Assortment and replenishment
Messika
By structuring its retail replenishment on Anaplan, Messika, a luxury jewellery house, secures decision-making and supports its international growth.

Store replenishment is one of the most structuring levers to ensure product availability and maintain a consistent in store offer. In retail, luxury and fashion networks, service level and inventory performance depend directly on the ability to supply stores at the right pace, with inventory aligned to actual demand.
This process relies on a series of daily decisions: setting target stock levels, prioritising stores, managing sales deviations, identifying stockout risks and updating parameters. Without robust steering, commercial volatility, large assortments and geographic dispersion quickly generate costly inefficiencies, particularly inventory related costs such as additional transfers, markdowns or lost sales.
Through numerous projects delivered on these topics, OneHive has supported organisations facing the same challenges. These experiences have allowed us to identify the levers that structure a truly high performing replenishment process and to design models that combine responsiveness, accuracy and operational consistency.
Our client engagements consistently highlight four major challenges:
Across our projects, we deliver a set of structuring components that secure the process and improve daily steering:
Results observed among our clients confirm the impact of a well structured, automated replenishment model:
Explore our full methodology, concrete examples and best practices:
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Assortment and replenishment
By structuring its retail replenishment on Anaplan, Messika, a luxury jewellery house, secures decision-making and supports its international growth.
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Discover answers to key questions about our services and approach.
The optimal level depends on local rotation, seasonality, store role and logistics constraints. A robust model combines ABC segmentation with the appropriate calculation method, such as target coverage, DRP or statistical approaches, updated daily to reflect real demand evolution.
The most structuring indicators are availability rate, stockouts and missed opportunities, stock coverage and demand variability. Daily updates enable exception based steering that is both faster and more reliable.
This balance is achieved through differentiated stock targets, automated recalculation of critical parameters and short to mid term stock projections. This combination prevents cumulative deviations and improves commercial performance without increasing costs.
Sales histories must be reprocessed, OMS rules integrated and allocation priorities defined consistently across channels. This ensures accurate reflection of ship from store, pick in store and web sales fulfilled by stores, often invisible in traditional steering.
A well designed model improves availability, reduces tied up inventory, increases responsiveness to market changes and stabilises network level trade offs. The result is better sales capture, fewer stockouts and more predictable steering for Retail and Supply Chain teams.
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