Replenishment decisions optimization at SKU level

Solution

How ROPROQ generates economically differentiated replenishment decisions

Economic reorder point and order quantity logic

Each SKU-location is solved through economic optimization

For each SKU-location-day

ROPROQ evaluates feasible replenishment alternatives and identifies the combination of reorder point and reorder quantity where total expected variable cost reaches minimum.

The calculation combines:

holding cost
stockout cost
ordering cost

The resulting minimum defines the economically optimal replenishment trajectory under current supply conditions.

TC = HC + SC + OC

TC = total cost

HC = holding cost

SC = stockout cost

OC = ordering cost

The optimum differs across products, locations, and supply situations.

Demand and supply variability are carried directly into each decision

ROPROQ operates on empirical distributions rather than point assumptions.

ROPROQ evaluates feasible replenishment alternatives and identifies the combination of reorder point and reorder quantity where total expected variable cost reaches minimum.

For each SKU-location combination, the system continuously updates:

demand distributions
lead-time distributions
receipt density distributions

These distributions are refreshed daily and used directly inside the optimization process.

This avoids reliance on average values plus generic safety buffers.

Decision quality depends on distribution shape, not only on mean values.

The output is daily executable replenishment plans

ROPROQ generates daily replenishment decisions at SKU-location level.

Typical output includes:

purchase order quantities
replenishment timing
higher-echelon requirements

In steady state, decisions are produced unattended except for explicitly defined safety stop situations.

The system is designed to produce decisions, not dashboards.

Independent analytical runtime above ERP

ROPROQ operates independently from transctional systems.

The system reads fixed daily snapshots, performs optimization in a separate analytical environment, and writes decisions back through a narrow interface.

Each run remains reproducible through pinned data and code versions.

Implementation begins through parallel run:

daily comparison
anomaly review
parameter adjustment
controlled cut-over

Transactional systems remain unchanged.

Test the decision logic on your own data

ROPROQ determines optimal reorder point and reorder quantity by evaluating the economic outcome of different replenishment strategies.

Test calculator

You can explore this logic directly using an interactive calculator.

The tool allows you to:

input your own demand, lead time, and cost assumptions
evaluate alternative ROP/ROQ combinations
compare optimized decisions with manually defined parameters
observe the impact on service level, inventory, and total cost

The objective is not to simulate scenarios, but to understand how economically differentiated replenishment decisions are formed.

Designed for desktop use. Processes one SKU-location combination at a time.

Decision transparency

Each replenishment line remains traceable to dominant cost drivers and active constraints.