Data Ingestion & Patterns
We connect to transaction logs, fill-level sensors, or operational exports to detect consumption patterns across locations and time periods.
Asset Replenishment
Predict when and where to restock, before the shortage hits.
Use demand pattern analysis to move from reactive dispatching to forecast-driven replenishment: fewer empty assets, lower logistics costs, better service levels.
The Challenge
Reactive restocking costs more and serves worse.
When replenishment is based on fixed schedules or manual checks, some assets run empty while others get restocked too early, wasting logistics capacity and leaving customers underserved.
Emergency dispatches are expensive. Missed replenishment windows erode trust. And without a demand signal, teams cannot prioritize where to focus first.
Empty assets
Customers encounter out-of-stock or out-of-service points
Emergency runs
Unplanned dispatches at premium cost
Wasted capacity
Restocking assets that did not need it yet
No visibility
Teams cannot see which locations need priority
The Approach
From raw data to optimized replenishment schedules.
Data IngestionPattern DetectionDemand ForecastDepletion ModelReplenishment WindowsRoute Optimization
Demand Forecast & Depletion
Forecast consumption rates per asset and location, accounting for seasonal variation, day-of-week effects, and external drivers like events or holidays.
Replenishment & Routing
Generate optimal restocking windows and group locations into efficient routes, balancing service levels against logistics cost.
Outcomes
What forecast-driven replenishment delivers.
Fewer empty assets
Restock before customers experience a shortage
Lower logistics cost
Fewer emergency runs, smarter route grouping
Better service levels
Higher asset availability where it matters most
Full visibility
See which locations need priority at a glance
In Practice
What this looks like
Illustrative scenario
Cash & ATM logistics
A cash logistics operator managing hundreds of ATMs across a region was running on fixed replenishment schedules, regardless of actual demand. The result: high-cash ATMs received unnecessary visits, while busy locations ran empty over weekends, triggering expensive emergency dispatches.
RivNox built a demand-driven replenishment model starting from transaction history the operator already had. Within a short PoC cycle, a usable tool was in place that predicted depletion per ATM and generated weekly replenishment windows, prioritizing by urgency, not by fixed schedule.
−35%
Emergency dispatches
+18%
Cash availability on peak days
Short PoC
From kickoff to first live model
Fit Check
Is this the right solution for you?
Best fit
- Networks of distributed physical assets that need periodic restocking.
- Operations where empty assets directly impact customer experience or revenue.
- Teams managing replenishment with fixed schedules or manual field checks.
- Companies with transaction or sensor data they are not yet using for planning.
Usually not ideal
- Single-location operations with very predictable, uniform consumption.
- Situations where no historical consumption or transaction data is available.
- Use cases where a simple time-based schedule already works well enough.
Ready to stop restocking blind?
Let's explore whether forecast-driven replenishment is the right fit for your operations.
Book a 30-minute discussion to see if there's a match