Bake What Sells. Price What It Costs. Keep the Margin
The Operational Challenges Eating Your Margin
Intuition-Based Production
Batch sizes are set by habit and memory, not sales history, so slow days create waste and busy days create sellouts.
The Unsold-Goods Tax
Every item marked down or donated at close is a permanent margin loss and the largest controllable drain on profit.
Recipes Priced in the Dark
Ingredient prices move constantly, but menu prices don't. Best sellers quietly become your worst earners.
Over-Ordering by Default
Purchasing runs on risk aversion, tying up cash in perishable stock that spoils before use and inflates COGS.
Channel Margins You Can't See
Retail, wholesale, online, and custom orders carry different costs, so owners grow the busiest channel, not the best one.
Hours Lost to Manual Reporting
Owners spend hours every week stitching POS exports, invoices, and payroll together just to see what already happened.
The Cost of Doing Nothing
15.8%
Bakery's share of all unsold food retail value
Breads and bakery rank among the highest-waste departments in US food retail, accounting for 15.8% of the retail value of all unsold food.1
38%
Of US food that goes unsold or uneaten
$7 : $1
Average savings per $1 spent cutting waste
- Bakery share of unsold food value: Pacific Coast Food Waste Commitment, retail food waste study, published via ReFED, April 2024. https://refed.org/articles/grocery-stores-report-significant-progress-in-reducing-food-waste-new-study-finds
- US food that goes unsold or uneaten: ReFED, U.S. Food Waste Report / “The Problem,” 2025. https://refed.org/food-waste/the-problem/
- 7:1 return on waste-reduction investment: Champions 12.3 (WRAP & World Resources Institute), The Business Case for Reducing Food Loss and Waste: Restaurants, 2019. https://champions123.org/publication/business-case-reducing-food-loss-and-waste-restaurants
Get the Omni-Channel Retail Growth Roadmap
Omni-Channel Strategy 101: Roadmap to Retail Growth
Step-by-step guide
- What true omni-channel integration looks like
- How to quantify the cost of manual processes
- Improve inventory turnover and stock accuracy
- Unify visibility across ops, marketing, and finance
- A practical roadmap with measurable ROI
How Data Pilot Solves It
Production That Matches Demand
We connect POS history to forecasting models that predict daily sales by product. Your baker starts each morning with a data-based recommendation and adjusts with judgment, closing the gap between baked and sold.
Recipes You Can Actually Price
We build live recipe costing that connects supplier invoices to your recipes and POS history, producing an item-level P&L for every SKU and flagging margin erosion the moment ingredient prices move.
Purchasing Tied to the Forecast
We connect demand forecasts to your bill of materials so ingredient orders match what you will actually produce, factoring in stock on hand, lead times, and minimum order quantities. Spoilage and over-ordering shrink.
Margins You Can Finally See
We replace scattered spreadsheets with unified dashboards showing waste, margin by SKU and channel, labor alignment, and forecast accuracy across every branch, so leadership decisions run on facts, not feel.
High-Value Use Cases
Daily Demand Forecasting by SKU
Business Problem:
Production volumes are set by habit, causing waste on slow days and sellouts on busy ones.
Data/AI Solution:
Predictive models forecast next-day demand by product and branch using POS history and seasonality.
Projected Business Value:
20-50% reduction in forecast error versus manual ordering methods.
Typical Tech Stack:
Square/Toast POS, BigQuery, Prophet/ARIMA, Looker
Recipe & SKU Profitability
Business Problem:
Owners know what sells, not what earns. Underpriced items quietly subsidize the rest of the menu.
Data/AI Solution:
Item-level P&L connecting ingredient prices, labor, and POS sales, with alerts on margin erosion.
Projected Business Value:
10-15% typical profit lift from menu engineering built on accurate costing.
Typical Tech Stack:
POS, supplier invoices, QuickBooks, Python, Looker
Ingredient Purchase Planning
Business Problem:
Habit-driven ordering ties up cash in perishable stock and inflates COGS through spoilage.
Data/AI Solution:
Demand forecast connected to bill of materials, calculating exact weekly ingredient needs.
Projected Business Value:
20-30% lower inventory levels while maintaining or improving fill rates.
Typical Tech Stack:
POS, supplier invoices, BigQuery, automated PO workflows
Waste Tracking by SKU
Business Problem:
Waste is recorded loosely or not at all, so nobody knows which products drive the loss.
Data/AI Solution:
SKU-level waste capture feeding a daily dashboard ranking products by margin lost.
Projected Business Value:
26% average waste reduction within the first year of measuring.
Typical Tech Stack:
POS, tablet waste logging, BigQuery, Looker
Demand-Aligned Labor Scheduling
Business Problem:
Overtime on slow days and scrambling on busy ones. Labor hours don’t track revenue.
Data/AI Solution:
Forecasted daily volume mapped to tiered staffing templates for production and counter.
Projected Business Value:
4-12% lower labor costs when schedules follow demand, with better service.
Typical Tech Stack:
POS, 7shifts/Homebase, payroll data, BI dashboards
Channel Profitability Analysis
Business Problem:
Wholesale grows because it’s predictable while consuming high-margin retail capacity.
Data/AI Solution:
Cost allocation assigning ingredients, labor, packaging, and delivery to each channel.
Projected Business Value:
2-7 percentage-point lift in return on sales from pricing programs.
Typical Tech Stack:
POS, QuickBooks, payroll, Python cost models, Looker
Ingredient Cost Alerts
Business Problem:
Flour and butter prices move monthly; menu prices move yearly. Margin erodes silently.
Data/AI Solution:
Automated invoice ingestion recalculates recipe costs and flags SKUs crossing thresholds.
Projected Business Value:
A 1% realized price improvement lifts operating profit by about 8%.
Typical Tech Stack:
Invoice ingestion, QuickBooks, Python, email/Slack alerts
Multi-Branch Performance Analytics
Business Problem:
Each location orders independently, with no visibility into over-production or stockouts.
Data/AI Solution:
Branch-level dashboards comparing forecast accuracy, waste, sell-through, and margin.
Projected Business Value:
Up to 15% lower operating costs from lean, data-driven operations.
Typical Tech Stack:
Multi-location POS, BigQuery, Looker
Marketing Impact Analysis
Business Problem:
Promotions run without a clear read on whether they move sales, making spend hard to justify.
Data/AI Solution:
Campaign activity connected to POS outcomes, showing which promotions drive revenue.
Projected Business Value:
15-20% of marketing spend freed up by integrated marketing analytics.
Typical Tech Stack:
POS, Meta/Google Ads, GA4, BigQuery, Looker
Automated Owner Reporting
Business Problem:
Owners spend 5-8 hours a week reconciling spreadsheets to see what already happened.
Data/AI Solution:
Automated pipelines consolidating sales, waste, cost, and labor into one live view.
Projected Business Value:
25-40% lower planning and administration costs from automation.
Typical Tech Stack:
POS, QuickBooks, Fivetran/Make, BigQuery, Looker
- Forecasting & inventory benchmarks: McKinsey, AI-driven forecasting research: forecast error reductions of 20-50%, lost sales down up to 65%, inventory reductions of 20-30% while maintaining fill rates, and administration costs down 25-40%. https://www.mckinsey.com/capabilities/operations/our-insights/ai-driven-operations-forecasting-in-data-light-environments
- Menu engineering profit lift: Hospitality industry studies on menu engineering: operators applying menu engineering built on accurate recipe costing typically report profit increases of 10-15% (widely cited hospitality research benchmark).
- Labor scheduling & operating costs: McKinsey, “Smarter schedules, better budgets”: activity-based labor scheduling cuts store labor costs by up to 12% (4-12% captured in practice) while improving service; the same research notes lean-retailing initiatives have yielded up to 15% reductions in operating costs. https://www.mckinsey.com/industries/retail/our-insights/smarter-schedules-better-budgets-how-to-improve-store-operations
- Pricing & return on sales: McKinsey, “Turning pricing power into profit”: systematic pricing-excellence initiatives typically translate into a 2-7 percentage-point increase in return on sales. https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/turning-pricing-power-into-profit
- Price improvement to profit: McKinsey, “The power of pricing”: a 1% price increase generates roughly an 8% increase in operating profits, volume constant. https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/the-power-of-pricing
- Marketing analytics savings: McKinsey, “Using marketing analytics to drive superior growth”: an integrated marketing-analytics approach can free up 15-20% of marketing spending. https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/using-marketing-analytics-to-drive-superior-growth
Proven Results
Multi-Branch Bakery: From Gut-Feel Orders to Demand Forecasting
Challenge: Branch managers across multiple cities ordered next-day stock by gut feel, not data, causing mismatched supply across SKUs and locations. Best sellers ran out while slow movers piled up and were written off as waste.
Solution: Built predictive models forecasting daily SKU demand across branches, combining POS history with weather, search trends, and social signals, plus a live sales dashboard replacing manual reports.
Key Outcomes:
- 15-30% improvement in demand forecast accuracy
- 20-25% reduction in overstock and stockout discrepancies
- 50-70% less manual sales and campaign reporting time
- Branch orders aligned to what each location will actually sell
Fashion Retail Brand: Sales & Inventory Analytics
Replaced trend-based guessing with real-time sales analytics, helping balance stock and reduce both overstock and stockouts.
How to Get Started
Assessment
Forecasting
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Profitability
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Frequently Asked Questions
How long does a bakery data project take?
It depends on your systems, data quality, and scope. We usually start with an assessment, then phase pilots by priority.
Do we need to replace our POS or accounting tools?
Usually not. We connect and improve the systems you already use.
What determines the cost of a project?
Cost depends on project scope, data sources, locations, integration complexity, and business needs.
What kind of data do you typically work with?
We work with POS sales, ingredient costs, supplier invoices, recipes, waste logs, labor, and channel data.
What if our sales data is messy or waste untracked?
That is common. We audit and clean the data first, then prioritize the fixes that matter most.
How do you decide which use cases to start with?
We prioritize by financial leakage and feasibility. Demand forecasting usually comes first.
Will our head baker have to follow a computer's orders?
No. The forecast is a starting point your baker adjusts with judgment, and the model learns from results.
How do you measure whether the work is successful?
We capture baseline metrics before the pilot and track them against agreed targets throughout.
Can you support both strategy and implementation?
Yes. We support assessment, roadmap, integrations, forecasting, dashboards, automation, and adoption.
What happens after the initial project is complete?
Most bakeries phase in recipe costing, purchase planning, or labor scheduling next. We also offer managed analytics.