Afresh Production Planning: Frequently asked RFP questions

Afresh Production Planning gives grocery retailers clear, AI-powered guidance on what to produce, when, and how much, for items made in the store. It's designed to optimize production decisions within a workflow that store teams can actually execute and adhere to, across departments like bakery, deli, meat and seafood, produce, prepared foods, and more.

1. How does Afresh generate a production recommendation?

Afresh combines a demand forecast, an on-hand inventory estimate, and shelf-life and merchandising inputs into a single recommended quantity for each item, store, and day.

Production recommendations weigh multiple inputs together

Afresh generates recommended production quantities and timing for each store, weighing on-hand inventory, shelf life, display minimums, and forecasted demand together rather than as separate steps. Forecasts run at the SKU, store, and day level at minimum, with configurable intraday production windows by shift or time of day for high-volume items and departments, so a bakery's morning rush and a deli's lunch rush can each get their own recommendation rather than one blended daily number.

Afresh gives recommendation transparency

Afresh can show the specific factors behind a given production recommendation, including forecast, minimum display standards, and how much of an item is expected to spoil, so store and corporate teams can see what's driving a number rather than treating it as a black box. That transparency feeds directly into execution: Afresh's Production Planning solution provides clear guidance on what to produce, when, and how much, inside a workflow designed to be genuinely followed, with items organized into groups and sequenced so produced items are ready for the floor at the right time.

2. How does Afresh calculate inventory position for production items?

Afresh continuously estimates on-hand inventory for production items rather than relying on a manually maintained perpetual inventory.

Before a store starts its production workflow, Afresh pre-populates an inventory estimate for each item using scale data as one of the inputs. The associate then reviews or enters inventory, correcting any estimates that don't match what's actually on hand, and whether it's the original estimate or an associate's correction, the resulting on-hand quantity feeds directly into the production recommendation, helping prevent both overproduction and out-of-stocks. Afresh maintains this estimated inventory for every production item at every store, updated continuously based on relevant item and production data.

3. How does Afresh manage recipes and ingredients?

Afresh can work with a retailer's existing recipe data where available, or offer its own recipe management capability while building it out as well.

Recipe data, from system integration through cost analysis, is handled end to end

Afresh integrates with and accepts data from existing recipe management systems, including Upshop, and has its own recipe management system to extend that foundation. Afresh supports tecipe master data inbound, including ingredients, steps, yields, packaging, label text, allergens, and compliance attributes. Afresh's recipe management system includes recipe cost roll-ups and margin analysis, using item costs and yields that build on the same yield factors already used to optimize production quantities, giving merchandising and operations teams a shared view of cost, yield, and production performance.

4. How does Afresh connect production planning to ingredient ordering?

Production and ingredient ordering run on the same underlying forecast, not as two systems reconciled after the fact.

Afresh links source items and finished production items

Afresh's item mapping resolves items that don't sell the way they're ordered, a common pattern in bulk and transformed fresh categories, connecting a source item to what it eventually becomes even when there's no one-to-one relationship between the two. That mapping is what lets Afresh define and manage relationships between orderable source items and the finished items they become, including conversion modeling for production recommendations, so ordering recommendations for raw source items already account for downstream production needs.

Afresh optimizes production and ordering forecasts and recommendations together

Afresh's replenishment solution uses the same forecast intelligence as its production planning tool, so ingredient replenishment decisions and production item decisions are optimized together at the item, store, and day level.

If chicken enchiladas go on promotion, Afresh anticipates the resulting jump in demand, automatically increases the bulk order of whole chickens with enough lead time for the store to receive them, and generates the production plan to match, accounting for how promotional demand ramps up and tapers off.

5. How does Afresh handle safety stock and production minimums?

Afresh builds minimums into the recommendation itself rather than layering a separate buffer on top.

Minimums are built into the recommendation logic and are configurable by SKU and store

Afresh supports safety stock in Production Planning by incorporating a retailer's merchandising standards as minimum required quantities for each production run, built directly into the production quantity recommendation logic; recommendations won't fall below these configured minimums.

Afresh generally avoids a separate static safety stock buffer, since for perishable prepared foods, adding an incremental buffer on top of a recommendation can materially increase shrink, so aligning to required minimums is meant to deliver shelf standards while minimizing unnecessary overproduction.

6. How does Afresh account for promotions, seasonality, and local events?

Most demand-shaping factors are learned automatically from historical data, without requiring a retailer to manually configure them.

Promotional impact is ingested daily and weighted by performance

Promotional forecasting is a core capability: forward-looking promotional data, including pricing, ad position, and display type, is ingested daily and incorporated into the demand model. Afresh applies promotional aggressiveness factors using retailer-provided promotional inputs and learns differentiated promotional lift from historical performance, and a weekly hotspot mechanism adds extra weighting to key promotional displays where more lift needs to be captured.

Seasonality, holidays, and local conditions are learned and factored in automatically

Weekly, monthly, and annual seasonal patterns are learned automatically by Afresh's deep learning model at the item, store, and day level, with no manual configuration required. Afresh's forecast automatically accounts for major holidays and major sale or promotional events, and provides upstream, long-term forecasts for resource planning.

7. How does Afresh handle production across different departments?

Production Planning is built around the reality that bakery, deli, meat and seafood, and produce each run on their own cadence, constraints, and prep work.

Production cycles are configured per department and item, and can run more than once a day

Afresh supports multiple departments per store, including bakery, deli, meat, seafood, produce, and prepared foods, and generates recommendations at the item, store, and intraday cycle level. Each department can have its own production cadence, and each item can have its own cycle within that cadence, with cycle parameters like production time, lead time, set time, and on-shelf time configurable per department and item.

Afresh uses department-specific intelligence to tailor production plans

Meat: Afresh organizes meat cuts by subprimal, tying an item like T-bone steaks directly to the whole beef loin it comes from and accounting for real trim loss and yield. When an item can come from more than one subprimal, Afresh recommends whichever balances shrink risk and margin, for example defaulting a stir-fry item to chuck roll when that's already being cut.

Bakery, multi-step prep: For items that need thawing, or proofing before they can be baked, Afresh schedules each step so the finished item is ready on time, for example pulling croissant dough from the freezer the night before with enough time to thaw, proof, and bake before the morning rush.

Deli, intraday runs: A single item's daily production can split across multiple runs sized to actual demand throughout the day: a deli might see a plan for 36 rotisserie chickens split into 12 at 10 AM for the lunch rush, 16 at 2 PM, and 8 at 5 PM for dinner, each run keeping the case full without overproducing.

Bakery, produce, and deli, ingredient batching: Afresh calculates shared ingredient needs across overlapping recipes, so when several fruit bowls draw on the same fruits, the system recommends one prepped batch per fruit instead of cutting each bowl's ingredients separately, cutting down on prep time while keeping cut fruit fresh.

8. What integrations does Production Planning support?

Afresh is designed to work alongside a retailer's existing systems, taking data in through a feed or API in whatever format those systems already produce it in.

Sales, labor, recipe, and scale systems all feed into Afresh on a near real-time basis

POS and sales transaction data, by SKU or PLU, is ingested daily with optional intraday feeds, delivered via batch file or API. Afresh also ingests WFM schedules and ingests recipe master data while pushing item and label updates to and from existing scale management and recipe systems, such as Upshop and any legacy solution, via API or export and import file processes, with versioning and effective dating supported for recipe data.

Afresh connects to enterprise and legacy systems, and moves data in whatever format a retailer needs

Afresh is designed to be system and ERP agnostic, working with retailers on whatever data format and transfer method, API, file transfer, or message queue, fits their existing systems, including SAP and legacy or mainframe environments.”

Data migration and reconciliation are handled as part of implementation

Afresh provides a data migration approach covering recipes, item mappings, store configuration, and historical data for forecasting model training, and absorbs the majority of the data mapping and clean-up work. Mapping documentation and a repeatable reconciliation process keep data in parity between systems throughout.

9. Does Afresh support reporting and analytics?

Afresh tracks production performance from the store floor up to corporate, with reporting built for both.

Execution is tracked through compliance scorecards, exception dashboards, and an audit trail

Afresh provides execution compliance scorecards at the store, department, category, and item level, with metrics including actual versus recommended quantity, on-time completion against a ready-by target, conversion rate (how often a recommendation was acted on), and coverage (items with captured actuals); the scorecard's own parameters are configurable, including the days-after-set window for capturing actuals, production start and end capture windows, pre-cycle set lead time, and the tolerance threshold that separates compliant from exception.

Corporate-level exception dashboards identify stores or departments that missed a step in the production planning process, such as failing to set a cycle, confirm production, or capture inventory, with drill-down from enterprise down to region, district, store, department, and item, and exception lists exportable or deliverable as scheduled emails to accountable managers. Production override governance is tracked at each level, store and corporate, with audit trail functionality covering overrides, system changes, and user activity, and override performance surfaced through a weekly scorecard.

Dashboards, variance reporting, and access controls give both store and corporate teams what they need

Reporting dashboards track store production rates (over or under production), skipped production rates, on-hand inventory and adjustments, production history lookup, and space-to-sales using a retailer's defined merchandising display standards, alongside variance reporting at the store and item level covering recommendation override performance and shrink variance, and facility-level reporting on production adherence and shrink.

All reports are accessible using role-based access control, so store, district, and corporate users see only the data relevant to their role, and reports roll up based on organization structure (total, district, store, department), with both operational reporting for stores and analytical reporting for corporate users provided; custom email alerts can be set up for specific metrics and thresholds. Production reports are navigable by category, subcategory, department, and store, and filterable by location and date range, with reports exportable to CSV or XLSX and custom report views savable and shareable by user.

10. How does Afresh use AI in Production Planning?

Afresh applies AI across demand forecasting, inventory, data quality, and production recommendations.

Production and inventory are modeled as probability distributions, not single numbers

Afresh's AI engine uses machine learning to weigh many variables together, including store orders, store sales, shipments, retail price, promotion vehicles, store displays, seasons, holidays The resulting demand forecast is a probability distribution rather than a single point value, and Afresh also produces a probability distribution for projected inventory that accounts for factors like perishability and shelf life, using these distributions to produce a recommendation that balances shrink, sales, freshness, and store presentation standards.

Afresh's Data Engine is built to handle messy fresh data

Fresh data is inherently messy: different units of measure, yields and cut tests, recipes, generic hot bar UPCs, and register mis-scans, among other issues. Afresh's system is built to understand these kinds of nuances and applies AI in the context of that uncertainty, rather than assuming the underlying signals are the entire truth.

11. How do I submit an RFP?

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