Quick calculators and advanced simulators for e-commerce & retail to make data-driven business decisions.
E-commerce Excess Inventory
ActiveSimulate inventory scenarios for your online store. Predict overstock and optimize working capital.
Seasonal Retail Demand
ActivePredict demand peaks and valleys. Simulate inventory by season.
Fashion Store Inventory
ActiveSimulate fashion inventory by season, size, and color.
Electronics Inventory Turnover
ActiveSimulate depreciation and turnover of electronics inventory.
Seasonal Demand Planning Calculator
ActiveProject monthly demand using 12 seasonal factors, identify peak and trough months, and plan staffing or inventory adjustments with appropriate lead time.
EOQ and Reorder Point Calculator
ActiveCalculate the economic order quantity (EOQ), reorder point (ROP) and safety stock for any SKU. Optimize inventory cost and service level with the Wilson model.
Sector context
An SMB e-commerce operator faces three simultaneous fronts: inventory sitting still and tying up working capital, seasonal demand under-sizing stock at peaks and over-sizing it in valleys, and a customer acquisition cost (CAC) climbing with advertising pressure. Every product that doesn't move is trapped cash; every stockout on Black Friday is unrecoverable lost revenue. The challenge: define when to reorder (ROP), how much to reorder (EOQ), and how to react to the seasonal cycle without chronic overstock. The e-commerce simulators model that cycle.
Key metrics
Indicators an SMB operator in the sector should know before modeling decisions.
Inventory turnover
Cost of goods sold / average inventory. Healthy e-commerce turns inventory 6–12 times a year; below 4 signals trapped capital and obsolescence risk.
Stockout rate
% of SKUs out of stock during the period. Above 5% sustained erodes reputation and pushes customers to the competitor.
Sell-through rate
Units sold / units received (fashion, electronics, short-cycle products). Falling below 60% usually demands a discount sweep with sacrificed margin.
AOV (Average Order Value)
Total revenue / orders. Its evolution reflects upsell and cross-sell efficiency; if it drops, the mix is shifting toward cheaper items.
Gross margin per SKU
The top-seller isn't always the most profitable. Knowing margin per SKU lets you prioritize ad spend and catalog placement where it actually pays.
How to pick the right simulator
If frozen inventory is the pain, the inventory excess simulator quantifies the financial cost of stalled stock and models clearance. If the question is operational (when and how much to reorder), the inventory ROP/EOQ simulator resolves safety stock, reorder point, and economic order quantity. For highly seasonal categories (home decor, fashion, electronics), the seasonal demand simulator adjusts projections against past cycles. For fashion specifically, the fashion stock simulator models sell-through per collection and seasonal depreciation. And if you sell electronics with a short life cycle, the electronics turnover simulator anticipates obsolescence.
Practical example
Hypothetical case in US dollars. Plug your real numbers into the simulator to validate your own scenario.
An e-commerce sells consumer electronics with $480,000 USD annual revenue and an average inventory of $95,000 USD, i.e. inventory turnover of 5.05×/year. The cost of working capital plus storage and insurance adds up to 18% annually. Holding cost: $17,100 USD/year. The simulator models a planning improvement that raises turnover to 7.5× (average inventory falls to $64,000 USD): new holding cost $11,520 USD, saving $5,580 USD/year. It also frees $31,000 USD in working capital that can be redeployed to higher-margin SKUs. The simulator lets you test sensitivity to lead time, seasonal demand, and target safety stock.
Common modeling mistakes
Traps we see when reviewing sector planning. Avoid them before closing your own model.
Applying EOQ to SKUs with volatile demand
EOQ assumes relatively stable demand. In short-life-cycle categories (fashion, electronics, gaming) the economic-lot calculation becomes irrelevant; use pull or vendor-managed inventory instead.
Confusing stockout with bin emptiness
A SKU available in the warehouse but un-synced to the storefront still generates a checkout-time stockout. Model real customer-facing availability, not accounting inventory.
Ignoring promotion effects
A Black Friday sale can sell in 3 days what normally moves in 6 weeks. If your ROP model uses average annual demand, stock will collapse before the peak.
Trusting stated supplier lead time
The supplier claims 14 days; actual average is 22 with a 6-day standard deviation. Use measured lead time, not declared, and add safety stock to the deviation.
Scope and limitations
E-commerce simulators assume reasonably predictable demand. Viral launches, logistics shocks (strikes, weather), and sharp customs-policy changes aren't modeled: they need tactical response, not safety-stock recompute. For broad catalogs (>2,000 active SKUs), exception management beats the flat model: prioritize A items (top 20% driving 80% of revenue) and review B/C quarterly.
Start here
The simulators with the most adoption in the sector. Each tackles a different business question.
Inventory excess
Quantifies the financial cost of stalled stock and models clearance.
ROP / EOQ
When and how much to reorder: safety stock + reorder point + economic order quantity.
Seasonal demand
Adjusts projections by past cycles and campaign peaks.
Fashion stock
Sell-through per collection and seasonal depreciation.
Electronics turnover
Anticipates obsolescence in short-life-cycle categories.