Model the factory you have. Branch the factory you want.
Manufacturing operations are systems of interacting processes. The difficult part is not modeling any one of them. The difficult part is understanding how they interact through time.
One operational model of the factory.
Materials arrive. Machines transform them. Operators move between tasks. Products accumulate, wait, move, change state, and leave one process only to become the input to another.
Campaigns run. Changeovers consume time. Buffers fill. Machines slow down. Equipment fails. Labor availability changes. Quality events create rework or scrap. A constraint in one area becomes a problem somewhere else.
TOKNITH creates one operational model of the manufacturing operation, where discrete work, continuous processes, machines, people, materials, automation, and physical constraints operate against the same clock.
Model it. Run it. Branch it. Change it. See what actually happens.
Many systems. No single model of how they behave together.
Manufacturers already run MES, ERP, WMS, PLCs and controls, SCADA, historians, scheduling systems, quality systems, maintenance systems, robotics and automation, engineering tools, and simulation tools.
A production schedule says what should run. A machine controller controls what is happening now. An MES records production. A simulator models a process. A digital twin represents an asset or a facility.
What these systems generally do not provide is one executable model of how the entire operation behaves together.
Manufacturing decisions often require a different question: what happens if we change the operation?
What happens if we
- Add another shift?
- Change the product mix?
- Increase demand?
- Change campaign sizes?
- Move a machine?
- Add a filler, press, oven, robot, or packaging cell?
- Change batch sizes?
- Alter buffer capacity?
- Change staffing?
- Introduce a new SKU?
- Add automation?
- Remove a constraint?
- Change the sequence of production?
- Reduce available equipment?
- Experience a machine failure?
- Build a new line?
- Redesign the facility?
One manufacturing model
Events happen. Processes evolve. Agents act. The model resolves all three.
Production starts and completes. Material arrives. A batch is released. A machine changes state. A changeover begins and completes. A pallet is completed. An inspection occurs. A machine fails. Maintenance begins and completes. An order is released. A shift begins. An operator becomes available.
Temperature, pressure, level, flow, speed, concentration, curing, drying, heating and cooling, tank levels, conveyor movement, accumulation, energy consumption, and process conditions change continuously.
Operators, technicians, forklifts, AMRs and AGVs, robots, machines, vehicles, material handling equipment, inspection resources, and production cells move, interact, and make decisions according to their modeled rules.
The factory is more than the line.
A change to production rate can affect packaging. Packaging can affect palletizing. Palletizing can affect finished-goods space, which affects material movement, forklift demand, and labor. A machine constraint can change campaign completion, and campaign completion can change shipping availability. The model follows those consequences.
- Receiving
- Raw material storage
- Staging
- Material preparation
- Batching
- Processing
- Assembly
- Machining
- Forming
- Filling
- Cooking
- Curing
- Testing
- Inspection
- Packaging
- Palletizing
- Finished goods
- Warehouse movement
- Shipping
- Utilities
- Maintenance
- Quality
- Labor
- Automation
- Internal transportation
How production actually moves
- Material
- Preparation
- Process
- Buffer
- Process
- Inspection
- Pack
- Palletize
- Finished Goods
Real operations are rarely a simple chain of machines. TOKNITH represents the actual flow, with multiple products and routes, parallel and alternate equipment, shared resources, batch dependencies, buffers, rework, scrap, quality holds, changeovers, labor constraints, equipment availability, and downstream capacity. The result is not merely a production schedule. It is a model of how production moves through the facility.
Sequencing has operational consequences.
A factory may run campaigns, producing related products together to reduce cleaning, setup, tooling changes, material changes, temperature changes, configuration changes, quality checks, and packaging changes.
TOKNITH models those dependencies directly, and compares alternative production sequences rather than assuming the sequence is optimal. A campaign can affect:
- Machine utilization
- Material consumption
- Labor requirements
- Buffer levels
- Downstream availability
- Changeover duration
- Cleaning requirements
- Production completion
- Shipping availability
Campaign A → B → C, or A → C → B?
The difference may look small on a schedule.
Inside the operation it can change changeover time, labor requirements, machine utilization, downstream starvation, finished-goods timing, and shipment completion.
TOKNITH makes those alternatives branches of the same operational model.
Machines are operational participants.
Machines are not static objects. They interact with materials, batches, operators, robots, conveyors, buffers, inspection systems, packaging equipment, and other machines, so equipment behavior propagates through the operation. A machine can carry:
- Processing rate
- Capacity
- Operating states
- Setup states
- Changeover rules
- Availability
- Downtime
- Maintenance requirements
- Fault conditions
- Recovery behavior
- Product-specific constraints
- Tooling requirements
- Upstream dependencies
- Downstream dependencies
A machine running below its expected rate is not simply a lower KPI. It can change the state of everything downstream.
Buffers are physical parts of the operation.
A downstream machine may be available but starved. An upstream machine may be available but blocked. A buffer may absorb the difference for a while. If the imbalance persists, the physical operation changes. TOKNITH models:
- Buffer capacity
- Queue length
- Accumulation
- Minimum and maximum levels
- FIFO behavior
- Staging capacity
- Blocking
- Starvation
- Replenishment
- Downstream demand
- Material availability
The model lets that behavior emerge.
Some processes cannot be represented as events.
Temperature, pressure, flow, tank level, mixing, heating, cooling, drying, curing, chemical concentration, conveyor speed, accumulation, energy, and residence time evolve over the model's clock.
Discrete events interact with them. A batch begins. A continuous process changes state. A target condition is reached. A discrete completion event occurs. The next process becomes available, and the downstream process begins.
The same clock resolves the interaction.
Packaging is part of manufacturing.
Production may flow through primary and secondary packaging, labeling, case packing, cartoning, wrapping, palletizing, inspection, serialization, and finished-goods staging.
A production process can be constrained by something that is technically downstream. If packaging cannot keep up, upstream production eventually feels the consequence.
TOKNITH models that interaction rather than assuming production and packaging are independent.
People and automation
Shifts, breaks, absenteeism, staffing levels, skills, certifications, cross-training, task assignments, operator travel, machine coverage, maintenance, material handling, inspection, and supervision. A line can have enough machine capacity on paper and still miss production because there are not enough qualified people at the right time. The model exposes that difference before the change is made.
Robots, robotic cells, AMRs, AGVs, automated storage, conveyors, palletizers, sortation, inspection, and machine vision, with their charging, travel, handoffs, buffers, faults, and maintenance. A faster machine may simply move the constraint downstream. An AMR fleet can have the theoretical capacity and still be limited by travel, charging, congestion, or pickup availability. A robotic cell may raise throughput and still need more staging or downstream capacity.
The question is not how many units per hour a machine can produce. It is what the automation actually does to the operation.
Quality, rework, and scrap
- Hold
- Investigation
- Rework
- Reprocessing
- Inspection
- Release
Manufacturing output is not always successful output. A failed batch does not simply disappear from a production report. Inspections, failure rates, holds, rework, scrap, quarantine, and release are part of the operational model, and a quality event propagates through the operation like any other.
Rehearse the failure before it happens
- A critical machine is unavailable for 45 minutes. What is lost, and where does it propagate?
- A machine runs at 80% availability instead of the planned 95%. What changes?
- Two operators are unexpectedly unavailable. Where does the constraint appear?
- A robotic cell goes down. Is there a manual fallback?
- A required utility becomes constrained. Which processes feel it, and how fast?
Capacity is rarely one number.
A factory has theoretical machine capacity, practical capacity, and labor, material, buffer, storage, utility, packaging, and shipping capacity. The actual constraint moves with product mix, campaign sequence, staffing, downtime, changeovers, batch size, automation, demand, and operating hours.
Instead of asking whether a line can produce 10,000 units per day, TOKNITH tests those conditions together.
Can this operation produce the required mix, under the actual constraints, and still complete the downstream work on time?
New product introduction
A new product can change an existing operation dramatically. The existing operation becomes the baseline and the proposed product becomes the branch, so the current operation can be compared with the current operation plus the new product before committing to the change.
- New SKUs
- New materials
- New process requirements
- New packaging
- New quality requirements
- New production sequences
- New equipment
- New labor requirements
Facility and line design
Manufacturing changes often begin with a physical question. Where should the equipment go? How much space is needed? Where should material enter? How far do operators travel? Where should buffers be located? Where does congestion appear when production increases? A proposed layout can become a branch of the existing operation.
- Equipment placement
- Production cells
- Material flow
- Operator movement
- Forklift movement
- AMR/AGV routes
- Buffers
- Storage
- Staging
- Utilities
- Production areas
- Packaging
- Shipping
Brownfield and greenfield
Manufacturers rarely get to start from scratch. Most changes happen inside an existing facility. TOKNITH supports both, and both use the same underlying model.
Start with the operation that exists. Existing facility, branch, modify, run, compare. Test equipment additions and relocation, new products, automation, staffing, layout changes, new buffers, and process changes.
Start with the operational requirements. Requirements, proposed facility, model, run, modify, compare. Evaluate the operation before construction or commissioning.
Introducing a new product family.
Consider a facility producing multiple products across several production areas, with shared equipment, batch processes, campaigns, changeovers, operators, material handling, packaging, palletizing, finished-goods staging, quality inspection, and planned maintenance.
The business wants to introduce a new product family. The question is not simply whether there is enough machine capacity. The team needs to know:
- Can the product run on the existing equipment?
- What campaign sequence should be used?
- How much changeover time is added?
- Does it consume shared resources?
- Does packaging become the constraint?
- Is additional labor required?
- Does material movement increase?
- Does finished-goods space become constrained?
- What happens during peak demand?
- What happens if a critical machine goes down?
- Can the existing facility support the product?
- Would automation change the answer?
Branches of the same model
| Branch | What changes |
|---|---|
| A | Current production mix |
| B | New product added |
| C | New product and an additional shift |
| D | New product and automation |
| E | New product and additional equipment |
| F | New product and a different campaign sequence |
| G | New product at peak demand |
| H | New product with a critical equipment failure |
The result is not a collection of isolated simulations. It is a set of futures derived from the same operational model.
Branch the factory
- Existing Operation
- Branch
- Change
- Run
- Measure
- Compare
- Modify
- Accept
- Campus
A proposed change does not require a separate model. The branch is a controlled future of the operation, not a second source of truth.
Is the factory behaving the way we expected?
The value of the model does not end when the design is approved. The operation moves from design to rehearsal to an accepted decision to live operation, and the same model stays connected to it through Campus.
The model establishes expected behavior. The floor provides observed behavior. TOKNITH compares them, and when reality differs, the difference becomes part of the model's operational state.
Reality does not get rewritten to make the model look correct.
An example residual
| State | Machine cycle |
|---|---|
| Expected | 42 seconds |
| Observed | 47 seconds |
| Residual | +12 % on the station's own cycle time |
The point is not that a KPI changed. The model identifies where the disagreement belongs, keeps the last-seen state, the expected state, the residual, and the owner, and lets the organization investigate what changed.
The picture is not allowed to lie.
Visualization is not the source of truth. The model is. The picture is derived from it, which makes visualization a direct representation of operational state rather than another dashboard.
- Material waiting shows as waiting.
- An unavailable machine shows as unavailable.
- A full buffer is full.
- An occupied operator is occupied.
- A traveling robot is traveling.
- A process still evolving shows that state.
Manufacturing applications
TOKNITH supports manufacturing decisions across the lifecycle of the operation.
Evaluate layouts, equipment placement, material flow, staffing, buffers, and capacity before committing to construction or modification.
Understand practical capacity under real product mixes, labor constraints, changeovers, downtime, and downstream limitations.
Model production processes and understand how changes propagate through the operation.
Evaluate automation as part of the entire production system rather than as an isolated equipment purchase.
Understand staffing, skills, task allocation, travel, coverage, and shift requirements.
Test production sequences, campaigns, batches, changeovers, and resource constraints.
Create branches of the operation and compare alternative futures.
Rehearse equipment failures, labor shortages, utility constraints, quality events, and other disruptions.
Evaluate alternative operating strategies against measurable operational outcomes.
Rehearse proposed equipment, layouts, processes, and automation before connecting them to the physical operation.
Connect the model to the floor and continuously compare expected and observed behavior.
From opportunity to operation
- We have an idea.
- Can it work?
- What would we have to change?
- What happens under normal conditions?
- What happens at peak?
- What happens when something fails?
- Which design is better?
- Let's build it.
- Is reality behaving as expected?
TOKNITH provides the model across that entire journey. The same operation can be modeled, branched, rehearsed, compared, accepted, and connected.
Manufacturing without the boundary.
Those systems can remain exactly where they belong.
- Not an MES.
- Not a PLC programming environment.
- Not an ERP.
- Not a traditional discrete-event simulator.
- Not simply a digital twin.
- Not a visualization layer.
TOKNITH provides the operational model that lets the physical process and operational work be understood together. The factory is not just a collection of machines. It is a system evolving through time. TOKNITH models the system.
The moat is the model, and the fact that everything else is required to agree with it.
Continuous state, discrete events, agents, physical assets, materials, labor, automation, real-world observations, and branches all operate against the same temporal and state model.
The same model drives the visualization. The same clock resolves production events. The same state receives observations from the floor. The same model can be branched to test a proposed future, and when that future is accepted, the model can be connected to the real operation.
One model. One clock. One operational truth.
Build the factory before you change the factory.
Model the operation you have. Branch the operation you want. Rehearse the change. Understand the consequences. Decide with confidence. Then connect the model to reality.
One clock. One model. Many futures. One reality.
Model the operation. Rehearse the change. Connect it to reality.
Request a Demo