Replenishment moduleAI Assistant · MCP

    AI-Powered Retail Replenishment

    Automatically determine what each store needs, when it needs it, and how much inventory to send from your central warehouse. Fixed min/max levels and hand-built store orders leave some stores out of stock while others overflow; buffers.ai recalculates every product at every store, every day.

    Built forReplenishment, allocation and merchandising teams that ship from a central warehouse to a chain of stores, and the store managers who'd rather not build orders by hand.

    See the features
    ~75%
    Replenishment automated
    2–3 hrs
    Saved per store manager, daily
    ~10%
    Stockout-rate reduction
    TODAY'S STORE ORDERS1 SKU · RECALCULATED DAILYCENTRALWAREHOUSESTORE 01+16STORE 02+4STORE 030STORE 04+18ON HANDSTORE ORDERDYNAMIC BUFFER
    AI Assistant
    • Question: The inventory in store 005 is low under category Teenagers. Answer: Based on my analysis, I suggest creating a push of 300 units to store 005. Would you like me to create it, or do you want to see the breakdown first?
    • Question: Why didn't store 012 get the new denim collection this week? Answer: The warehouse held 1,140 units against 1,600 units of store demand, so the stock went to the 18 stores with the highest sales potential. Store 012 ranked 23rd. Should I reserve part of Thursday's delivery for it?
    • Question: Which stores will run out of sunscreen before the weekend? Answer: Four stores hit zero by Friday: 003, 007, 015 and 021. Tomorrow's orders already cover three of them; store 015 needs 60 more units. Want me to add them to tomorrow's order?
    Replenishment: Each store's order fills the gap to its Dynamic Buffer

    Central warehouse → Stores

    1. Step 1

      Central warehouse

      The stock you have available to send, plus your local and global constraints.

    2. Step 2

      Daily store decision

      A dynamic buffer for every product at every store decides what to send, when, and how much.

    3. Step 3

      Stores & channels

      Store orders ready to ship, with online and wholesale allocation balanced against the shelves.

    New · AI Assistant

    Run Replenishment by conversation

    Control the Replenishment module with a conversational AI Assistant and query your own data in plain language. Ask about a store, a category or a SKU, get a recommendation, and let the assistant carry it out once you approve.

    • Control the system by conversationCreate a push, draft a purchase order or change the plan just by asking. The assistant prepares the action and carries it out once you confirm.
    • Query your data in plain languageAsk about any store, category, SKU or month the way you'd ask a colleague. No reports to build, no filters to set.
    • See the reasoningEvery answer comes with the numbers behind it, and you can open the full breakdown before deciding.
    MCP supportedModel Context Protocolbuffers.ai supports the Model Context Protocol (MCP), so the AI assistants and agents your team already uses can query your data and work with the system too.

    Try asking

    • Which stores are below their Dynamic Buffer in Teenagers?
    • Push the new summer collection to the top 20 stores.
    • Why is store 008 overstocked on sandals?
    • How much should we hold back for online orders this week?
    • Stop replenishing items that sell under 2 units a month at store 014.
    • Which of today's orders would break a size set?
    Capabilities

    Ten capabilities, one daily store order

    Every capability feeds the same decision: the right quantity of each product for each store. Launches, store openings, holidays, promotions and online demand adjust it instead of being planned in separate spreadsheets.

    / Replenishment

    Dynamic Buffer

    Recalculate the optimal stock level every day

    Calculates the optimal stock level for every product at every store on a daily basis, so targets follow the latest demand instead of a fixed level set months ago.

    Example: one product's buffer at one store over two weeks

    Basic Tee · Store 01

    DOI 3 days · recalculated every day

    • Fixed level 12
    • Low 9
    • Today 18
    Function
    Daily optimal stock-level calculation per product and store
    Optimizes for
    Availability with the least inventory
    / Replenishment

    First Allocation

    Send new products to stores in the right sizes from day one

    When a new product arrives at the warehouse, buffers.ai handles its first distribution to stores. You set rules with the maximum quantity per store and department. buffers.ai builds each department's size curve automatically from its sales history, with no hand-maintained size ratios. It then splits each store's allocation by that curve and decides how many units of each size to send.

    Example: the size curve for one department, learned from 356,005 units sold

    Jersey Basic T · Ladies

    356,005 units · 1,907 SKUs

    Max per size 6 = 23 units

    • XS 4
    • S 6
    • M 6
    • L 4
    • XL 3
    • XXL 0
    Function
    First distribution of new products, split by size curves built from sales history
    Optimizes for
    A size mix that matches each department's customers from the first shipment
    / Replenishment

    Replenishment

    Restock the right products at the right time

    Sends the right amount to each store within local and global constraints, so shelves stay stocked without tying up capital in excess inventory.

    Example: the warehouse holds less than the stores need

    Polo Shirt · warehouse → 4 stores

    30 units available · 38 needed

    The available stock goes first to the stores with the highest sales potential.

    Function
    Store-level restocking under local and global constraints
    Optimizes for
    Stockout reduction, inventory availability
    / Replenishment

    Store-to-Store Rebalancing

    Move stock from the store where it sits to the store where it sells

    The units one store isn't selling are often exactly what another store is missing. buffers.ai finds stock sitting above target at one store while another store is short of the same product and size, and turns it into store-to-store transfers. Slow sellers get another chance to sell at full price instead of waiting for a markdown, and short stores are covered without a new purchase. It matters most for slow-moving, high-value products, where a single unit in the wrong store is a lost sale in one place and dead stock in another.

    Example: mid-season, one size sitting in the wrong store

    Wool Coat · size M · 4 stores

    Warehouse sold out · no reorder this season

    • Moved store-to-store 3
    • New purchase 0

    Store 01 sold none of its four in eight weeks; stores 02 and 03 sold out of the size.

    Function
    Store-to-store transfers from stores holding excess stock to stores that are short
    Optimizes for
    Full-price sell-through without new purchasing
    / Replenishment

    Assortment

    Adjust your assortment dynamically

    Adjusts your product assortment as sales data comes in, surfacing best sellers and matching the mix to what customers are actually buying. Also used to set the initial assortment when a new store opens.

    Example: this week's assortment changes across six stores

    Tops · 6 stores

    Tail: under 2 units in 8 weeks

    • Best sellers added 3
    • Tail dropped 2
    Function
    Real-time, data-driven product-mix adjustment per store
    Optimizes for
    Best-seller identification, new-store product fit
    / Replenishment

    New Store

    Decide the opening quantities for a new store

    Helps decide how much of each product to send when a new store opens, before the store has any sales history of its own.

    Example: opening quantities for a store with no sales history

    Store 27 · opening order

    Opens in 3 weeks · no sales yet

    • Categories 5
    • Total 1,240
    Function
    Opening-quantity planning for new stores
    Optimizes for
    Right-sized opening stock
    / Replenishment

    Time Events & Promotions

    Plan stock around holidays, seasons and promotions

    Adjusts store stock levels for time-based events such as holidays and seasons, and for planned promotions, raising inventory ahead of the event and bringing it back down once it ends.

    Example: a store's buffer raised for a holiday, then brought back down

    Swimwear · Store 01

    Summer holiday · weeks 6–8

    • Base 10
    • Peak 19
    • After 10
    Function
    Stock-level adjustment for calendar events and promotions
    Optimizes for
    Event demand coverage without leftover stock
    / Replenishment

    Push

    Let the merch team increase inventory when needed

    Lets your merchandising team push a one-time stock increase ahead of a promotion or event, covering the demand spike without carrying the extra inventory afterward.

    Example: a one-time push for a window display

    Sunglasses · Store 01

    Merch push · weeks 3–4

    • Base 8
    • Push +12
    • After 8

    One-time: the extra units aren't replenished once the push ends.

    Function
    One-time manual stock increase for promotions or events
    Optimizes for
    Peak-demand coverage without overstocking
    / Replenishment

    Omnichannel/Online

    Balance inventory across online sales, physical stores and wholesale

    Splits inventory between online, physical stores and wholesale customers according to your policy, so no channel runs short while another sits overstocked. Wholesalers are supported as a channel alongside your own stores and online sales.

    Example: one product's warehouse stock split by channel policy

    Denim Jacket · central warehouse

    1,000 units on hand

    • Stores 620
    • Online 250
    • Wholesale 130
    Function
    Inventory allocation between online, physical stores and wholesalers
    Optimizes for
    Cross-channel availability, policy compliance
    / Replenishment

    Advanced Dashboard

    Global KPIs dashboard for fast decision making

    One dashboard surfacing KPIs from manufacturing, engineering, procurement, and supply chain, so managers can decide from current data instead of a weekly report.

    Example: chain-wide KPIs on one screen

    All stores · this week

    Updated daily · 6-week trend

    • AVAILABILITY
      97.4%
    • STOCKOUT RATE
      2.6%
    • DAYS OF STOCK
      21
    • AUTO ORDERS
      75%
    Function
    Real-time KPI reporting across manufacturing, engineering, procurement, and supply chain
    Optimizes for
    Decision speed
    Proof

    Results from live store networks

    Metrics reported by Replenishment customers, sourced from published case studies.

    Bath & Body Works

    Delta Israel Brands Ltd. — deployed across European stores and online channels

    ~70%
    Replenishment process automated
    2 mo
    Initial deployment time
    2
    Markets live (Israel, Germany)
    ~90%
    Target automation
    “Among the systems currently in use, buffers.ai has proven to be the strongest performer in supporting our operational needs.”
    — Diana Grohe, Commercial Manager, Bath & Body Works
    Read the Bath & Body Works case study

    RAI

    Grocery retailer managing 6,000–7,000 SKUs

    95%
    Replenishment process optimized
    2–3 hrs
    Saved per store manager, daily
    ~5%
    Stockout rate reduction (8–10% → ~4.5%)
    1–2 mo
    Expected full ROI
    Read the RAI case study
    / Replenishment

    Priced per store, published up front

    From $175 down to $25 per store per month, with a 5-store minimum. Estimate the cost for your store network in under 30 seconds.

    See pricing
    Go deeper
    Replenishment pricingFrom $175 down to $25 per store per month. Get an instant estimate for your store count.Replenishment for luxury fashionSlow-moving, high-value SKUs: size-level buying, boutique allocation and in-season rebalancing.Security & data handlingHow buffers.ai handles the sales and inventory data you share with it.
    Replenishment FAQ

    Frequently Asked Questions

    The platform

    One platform, three modules

    Each module is licensed on its own. Start with the one that solves today's problem and add the others when you need them.