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Center store technology uses a brittle approach to calculating inventory: Yesterday’s inventory + shipments - sales and scanouts = current-day inventory.
This method requires data to be perfect at all times – and that’s just not possible in fresh.
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Center store technology uses a brittle approach to calculating inventory: Yesterday’s inventory + shipments - sales and scanouts = current-day inventory.
This method requires data to be perfect at all times – and that’s just not possible in fresh.
Calculating fresh department inventory requires technology that’s built to identify and adjust for uncertainty and errors.
Solutions that take a truly fresh-first approach leverage machine learning, probabilistic forecasting, perishability models, and data validation and testing.
While shelf-stable items are relatively similar, there are countless circumstances that impact how and when fresh food should be replenished.
Without a fresh-first approach, every store’s inventory estimations and order recommendations are wrong, which impacts everything from workflow adherence to in-stock rates.
As the most dynamic category, fresh teams need purpose-built technology that considers the full range of characteristics of every item.
Solutions should identify and adapt to variables such as perishability, seasonality, changing display sizes, misscans, edge cases, and shrink.
In fresh departments using legacy technology, order writers often revert to manual calculations and rely on over-ordering to keep shelves fresh and full.
These workarounds create excessive shrink and inaccurate data, causing store teams to spend hours triaging inventory discrepancies and managing full backrooms.
Fresh teams need a trustworthy solution to help them achieve operational excellence and keep customers happy, even in a world of inevitably messy data.
Accurate and reliable fresh technology will account for the variable nature of fresh, flag any suspicious data inputs, and require minimal maintenance for IT teams.