Custom Retail Inventory Management Software

Product hierarchy that models your range accurately: style, colour, size, or any attribute combination your buying team works with. Barcode assignment at the variant level with support for supplier barcodes, internal barcodes, and GS1 EPC SGTIN encoding for RFID-tagged inventory. GS1 SGTIN (Serialised Global Trade Item Number) encodes both the product identity and a unique serial number into the RFID tag so individual units can be tracked at item level, not just quantity level -- the foundation for RFID-enabled cycle counting and item-level shrinkage attribution.

Where Zebra FX-series fixed RFID readers are deployed in stockrooms or on fitting room doors, the inventory system receives item-level read events and updates stock positions without a manual count. For stores without RFID infrastructure, barcode-based variant identification using a Zebra mobile scanner connects to the same inventory records through the warehouse receiving and stock count workflows. Bundle and kit management tracks component stock separately from finished kit stock so a promotional bundle does not misrepresent the availability of individual components. Product import via spreadsheet or supplier EDI feed includes validation before it touches live stock records. Attribute-based filtering across the full catalogue supports buying team analysis by category, season, supplier, or margin band.

Per-location stock levels at the variant level updated in real time after every sale, goods receipt, write-off, and transfer. Real-time stock deduction at the point of sale is the critical integration: when a sale is rung through the POS (whether Shopify POS, Square, or a bespoke till system), the inventory management system receives the sale event via webhook or API and deducts the sold variants from the location's stock record immediately. Nightly batch reconciliation is not sufficient for a multi-location retailer -- a stock position that is six hours old creates overselling in click-and-collect and store-to-store transfer errors.

Shopify, WooCommerce, and Magento stock sync is maintained via webhook (inventory_level.update events in Shopify, WooCommerce's order.completed webhook) so available quantity on the e-commerce front end reflects the physical stock position at each location. ABC/XYZ inventory classification segments the SKU catalogue by revenue contribution (A: top 80%, B: next 15%, C: bottom 5%) and demand variability (X: stable, Y: seasonal, Z: erratic) so buying team effort and safety stock investment is directed at the SKUs that matter most. Stock reservation for click-and-collect deducts available quantity at the fulfilment location before the customer collects. Allocation views show the buying team where stock is concentrated before a new delivery arrives.

Supplier product catalogue with cost prices, minimum order quantities, and lead times per supplier. Lead time variability is tracked per supplier alongside the nominal lead time -- if a supplier nominally delivers in 14 days but has delivered in 9 to 21 days over the last 12 POs, the system uses the variability data in safety stock calculations rather than assuming the nominal figure. Supplier lead time variability is one of the most commonly ignored inputs in retail replenishment logic, and it is the reason safety stock calculations drift from theory when the nominal lead time is used uncritically.

PO creation from the buying team dashboard or auto-generated by reorder point rules. Barcode label generation using Zebra ZPL (Zebra Programming Language) prints GS1-compliant product labels at goods receipt for any items that arrive without compliant labels -- the label print job is triggered from the goods receipt workflow without a separate label design step. PO approval workflow has configurable spend thresholds and approver routing. Invoice matching against the goods receipt flags quantity and price variances for the finance team to resolve before payment is approved. ERP integration with SAP MM, Oracle Inventory, or NetSuite passes PO, goods receipt, and invoice matching data to the financial system automatically without the finance team re-entering the transaction.

Configurable minimum and maximum stock levels per variant per location, or aggregated across locations for centralised replenishment. Safety stock calculation uses the formula: safety stock = Z * sigma_d * sqrt(L), where Z is the service level z-score (1.65 for 95% service level), sigma_d is the standard deviation of daily demand over the trailing period, and L is the supplier lead time in days. This means the reorder point adjusts automatically as demand patterns shift -- a variant with stable demand carries a lower safety stock than one with erratic demand at the same average velocity. Economic order quantity (EOQ) optimisation minimises the combined cost of ordering frequency and holding cost: EOQ = sqrt(2DS/H), where D is annual demand, S is order cost per PO line, and H is annual holding cost per unit.

Demand forecasting with LightGBM or Facebook Prophet (seasonal decomposition) projects forward demand using historical sales, incorporating seasonal decomposition for holiday spikes -- the weeks before Christmas, summer peak for seasonal categories -- so reorder points automatically increase ahead of known high-demand periods rather than reacting to stockouts after they happen. Automatic PO generation fires when stock falls to the calculated reorder point, using the preferred supplier and EOQ-derived order quantity. Exception reporting shows variants approaching reorder point for buying team review before a bank holiday or a known promotional period.

Write-off recording at the variant and location level with reason codes: damaged, expired, theft, unknown shrinkage. Cycle count workflow for continuous inventory accuracy: a fraction of the SKU catalogue is counted each day (ABC-weighted -- A-class SKUs counted more frequently than C-class) so every variant is counted on a rolling cycle without a full store closure. Cycle count variance -- the difference between the system position and the physical count -- is reviewed and approved before stock levels are adjusted. This approval step prevents unauthorised stock adjustments and ensures every change has an authorised explanation in the audit trail.

RFID-enabled cycle counting, where Zebra FX readers are deployed in the stockroom, reduces the physical count time significantly: a reader scanning the stockroom passively identifies tagged items without individual scanning. Shrinkage variance reports break out cycle count variances by location, product category, reason code, and time of day -- time-of-day correlation can identify shift patterns or staffing gaps associated with theft-related shrinkage. Shrinkage rate by location compared across the estate makes outlier locations visible without manual calculation. The loss prevention data that moves shrinkage conversations from anecdotal to evidence-based.

Supplier profiles with contact information, payment terms, lead times, minimum order requirements, and lead time variability history. Cost price by supplier per variant so the system knows which supplier to use when generating an automated PO based on current cost rather than the last manually updated price list. Price variance alerts fire when a goods receipt cost differs from the PO cost by more than a configured threshold -- flagging the discrepancy for the buying team to resolve with the supplier before the invoice is approved for payment rather than after.

Margin reporting at product, category, and supplier level uses current cost prices against selling prices from your POS or e-commerce platform. SAP MM, Oracle Inventory, and NetSuite ERP integrations pass landed cost data (including duty and freight allocated to PO lines) into the margin calculation so the reported margin reflects the true cost of delivered goods rather than just the ex-works price. Supplier performance tracking covers on-time delivery rate, order fill rate (percentage of ordered lines delivered in full), and return rate per supplier, over rolling 12-month and trailing-quarter windows. A supplier delivering at 92% fill rate on a 14-day lead time is a different purchasing risk than one delivering at 99% fill rate on a 21-day lead time -- the system surfaces both dimensions so buying decisions are based on margin and reliability rather than habit.