Your Simulation
Should Be
Running Your Plant.
You built a simulation — then production changed, and the model stayed behind. Twinloop builds Digital Twin environments that stay connected to your real system, so you can explore failure modes, test decisions, and catch problems before they hit the floor.
within 18 months
production-live model
before any risk
NDA from first call · Scope before commitment · Zero surprise change orders
STATUS: SHELF-WARE DETECTED
This simulation is no longer representative of the real system. Decisions made from this model carry significant operational risk.
Composite diagnostic — based on common engagement findings
The Problem
How a working simulation
becomes shelf-ware
It happens at every company. The model was useful on day one. Then production changed, and the simulation didn't.
-
Day 1
Simulation deployed — accurate and connected
Data feeds are live. Failure modes match reality. Teams trust the model enough to make decisions from it. This is the peak of Digital Twin value.
-
~6 Months
First update skipped — drift begins
Production added a new process step. "We'll update the model next sprint." The sprint closed. The update never happened. The gap is small — but it's open now.
-
~12 Months
Model diverges — teams stop trusting it
Three layout changes, one equipment upgrade, two new SKUs. The simulation reflects none of them. Predictions start contradicting what production engineers know to be true. Usage drops.
-
18+ Months
Shelf-ware — model exists, nobody uses it
This is where 73% of industrial digital twins end up. The tool that cost $200K to build makes zero decisions per quarter. It sits in a folder labeled "simulation_FINAL_v7."
This is the problem Twinloop was built to solve.
We build the connection layer between your simulation and your production system — and we keep it current so the model never drifts again.
Technical Capability
We work in your stack,
not against it
No vendor lock-in. We assess your existing simulation assets and build integrations to your production environment — whatever platform you're already on.
Simulation Environments
AnyLogic
Discrete Event / Agent-Based
Arena / Simio
Discrete Event Simulation
MATLAB / Simulink
Multiphysics / Controls
ANSYS
Structural / Thermal / CFD
Modelica / Dymola
System-Level Dynamics
FlexSim
3D Discrete Event
Production Integration Targets
OSIsoft PI / AVEVA
Process Historian
Ignition SCADA
SCADA / HMI
SAP Manufacturing
MES / ERP
Plex / Infor
Cloud MES
OPC-UA / MQTT
Industrial Protocol
SQL / InfluxDB
Time-Series Data
Not on this list? Discovery starts with an audit of your existing environment. If it has an API or data export, we can likely integrate it.
How It Works
Three phases, each with
a defined deliverable
You know what you're getting before any work begins. No scope creep, no surprise change orders.
Model Discovery
~2 weeksWe review the current state of your physical assets, virtual assets, and production data. We assess reuse of existing simulation assets, integration possibilities, and define the Digital Twin approach that fits your operation.
A detailed, phased plan to develop Digital Twin capability within your company.
Model Build & Development
6–12 weeksWe develop the simulation assets needed to address your target failure modes. Configuration and parameters are built so users can explore the variable space — not just run predefined scenarios.
A simulation model you can actually run what-if scenarios against.
Orchestrate Assets
OngoingWe build an integrated solution that automates and orchestrates your virtual assets — connecting simulation to production data and surfacing insights for both technical and non-technical users.
A usable application that keeps the simulation current as your system evolves.
Every engagement starts with a free 30-minute conversation.
No prep required. You'll leave knowing exactly which phase fits today.
The Evidence
The data is not subtle
Industry research on Digital Twin ROI — and what we've seen firsthand in client engagements.
of organizations with mature Digital Twin programs report faster root-cause identification
Capgemini / OpenText, 2024
reduction in product development time for companies using simulation-driven design processes
McKinsey Global Institute, 2023
reduction in unplanned downtime for manufacturers using simulation-based maintenance planning
Aberdeen Group, 2023
Client engagements — anonymized by NDA
A Tier-1 aerospace supplier had simulation models that covered planned maintenance scenarios but not cascade failures across subsystems. We built an integrated Digital Twin that connected live sensor data to the existing model. The first month of production monitoring identified a failure sequence the original simulation had never explored. Downtime events dropped 40% in the following quarter.
A specialty chemical producer had a process simulation that had not been updated since the last major equipment upgrade. The real system had drifted significantly from the model. We rebuilt the simulation against current process parameters and connected it to the production historian. Operators identified an underperforming parameter range within 30 days of the new model going live.
A regional distribution operator was planning a facility layout reconfiguration and had no simulation capability. We built a discrete-event simulation of the new layout in parallel with the construction timeline. The team ran 30+ layout configurations before the first forklift moved — and identified a throughput bottleneck that would have cost two weeks of manual correction post-launch.
A food production operator ran a simulation team that manually re-entered line parameters after every product changeover — a process frequently skipped under production pressure, leaving the model stale and unusable between runs. We built an automated integration between the production scheduling system and the simulation engine. Parameters now sync at every changeover, without human intervention.
About the Founder
Jose Lara
Founder · Twinloop
Credentials
MIT BS Mechanical Engineering 2014 · BU MS Mechanical Engineering 2016 · BC MBA Candidate 2028
Jose Lara is a Digital Twin engineer with 10 years of experience building and integrating simulation models for manufacturing, process, and logistics operations. He holds a BS in Mechanical Engineering from MIT and an MS in Mechanical Engineering from Boston University, with an MBA in progress at Boston College. His work focuses on closing the gap between static simulation assets and live production environments — connecting models to the systems they were built to represent.
"I built Twinloop because I kept seeing the same problem: simulation models built with care, then abandoned when the real system changed. The model becomes shelf-ware, and the team goes back to making decisions without it. Twinloop exists to keep the loop closed — so the simulation stays connected to the system it was built to represent, and the team can actually use it."
Engagement Model
Pick the phase that fits
where you are today
Each phase is scoped, deliverable-defined, and priced transparently. Most clients start with Discovery and grow from there.
Phase 01
Discovery
For the production owner who has simulation assets collecting dust and wants to understand exactly what it would take to connect them to live data.
One-time
A clear plan before you commit to a full build.
- Asset Inventory: full audit of existing simulation models, production data sources, and integration points — documented in plain language
- Gap Analysis: side-by-side comparison of what your simulation currently covers vs. the failure modes your operation actually faces
- Digital Twin Roadmap: phased plan with defined deliverables, integration targets, and effort estimates — scoped to your operation, not a template
Most clients discover they already have 60–80% of the assets they need. Discovery turns that inventory into a clear build plan — before you commit to a full engagement.
Phase 02
Build
For the operations leader who has done a discovery and is ready to build a simulation that reflects how the real system actually behaves today.
Project-scoped
A simulation model that stays current as your system evolves.
- Model Development: simulation assets built or updated to cover your defined failure modes and variable space
- Production Integration: live data pipeline connecting your physical system to the simulation — so the model reflects the current state, not the last known state
- User Interface: configuration layer for both technical and non-technical users to run scenarios without touching the underlying model
Scoped transparently on the discovery call — no surprise change orders. Every engagement starts with a fixed deliverable list.
Phase 03
Orchestrate
For the engineering team that has a live Digital Twin and wants an ongoing partner to keep it connected, updated, and relevant as the real system changes.
Retainer
A simulation that stays useful as the real system evolves.
- Model Maintenance: monthly updates to reflect mechanical changes, layout revisions, and new configuration rules
- Scenario Expansion: new failure modes and edge cases added as your operation reveals them — the model grows with your understanding
- Insight Reviews: monthly debrief on what the simulation flagged, what was validated in production, and what to test next
A simulation that isn't maintained becomes shelf-ware again within 18 months. Orchestrate keeps the loop closed.
Digital Twin Operations Partner
Ongoing Engineering Retainer — Active Simulation Management
For engineering teams that have a live Digital Twin and need an active partner to keep it connected, current, and technically sound as the real system evolves. Not advisory — hands-on engineering: model versioning, integration monitoring, scenario expansion, and quarterly technical alignment with your production and capital planning teams.
- Model Versioning — structured update process for every mechanical change, layout revision, or configuration rule — documented change log and rollback capability included
- Integration Monitoring — active oversight of the production data pipeline feeding your simulation — alerts when a source drifts, fails, or changes schema before the model goes stale
- Scenario Expansion — new failure modes and edge cases added as your operation reveals them — the model grows with your understanding of the real system
- Quarterly Technical Review — 90-day alignment between your simulation accuracy metrics and upcoming capital and process changes — so the model stays ahead of production, not behind
Questions We Get Every Week
If it's on your mind, a production owner or engineering leader somewhere has already asked it.
Yes. The Discovery phase starts with an audit of your existing assets. Most clients have 60–80% of what they need already. We assess reuse potential before recommending any new tooling.
Usually worth updating — but it depends on how far the real system has drifted from the model. Discovery answers that question directly with a documented gap analysis before you commit to a build.
We build a data pipeline between your production historian, MES, or control system and the simulation model. The specific integration depends on your data sources and simulation platform — we scope that during Discovery.
No. We build a configuration layer so non-technical users can run scenarios without touching the underlying model. Technical users can go deeper. Both groups get something useful from the same system.
Manufacturing, process industries (chemical, food & beverage, aerospace), and logistics. The simulation methodology transfers across industries — what changes is the domain knowledge and integration target.
Networking infrastructure, cybersecurity insurance, and physical hardware installation are outside scope. We build the software and data layer — we do not do field work on physical systems.
How We Engage
Engineering-first.
No black boxes.
We work with your existing simulation assets and production environment — not a proprietary platform you're locked into. Every integration we build is documented, version-controlled, and owned by your team when the engagement ends.
Platform-agnostic by design
We assess your existing stack in Discovery before recommending any new tooling. If you already have 60–80% of what you need, the roadmap reflects that — no vendor upsell.
Deliverable-defined engagements
Every phase has a defined output before work begins. You know what you're getting before you commit to the next phase — no surprise change orders, no open-ended retainers without scope.
NDA from the first conversation
Your process data, system architecture, and failure modes are confidential. We operate under NDA before you share anything technical — standard practice, not an optional add-on.
Remote-first, on-site available
Based in Greater Boston. Every engagement works over video — same rigor, same deliverables. On-site available for New England facilities when the scope warrants it.
Industries Served
Discrete Manufacturing
Assembly, machining, production lines
Chemical & Process
Batch and continuous process operations
Aerospace & Defense
Tier-1 suppliers, subsystem testing
Logistics & Warehousing
Facility layout, throughput optimization
Food & Beverage Production
Line configuration, changeover modeling
Jose — Founder, Twinloop
Digital Twin Engineer · Greater Boston Area
"I built Twinloop because I kept seeing the same problem: simulation models built with care, then abandoned when the real system changed. The model becomes shelf-ware, and the team goes back to making decisions without it. Twinloop exists to keep the loop closed — so the simulation stays connected to the system it was built to represent, and the team can actually use it."
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