Build a range-based forecast using multiple scenarios (conservative, base, optimistic). Use short lead-time suppliers or smaller initial orders to minimize risk. Track leading indicators like social momentum, search trends, and repeat purchase rates to validate steady-state assumptions before committing to large POs.

Compare against historical seasonal patterns, check for sudden spikes or anomalies, validate against recent sales trends and marketing calendar. Cross-reference with vendor scorecard data to ensure realistic lead times. Start with conservative quantities and use a phased approach—initial PO plus replenishment buffer.

Yes. CommerceOS by Endless consolidates inventory from multiple sources into a single source of truth. Real-time API connections to Shopify, NetSuite, QuickBooks, 3PL WMS systems, and EDI networks automatically reconcile stock levels, track inbound shipments, and adjust forecasts based on unified data across channels.

Set channel-specific inventory allocation rules and safety stock buffers. Reserve minimum floor stock for DTC, use separate reorder points by channel, and create alerts when wholesale orders would drop DTC inventory below threshold. CommerceOS manages multi-channel inventory with automatic allocation based on priority rules.

Track PO commitments vs. actual landed costs (COGS + freight + duties). Use invoice data to update actual spend, not PO estimates. Calculate: OTB Budget - (Committed POs + Invoiced Receipts) = Available Spend. CommerceOS tracks landed costs from PO to invoice, providing real-time visibility into actual cash deployed.

Signs you've outgrown Excel: forecast takes more than 2 hours to update, multiple versions exist, errors cause costly mistakes, you can't scale to 50+ SKUs or multiple channels, inventory visibility lags reality. Most brands transition at $2M–$5M revenue or when manual processes create stockouts or overstock worth $50K+.

Work backward from docks-by dates, add vendor lead-time + transit + buffer; lock critical SKUs 90–150 days ahead. Start with highest-velocity items and seasonal winners. Build in 2–3 week buffer for international suppliers. Use historical sell-through rates to size initial commitment vs. replenishment flexibility.

Shorten PO intervals, kill slow SKUs, bundle intelligently, and use liquidation triggers tied to aging thresholds. Set velocity-based reorder points (ABC analysis), establish aging rules (liquidate at 90/180 days), bundle slow movers with fast, and create automated alerts when turns drop below 4× annually.

Commit only to minimum viable quantity for next 13-week forecast, plus safety stock for lead-time variability. Keep remaining allocation flexible with suppliers via rolling 13-week forecasts updated monthly. Use historical forecast accuracy to size commitment—if forecasts are ±20%, commit conservatively. Consider dual sourcing for critical components to reduce single-supplier risk.

Calculate safety stock separately for each stage. Raw material safety stock = (avg daily consumption × lead time variance) + demand variability buffer. Finished goods safety stock = (avg daily sales × lead time) + demand variance. Total buffer = max(raw material risk, finished goods risk), not additive. Track raw-to-finished conversion time to synchronize ordering cycles and minimize total pipeline inventory.

Export Excel forecasts via CSV to a platform with machine learning capabilities. AI models need historical sales data, seasonality patterns, promotional calendars, and inventory levels. As actual sales arrive, the model compares forecast vs. reality and adjusts parameters. Start with a 70/30 split: use Excel for next 3 months, AI for months 4–12. Gradually expand AI's role as accuracy improves (typically 20–30% better than manual forecasts after 3–6 months of learning).

Start with SKU velocity ranking (ABC analysis) to identify your 20/50/20 split. Build historical sales baseline for top SKUs, establish initial safety stock formulas, and create weekly review cadence. Focus on highest-risk items first (fast movers, long lead times, high value). Set up alerts for stockouts and overstock. Only after 8–12 weeks of disciplined planning should you expand to full SKU coverage or advanced modeling.

Weekly planning meeting: ops shares sales trends and stock position, finance provides cash constraints and open-to-buy budget, supply chain confirms lead times and supplier capacity. Use a shared planning tool (not email) with version control. Set decision thresholds: ops can approve POs under $10K independently; above requires finance sign-off. Monthly executive review of forecast accuracy. Quarterly reset of safety stock and budget allocation by category.

AI is ready now for replenishment of stable, predictable SKUs. Autonomous purchasing works when you have 12+ months of sales history, consistent demand patterns, reliable lead times, and clear supplier MOQs. AI still requires human oversight for new products, seasonal spikes, promotions, and supplier relationship changes. Most brands start with AI recommendations that require approval, then gradually automate low-risk, high-frequency items.

Yes. Most AI demand planning platforms offer trial periods using your historical sales data. You'll need at least 12–24 months of sales history, SKU-level data, and basic master data (product attributes, seasonality markers). The platform will run a backtest against your historical data to show predicted accuracy. Look for platforms that explain their methodology, show confidence intervals, and allow you to manually override AI recommendations while the model learns.

Consolidate inventory feeds into a single operations layer that sits between your channels and suppliers. Use APIs or EDI to pull real-time inventory from Shopify, NetSuite, or 3PL systems. Calculate demand separately by channel (B2B often has longer lead times and higher order quantities), then aggregate total demand and compare to unified inventory position. Tools like CommerceOS integrate multi-channel inventory into one dashboard, making it possible to forecast across channels without manual reconciliation.