Why Are We Experiencing Simultaneous Stockouts and Overstock

TL;DR

Simultaneous stockouts and overstock are not two separate problems. They are symptoms of one systemic failure in how inventory is planned, positioned, and managed. The root causes include inaccurate forecasts, static safety stocks, cross-functional misalignment, and the bullwhip effect. Fixing it requires addressing the planning process itself, not just adding more inventory.

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Finance looks at the balance sheet and sees record-high inventory levels. Operations looks at the warehouse and sees empty shelves on the products customers actually want. Both are right. And both are frustrated.

This situation, where a company carries too much of the wrong products while running out of the right ones, is far more common than most leaders realize. It has a name: the bimodal inventory problem. And it costs the global economy roughly $1.7 trillion annually in what researchers call inventory distortion.

If your team is asking “why are we experiencing simultaneous stockouts and overstock,” you are not dealing with a procurement mistake or a one-time forecasting miss. You are looking at a structural planning failure that feeds on itself. This article breaks down exactly why it happens, how to diagnose which causes apply to your business, and what actually fixes it.

Struggling with this pattern now? Explore inventory optimization approaches built for mid-market product companies.

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What “Simultaneous Stockouts and Overstock” Actually Means

Stock-outs and excess inventory seem like opposite problems requiring opposite solutions. But they are actually two symptoms of the same root cause: suboptimal inventory management.

The formal term for this pattern is bimodal inventory distribution. Supply Chain Partners defines it as an aggregate inventory view exhibiting two distinct peaks: one showing inventory shortages and backorders, the other showing excessive positions and overstocked SKUs. When you plot your inventory health across your full portfolio, instead of seeing a single bell curve centered near your target, you see two humps at opposite extremes.

Here is what makes it so destructive: inventory distortion now represents 6.5% of global retail sales. Out-of-stocks alone account for $1.2 trillion of that total, while overstocks add another $554 billion. And research from ToolsGroup indicates that stockouts reduce annual revenue by 2 to 5 percent while overstock absorbs 20 to 30 percent of working capital.

Most companies experiencing this paradox don’t realize both problems share the same DNA. They treat stockouts as a purchasing problem and overstock as a warehousing problem. They assign different teams to fix each one. And those teams, working independently, often make the other problem worse.

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Why This Paradox Is So Common

The intuitive reaction to a stockout is simple: buy more. But when a planner panic-buys to fill a gap on one SKU, they consume warehouse space and purchasing budget that was needed for other items. Those items then go short. The cycle repeats.

Practitioners in the Demand Driven Institute community have a telling observation about this moment. The first time a planner sees the bimodal distribution curve plotted from their own data, there is usually a pause. It confirms what many have felt for years: the problems are systemic, not personal. And it challenges the assumption that chasing more precise forecasts alone will fix things.

The RFgen Digital Inventory Report found that 44% of inventory-handling companies struggle with overstocking and understocking simultaneously. Perhaps not coincidentally, 40% of those same respondents cited inaccurate inventory data as a significant challenge.

This is not a rare edge case. It is the default state for companies that have outgrown their planning tools but have not yet built a planning process to match their complexity.

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The Seven Root Causes of Simultaneous Stockouts and Overstock

Understanding why you are experiencing simultaneous stockouts and overstock requires looking beyond surface-level symptoms. Here are the seven structural causes that create the bimodal inventory pattern.

1. Inaccurate or Biased Demand Forecasts

This is the most common driver. When forecasts fail to reflect true demand patterns, companies either buy too much or too little. The error is rarely balanced: forecasts tend to overestimate slow-moving items (creating overstock) and underestimate fast-moving or volatile ones (creating stockouts).

Many mid-market companies still rely on spreadsheets or basic ERP modules that use simple moving averages. These tools cannot account for promotional lifts, channel shifts, regional differences, or new product introductions. When demand signals are noisy, late, or modeled with static logic, inventory plans lag reality by weeks or months.

Practitioners on Reddit’s r/SupplyChainLogistics regularly point out that even companies with forecasting tools in place struggle because the underlying data is messy. Shaped demand (orders constrained by what was available rather than what customers actually wanted) corrupts historical patterns and makes the next forecast less accurate, not more.

For a deeper look at forecasting approaches that address these gaps, see our guide on multi-horizon demand forecasting.

2. Static Safety Stock and Set-and-Forget Reorder Policies

Safety stock is supposed to absorb variability. But in most organizations, safety stock levels are calculated once during system implementation and then rarely touched again. Lead times change. Demand patterns shift. Supplier reliability fluctuates. The buffers stay frozen.

TransImpact identifies static lead times and set-and-forget safety stock policies as two of the most common root causes of both stockouts and overstock. The failure mode is the same in both directions: a safety stock and replenishment strategy that was not designed for the actual variability of demand and supply.

The result is predictable. Products with declining demand carry bloated safety stock that nobody ever draws down. Products with growing or volatile demand carry buffers that were adequate two years ago but get blown through regularly now.

3. Cross-Functional Misalignment and S&OP Failure

Sales wants more inventory to avoid disappointing customers. Finance wants to minimize working capital. Operations wants stable production schedules. Marketing launches promotions without telling anyone in the warehouse. Without shared KPIs and a single consensus plan, everyone optimizes their own silo and the inventory profile fractures.

This is the failure mode that makes experienced supply chain leaders wince, because many companies technically “have S&OP” but it is theater. Practitioners at Invent AI describe the most common version: S&OP becomes a reporting exercise rather than a decision-making forum. Teams present numbers, review variances, and leave without changing anything. That version adds meeting overhead without improving outcomes.

A real S&OP process produces a single plan of record that finance, sales, and operations all commit to. Without it, you get conflicting signals flowing into the same planning system, and the bimodal pattern emerges naturally. If your S&OP cadence needs work, our guide on running an effective S&OP covers the essentials.

4. Wrong Inventory in the Wrong Location

A company might have adequate total inventory across its network but terrible availability at the points where customers actually buy. Inventory sitting in a central warehouse does not serve a customer whose local distribution center is empty.

As SCMR notes, inventory that sits idle in one location while stores or DCs run out of the exact products customers want represents the worst of both worlds: simultaneous overstock and stockouts. The problem is not how much inventory you hold. It is where you hold it.

This cause is especially painful for mid-market distributors and manufacturers operating across multiple warehouses or regional distribution points. Aggregate inventory metrics can look healthy while individual locations are in crisis.

5. The Bullwhip Effect and Reactive Overbuying

The bullwhip effect is one of the most studied phenomena in supply chain management, and it directly explains why companies experience simultaneous stockouts and overstock across different parts of their network.

Here is how it works: a small change in consumer demand, say 5%, gets amplified as it moves upstream. The retailer orders 10% more as a buffer. The distributor, seeing the spike, orders 20% more. The manufacturer, seeing that, orders 40% more from its suppliers. A modest demand signal becomes a massive overreaction.

The 2021 holiday season illustrated this at scale. Stockouts were up 250% compared to 2019. Retailers, desperate to fill shelves, over-purchased aggressively. Then months of delayed orders arrived all at once, swinging from bare shelves to inventory surpluses almost overnight.

The irony of the bullwhip effect, as ScienceInsights observes, is that it simultaneously creates too much inventory at some points in the chain and too little at others.

6. Seasonality, Promotions, and New Product Blind Spots

Seasonal fluctuations, promotional campaigns, and new product introductions all create demand spikes that standard planning systems handle poorly. Promotional uplift that is not factored into the forecast leads to stockouts during the event and overstock of non-promoted substitutes. Seasonal items ordered too early sit in the warehouse for months, tying up capital that could fund replenishment of everyday items.

New product introductions are particularly tricky because there is no historical data to forecast from. Companies either bet conservatively (risking stockouts during the critical launch window) or aggressively (risking obsolescence if the product underperforms). Without structured scenario planning, these launches become coin flips.

7. Poor Data Hygiene and Supply Chain Visibility Gaps

You cannot fix what you cannot see. Only 6% of companies report full visibility across their supply chain. The other 94% are making planning decisions with incomplete, delayed, or inaccurate information.

Common data problems include incorrect on-hand counts, miscategorized SKUs, unrecorded transfers between locations, and phantom inventory (the system says it is there, but it is not on the shelf). Circana draws an important distinction between “true” stockouts and “phantom” stockouts, where inventory exists in the system but is not actually available to fulfill orders.

For mid-market companies running on ERPs like NetSuite or SAP Business One, the gap between what the system shows and what the warehouse reflects can be enormous. Practitioners on Reddit’s r/Netsuite frequently discuss the limitations of native demand planning modules and the need for external forecasting tools when SKU complexity grows.

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The Self-Reinforcing Cycle: How Stockouts Create Overstock (and Vice Versa)

None of the top-ranking pages on this topic explain what might be the most important dynamic: stockouts and overstock actively create each other.

Here is the cycle:

Stockout occurs → Planner panic-buys to prevent recurrence → Excess inventory arrives → Warehouse space fills up → Cash gets tied up in slow-moving stock → Purchasing budget is consumed → High-velocity items cannot be restocked → New stockout on a different SKU → Cycle repeats.

Circana describes the mechanism clearly: as stockouts persist, order quantities tend to increase to account for the lagging inventory, potentially leading to excess when shipments finally arrive.

There is also a working capital dimension that rarely gets discussed. Overstock literally funds stockouts. When too much cash is tied up in excess inventory, it limits the value of orders that can be placed each month. A company sitting on $2 million in slow-moving stock may not have the purchasing budget to buy $200,000 worth of the items actually selling.

This is why “just buying more” without fixing the underlying planning system makes both problems worse. Every dollar invested in the wrong SKU is a dollar not available for the right one.

For a detailed look at reducing inventory without sacrificing service, see our guide on cost savings through inventory reduction.

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How to Diagnose Which Causes Apply to You

Knowing the seven root causes is useful. Knowing which ones are hitting your business is actionable. Here is a straightforward self-diagnostic framework.

Step 1: Map Your SKU Health

Export your current stock levels by item. Tag each SKU into one of three categories: chronically short (required repeated expediting in the past month), chronically over (untouched or above target for weeks), or healthy (within target range). If both the “short” and “over” groups represent more than roughly 25% of your portfolio, you are dealing with bimodal inventory distribution.

Step 2: Measure Forecast Accuracy by Product Family

Calculate MAPE (Mean Absolute Percentage Error) or bias by product family, not just as a company-wide average. A 20% aggregate MAPE can hide the fact that half your families are at 10% error and the other half are at 40%. The families with the worst accuracy are almost certainly the ones driving both your stockouts and your overstock.

Step 3: Audit Your Safety Stock

Ask a simple question: when was safety stock last recalculated? If the answer is “during ERP implementation” or “I’m not sure,” that is a root cause. Pull the date stamps and compare current demand variability and lead times against what was assumed when the buffers were set.

Step 4: Evaluate Your S&OP Process

Does your S&OP process produce a single plan of record that purchasing, production, and finance all execute against? Or is it a monthly slide deck review where people nod and then go back to their own spreadsheets? If no decisions change as a result of the meeting, you have the reporting-exercise failure mode.

Step 5: Check Inventory by Location

If you operate from multiple warehouses or distribution points, aggregate numbers will mislead you. Break availability down by location and channel. You may find that your total network inventory is adequate but badly mispositioned.

After running through these steps, you will have a clear picture of which root causes are active in your business. To quantify the financial impact, try the BetterDemand ROI calculator.

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Breaking the Cycle: What Actually Works

Each solution here maps directly to the root causes above. Generic advice like “improve your forecasting” is not enough. The fix needs to be specific.

For Forecast Inaccuracy (Cause 1): Multi-Horizon Demand Forecasting

Replace static spreadsheet models with continuously updated forecasting that adapts as demand patterns evolve. A multi-horizon approach generates short-term signals for replenishment, medium-term views for production and purchasing, and long-term projections for capacity and financial planning. The forecast should incorporate promotional lifts, seasonal patterns, and new product introductions rather than treating them as one-time manual overrides.

For Static Safety Stock (Cause 2): Differentiated, Dynamic Buffers

Stop applying uniform service-level targets across all SKUs. High-margin, high-velocity items justify higher availability targets and larger buffers. Low-volume or end-of-life items do not. Recalculate safety stock at least quarterly based on current demand variability, supplier lead times, and lead time variability. The math is not complicated, but the discipline of doing it regularly is where most companies fall short.

For Cross-Functional Misalignment (Cause 3): Real S&OP with Decision Authority

Build an S&OP process that produces a single consensus plan and gives participants the authority to act on it. Shared KPIs (not departmental ones) keep everyone aligned. The monthly cadence should include demand review, supply review, pre-S&OP reconciliation, and an executive meeting where trade-offs are decided, not deferred. For companies that need help building this muscle, BetterDemand offers S&OP facilitation as part of its advisory subscription.

For Location Mismatch (Cause 4): Network-Level Inventory Positioning

Optimize inventory placement across your distribution network, not just total quantities. This means analyzing demand patterns by location and channel, calculating optimal stock positions at each node, and rebalancing regularly. Companies that get this right can boost service levels by 3 to 5 percent while reducing overall inventory by 15 to 30 percent.

For the Bullwhip Effect (Cause 5): Consumption-Based Signals

Move from forecast-push to demand-pull where possible. Share point-of-sale or actual consumption data upstream rather than relying on order signals that have already been amplified. Shorter review cycles and smaller, more frequent orders dampen the amplification that creates the bullwhip.

For Seasonal and Promotional Blind Spots (Cause 6): Scenario Planning

Build promotional and seasonal demand into the forecast as structured events with expected uplift ranges, not as last-minute adjustments. Run scenarios before committing to inventory buys: what happens if the promotion lifts 20% instead of 40%? What if the new product launch is delayed by two months?

For Data Hygiene Gaps (Cause 7): Cycle Counting and System Integration

Implement regular cycle counting programs to catch data errors before they cascade into planning failures. Integrate your forecasting and replenishment tools directly with your ERP so that demand signals drive purchasing decisions automatically, reducing the manual handoffs where errors creep in. For companies on NetSuite, SAP, or Microsoft Dynamics, this ERP integration step is often the single highest-impact improvement.

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Glossary of Related Terms

Inventory Distortion: The combined cost of out-of-stocks and overstock across a supply chain. Estimated at $1.7 trillion globally in 2024.

Bimodal Inventory Distribution: An inventory profile showing two peaks, one at shortage levels and one at excess levels, rather than a healthy single peak near target. The hallmark of simultaneous stockouts and overstock.

Bullwhip Effect: The amplification of demand signals as they move upstream through a supply chain. Small fluctuations in end-consumer demand become large swings in supplier orders.

Safety Stock: Extra inventory held as a buffer against demand variability and supply uncertainty. Becomes a problem when set statically and never recalculated.

Demand Sensing: The use of near-real-time data (point-of-sale, web traffic, weather, social signals) to detect demand shifts faster than traditional forecasting methods.

S&OP (Sales and Operations Planning): A monthly cross-functional process that aligns demand, supply, and financial plans into a single consensus plan of record.

Forecast Value Add (FVA): A metric that measures whether each step in the forecasting process (statistical model, analyst override, management adjustment) actually improves accuracy or degrades it.

SKU Rationalization: The process of evaluating which products to keep, modify, or discontinue based on profitability, demand, and strategic fit.

OTIF (On Time In Full): A service-level metric measuring the percentage of orders delivered to customers both on time and with the complete quantity ordered.

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Frequently Asked Questions

Why do stockouts and overstock happen at the same time?

They share the same root causes: inaccurate forecasts, static safety stocks, cross-functional misalignment, and poor inventory positioning. These planning failures push too much inventory toward slow-moving items while starving fast-moving ones. The two problems are not opposites on a spectrum. They are twin symptoms of a single systemic issue.

What is bimodal inventory distribution?

Bimodal inventory distribution describes an inventory profile with two distinct peaks: one cluster of SKUs stuck in shortage or backorder status and another cluster in excess or overstock. It is the statistical fingerprint of the simultaneous stockouts and overstock paradox. If both groups represent more than 25% of your SKU portfolio, you are dealing with a bimodal pattern.

How much does inventory distortion cost?

According to IHL Group, global inventory distortion cost $1.7 trillion in 2024, representing 6.5% of global retail sales. Out-of-stocks accounted for $1.2 trillion while overstock added $554 billion. At the company level, stockouts typically reduce annual revenue by 2 to 5%, and overstock absorbs 20 to 30% of working capital.

Can adding more inventory fix the problem?

No. Adding more inventory without fixing the planning process usually makes things worse. The additional stock tends to pile up on items that are already over-target, consuming warehouse space and purchasing budget needed for items that are actually short. This is the self-reinforcing cycle where overstock directly causes new stockouts.

What is the bullwhip effect and how does it relate?

The bullwhip effect is the amplification of demand signals as they travel upstream through a supply chain. A 5% increase in consumer demand can balloon into a 40% spike in factory orders. This amplification simultaneously creates excess inventory at some points in the chain and shortages at others, making it a direct contributor to the simultaneous stockouts and overstock paradox.

How do I know if my S&OP process is working?

Ask whether your S&OP process produces a single plan of record that all functions execute against and whether real decisions are made in the meeting. If participants present data, review variances, and leave without changing any plans, you have a reporting exercise rather than a planning process. Effective S&OP should measurably improve forecast bias, reduce inventory, and increase service levels over time.

What is the first step to fixing simultaneous stockouts and overstock?

Start with the diagnostic: export your stock levels, tag each SKU as chronically short, chronically over, or healthy, and see how your portfolio splits. Then measure forecast accuracy by product family and check when safety stocks were last recalculated. This gives you a clear picture of which root causes are active before you invest in solutions.

How can mid-market companies afford to fix this?

Mid-market companies ($10M to $5B in revenue) often assume that advanced planning tools require enterprise-scale budgets and multi-year implementations. That is no longer the case. Solutions like BetterDemand combine AI-powered forecasting with embedded supply chain expertise in a subscription model starting at $1,000 per month, with guaranteed measurable improvements within 90 days.