Connect what you use.
CSV sales history works for any POS; a supported POS can sync catalog items when enabled.
How it works
From sales history to a smarter order.
See the real sample workspace turn sales signals into a practical order guide.
Forecast
A real sample workspace showing forecast, order guide, and purchase signals together.
Built around the order day you already have.
Review your next order cycle.
See demand signals before your next purchasing decision.
Order with context.
Use recommended quantities and plain-language reasoning to make the final call.
Under the hood
Three layers, each with a specific job.
Open each layer for a plain-English explanation. Forecast availability and automatic refresh timing depend on plan.
01Statistical baseline
Finds each item's normal trend and seasonal pattern.
StocksSense reads each product's monthly sales history. With enough history, Holt-Winters tracks both trend and repeating seasonal changes. With less history, a simpler Holt model avoids pretending it knows a seasonal pattern yet. An outlier check smooths an unusual spike against nearby months before it can distort the forecast.
02Reliability-tuned safety stock
Adds a bigger buffer when an item's history is less predictable.
StocksSense tests past forecasts against what sold next. Stable items get a tighter safety-stock buffer. Volatile items get more protection and a shorter trustworthy forecast window. Confidence labels show which numbers have strong history behind them.
03Weather & event context
Lets paid accounts account for a known local demand change.
When this paid feature runs, StocksSense checks available local weather and event data and applies a limited adjustment to the statistical forecast. A heat wave can raise expected cold-drink demand, for example, but it cannot erase months of sales history. Automatic nightly Pro and beta refreshes reuse the latest context already available to the account; they do not promise a brand-new live web search every night.
Feature guide
What each feature actually does.
Open any item to see its job and exactly where it lives in StocksSense. Paid features are labeled.
Forecasting & ordering
6-month rolling forecast
Shows expected monthly demand for each product. It recalculates when new sales data is added.
Find it in: ForecastsAutomatic outlier correction
Stops one unusual sales spike or drop from pulling the next six months too far in the same direction.
Find it in: ForecastsReliability-based safety stock
Uses past forecast accuracy and sales volatility to choose a smaller or larger reorder buffer for each product.
Find it in: Forecasts and Order GuideReorder list
Turns the forecast into a quantity to buy, estimated cost, and a short reason for each recommendation, sized to your order cycle.
Find it in: Order GuidePriority flags
Labels items High, Normal, or Low so urgent purchases rise to the top of the list.
Find it in: Order GuidePrintable reorder sheet · paid plans
Opens a clean supplier checklist with quantity and estimated cost, ready to print or save as a PDF.
Find it in: Order Guide → Print checklistActuals vs. forecast tracking
Lets you enter what actually sold, compare it with the forecast, and feed recent misses back into future forecasts.
Find it in: ReportIngredient intelligence · paid plans
Recipe mapping
Connects a menu product to each raw ingredient and records how much one serving uses.
Find it in: Order Guide → Ingredient forecastsSuggested recipes
Drafts a practical ingredient list for a menu item. You review and edit it before anything is saved.
Find it in: Order Guide → Ingredient forecastsLive ingredient rollup
Multiplies each product forecast by its recipe so you see total raw-ingredient demand without spreadsheet math.
Find it in: Order Guide → Ingredient forecastsInsights · paid plans
Per-product insight
Explains in plain language why a product's reorder number changed. It uses that product's sales and forecast data, then streams the explanation on demand.
Find it in: Order Guide → InsightBusiness health score
Combines your sales, pricing, cost, and margin data into a score with specific strengths, risks, and next actions.
Find it in: Insights → Business healthCompetitor pricing analysis
Searches public web results for nearby restaurants, then compares their visible menu and price information with your own data.
Find it in: Insights → CompetitorsIntegrations & data
POS catalog sync · paid plans
Connect a supported POS when enabled for your account to sync catalog items. Upload historical sales with CSV to forecast.
Find it in: Import → Connect POSCSV import
Uploads a CSV export from any POS. StocksSense lets you map the columns before it saves the data.
Find it in: Import → Upload CSVNightly refresh · Pro and beta
Rebuilds forecasts automatically at 03:00 UTC for active Pro and beta accounts. Other accounts recalculate when a forecast is requested after their data changes.
Find it in: Runs automatically; results appear in ForecastsGo deeper
The arithmetic, written out.
The same decisions this page automates, worked through by hand — useful whether or not you ever use StocksSense.
How to export your sales history from Lightspeed Restaurant
All guidesExport Lightspeed Restaurant's Product Mix Report as a CSV, one calendar month at a time, and combine the quantities into a monthly sales history.
How to export your sales history from ToastFind Toast’s Product mix report, export sales by month, understand its quantity and modifier settings, and combine the results for forecasting.
The two ways ordering by feel goes wrong (and why you only hear about one)Under-ordering can become a public stockout review. Over-ordering can disappear into the waste bin. See the arithmetic and a practical way to check both.
Safety stock for restaurants: why a flat 20% buffer is the wrong answerA flat safety-stock percentage treats every ingredient alike. See how StocksSense sizes the buffer from each product's reliability and demand.
What three months of sales history can and cannot tell youThree months can show a starting level and direction. Learn what a short sales history supports, what it cannot prove, and how to use the forecast carefully.
How to export your sales history from SquareThe exact Square Dashboard path to a CSV of item sales, which report to pick, what date range to use, and how to reshape it into a forecastable file.
How much should a restaurant order? A worked exampleOne product, one year of sales, and the whole ordering decision done by hand — average, trend, seasonality, and the buffer you cannot calculate from one year.
Holt-Winters, explained for people who run kitchensThe forecasting method behind most demand software, in plain language: level, trend and seasonality, what each one does to your order, and what none of them can see.