Frequently Asked Questions
RSI Q&A
What does “right-sized inventory” mean in supply chain management?
Short Answer:
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.
Why It Matters:
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. How to Think About It:
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.
Example:
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.
Key Takeaway:
Right-sized inventory = enough to win and retain customers, lean enough to protect profitability
How can companies reduce inventory without hurting service levels?
Short Answer:
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.
Five Practical Levers:
Segment products by demand profile → Treat stable SKUs differently from erratic ones.
Simulate service-level trade-offs → Use simulation models to test the impact of lower safety stock before acting.
Address upstream variability → Shorten supplier lead times or improve production reliability.
Rationalize slow movers → Pare down SKUs that add complexity without customer value.
Rebalance across items and locations → Excess in for one item or one DC often masks shortages for other items and locations.
Example:
One RSI client reduced overall inventory 18% while raising service levels from 92% to 96% — by cutting waste where it didn’t impact customers.
Key Takeaway:
Inventory cuts should come from precision, not blunt force. Done right, you serve customers better with less.
What’s the difference between inventory optimization based on demand forecasting versus simulation-based optimization?
Statistical forecasting predicts demand from history; simulation tests thousands of scenarios to choose stocking policies under uncertainty.
Forecast-based Optimization:
Relies on accurate forward-looking forecasts.
Works for highly stable, high-volume items.
Struggles with volatile, intermittent, or “long-tail” SKUs.
Simulation-based Optimization:
Models thousands of demand scenarios, including variability and uncertainty.
Tests how different stocking policies perform across those scenarios.
Provides a distribution of outcomes rather than a single forecast.
Analogy:
Forecast = a weather prediction for tomorrow
Simulation = running 10,000 weather scenarios to prepare for both sunny skies and thunderstorms
When to Use Which:
Very stable, high-volume SKUs → models using forecasts may be fine.
High uncertainty, long-tail, seasonal, or critical-service SKUs → simulation adds major value.
Key Takeaway:
Forecasting tells you what might happen. Simulation tells you what will probably happen and what to do about it.
How do you calculate the right amount of safety stock with uncertain demand?
Short Answer:
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.
Traditional Formula Approach:
Safety stock = (Service Factor) × (Standard Deviation of Demand) × (√Lead Time).
Works well in textbooks, often fails in the real world.
Simulation Approach:
Run thousands of possible demand/lead time outcomes based on actual demand history.
Test different safety stock levels against actual service level goals.
Pick the level that balances cost vs. customer impact for each item/location combination.
Example:
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.
Key Takeaway:
Don’t just calculate safety stock — test it under uncertainty. Simulation is how you know if your safety stock is really right-sized.
What are the risks of carrying too much inventory?
Short Answer:
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.
Top Five Risks:
Working capital drag → Money tied up in stock can’t be invested elsewhere.
Storage and handling costs → Warehousing, insurance, labor.
Obsolescence and expiry → Especially risky in seasonal or short-lifecycle products.
Hidden complexity → More SKUs = more forecasting errors, more stockouts.
Complacency → Excess stock can mask supply chain problems instead of fixing them.
Example:
A consumer goods firm was carrying 6 months of excess stock. When demand shifted, 30% of their inventory had to be written off.
Key Takeaway:
Inventory is an asset only if it’s the right inventory. Otherwise, it’s a liability.
Schedule a Call
Find out if Right Sized Inventory is right for your business and inventory needs. Set up a call with one of our team and we’ll walk you through a demo, identify trouble spots in your business, and see how implementing RSI can transform your business through data-driven inventory optimization.