reorder points that have not been updated since a supplier changed shipping schedules.

The fix: liquidate or reduce service on slow movers, raise protection for critical A items using current demand and lead-time variability, update reorder points by location, and transfer existing excess before issuing new POs. Total inventory drops. Service improves. Working capital is freed for things that matter.

NTT DATA documented a similar pattern with a forestry products manufacturer: excessive slow-moving stock alongside frequent stockouts of fast movers. By adjusting safety stock to localized SKU demand at each distribution center, the company improved service levels while reducing inventory value, with nearly €1.5 million per location identified as recoverable. Source

For more on how to reduce inventory without losing service, that guide covers the financial mechanics in depth.

Metrics That Tell You Whether It Is Working

Metric	Why It Matters
Fill rate / line fill	Shows whether demand is fulfilled from stock
OTIF	Shows customer-facing delivery performance
Stockout frequency	Shows how often availability fails by SKU-location
Lost sales estimate	Recovers the true demand signal
Inventory turns	Shows working-capital efficiency
Days of supply by segment	Shows exposure, but should not be used alone
Excess and obsolete inventory	Shows dead or risky stock
Forecast bias	Shows whether plans consistently over- or under-call demand
Forecast error at SKU-location level	Shows replenishment-level accuracy
Lead-time variability	Shows supply uncertainty
Expedited freight spend	Shows hidden cost of poor planning
Inventory record accuracy	Shows whether ERP/WMS data can be trusted

Track these together. An improvement in fill rate that comes with rising expedite costs is not a win. A drop in inventory that comes with rising stockouts is not a win either.

Quick Checklist

• Identify stockouts by SKU-location, not only total fill rate.
• Identify excess by SKU-location-age, not only inventory dollars.
• Mark stockout periods in demand history.
• Estimate lost sales.
• Segment SKUs by value, velocity, variability, lead time, and substitutability.
• Assign service levels by business impact.
• Recalculate safety stock using current demand and lead-time variability.
• Update reorder points and min/max settings.
• Check MOQs, order cycles, and supplier lead-time reliability.
• Rebalance stock before buying more.
• Review inventory accuracy and cycle count results.
• Use S&OP to align demand, supply, finance, and customer priorities.

FAQ

Can you really reduce stockouts and excess inventory at the same time?

Yes. The two problems are not opposites. They are usually symptoms of the same planning failure, specifically inventory that is in the wrong place, wrong SKU, or wrong quantity based on outdated assumptions. Fix the allocation and policy, and both improve.

Does better forecasting automatically reduce excess inventory?

No. Better forecasting reduces uncertainty, which means you can hold less safety stock for the same service level. But the forecast must flow into updated safety stock calculations, reorder points, and replenishment decisions. A better forecast that does not change a purchase order changes nothing.

Is safety stock a sign of poor planning?

Not at all. Safety stock is a calculated risk buffer against demand and supply uncertainty. Poorly sized safety stock, either too much everywhere or not enough on critical SKUs, is the sign of poor planning. Right-sized safety stock is the goal.

What is the single fastest first step?

Segment your inventory by SKU-location and compare stockouts, excess, margin, and velocity side by side. The first win almost always comes from moving working capital away from slow movers and toward constrained, high-value items that are stocking out.

Why do blanket inventory cuts make things worse?

Because the excess and the shortage are rarely on the same items. A 15% reduction applied uniformly trims protection on critical A items just as aggressively as it trims dead C stock. Service drops on the SKUs that drive revenue while the real excess barely moves.

How does AI help reduce both stockouts and excess inventory simultaneously?

AI can continuously update demand signals, detect forecast bias, recalculate safety stock dynamically, simulate supply disruptions, and prioritize planner exceptions. But it requires clean data, accurate inventory records, defined service targets, and human governance of trade-offs.

What role does S&OP play?

S&OP is where the trade-offs become explicit decisions. It aligns the demand plan, supply response, financial targets, and customer commitments into one executable plan. Without it, each function optimizes independently, and the company ends up with conflicting signals that produce both stockouts and excess.

How long does it take to see results?

Companies that fix the demand signal, recalculate safety stock, and reposition inventory often see measurable improvement within one to three months. Vendor-reported examples include 20% inventory reduction with improved fill rates within 90 days, though results vary by data quality and organizational readiness.

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If your team is carrying excess inventory while still expediting orders and missing service targets, the issue is probably not one bad forecast. It is the connection between forecasting, inventory policy, positioning, and monthly decision-making. BetterDemand helps mid-market product companies build a live demand forecast and turn it into supply chain decisions that actually get executed.