Retail Analytics That Give Every Team One Live Number to Work From

Significant changes in demand patterns, availability, lead times, and OTIF rates are detected automatically by Solvoyo’s cloud engine — surfacing exceptions with context and recommended actions rather than raw alerts that require planners to build their own analysis before deciding what to do. Every exception is prioritized by business impact, so teams focus attention where it matters most rather than working through a uniform queue of issues with vastly different consequences.

Excess stock, stockouts, slow-movers, perishable waste, and at-risk inventory are identified continuously and quantified in financial terms — giving supply chain, category, and finance teams a shared, real-time view of inventory health rather than discovering working capital problems or spoilage costs at period-end. Dead stock and obsolescence risk are flagged before write-offs become unavoidable, while waste is traced to its root causes at the store, category, and SKU level — whether excess ordering, inaccurate shelf-life forecasting, poor markdown timing, or supply chain delays. Recommended actions are generated automatically for each issue type, from reallocation and markdown timing to supplier return coordination, so teams act on the highest-impact opportunities rather than applying blanket policies that address symptoms without fixing the underlying planning failures driving them.

Supplier performance is tracked continuously across OTIF rates, lead time reliability, fill rates, and order accuracy — giving procurement and supply chain teams a data-driven view of which vendors are protecting service levels and which are introducing the variability that drives safety stock inflation, emergency buys, and stockouts downstream. Vendor scorecards are built automatically from actual transaction data rather than self-reported metrics, making performance reviews faster, more objective, and harder to dispute. Chronic underperformers are identified at the SKU and lane level — not just at the supplier level — so root cause analysis goes deep enough to distinguish a systemic reliability problem from an isolated product or route issue, and corrective conversations with suppliers are grounded in specific, quantified evidence rather than general impressions.

Store performance is analyzed through multi-dimensional clustering that groups locations by revenue profile, sales price positioning, physical capacity, size-selling patterns for apparel, and category contribution mix — giving merchandising and supply chain teams a structured, data-driven basis for localization decisions rather than relying on geography or store format alone. Clusters built on how stores actually sell — not how they are administratively grouped — surface assortment localization opportunities that aggregate reporting would never reveal: the regional stores where a size curve diverges significantly from the chain average, the categories where a cluster of high-revenue locations is systematically undersupplied, or the format group where a promoted item consistently underperforms against plan. Analytics capabilities combine data from multiple internal and external sources, providing automated analysis and diagnostics to drive insights from big data and support faster, easier decision-making — so category and regional teams act on localization opportunities with analytical confidence rather than anecdotal observation.

Optimize employee shift schedules to store traffic and sales forecasts, in line with skills, labor laws, and regulations — with real-time traffic data informing how staff are distributed across checkout, fitting rooms, shop floor, and storage, and KPI tracking across retention, productivity, and hours.

Solvi, Solvoyo’s AI planning agent, goes beyond surfacing what is happening to recommending exactly what to do — identifying root causes behind exceptions, quantifying the business impact of inaction, and generating prioritized action items planners can review and execute directly within the platform.