Case Study Supply Chain & Inventory · 2026

The Inventory
Blind Spot

A distributor was overstocked on slow sellers and stockedout on bestsellers — simultaneously. The root cause wasn't procurement. It was invisible data.

Rp 1.2B
Capital trapped in dead stock
43%
Stockout rate on top SKUs
6 wks
To full inventory intelligence
Rp 890M
Capital unlocked in 90 days
01 · The Problem

Overstock and stockout —
at the same time.

This should be mathematically impossible. Yet it's one of the most common inventory pathologies we encounter. The warehouse was full. But it was full of the wrong things. And the right things kept running out.

01
📦
Dead Stock Accumulation
340+ SKUs with zero movement in 90+ days. Rp 1.2B in capital sitting on shelves, generating storage costs and zero revenue. No one had mapped this.
02
🚨
Chronic Stockouts on Winners
Top 20 SKUs (representing 67% of revenue) ran out of stock an average of 8.3 times per quarter. Each stockout caused emergency re-orders at premium rates.
03
🔮
Zero Demand Forecasting
Procurement decisions were based on gut feel and last month's orders — not velocity trends, seasonality, or lead time data. Every reorder was a guess.
340
SKUs with 90-day
zero movement
Rp 1.2B
Capital locked in
dead inventory
8.3×
Avg stockouts per
quarter per top SKU
+34%
Premium paid on
emergency reorders
Rp 78M
Storage cost on
dead stock (per qtr)
02 · Data Analysis

Mapping 2,800 SKUs.
Finding the truth.

We pulled 24 months of sales data, purchase orders, supplier lead times, and warehouse movement logs. After cleaning and cross-referencing, we built the first complete picture of inventory health this business had ever seen.

SKU Performance Matrix — Top 20 Revenue Drivers
Sales velocity, stock level, reorder status, and margin contribution per product
Product Monthly Velocity Current Stock Days Remaining Reorder Lead Time Margin Status
SKU-A041 · Premium Filter 📈 420 units/mo 38 units 2.7 days 14 days 38% ⚠ CRITICAL LOW
SKU-B012 · Industrial Valve 📈 284 units/mo 12 units 1.3 days 21 days 44% ⚠ CRITICAL LOW
SKU-C088 · Standard Gasket 📊 156 units/mo 2,840 units 548 days 7 days 18% OVERSTOCK ×18
SKU-D203 · Pump Assembly 📈 198 units/mo 74 units 11 days 18 days 52% ⚠ REORDER NOW
SKU-E019 · Legacy Connector 📉 3 units/mo 890 units 890+ days 3 days -4% DEAD STOCK
SKU-F301 · Pro Seal Kit 📈 312 units/mo 224 units 21 days 14 days 41% ✓ HEALTHY
SKU-G445 · Bulk Adhesive 📉 8 units/mo 1,240 units 465 days 5 days 12% DEAD STOCK
Inventory Capital Distribution
Where your Rp 2.1B in stock is actually allocated
Dead Stock (0 mv / 90d) Rp 1.2B · 57%
57%
Overstocked (>90d supply) Rp 420M · 20%
20%
Healthy Range (15–45d) Rp 315M · 15%
15%
Critically Low (<14d) Rp 168M · 8%
8%

Key finding: 77% of capital is locked in stock that either doesn't move or is massively oversupplied — while the top revenue generators are running out.

Stockout Frequency — Top SKUs
Number of stockout events per month across high-revenue products
20 15 10 5 0 Jul Aug Sep Oct Nov Dec Jan Stockout events trending upward →
03 · The Solution

From gut feel
to data-driven procurement.

We didn't recommend new software. We built an analytical layer on top of what they already had — turning raw transaction data into a living inventory intelligence system.

01
Full SKU Velocity Mapping
Calculated 30/60/90-day sales velocity for every SKU. Identified the 80/20 split: 22% of SKUs drove 78% of revenue. Everything else needed a liquidation or discontinuation decision.
Identified Rp 1.2B in capital to unlock
02
Dynamic Reorder Point Calculation
Built reorder triggers for each top SKU based on actual lead time × safety stock formula. Eliminated "reorder when it feels low" with precision reorder points that account for supplier variability.
Stockout risk reduced by 74% on critical SKUs
03
Dead Stock Liquidation Roadmap
Ranked 340 dead-stock SKUs by recovery potential. Created a tiered plan: discount bundling for near-dead items, supplier return negotiation for recent overbuys, write-off recommendation for aged stock.
Rp 890M capital recovery projected over 90 days
04
Seasonal Demand Forecasting Model
Analyzed 24 months of sales patterns to identify seasonal peaks per SKU category. Procurement now aligns 6–8 weeks ahead of demand spikes instead of reacting to them.
Emergency reorder premium cost eliminated (saved Rp 34% markup)
04 · Results — Before & After

Same business.
Completely different operation.

90 days post-implementation. No new software purchased. No headcount added. Just better data and better decisions.

// BEFORE
❌
Rp 1.2B locked in dead stock, generating zero revenue and Rp 78M/qtr storage cost
❌
Top SKUs stocking out 8.3 times per quarter — lost sales, emergency reorders at +34% premium
❌
Procurement based on gut feel and last month's numbers — no velocity data, no lead time logic
❌
Zero visibility into which products were growing, dying, or seasonal
// AFTER 90 DAYS
✅
Rp 890M recovered through liquidation roadmap — capital redeployed into high-velocity SKUs
✅
Critical SKU stockouts reduced by 74% — dynamic reorder points active for top 50 products
✅
Every reorder decision backed by velocity data + lead time buffer — no more guessing
✅
6-month seasonal forecast live — procurement team now operates 6–8 weeks ahead of demand
Rp 890M
Capital unlocked
in 90 days
−74%
Stockout reduction
on critical SKUs
−34%
Emergency reorder
premium eliminated
Rp 78M
Storage cost saved
per quarter
6 mo
Demand forecast
horizon (was: 0)

Is capital hiding in your warehouse while your best sellers run dry?

We've seen this pattern across dozens of distributors. The fix is always in the data.

Muhamad Reva · Business Efficiency Partner