Case: Inventory Is Rising While Stockouts Continue
How can a warehouse be full and the customer shelf still be empty? That is the paradox in this case: the company does not have “too little inventory” - it has the wrong inventory, in the wrong place, at the wrong time.
- Rising inventory with stockouts is usually a mismatch problem, not a simple shortage problem.
- Diagnose across four cuts: SKU mix, location, timing, and inventory accuracy.
- The key metrics are fill rate, stockout rate, inventory turns, days inventory, forecast bias and aged stock percentage.
- Most cases need segmentation: A-items need high availability; C-items should not absorb working capital.
- The fix is rarely “buy more stock”; it is usually better allocation, replenishment rules, demand sensing and cleanup of dead stock.
- The best interview answer separates aggregate inventory from available inventory at the demand point.
Big Picture: The Inventory Paradox
When stockouts continue despite rising inventory, the first move is to stop looking at total inventory as one number. Total inventory can rise because slow movers are piling up, while fast movers are unavailable in the locations where demand is happening.
Core Explanation: Diagnose the Mismatch Before Recommending More Stock
The interviewer is testing whether you can think like an operations manager, not just calculate reorder points. If you recommend “increase safety stock” too early, you may worsen the problem: more cash blocked, more obsolete inventory, and still no product at the customer-facing node.
Use this sequence:
The 2x2 That Makes the Case Click
The fastest way to structure this case is to map products by demand velocity and inventory availability. It immediately shows where the business is bleeding service and where it is trapping cash.
Read the matrix like a manager:
- Protect: fast-moving SKUs with low availability. These cause stockouts, lost sales and customer frustration.
- Cash Trap: slow-moving SKUs with high stock. These explain why inventory is rising.
- Win Zone: fast-moving SKUs with enough stock. Maintain service, but do not blindly overstock.
- Ignore Carefully: low-demand SKUs with low stock. Keep them only if they are strategic, regulated, or needed for assortment completeness.
The Six Metrics You Must Track
Do not diagnose this case only through stories. Bring 4-6 metrics and show what each one proves.
A good answer connects the metrics: if aged stock is rising while A-item fill rate is falling, the company has a mix problem. If inventory turns are falling while stockout rate rises in specific cities, the company has a location-allocation problem.
Worked Example: Same Inventory, Different Service Outcome
Suppose a retailer has two SKUs:
Total inventory is 320 units. That sounds comfortable because total weekly demand is only 110 units. But the fast mover has only 20 percent of its weekly demand covered, while the slow mover has 30 weeks of stock.
Case insight: buying more total inventory is not the first fix. Replenish SKU A, pause or markdown SKU B, and investigate why the planning system allowed such an uneven position.
Definitions You Can Say in One Breath
- Inventory: stock of materials, components, or finished goods held to meet future demand or production needs.
- Stockout: a demand event that cannot be fulfilled immediately from available stock at the required location and time.
- Service level: the probability of meeting demand without a stockout during a replenishment cycle.
- Fill rate: the share of demand units fulfilled immediately from available inventory.
- Safety stock: extra inventory held to protect against demand variability, supply variability, or forecast error.
Root Causes: What Could Be Going Wrong?
Once you understand the paradox, test root causes in a clean order. Do not jump randomly from forecasting to vendors to warehouse space.
If the issue is recurring and SKU-level, the natural next step is to revise setting inventory policy for a multi-product business. If the issue is shop-floor or warehouse replenishment, connect it with Kanban and pull-based replenishment.
Case Study: Nike’s Inventory Glut Shows Why Mix and Timing Matter
Nike’s FY23 inventory pressure showed how a company can hold too much inventory overall while still needing the right products in the right channels.
Nike told investors in its FY23 first-quarter results that inventory was elevated as delayed shipments arrived and supply-chain volatility continued (NIKE, Inc. FY23 Q1 results). The managerial lesson is powerful: stock arriving late does not automatically solve service problems. If demand has shifted, seasons have moved, or channels have different needs, late inventory can become excess inventory.
The primary driver was a timing and mix mismatch: inventory that was planned for one demand window reached the system after conditions had changed. Supporting drivers included longer supply lead times, channel-level demand shifts, product variety across styles and sizes, and the need to protect full-price selling while clearing slower inventory.
For an Indian parallel, think of a beauty or fashion marketplace such as Nykaa or Myntra during festive spikes. A warehouse can be full of adjacent shades, sizes or older styles, while the exact fast-moving shade or size in Mumbai, Bengaluru or Delhi NCR goes out of stock. India-specific mechanics make this harder: city-level demand variation, marketplace service-level promises, return flows, batch or expiry concerns in beauty, and promotion-led demand bursts.
So what: the best fix is not “increase inventory.” It is to improve demand visibility, align supply timing, and actively move or liquidate stock that is consuming working capital without improving service.
How AI Changes Inventory Rising While Stockouts Continue
AI makes this case more practical because it can detect mismatch faster than manual dashboards, especially when the company has thousands of SKU-location combinations.
- Demand sensing at SKU-location level: machine-learning models can use recent sales, promotions, weather, local events and search signals to update forecasts faster than monthly planning cycles.
- Inventory anomaly detection: AI can flag combinations such as “stock rising but fill rate falling” or “high inventory with negative forecast bias” before the monthly review.
- Replenishment recommendations: optimization models can suggest transfers, reorder quantities and safety-stock changes while respecting lead times, MOQs and service targets.
Practical student workflow: load the case facts, SKU table and company context into ChatGPT or NotebookLM, then ask: “Segment SKUs into protect, cash trap, win zone and ignore carefully; identify the top three root causes; recommend actions with metrics to track.” For deeper revision, read using AI for inventory optimisation and replenishment.
Interview Relevance
A retail chain says inventory value has increased by 25 percent over six months, but customer complaints about stockouts have also increased. How would you diagnose and solve the problem?
Say this line early: “I will not treat inventory as one pool. I will split it by SKU, location and demand velocity to see where service is failing and where cash is stuck.” That immediately signals structured operations thinking.
Common Mistake
The biggest mistake is recommending higher safety stock without diagnosing why inventory is already rising. It costs candidates because it solves the symptom with more working capital and may worsen obsolescence. One-line fix: first separate fast-moving stockouts from slow-moving overstock, then change policy by segment.