SUPPLY CHAIN

Supply chain

Test the network redesign before committing capital. Stress the inventory policy before the season does. Prove the distribution center can hit its rate before the automation order ships. SimWell builds working models of how your operation behaves, so the hardest calls get made on evidence instead of conviction.

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GIS-style supply chain network simulation showing distribution nodes, transport arcs, and an orange congestion cluster

The gap between the data and the decision

You have the data. You have planning tools. The decision is due this quarter, and it still won't close.

Supply chains have grown too interconnected for anyone to hold the whole system in their head. A supplier's lead time slips two weeks and the promise date survives on paper while safety stock quietly covers the gap. A mode shift that saves freight cost on one lane adds a day of variability that three DCs downstream end up absorbing. The consequences of the hardest decisions stay invisible until after the commitment is made, and by then they're expensive and lasting.

Inside that reality, the stall takes familiar forms:

The operation

The room can't converge.

Two experienced planners look at the same network and reach opposite conclusions, each backed by a different spreadsheet, and the call sits unmade while the cost of waiting compounds.

The tools show a snapshot.

What you need is what happens next: how the peak hits dock congestion, where the plan gives way under demand variability, what a lane change does to landed cost per SKU. Static tools can't show a moving system.

The capability lives in one person.

Where a modeling seat exists at all, it rests on someone whose departure would take it with them, and the software bought to fix that sits unused because nobody else can run it.

Nobody trusts the number.

The forecast performs at the mean, and the operation doesn't break at the mean. The breaks come at the surge, the simultaneous failures, the container that misses the vessel.

And it lands on you

The weight lands on one desk.

Someone signs the distribution-network build or approves the inventory plan for the year. All of that interaction, all of that risk, and nothing exists to show the room what each option sets in motion before the signature.

The decisions supply chain leaders own

Six decisions that constrain each other, which is exactly why they're hard to make one spreadsheet at a time.

None of these decisions arrives alone. The network design fixes what inventory staging can achieve. Inventory placement fixes what the DC has to absorb on a peak day. The DC's real throughput sets the staffing envelope, and every one of them determines how the network responds when a supplier goes down. Supply chain performance rides on six decisions that constrain each other, which is exactly why they're hard to make one spreadsheet at a time.

SimWell organizes its work around all six.

Capital & capacity

Whether to build, buy, or expand, and how much capacity to put in place for the demand you expect three years out. The new DC, the automation retrofit, the port allocation you commit to before the volume exists.

Routing & dispatch

How to assign trucks, containers, and crews when demand changes by the day and the lane.

Throughput & bottlenecks

Where the constraint really sits in the building or the flow, and what will move it, before spending to fix the wrong thing.

Staffing & scheduling

How many people are needed, where, and on what shift structure, to hit service levels without overspending on labor.

Contingency & risk

What breaks when a supplier, port, or lane goes down, and what the response should be before the disruption arrives.

Why the current approach stalls out

A design that works on average fails in October.

None of the above is a tooling indictment. The planning stack was built to calculate, forecast, and recommend, and it does those jobs. The decisions above ask a different question: what the whole system will do once you change it. Answering that requires representing variability, congestion, and the interactions between echelons, which is precisely what general-purpose tools were never built to carry.

Simplification helps when it removes noise. Simplification creates risk when it removes the dynamics that decide how the operation actually behaves. A network model that optimizes on averages will hand you a design that works on average and fails in October.

WHAT SIMWELL BUILDS

We build decision systems.

The core is a working replica of how your operation behaves, validated against the operation itself, down to the operating rules and the variability your planning tools average away. You run your options through it and watch what each one sets in motion before anything is committed.

The model answers the question once. The system makes the answer repeatable, so it's there whenever the decision returns. Your team runs it, reads the results, and makes the call. The work runs on top of the systems you already have, and when the engagement ends, the capability stays.

PROOF

Decisions made on evidence, not conviction.

Retail fulfillment network simulation

CASE STUDY

Could our stores fill online orders without driving up cost?

Testing dozens of fulfillment scenarios showed which stores should ship and which should stay satellites. Delivery times were cut in half, at lower transport cost and the same service level.

Read the case
Optimized recycling collection route across Canada

CASE STUDY

How do we route trucks against demand that swings by season?

RPM eco collects recyclables across Canada from customers whose volumes shift month to month. Predicting the weight waiting at each stop exposed how much capacity was being driven around empty. Empty pickups fell by as much as 90% in some provinces, with 30% more collected per pickup.

Read the case
Distribution center capacity and throughput planning

CASE STUDY

How much capacity will we need to meet the long-term forecast?

A large apparel company had to turn a strategic forecast into throughput requirements, account for peaks the averages hide, and convince stakeholders to approve the capacity case. A simulation of the logistics network gave them projections they could defend with evidence instead of assertion.

Read the case

HOW ENGAGEMENTS BEGIN

One decision, bounded scope, sized to the window the decision actually has.

  1. 01

    Frame the decision.

    One decision, the options on the table, the constraints that bind, and the criteria the room will judge by. If a model won't help, you hear that here, before anyone scopes anything.

  2. 02

    Build and validate the model.

    We build a right-sized model against the data you have. Perfect master data is never the entry requirement; the first pass runs on order history, operating rules, and the inputs your team already trusts, and the model itself surfaces which data gaps matter enough to close. Your planners review the logic as it takes form, in sessions scheduled around their planning calendar rather than on top of it.

  3. 03

    Deliver the decision package.

    Scenario results, documented trade-offs, stated assumptions, and a model your team keeps. Model logic and assumptions stay visible to your team throughout, and results are reviewed together rather than delivered as conclusions, so the recommendation you carry forward is one you can defend line by line.

WHY SIMWELL

Plenty of firms can build a model. Fewer can build one that holds up inside a real operation.

SimWell's consultants carry one of the deepest modeling benches anywhere, and many spent years inside plants, distribution networks, and logistics operations before they built models of them. They know why a design that clears every review meeting can still fail on the floor, and they build for the floor.

WHO THIS IS FOR

A decision this quarter where the downside is real.

SimWell works with supply chain teams facing a decision this quarter where the downside is real: capital committed for years, service missed, throughput lost shift after shift. If oversimplification has already failed you once, you're in the right place.

Discuss the decision

Bring one question. If modeling can support a defensible commitment, we'll show you the smallest scope that gets you there. If a model won't help, you'll hear that in the first call.

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RESOURCES

More on the decisions supply chain leaders own.

CSM

BLOG POST

Cutting Stock at Network Scale

Five plants. Eight customer orders. Watch where they land when cutting math takes over from geography.

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A retailer redesigns its supply chain network

CASE STUDY

A retailer redesigns its supply chain network

A network redesign guided by simulation cut buffer inventory by 20% while maintaining service coverage across the new footprint.

Read the case study
A manufacturer implements a supply chain digital twin

CASE STUDY

A manufacturer implements a supply chain digital twin

A digital twin of the end-to-end network lets the team compare recovery scenarios before disruption cascades into margin loss.

Read the case study