Most restaurant owners believe purchasing decisions should rely on intuition and experience alone. This myth costs thousands in wasted inventory and missed sales opportunities. Your historical sales data reveals purchasing patterns that gut feelings simply can't match.
Why historical data is so valuable
Your sales data tells stories you'd otherwise miss. Which dishes sell well on Monday? How much more fish do you sell in summer? These patterns repeat year after year, and a pattern we see repeatedly in restaurant financials shows that data-driven purchasing reduces food waste by 25-40%.
? Example:
Restaurant De Smaak analyzes their salmon sales:
- January 2024: 45 salmon portions per week
- July 2024: 78 salmon portions per week
- January 2025: 48 salmon portions per week
Pattern: 70% more salmon in summer months
What data you need
You don't need every number to get started. Focus on what matters most:
- Number of portions sold per dish per day/week
- Total revenue per day (to spot seasonal shifts)
- Number of covers per day (to predict busy periods)
- Special events (holidays, events, weather disruptions)
Your POS system provides most of this. Manual counting works too. Go back at least 12 months for reliable patterns.
Recognizing seasonal patterns
Most restaurants show clear seasonal trends. You can use these for smarter purchasing:
? Example patterns:
- Summer: +40% salads, +60% fish, -30% stews
- Winter: +50% game, +30% red wine, -40% rosé
- Holidays: +200% luxury ingredients (lobster, champagne)
Note these percentages and adjust your purchasing accordingly. Don't buy winter ingredients in summer, even if they're discounted.
⚠️ Watch out:
One-off events skew your data. Filter out outliers before looking for patterns. That wedding with 200 guests isn't your typical Saturday.
Using daily patterns for fresh purchasing
Weekdays have patterns too. This matters for fresh products with short shelf life:
- Monday often shows 40-60% less volume than weekends
- Friday/Saturday usually peak in evenings
- Sunday depends on your concept (lunch vs. dinner focus)
Use this for fish, meat, and fresh vegetables. Buy less on Monday, more on Thursday for weekend rush.
Forecasting with correction factors
Historical data provides your foundation, not your final answer. Always adjust for:
- Weather: rain means fewer guests, heat increases terrace seating
- Local events: festivals, sports matches, trade shows
- Your own actions: new menu launches, marketing campaigns
- Economic conditions: recession drives demand toward cheaper dishes
? Example correction:
Forecast based on data: 60 covers on Saturday
Adjustments: Football final (-20%), nice weather (+15%)
Adjusted forecast: 57 covers
Tools to organize your data
Excel works but gets messy quickly. Better options exist:
- POS system reports: most can export sales per dish
- Restaurant management software: tools like KitchenNmbrs link sales and purchasing data
- Simple spreadsheet: track weekly quantities per dish
The most important thing? You actually use it. A perfect database you never check won't help your bottom line.
From data to action
Collecting data is step one. Step two means adjusting your purchasing:
- Order 80% of your forecast for perishables (you can reorder mid-week)
- Order 100-110% for shelf-stable products (inventory doesn't hurt here)
- Keep buffer stock for popular dishes (running out costs more than slight overage)
Test your forecasts for a month and adjust. Perfect forecasting doesn't exist, but 80% accuracy beats guessing every time.
How do you adjust purchasing based on historical data? (step by step)
Collect 12 months of sales data
Export from your POS system: number of portions sold per dish per week. At least a full year for seasonal patterns. Filter out outliers (events, closed days).
Calculate averages per season and weekday
Create overviews: how much you sell on average per dish in winter/summer, per weekday. Note percentage differences between seasons for your top 10 dishes.
Forecast next week and order based on that
Use your averages, adjust for weather/events/promotions. Order 80% of forecast for fresh items, 100% for shelf-stable. Compare after a week: was your forecast accurate?
✨ Pro tip
Track your top 5 dishes for exactly 4 weeks to establish baseline patterns. These dishes typically represent 60-70% of your total food costs, so accurate forecasting here delivers immediate results.
Calculate this yourself?
In the KitchenNmbrs app you can do this in just a few clicks. 7 days free, no credit card.
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Frequently asked questions
How many months of data do I need at minimum?
What if my POS system can't generate detailed reports?
How do I handle new dishes without sales history?
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Sources consulted
- EU Verordening 852/2004 — Levensmiddelenhygiëne (2004) — Official source
- EU Verordening 853/2004 — Hygiënevoorschriften voor levensmiddelen van dierlijke oorsprong (2004) — Official source
- EU Verordening 1169/2011 — Voedselinformatie aan consumenten (2011) — Official source
- NVWA — Hygiënecode voor de horeca (2024) — Official source
- NVWA — Allergenen in voedsel (2024) — Official source
- Codex Alimentarius — International Food Standards (2024) — Official source
- FSA — Safer food, better business (HACCP) (2024) — Official source
- BVL — Lebensmittelhygiene (HACCP) (2024) — Official source
Food Standards Agency (FSA) — https://www.food.gov.uk
The HACCP standards shown in this application are for informational purposes only. KitchenNmbrs does not guarantee that displayed values are current or complete. Always consult the FSA or your local authority for the latest regulations.
Written by
Jeffrey Smit
Founder & CEO of KitchenNmbrs
Jeffrey Smit built KitchenNmbrs from 8 years of hands-on experience as kitchen manager at 1NUL8 Group in Rotterdam. His mission: give every restaurant owner control over food cost.
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