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Replenishment Optimization Software

Reorder Before The Shelf Ever Goes Empty.

Sophus optimizes inventory levels and replenishment frequency across every DC, site, and channel, trading stock off against sourcing and transportation cost instead of guessing on a fixed schedule.

Trusted by Growing Companies at Every Stage of Network Design Maturity

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The plan behind every reorder

Planning the right inventory level and replenishment frequency gets hard fast once a company runs a multi-echelon network and serves customers across multiple channels.

Replenishment means restocking inventory and moving goods so the right products are available at the right place and time. But what if it actually did that, optimized across every DC and channel at once?

That’s what Sophus Replenishment Optimization does. It optimizes inventory level and replenishment frequency between DCs and sites, trading them off against sourcing and transportation cost to land on a plan that protects margin and fill rate together.

See where your stock runs out first.

The Problem

Why replenishment gets complicated fast

One network, a thousand moving parts

Add a multi-echelon network and a handful of channels, and getting inventory levels and replenishment frequency right gets hard fast.

Stock decisions don't happen in a vacuum

None of it can be decided in isolation either. It all has to weigh against sourcing cost, transportation cost, and margin at the same time.

The Sophus Solution

Stock levels and cost, optimized together

Across Every DC

Sophus optimizes the inventory level and replenishment frequency between DCs and sites, instead of planning each location on its own.

Against Real Cost

It trades off inventory level and replenishment frequency against cost elements like sourcing cost and transportation cost, to land on the optimal replenishment and stock transfer plan.

How It Actually Works

Two Tools. One Signal.

Dastro: Data Automation

Keeps the signal current

Inventory position, sales history, lead times, and cost data get pulled and cleaned from your systems automatically, so the model never runs on a stale count.

Sophus X: Optimization Engine

Turns the signal into a plan

Sophus X sets the inventory level and replenishment frequency at every DC and site, weighed against sourcing and transportation cost together.

Build Around Your Replenishment Plan

Put the plan to work

Replenishment connects directly to how you forecast, stock, and design the network around it.

Cost to Serve

See the true cost behind every replenishment and stock transfer decision.

Demand Forecasting

Feed replenishment with a demand signal it can actually trust.

Shelf-Life Optimization

Factor product freshness and expiry directly into the replenishment plan.

Sourcing Optimization

Decide where to source from as part of the same cost trade-off.

Supply Chain Network Digital Twin

See replenishment flows reflected in a live model of your network.

Greenfield & Brownfield Analysis

Decide where a new DC would actually improve your replenishment network.

Common Questions

FAQs

What is replenishment optimization?

Replenishment is the process of restocking inventory and moving goods to ensure the right products are available at the right place and time. Replenishment optimization automates that decision, setting the right inventory level and replenishment frequency for every DC and channel instead of relying on a fixed reorder calendar.

What is the difference between replenishment optimization and inventory optimization?

Inventory optimization sets the target stock level and safety stock policy for each location. Replenishment optimization decides how often and how much to actually move to hit that target, trading it off against sourcing and transportation cost. The two work together: one sets the target, the other decides how to hit it.

How does replenishment work across a multi-echelon network?

In a multi-echelon network, stock at a central DC feeds regional DCs, which feed stores or customers directly. Sophus optimizes replenishment frequency and quantity at every echelon together, instead of planning each site in isolation and hoping the network balances itself out.

How do you balance inventory cost against service level?

Sophus trades off inventory level and replenishment frequency against cost elements like sourcing cost and transportation cost, so the plan protects customer fill rate without carrying more stock than the margin can support.

What data does replenishment optimization need?

At minimum, current inventory position, demand or sales history by location and channel, lead times, and cost data for sourcing and transportation. Dastro automates pulling and cleaning this data so the model always runs on current numbers.
Get Started

Ready To Reorder On Purpose, Not On Guesswork?

Stop choosing between too much stock and too many stockouts. Get one replenishment plan that balances both against real cost.