Frequently Asked Questions
RSI Q&A
What does “right-sized inventory” mean in supply chain management?
Short Answer:
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 do you calculate the right amount of safety stock with uncertain demand?
Short Answer:
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.
How can companies reduce inventory without hurting service levels?
Short Answer:
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 are the risks of carrying too much inventory?
Short Answer:
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.
What’s the difference between inventory optimization based on demand forecasting versus simulation-based optimization?
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.
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.