Take the warehouse you have. Branch it. Add the customer you want.
A 3PL does not have the luxury of designing every operation from scratch. The building already exists. Customers, labor, equipment, automation, and processes already exist. And then a prospective customer asks: can you fit us into this operation? TOKNITH allows the operator to answer with a model instead of a spreadsheet and a guess.
Start with the warehouse you actually have.
Build the existing facility as the baseline. Then run the operation. The baseline becomes the starting point for every branch.
- Building
- Storage
- Locations
- Inventory
- SKUs
- Orders
- Customer profiles
- Labor
- Shifts
- Equipment
- Docks
- Yard
- Conveyors
- Sortation
- Automation
- Robots
- AMRs/AGVs
- Processes
- Buffers
- Constraints
- SLAs
- Cutoffs
Branch the operation you already have.
Create a branch. Add the prospective customer. Change the demand profile. Add the required inventory. Apply the storage requirements. Add the pick profile. Add case and pallet activity. Apply the SLA. Set the outbound cutoff.
Run it. Now ask: can this customer fit into the operation? If not, what would we have to change to make it work?
The operation is not a spreadsheet.
A warehouse is a system of interacting behavior.
Slotting changes travel. Travel changes congestion. Congestion changes cycle time. Cycle time changes labor requirements. Labor changes wave completion. Wave completion changes dock timing. Dock timing changes yard activity. Automation changes all of them.
TOKNITH models those interactions rather than treating each metric independently.
Three kinds of behavior in the warehouse
Orders are discrete. Equipment is physical. People are agents. An order moves through discrete operational states. A conveyor continuously moves and accumulates material. A worker moves through the building as an agent. The same clock determines when these things interact. The warehouse is the interaction of all three.
Order release, wave release, pick completion, tote induction, sort completion, pack completion, trailer arrival, dock assignment, shipment departure, equipment failure, maintenance completion.
Conveyor movement, acceleration and deceleration, accumulation, zone capacity, buffer levels, travel, equipment motion, battery state, fluid or material processes where applicable.
Pickers, packers, replenishment workers, supervisors, forklifts, AMRs, AGVs, drivers, robots, mobile equipment. Agents can have location, skills, certifications, shift, break schedule, productivity, task state, availability, travel behavior, task-selection behavior, and interaction rules.
Congestion should emerge.
TOKNITH should not simply report "Congestion = 27%" after the simulation is finished. Congestion should emerge from the operation.
- Workers encounter other workers.
- Forklifts compete for travel paths.
- AMRs encounter traffic.
- Totes accumulate.
- Conveyor zones fill.
- Sortation capacity becomes constrained.
- Buffers reach limits.
- Downstream processes slow upstream processes.
The resulting congestion is a consequence of the modeled system.
Waves propagate through the operation.
- Release
- Allocation
- Picks
- Workers
- Totes
- Conveyance
- Sort
- Pack
- Staging
- Dock
- Cutoff
A wave is not simply a number of orders multiplied by an average pick rate. It propagates. Changes upstream can affect everything downstream. TOKNITH allows the resulting effects to emerge from the operation.
Labor is behavior. Automation is part of the operation.
Shifts, breaks, absenteeism, skills, certifications, cross-training, task assignment, travel, productivity distributions, pick, pack, replenishment, receiving, shipping, equipment operation, supervision. Workers can be modeled as agents whose location, availability, assignment, and behavior affect the operation. This makes labor more than a capacity number.
Capacity, speed, availability, buffers, faults, maintenance, charging, travel, handoffs, dependencies, upstream constraints, downstream constraints. A robotic system cannot be evaluated solely by its theoretical cycle rate. TOKNITH models the system around it.
Failure rehearsal: what happens when
- A sorter is unavailable for 15 minutes?
- AMR capacity falls by 20%?
- A conveyor zone goes offline?
- AS/RS capacity becomes unavailable?
- A robotic cell operates at 70%?
- Induction becomes constrained?
- A critical buffer fills?
- A key piece of equipment fails during peak?
- Labor availability drops unexpectedly?
Brownfield and greenfield
TOKNITH supports the reality of existing operations. Run the branch. Observe the propagation. Measure recovery. Compare alternatives. Then decide.
Start with the current facility. Model the existing operation. Branch it. Change the layout. Add automation. Change labor. Change slotting. Add the customer. Run the branch. Compare.
Start with the requirements. Design the facility. Define processes. Place equipment. Define labor. Define automation. Model expected demand. Run scenarios. Compare designs. Then move toward commissioning.
The customer-fit workflow
- Existing Facility
- Baseline Model
- Prospective Customer
- Branch
- Run
- Measure
- Modify
- Compare
- Commit
- Campus
The question changes from "Can we probably make this work?" to "What does the operation actually do when we put this customer into it?"
From opportunity to operation
| Stage | Question |
|---|---|
| Opportunity | Can we support the customer? |
| Design | What would the operation need to look like? |
| Rehearsal | What happens under expected and peak demand? |
| Commitment | What configuration are we actually choosing? |
| Implementation | What needs to change? |
| Campus | Is the live operation behaving like the model? |
| Improvement | What should we change next? |
The model remains the common foundation.
What the 3PL gains
- Can we fit the customer?
- Where will capacity break?
- What will congestion look like?
- How much labor will we need?
- Where should inventory go?
- What automation is justified?
- What happens at peak?
- What happens when equipment fails?
- What happens when labor is constrained?
- Which facility configuration works best?
- What changes before implementation?
- Is the live operation behaving as expected?
Run the operation before committing the operation.
Then connect the model to reality. The value is not simply a prettier simulation. It is the ability to understand the operation as a system. From opportunity to operation.
One clock. One model. Many futures. One reality.
Model the operation. Rehearse the change. Connect it to reality.
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