Warehouse & distribution center

The building runs hard all day, and orders still ship late.

More overtime hasn't fixed it. The last change just moved the backlog somewhere else. Somewhere in the building one bottleneck is setting the pace, and no report, walk-through, or time study can point to it.

A model can. Watch your building run, receiving to shipping. Find the real bottleneck, and see what each fix actually recovers before you commit to overtime, headcount, or a capital ask.

Book a call Download our capabilities
Sound familiar?

›The distribution center can't keep up, and no one can say why

›Some shifts run overtime while others sit idle, and service still slips at peak

›Automation might help, but how do we prove it before we invest?

›You're sizing a new building, and every number came from a source you can't test

  • The distribution center can't keep up, and no one can say why
  • Some shifts run overtime while others sit idle, and service still slips at peak
  • Automation might help, but how do we prove it before we invest?
  • You're sizing a new building, and every number came from a source you can't test

Why can't the building keep up, and why does every fix just move the backlog?

Because layout, slotting, pick logic, and labor interact under real variability, and a fix aimed at the visible symptom sends the constraint somewhere else. A building that can't keep up looks like a space problem. Most of the time the space is fine, and the flow is what's losing the throughput.

You've probably already run the elimination everyone runs. Each pass is competent, and each one dead-ends the same way.

Is it one building?

One site runs 50 cases an hour and a sister site runs 30, on the same equipment and the same process. Benchmarks can tell you the gap exists. They can't tell you whether it's the layout, the mix that building is handed, or the volume it carries, because a benchmark compares outputs and the answer lives in the interactions.

Is it one shift?

Day shift outproduces nights, or the other way around, and the handoff window quietly eats half an hour of every changeover. Comparing shifts fairly is nearly impossible from reports alone, because no two shifts are handed the same work.

Is it one step?

Picking looks slow until you notice the pickers waiting on replenishment. The fastest pickers post the most errors, and the rework lands downstream where nobody logs it. Standing and watching a process for fifteen minutes is a genuinely good technique, and it still can't see receiving, putaway, picking, packing, and shipping interact across a whole day.

Did something change, or is it the data?

Sometimes the trigger is real: a new layout, a new process, growth. Sometimes the stock is technically in the system and physically somewhere else, and the throughput problem you're chasing is really a data gap. Telling those apart from the numbers alone is close to impossible.

Is it the people, or the process making them slow?

Here's the question underneath all the others, and the one nobody wants to say out loud. A model of the building answers it without pointing at anyone: it shows what the flow does to the people inside it. That's usually the answer the team was hoping someone could prove.

How SimWell helps

This is where SimWell comes in, and where the guessing stops.

SimWell builds a working model of your building and runs it, receiving to shipping. It's a simulation: your real arrivals, order profiles, slotting, and rosters, moving through your real layout under the variability your floor actually sees. That's the difference from a walk-through or a time study. Those catch a moment; a simulation watches every part of the building interact at once, over a full day, the way the building really behaves. It reflects the work as it's done, not a textbook flow, which is the only reason its answer is worth trusting.

Run that way, the model finds the constraint that's setting your pace. Then it tests every lever against the same real demand and shows what each one actually recovers, and what each one costs.

Get the answers you need

Find the constraint · watch it move · rank the fixes

Every lever, ranked by what it recovers and what it costs.

CASES/HR 41 RECEIVING → SHIP RECEIVING PICKING PACKING Backlog here + 2 PICKERS

Same flow. Different fix. The constraint moves, and throughput barely does.

The point isn't the model. It's the decision on the other side of it. That ranking is the answer you came for. Sometimes the top of it is a re-slot or a staffing change that recovers throughput for almost nothing, and the new building comes off the table. Sometimes the building genuinely is out of room, and the model tells you that before you spend a year finding out the expensive way. Either way you stop weighing options you can't compare, and start choosing on what happened when you ran the scenario through your own building.

True prioritization provides you with tremendous value and sometimes that's enough. Some roads end there: a re-slot is a re-slot, and once it's ranked you can act on it. Others are big enough to be their own decision. If the answer points to automation or a new building, further simulation work can reveal how to understand the consequences of your moves before they happen.

The automation on the table

The automation vendors' numbers look strong. Will they hold at your volume and your mix?

This is the first of those deeper looks.

Vendor throughput specs are measured honestly, under ideal conditions: clean order profiles, steady arrivals, a mix that behaves. Your building has its own mix, its own peaks, and its own variability. The model runs the proposed design against your real orders and arrivals, next to the building you run today, and returns the throughput and payback the system will actually deliver, with the assumptions stated and the sensitivities run.

SimWell doesn't sell the building, the racking, or the robots. The model has no stake in which answer wins, which is precisely why you can take its number to the board. No vendor can make that case about its own product, however good the system is. What you walk away with is the risk section your CFO will scrutinize, already written and already tested.

A pilot is a genuine test, and it has a boundary worth naming: a pilot can't run peak season, and it can't run next year's mix. A model can, and it keeps answering as your volume and mix move.

A model settles the throughput question. It doesn't settle every question a multi-year commitment raises, and you should weigh those separately: whether the vendor and the product line survive the contract, what the system costs each year after you sign, how the WMS and ERP integration goes, what the install does to your operation while it ramps, and what your exit terms are. Those risks live outside any model, ours included.

Automated sortation conveyor system
Proof

A CPG online retailer building a brand-new fulfillment facility used a simulation to size the automated sorting system and validate its throughput before committing to the hardware. The model tested the proposed automation against the facility's real order and volume profile, so the capital decision rested on the building's own numbers rather than the vendor's demo conditions.

Read the case study →
Browns Shoes warehouse conveyor simulation
Proof

Browns Shoes, a Canadian footwear retailer, upgraded an automated warehouse system (ASRS and conveyor) to keep pace with rising demand, and used simulation to test the new automation against real demand before committing.

Read the case study →

The new building

If the answer really is more space, did you size it right, and what aren't you seeing before the concrete pours?

Once the capital is approved, nobody says the quiet part out loud: what if the size is wrong? Almost everyone carries that worry anyway. The architect gave you one number, the 3PL suggested another, and your own pallet counts produced a third. All three came from competent sources, and none of them can be tested, because each is a single static answer to a question the forecast will keep changing.

The model tests the candidate designs against the forecast itself, peak included, with service level and utilization visible per option, and it reaches the questions a ratio can't: whether the design survives year five, whether to build the growth in now or phase it, and what an automated version of the layout does to the footprint. SimWell tests whether the design holds. Pricing, permitting, and engineering the building belong to your architect and your builder, and we stay out of their lane.

Apparel capacity planning simulation
Proof

A large apparel company facing years of projected growth used simulation to understand its strategic storage and throughput capacity needs, testing the facility against a long-range demand projection with its peaks and valleys rather than an average. The model gave leadership the evidence to approve capacity ahead of the demand instead of behind it.

Read the case study →

Proof

Warehouse conveyor and sortation system simulation

How a distribution operation validated its facility design before it was built

A distribution operation planning a facility with 50+ inbound dock doors, staging areas, sortation, and a large conveyor system needed to know where the design would bottleneck before it was built. Truck unload alone was no fixed-time activity: it moved with product mix, dock assignment, and staging congestion, exactly the interactions a static calculation smooths away. The simulation found the constraints in the virtual building first, so the design could be corrected on the model instead of on the floor.

Read the case study →

How we engage

Where you start depends on what you already know

Four ways in. You pick the one that matches where you are, not a track you have to walk from the beginning.

  1. You're not sure which decision in the building to attack first

    Decision workshop. A focused session that surfaces and ranks the decisions where modeling pays off first: throughput, slotting, labor, layout, or the automation call. You leave knowing which one to model, why it ranks where it does, and what it takes to answer it.

  2. You know the decision, and you want it answered once

    Scoped project. You bring the specific question: where the building is losing throughput, whether the design holds the forecast, whether the automation pays off. We handle discovery, build, and validation on your data, and you keep the model and the answer, inside your environment.

  3. You know the decision, and you want your own team to own it

    Licensing and training. We license the right tools and train your engineers and analysts to build and run the models in-house. The capability becomes yours rather than something you keep buying.

  4. The decision comes back every cycle

    Managed services. Volume shifts, mix changes, and a model built once goes stale. We maintain and evolve the models alongside your team, so scenario runs become part of how the building plans, season after season.

You don't need perfect data to start. The model runs on data most buildings already have: the SKU master, arrivals, order profiles, locations, rosters. Where the data has gaps, building the model is usually what finds them, and finding them is worth something on its own.

What every path leaves behind

Whichever way you come in, the model, the logic, and the capability stay inside your organization, documented and maintainable, so the building keeps answering its own questions long after the work is done.

Book a call Download our capabilities

Resources

More on warehouse and distribution center decisions

Shipping dock loading simulation
Case study

Seeing the process in a new light

A recreational-equipment manufacturer used a simulation of its shipping dock to test loading strategies, and the results surfaced a change that let them ship 40% more per day. Proof that the value is often the insight the model makes visible, not just the metric.

Read the case study →
Warehouse simulation guide
Guide (gated)

Warehouse simulation: 22 questions to ask before you start

Plan smarter and avoid costly missteps before change hits the floor.

Get the guide →
Buying warehouse simulation
Guide (gated)

How to buy warehouse simulation: a practical guide for capital and operational projects

The questions warehouse leaders should ask before commissioning a model, capital project or operational.

Get the guide →

Get started

The backlog already knows where your constraint is.

The fastest way to know whether the building can do more is to put your own data in front of the question. Book a call and we'll tell you straight, even if the answer is a spreadsheet and an afternoon. Below a certain complexity, it is.

Book a call Download our capabilities