Case Study · Online Grocery · Dark Store Operations
Turning memory-based picking into a governed, location-first warehouse
A UAE-based online grocery operator was running dark-store fulfillment without reliable SKU-to-location data. Pickers relied on familiarity and search to find items, duplicate bin assignments created conflicting records, and there was no capacity model linking normal operations to peak-season slot requirements. Vorctis deployed an addressable storage programme — structured location grammar, machine-readable labels, master-data conflict resolution, and app-guided location-first scanning — giving the operation a governed slot model and measurable capacity visibility for the first time.
About the Customer
An online grocery operator running several dark stores across the UAE, managing high daily order volume, tight delivery windows, and multi-temperature picking, packing, and dispatch flows.
Problems
- Pickers relied on memory or time-consuming searching to locate items — no reliable SKU-to-slot data existed at scale.
- Duplicate SKU-location assignments produced conflicting bin records, creating confusion at shelf level and degrading pick confidence.
- Temporary or new pickers without store familiarity faced disproportionately long search times, widening the performance gap between experienced and inexperienced staff.
- Facings-based storage consumed shelf width without systematic slot governance or a structured location grammar.
- No capacity model existed to plan the difference between normal-season and peak-season slot configurations, leaving peak readiness unquantified.
Solutions
- Rolled out addressable storage using a structured zone/aisle/section/shelf/slot location grammar across all dark-store temperature zones.
- Deployed machine-readable bin labels and shelf labels with embedded barcodes, enabling scan-based location capture and confirmation.
- Captured and governed SKU-to-bin assignments in master data, establishing a single authoritative source for slot ownership.
- Built a duplicate-conflict detection and resolution workflow to clean conflicting bin records and prevent recurrence through governance controls.
- Modelled storage capacity by aisle, shelf, slot, and storage mode for both normal and peak-season configurations, quantifying the seasonal uplift available.
- Introduced app-guided location-first picking: scan the location barcode before the item barcode to anchor every pick to a verified, addressed slot.
Impact
Impact
Several key metrics improved, with exact numbers used only when approved.
Our approach
How We Solved It
Scan-based location capture across all zones and aisles — building a full inventory of physical storage slots and their barcoded addresses.
Duplicate detection and resolution across all SKU-location records; identification and remediation of no-location SKUs in the active assortment.
Machine-readable bin labels and shelf label generation, aligned to the zone/aisle/section/shelf/slot grammar for reliable scan-based confirmation.
Location grammar training, app-guided location-first picking, and master-data maintenance processes to sustain slot accuracy as assortment changes.
Ready to solve your operational problems?
Tell us about your operation and we will show what a similar improvement roadmap could look like.
Contact usContact
Contact Vorctis Digital
Share your operation context and we will come back with the right next step.
Request received
Your request was received.
Someone from Vorctis will be in touch shortly.