Simio Simulation and Scheduling Software

🎓 SIMULATION SCHOOL — Lesson 9
Your best improvement idea is the one you'll never test.

Here's the paradox: the higher the potential upside of a process change, the more of your operation it touches — and the more it touches, the less anyone is willing to risk trying it.

So the safe 2% ideas get implemented. The 20% ideas get discussed for three years and die in a steering committee.

Every operations leader has a list of these. Reconfigure the flow. Change the batching rule. Move the buffer. Run mixed-model instead of campaigns. All plausible. None testable, because the test costs a week of production and a very uncomfortable conversation if it fails.

This is exactly the problem a process digital twin exists to solve. In a virtual copy of your operation:

→ The catastrophic idea costs you an afternoon, not a quarter
→ You can run it a hundred times and see the distribution, not one lucky trial
→ You can bring evidence to the steering committee instead of conviction
→ Failure is information, not an incident report

The teams pulling ahead aren't having better ideas. They're testing more of the ones they already had.

Full article:
https://hubs.la/Q04ykmSB0

What's the idea your team has been sitting on because it's "too risky to try"?

Your AI assistant just learned to speak Simio.

Only two weeks until our next Solution Series webinar, "Using AI with Simio: New Tools and Techniques." Our engineers will demo two ways AI is changing how you model:
1️⃣ Embedded Neural Networks that make runtime decisions inside your models
2️⃣ New built-in MCP Servers that give LLMs direct access to Simio to generate logic, troubleshoot errors, run experiments, and answer questions from Simio's documentation

Stick around afterward for live Q&A with the experts.

📅 Wednesday, October 7 | 11am ET
👉 Save your spot: https://hubs.la/Q04y86R90

🧙‍♂️ "It's a dangerous business, Frodo, going out your door..."

But unlike Bilbo, you don't have to venture into the unknown when implementing operational changes!

This , remember:

🏔️ Every epic journey (even to Mount Doom) benefits from a map
🗺️ Every process improvement benefits from simulation

Test your changes in a virtual environment first - it's second breakfast for your risk management strategy!

What operational "quest" are you planning? Let simulation be your guide.

🎓 SIMULATION SCHOOL — Lesson 8
Your ERP produces a schedule. Reality produces a different one by 10am.

The schedule looked great Sunday night. Then:

• A machine went down
• A rush order came in from the customer you can't say no to
• Material slipped a day
• Quality held a lot

By mid-morning your plan is fiction and someone is rebuilding it in a spreadsheet.

Why static scheduling breaks: most MRP/ERP scheduling assumes infinite capacity, ignores variability, and can't represent the constraints that actually govern your floor — changeover families, tooling, operator certifications, tank cure times.

What simulation-based APS does differently:

✓ Models the capacity you actually have
✓ Includes variability instead of averaging it away
✓ Tests the schedule against disruption before you release it
✓ Reschedules against current conditions, not Sunday's conditions
✓ Optimizes across competing objectives instead of one

The output changes shape, too. Instead of "the schedule says Tuesday," you get "87% probability of Tuesday, 98% for Wednesday." One of those you can say to a customer.

Reported customer results with Simio APS: 20% throughput increase, 12% on-time delivery improvement, 25% lead time reduction.

See how simulation-based APS works:
https://hubs.la/Q04xZFSK0

How many hours a week does your team spend rebuilding a broken schedule?

🎓 SIMULATION SCHOOL — Lesson 7
What a health system learned about its own bottleneck

Northwell Health was looking at a projected 10–30% increase in annual emergency department visits. The question in front of them: can we absorb that, and what breaks first?

Guessing was not an option. Neither was experimenting on an emergency department.

So they built one in software — a discrete event model of ED operations, built from historical data and validated with the clinical staff who actually work the floor.

What it told them:

📊 They could absorb a 20% volume surge with minor staffing adjustments
📊 Past 20%, the constraint was nighttime nurse utilization, already running at 87%
📊 The limiting factor was not beds, not space, not triage — it was one resource at one time of day

That last point is the lesson. Almost everyone guesses wrong about their own bottleneck, and the wrong guess is usually the expensive one. Beds cost millions. Night-shift staffing is a scheduling decision.

Read the full case study:
https://hubs.la/Q04xN2G80

If someone asked you to name your true constraint right now, could you prove it?

🎓 SIMULATION SCHOOL — Lesson 6
Five habits that separate models that get used from models that get shelved

1. Start with a decision, not a diagram.
❌ "Let's build a model of the plant."
✅ "Should we add a second shift in cell 4, or a machine?"
A model with no decision attached becomes a very expensive drawing.

2. Build the smallest model that answers it.
Detail is seductive and expensive. Add complexity only when the answer changes because of it.

3. Connect real data early — before the model is finished.
Early data exposes bad assumptions while they're still cheap to fix, and it builds credibility with the people whose numbers you're using.

4. Make objects you'll use again.
Your industry has patterns — the same cell, the same ward, the same dock. Build them once as reusable objects. The second project takes a third of the time.

5. Design for someone else to run it.
The model that survives is the one an operations manager can open on a Tuesday, change two inputs, and get an answer from. Interfaces and clear outputs are not decoration.

The meta-lesson: successful simulation is rarely about technical sophistication. It's about answering a real question credibly enough that someone acts on it.

Our field-notes white paper, Tips for Successful Practice of Simulation:
https://hubs.la/Q04xFyWC0

Which of these did you learn the hard way?

🎓 SIMULATION SCHOOL — Lesson 5
Where AI fits into a simulation model (and where it doesn't)

Everyone wants AI in their operation. Fewer people can say what it would decide.

Here's how the two technologies actually help each other:

Simulation gives AI a training ground. Real operational data is scarce, biased toward normal conditions, and expensive to break on purpose. A model generates as much synthetic data as you want, including the disasters.

AI gives simulation better decisions. Embed a neural network at a decision point and the model stops using a static rule and starts using a learned one — which supplier, which machine, which job next.

Together they let you test an algorithm before it touches production. This is the part people skip. You would not deploy a scheduling algorithm to the floor untested. A digital twin is where you test it.

In Simio specifically: native neural network support with no coding required, a built-in trainer powered by TensorFlow, ONNX support for models your data science team already built, and Python for everything else.

The question worth asking your team: if the AI recommended something surprising tomorrow, where would you check whether it was right?

More on AI optimization: https://hubs.la/Q04xysX60

🎓 SIMULATION SCHOOL — Lesson 4
Digital twin projects don't fail on the model. They fail on the data.

Three headaches kill more twin projects than anything technical:

1. Missing data — the twin goes blind
Nobody logged changeover times. Resource availability lives in someone's head. Quality data exists in a binder.
→ The fix: start with reasonable estimates and collect properly as you go. Use the model itself to tell you which gaps actually move the answer — usually it's two or three, not fifty.

2. Quality issues — bad data, good model, wrong answer
Legacy systems produce inconsistent units, outliers, timestamps that disagree with each other.
→ The fix: automated validation rules plus a human who knows the process. Audit on a schedule, not when something looks weird.

3. Integration roadblocks — the systems won't talk
MES, ERP, and the historian each have their own format and their own gatekeeper.
→ The fix: phase it. Start with a file drop. Move to a database connection. Get to APIs when you've earned the trust.

The pattern in all three: don't wait for perfect data. Teams that hold out for clean data never start. Teams that start with estimates find out which data actually matters — and then go get that.

Full article with the practical fixes:
https://hubs.la/Q04xphQC0

Which of the three is your blocker right now?

🎓 SIMULATION SCHOOL — Lesson 3
Process twin or product twin? They are not the same purchase.

This is where a lot of digital twin budgets get spent on the wrong thing.

🔧 Product digital twin — a virtual copy of a thing. A pump, an engine, a machine. It tells you how that asset is performing and when it will fail. Enormously valuable if your problem is asset health.

🔄 Process digital twin — a virtual copy of how work flows. Orders, people, materials, queues, handoffs. It tells you why the line is slow and what happens if you change it.

The test:

Is your pain "this equipment keeps breaking"? → product twin.
Is your pain "we're always late and nobody can tell me why"? → process twin.

Most operations leaders describe a process problem and get pitched a product solution, because product twins are easier to demo. Then a year later the equipment is well-monitored and the orders are still late.

Full breakdown of the difference — and how to tell which one your business actually needs:
https://hubs.la/Q04x80kp0

Which one is your organization actually buying?