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ITEM=CROISSANT_BUTTER BATCH=06:00 PROOF=42_MIN ITEM=SALMON_ATLANTIC TEMP=FROZEN CONF=0.89 ITEM=BANANA_PETITE REC_ORDER=64 STORE=214 ITEM=CHEDDAR_SHARP_8OZ TURN=+7PCT ITEM=ROTISSERIE_CHX

ITEM=CROISSANT_BUTTER BATCH=06:00 PROOF=42_MIN ITEM=SALMON_ATLANTIC TEMP=FROZEN CONF=0.89 ITEM=BANANA_PETITE REC_ORDER=64 STORE=214 ITEM=CHEDDAR_SHARP_8OZ TURN=+7PCT ITEM=ROTISSERIE_CHX

ITEM=CROISSANT_BUTTER BATCH=06:00 PROOF=42_MIN ITEM=SALMON_ATLANTIC TEMP=FROZEN CONF=0.89 ITEM=BANANA_PETITE REC_ORDER=64 STORE=214 ITEM=CHEDDAR_SHARP_8OZ TURN=+7PCT ITEM=ROTISSERIE_CHX

Probabilistic Decision Making

Decisions that consider every tradeoff

Your teams make thousands of calls a day on what to order, what to prep, what to count, and what the DC should buy.

Afresh built AI that analyzes demand, inventory, shelf life, and margin together, so each decision accounts for what could sell, what's really there, and what it costs to get it wrong.

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Most decisions are made with incomplete information

Every order, production plan, and vendor request goes in without anyone knowing exactly what will sell. Most systems handle that uncertainty with a fixed safety stock. For a canned soup with a six-month shelf life, that works. On avocados that turn in 72 hours, the same buffer becomes shrink.

Demand that won't hold still

Season, price, weather, promotions, and new items with no history all move demand in ways a point forecast can't capture.

Inventory that drifts

Spoilage, mis-scans, and product received by the case but sold by the pound go unrecorded, so the perpetual inventory drifts further from the shelf every day.

Shelf life that sets the clock

Every fresh item has a window to sell before it becomes shrink, and that window changes by item, season, and delivery schedule.

Afresh models what could happen, not just one outcome

ITEM=CROISSANT_BUTTER BATCH=06:00 PROOF=42_MIN ITEM=SALMON_ATLANTIC TEMP=FROZEN CONF=0.89 ITEM=BANANA_PETITE REC_ORDER=64 STORE=214 ITEM=CHEDDAR_SHARP_8OZ TURN=+7PCT ITEM=ROTISSERIE_CHX

ITEM=CROISSANT_BUTTER BATCH=06:00 PROOF=42_MIN ITEM=SALMON_ATLANTIC TEMP=FROZEN CONF=0.89 ITEM=BANANA_PETITE REC_ORDER=64 STORE=214 ITEM=CHEDDAR_SHARP_8OZ TURN=+7PCT ITEM=ROTISSERIE_CHX

ITEM=CROISSANT_BUTTER BATCH=06:00 PROOF=42_MIN ITEM=SALMON_ATLANTIC TEMP=FROZEN CONF=0.89 ITEM=BANANA_PETITE REC_ORDER=64 STORE=214 ITEM=CHEDDAR_SHARP_8OZ TURN=+7PCT ITEM=ROTISSERIE_CHX

Demand forecasts see the bigger picture

Most forecasts reduce demand to a single forecast number. Afresh models every likely outcome for every item, at every store and DC, every day.

Afresh accounts for seasonality, holidays, weather, and price; forecasts promotion ramp-up, peak, and ramp-down separately; forecasts new items from day one using similar ones; combines every selling form; and forecasts by daypart, rolling up to the DC.

Most forecasts reduce demand to a single forecast number. Afresh models every likely outcome for every item, at every store and DC, every day.

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Considering the range of probable demand

Instead of predicting that 10 cases of mushrooms will sell, the forecast estimates the demand likelihood for various quantities will sell. That range of probabilities increases the accuracy of every downstream decision.

Inventory estimates that reflect reality

Perpetual inventory is arithmetic: what came in minus what the system knows went out. In fresh, a lot happens without recording a transaction.

Afresh's Intelligent Inventory accounts for a full range of probabilities instead of treating a single inventory number as the truth. Understanding uncertainty allows for more accurate inventory estimates.

Perpetual inventory is arithmetic: what came in minus what the system knows went out. In fresh, a lot happens without recording a transaction.

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10% to 40% more accurate

Afresh estimates the likely on-hand position from sales, shipments, counts, perishability, and the historical signal reliability across the shelf, backroom, in transit, and DC.

Shelf life built into every estimate

Perishability shapes both Afresh's on-hand estimate and order recommendations. Product likely lost to unrecorded spoilage drops out of the on-hand estimate, and incoming product gets enough time to sell before it becomes shrink.

The result is decision making built around the item's actual selling window, so product doesn't sit in the backroom until it goes bad.

Understanding that fresh food acts differently

Afresh builds shelf-life models for each item across produce, meat and seafood, deli, and bakery, so ordering recommendations optimize sell-through.

Unified decision making

For each item, Afresh analyzes demand, inventory, and perishability, then simulates thousands of possible decisions. Each one gets scored on availability, waste, freshness, labor, and margin, and Afresh picks the one with the best overall result.

The same engine runs stores, production, and DCs, accounting for item-specific display minimums, freshness standards, and dynamic safety stock to optimize all decisions in tandem.

Weighing tradeoffs to maximize profit

Every decision, especially in fresh, comes with a tradeoff. Fuller displays sell more but waste more. Frequent production keeps hot cases fresh but costs labor. Afresh considers every factor to optimize recommendations for profitability.

One intelligence engine syncs every decision

The same decision engine generates order, production, and DC buying recommendations, so everything follows the same view of demand and inventory.

Store orders

Weighs the risk of a deli out-of-stock against shrink, freshness, display requirements, and the next delivery.

Production plans

Decides how much to prepare for each daypart based on likely demand and what's already available.

DC buying

Sizes an opportunity buy on blackberries to what stores can sell before the fruit turns.

Built into your teams' daily workflows

Store Ordering

Afresh weighs likely demand, estimated inventory, perishability, delivery schedules, and store requirements to recommend how much of each item to order.

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Period Ending Inventory

Daily inventory estimates pre-populate every item at period close, so teams review and adjust instead of counting from zero.

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Production Planning

Daypart forecasts, current inventory, and perishability help determine how much to prep, cut, cook, or bake for each production run.

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DC Buying

Store-level demand and inventory roll up so buyers size POs and opportunity buys against what stores will actually move.

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Store Ordering

Afresh weighs likely demand, estimated inventory, perishability, delivery schedules, and store requirements to recommend how much of each item to order.

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Production Planning

Daypart forecasts, current inventory, and perishability help determine how much to prep, cut, cook, or bake for each production run.

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Period Ending Inventory

Daily inventory estimates pre-populate every item at period close, so teams review and adjust instead of counting from zero.

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DC Buying

Store-level demand and inventory roll up so buyers size POs and opportunity buys against what stores will actually move.

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25%

shrink reduction

sales growth

94%

user adherence

12.5B

items trained on Afresh

Start with clean data.
Finish with action

These models run on Afresh's data foundation. That gives Afresh a current view of demand, inventory, and perishability across the business. Afresh's agents then turn each decision into concrete actions, with your team in the loop where it matters.

Decisions that consider every tradeoff

Decisions that consider every tradeoff