Fomuta Goxoze gives multi-location retailers a single place to watch stock levels, respond to shortages, move inventory between stores, and plan ahead using real sales history.
Each capability connects to the others. Stock alerts trigger transfer requests. Forecasting informs reorder points. Dead-stock reports free capital for better-performing lines.
The dashboard pulls live inventory data from every connected store location and displays it in a single unified view. No more calling individual stores or waiting for end-of-day reports. When a sale happens at your downtown location, the count updates immediately across the platform.
Retailers with five stores see the same depth of information as those with fifty. The interface scales without becoming cluttered. Filter by location, product category, SKU, or supplier to focus on what matters in that moment.
A downtown flagship store and a suburban outlet move inventory at different rates. The platform lets teams set reorder thresholds per SKU per location, so alerts fire at the right moment for each store rather than using a single blanket rule that fits none of them well.
Alerts reach the right people through email or in-app notifications. Set who receives alerts for which stores so district managers and store staff each get relevant information without noise.
When one store is running low and another has surplus, the platform surfaces that opportunity automatically. Staff can initiate a transfer request directly from the dashboard. The request routes to the sending store for confirmation, then updates both locations' inventory counts when the transfer is marked complete.
Every transfer creates a paper trail. Managers can see pending, in-transit, and completed transfers at a glance. This removes the informal phone-call system that most multi-location retailers rely on and replaces it with a documented, trackable process.
The forecasting module uses historical sales data from the same period in prior years to project demand for the weeks and months ahead. It works at the location level, so if your lakeside store sells more outdoor gear in summer while your city location stays steady year-round, the forecasts reflect that difference.
Forecasts inform suggested reorder quantities and timing. Teams can review projections, adjust for known factors the system cannot see (a planned promotion, a local event), and use the output to negotiate with suppliers earlier.
Dead stock is inventory that has not sold within a defined period. It occupies shelf space, ties up working capital, and often goes unnoticed until a physical count reveals the problem. The platform generates regular reports that flag slow-moving and stagnant SKUs by location before they reach that point.
Reports show how long each item has been sitting, its current quantity, and its carrying cost estimate. This gives buyers and merchandisers the information they need to decide whether to discount, consolidate to one location, return to supplier, or discontinue the line entirely.
The platform fits into existing workflows rather than replacing them. These are situations where teams find it most useful.
Store managers open the dashboard at the start of each day to see overnight changes, check for any alerts that fired, and confirm that incoming transfers are on schedule. The full picture in under two minutes.
Before placing orders with suppliers, buyers pull the seasonal forecast for each location. They compare last year's sell-through by store and adjust quantities accordingly rather than ordering the same amounts as the previous year.
A low-stock alert fires for a fast-moving item at one location. The platform shows that a nearby store has surplus. A transfer request is initiated, approved, and dispatched within the same shift. No calls, no emails, no waiting.
Merchandising teams export the dead-stock report and review which SKUs have stalled at which locations. They make markdown, consolidation, or discontinuation decisions based on actual movement data rather than gut feel.
When a new location opens, it connects to the same platform the rest of the network uses. The new store's inventory appears in the dashboard immediately, and the same alert rules and transfer workflows apply from day one.
The onboarding process is structured to get retailers operational quickly without disrupting existing operations.
The first conversation focuses on understanding the retailer's current inventory process, the number of locations, the systems already in use, and what specific problems are most pressing. No generic demos. The conversation shapes what comes next.
A live walkthrough of the platform configured to reflect the retailer's product categories and location structure. The team shows how the specific features relevant to that retailer's situation work in practice, not in theory.
The technical team connects the platform to the retailer's existing point-of-sale and ERP systems. Historical sales data is imported so forecasting and dead-stock tools have a baseline to work from on day one.
Training sessions run separately for store-level staff, district managers, and buyers. Each group learns the parts of the platform relevant to their role. Documentation stays accessible after training for ongoing reference.
After go-live, a dedicated support contact remains available for questions. The platform team monitors for integration issues and releases updates on a regular schedule. As the retailer's network grows, the platform scales with it.
The Fomuta Goxoze team combines software engineering with direct retail operations experience.
Nadia spent eight years in retail operations before moving into product development. She shapes the platform's feature roadmap based on what actually slows inventory teams down.
Marcus leads the engineering team responsible for the real-time data pipeline and integration layer. He focuses on reliability and the speed of data updates across the platform.
Priya manages onboarding and ongoing support for all retailer accounts. She runs training sessions and maintains the documentation that helps teams get the most from the platform.
Jordan built the forecasting models that power seasonal demand projections. The work draws on retail sales pattern research to produce location-specific demand curves.