About Production Intelligence
Turning Production Data into Decisions
Production Intelligence helps manufacturers turn production and inspection data into practical, operational improvements.
Most production environments already generate vast amounts of data—timestamps, machine states, inspection results. Yet critical decisions are still made without a clear understanding of cause and impact.
Production Intelligence bridges this gap by transforming existing data into probabilistic decision support, enabling forward-looking control of manufacturing systems under uncertainty.
Founder
Marc Philip Hermans
Founder – Production Intelligence
Marc Hermans is a manufacturing and simulation specialist with over 20 years of experience in discrete-event simulation, production systems, and data-driven decision support in industrial environments.
His work builds on extensive experience in manufacturing system analysis and simulation, combined with advanced research in probabilistic modeling.
He is currently completing a PhD at the University of Miskolc (Logistics / Computer Science), focusing on:
- Reinterpreting production KPIs such as OEE as probabilistic state variables
- Modeling production systems as stochastic processes
- Supporting maintenance and operational decisions using Bayesian methods
This research forms the foundation of the Production Intelligence approach.
Mission
Manufacturers do not lack data.
They lack clarity on what the data actually means for decisions.
Production systems are often managed using retrospective indicators:
- OEE
- scrap rates
- downtime statistics
These describe the past—but do not guide action under uncertainty.
Production Intelligence exists to change this.
The mission is to move from:
- measurement → understanding
- understanding → decision support
- decision support → operational impact
Credibility
The approach is grounded in both industrial practice and formal research.
Industrial & Technical Background
- Certified training in Siemens TIA environments (SERV1, SERV2)
- Advanced training in Plant Simulation and industrial system modeling
- Experience with simulation-based analysis of production and logistics systems
Research Foundation
- PhD research in probabilistic production modeling
- Development of Bayesian models for maintenance and operational decision support
- Integration of simulation data and real production data into unified analytical frameworks
Application Domain
- Manufacturing systems analysis
- Production logistics and flow systems
- Performance and reliability evaluation using real production data
How We Work
Production Intelligence follows a consulting-led, pilot-first approach.
Rather than introducing new tools or systems, the focus is on extracting value from data that already exists.
Typical engagement structure:
- Exploration
Understanding the production system, data availability, and key questions - Analytical Modeling
Transforming production data into structured, interpretable models - Probabilistic Evaluation
Quantifying uncertainty and evaluating decision scenarios - Operational Integration
Translating insights into actionable decisions on the shop floor
This approach ensures that results are:
- grounded in real production conditions
- interpretable for decision-makers
- directly applicable in operations
Positioning
Production Intelligence does not aim to replace existing systems.
It extends them.
By reinterpreting production data as a source of probabilistic insight, it enables manufacturers to move from reactive management toward informed, forward-looking control.