Frequently Asked Questions

Right-sized inventory is the minimum stock that consistently meets demand while limiting working capital, carrying costs, and obsolescence risk.

Right-sized inventory is the optimal amount of stock a business should carry of a given item at a given location to meet customer demand reliably while minimizing excess working capital, carrying costs, and risk of obsolescence.

Most companies both hold too much inventory (tying up cash, creating waste) on items and too little (causing lost sales and service failures) on other items. Right-sizing finds the balance point.

• Not “lean” at all costs → Cutting inventory too aggressively risks stockouts.
• Not “just in case” stockpiles → Over buffering drains profitability.
• The right size depends on demand variability, lead times, and service-level goals.

A distributor carrying $5M of excess stock was able to free $1.2M in working capital while improving service level reliability by shifting inventory from some items to other items using right-sized inventory optimization guided by simulation.

RSI uses Monte Carlo simulation to generate thousands of possible future demand scenarios, allowing us to quantify real-world uncertainty and determine the optimal inventory and reorder rules for each item at each location.

Real demand is messy. Peaks, troughs, and variability rarely fit simple formulas. By simulating many plausible futures instead of relying on averages, RSI identifies the inventory strategy that performs best across uncertainty. This leads to higher service levels, lower stockouts, and more efficient working capital.

Suppose an item has highly volatile weekly demand. Instead of assuming “typical” demand, RSI generates thousands of weeks of simulated demand based on the item’s statistical pattern (trend, seasonality, variability, intermittency).

• For each simulated future, RSI tests different reorder points and order quantities.
• We then select the strategy that delivers your targeted fill rate at the lowest total cost.

This turns uncertain demand into a clear, data-driven stocking decision.

Reduce inventory by targeting variability: segment SKUs, simulate service levels, fix lead-time noise, trim slow movers, and rebalance across locations.

You reduce inventory safely by balancing inventory across items and locations. Cutting “just in case” stock indiscriminately backfires; the smarter play is to align inventory with actual demand risk for each item/location combination.

1. Segment products by demand profile → Treat stable SKUs differently from erratic ones.
2. Simulate service-level trade-offs → Use simulation models to test the impact of lower safety stock before acting.
3. Address upstream variability → Shorten supplier lead times or improve production reliability.
4. Rationalize slow movers → Pare down SKUs that add complexity without customer value.
5. Rebalance across items and locations → Excess in for one item or one DC often masks shortages for other items and locations.

One RSI client reduced overall inventory 18% while raising service levels from 92% to 96%, by cutting waste where it didn’t impact customers.

Statistical forecasting predicts demand from history; simulation tests thousands of scenarios to choose stocking policies under uncertainty.

Forecast = a weather prediction for tomorrow

Simulation = running 10,000 weather scenarios to prepare for both sunny skies and thunderstorms

• Very stable, high-volume SKUs → models using forecasts may be fine.
• High uncertainty, long-tail, seasonal, or critical-service SKUs → simulation adds major value.

Use simulation to size safety stock by testing many demand and lead-time outcomes against your service-level goal.

Traditional formulas (like safety stock based on Normal distribution) assume demand is stable and predictable. In reality, demand is uncertain, which makes simulation the best way to size safety stock.

A parts distributor found that formula-based safety stock caused overstock of stable items and stockouts of slow movers. Simulation reallocated buffers, cutting working capital by 12% and improving fill rates.

Excess inventory ties up cash, raises storage costs, risks obsolescence, adds complexity, and hides real supply chain issues.

Excess inventory drains profitability and flexibility by unnecessarily tying up working capital.

1. Working capital drag → Money tied up in stock can’t be invested elsewhere.
2. Storage and handling costs → Warehousing, insurance, labor.
3. Obsolescence and expiry → Especially risky in seasonal or short-lifecycle products.
4. Hidden complexity → More SKUs = more forecasting errors, more stockouts.
5. Complacency → Excess stock can mask supply chain problems instead of fixing them.

A consumer goods firm was carrying 6 months of excess stock. When demand shifted, 30% of their inventory had to be written off.