USING POS DATA TO FORECAST MARYLAND CANNABIS INVENTORY DEMAND

Accurate inventory forecasting is one of the hardest parts of cannabis retail. Order too little and popular products disappear from the menu. Order too much and cash becomes trapped in slow-moving stock. Maryland dispensaries can reduce both risks by turning everyday POS activity into a structured demand-planning process.

A well-configured point-of-sale for Maryland dispensaries can provide the transaction history needed to forecast by SKU, category, location, and selling period. Instead of buying mainly from intuition or vendor recommendations, teams can compare what customers actually purchased, how quickly products moved, and how much stock remained between deliveries.

Forecasting is not about predicting every future sale precisely. A useful forecast warns buyers about changing demand early enough to adjust purchasing before a stockout or overstock problem becomes expensive.

Start With Clean Historical POS Data

Forecast quality depends on input quality. Before building formulas, make sure sales history reflects real customer demand rather than duplicate SKUs, canceled transactions, or catalog errors.

Useful fields include:

• units sold by SKU and category;
• inventory received;
• stockout periods;
• promotional discounts;
• current quantity on hand.

Bad source data produces confident-looking forecasts that are still wrong. Catalog hygiene and inventory reconciliation should come before advanced forecasting.

Separate Sales From Lost Demand

A POS records what was sold, not what shoppers wanted when an item was unavailable. If a popular product sells out for several days, recorded sales alone can understate demand.

Flag stockout periods and review substitution behavior. When one SKU disappears, customers may switch to another brand, size, or price tier. That helps buyers determine whether demand belongs to the exact product or the broader category.

Forecast Categories Before Individual SKUs

SKU-level planning is useful, but category trends provide context. Flower, vapes, edibles, concentrates, and pre-rolls may respond differently to promotions, seasonality, and pricing.

Compare categories by:

• weekly unit sales;
• revenue and margin;
• inventory turnover;
• stockout frequency.

If total pre-roll demand rises while one product declines, the issue may be the SKU rather than the category. Category analysis helps buyers separate broad demand shifts from isolated product performance.

Use Rolling Averages as a Starting Point

A simple rolling average can be more useful than a complicated model nobody understands. Compare a short sales window with a longer baseline.

Useful views include:

• trailing 7 days;
• trailing 28 days;
• same period last month;
• same period last year, when reliable history exists.

When short-term velocity moves consistently above or below the longer baseline, demand may be changing.

Do Not Let One Weekend Rewrite the Forecast

Promotions, holidays, weather, and local events can create temporary spikes. Mark those periods so buyers understand why sales changed.

Translate Demand Into Reorder Points

A forecast becomes useful when it changes purchasing decisions. A reorder point is the inventory level at which a new order should be considered.

It should account for:

• expected daily demand;
• supplier lead time;
• current stock;
• confirmed inbound inventory;
• safety stock.

If an SKU sells 10 units per day and replenishment takes five days, expected lead-time demand is about 50 units before adding a buffer.

Safety stock should cover uncertainty, not compensate for weak ordering discipline.

Keep Forecasts Connected to Maryland Inventory Controls

Forecasting is a purchasing tool, but its starting quantities should reconcile with physical inventory and regulated records. An incorrect on-hand number can produce a bad purchase recommendation even when the demand model is sound.

For the current regulatory text that teams can use as a reference when designing inventory workflows, see the Maryland dispensary inventory-control requirements.

A useful dashboard should distinguish sellable inventory from committed, inbound, or otherwise unavailable stock. The forecast should use the quantity genuinely available to satisfy expected demand, not simply the largest inventory number visible in the system.

Resolve Inventory Differences Before Ordering

If the POS reports 20 units while physical inventory shows 14, investigate the difference before using either number as the basis for an order.

A practical sequence is:

1. verify the physical count;
2. review recent sales and adjustments;
3. inspect SKU or package mappings;
4. resolve the inventory record;
5. update the forecast.

Forecasting should consume reconciled data rather than mask discrepancies.

Remove Promotional Distortion

Discounts can increase unit velocity sharply. A product that sells well during a major promotion may not continue at that pace when its normal price returns.

Compare:

• regular-price velocity;
• promotional velocity;
• gross profit;
• post-promotion sales.

A demand forecast should represent normal purchasing behavior unless another promotion is intentionally part of the future plan.

Treat New Products Differently

New SKUs lack enough history for a reliable trend. Start with conservative quantities and use comparable products as reference points.

Watch the First Reorder Closely

The initial buy often reflects merchandising strategy more than demand. The first reorder is more informative because it follows real customer purchases.

Build a Weekly Forecast Review

Forecasting works best as a recurring process. A weekly review can focus managers on exceptions instead of every SKU.

Review:

• products below reorder point;
• fast-rising or declining velocity;
• repeated stockouts;
• excess days of supply;
• forecast versus actual sales.

The most valuable forecast changes a decision before the problem reaches the shelf.

Measure Forecast Accuracy

Compare predicted demand with actual results. Perfect accuracy is unrealistic, but repeated errors show where the method needs improvement.

If forecasts run high, examine promotions, seasonality, and excessive safety stock. If they run low, review stockout-adjusted demand, recent growth, and supplier delays.

Final Takeaway

POS data gives Maryland cannabis retailers a practical foundation for inventory forecasting because it connects customer purchases with product velocity, promotions, stock levels, and replenishment. The strongest process starts with clean historical data, separates stockouts from low demand, analyzes categories before SKUs, and converts expected sales into realistic reorder points.

Retail teams should also account for supplier lead times, new-product uncertainty, physical inventory, and confirmed inbound orders. Forecasts improve when managers compare predictions with actual results and adjust the process.

Demand planning is most effective when it becomes an early-warning system for purchasing. With disciplined POS reporting and regular review, Maryland dispensaries can reduce avoidable stockouts, limit excess inventory, protect working capital, and keep product availability closer to what customers are actually buying.