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.

Discuss Your Use Case

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 can't 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 didn't need it yet

No visibility

Teams can't see which locations need priority

The Approach

From raw data to optimized replenishment schedules.

Data Ingestion & Patterns

We connect to transaction logs, fill-level sensors, or operational exports to detect consumption patterns across locations and time periods.

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 three weeks, 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

3 weeks

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 aren't 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