Improving Industrial Performance​

Unlock the full potential of your operations — accelerate, streamline, and fortify your industrial performance.​

Identify and act efficiently on the key levers of industrial performance — from reducing lead time to enhancing resilience and operational cost efficiency.

Reaching industrial performance target is often a difficult venture, as it can be strongly impacted by market volatility, technical constraints, and organizational inertia. We help our clients take control of this complexity with a systemic, model-driven approach. Our contributions typically focus on:​

  • Building a clear understanding of the dynamics of your industrial system by mapping how production flows and critical resources (tools, machines, operators, storage, etc.) interact under real-world conditions.​
  • Identifying key levers that influence throughput, variability, resource utilization, or cost-efficiency — across operations, supply chain, and governance.​
  • Testing scenarios and solutions through simulation to anticipate impacts, mitigate risks, and avoid costly surprises.​
  • Aligning teams around a shared understanding of challenges and options — bridging gaps between engineering, operations, and management.​
  • Enabling continuous improvement, by building digital assets (systemic industrial models, industrial configurations, industrial evolution scenarios, digital twins) that evolve with the industrial system and remain useful over time.

Our approach helps organizations move beyond surface-level fixes, enabling them to deliver measurable, lasting improvements in lead time, resilience, and cost control.​

Projects Snapshots

Risk Management

Stelliant – Stress of an automobile factory under various feared events

Railways

SNCF – Performance analysis of Signaling on high-speed train line

Project Snapshots – Risk Management

Stress of an automobile factory under various feared events​

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Client Need

A major European insurance provider sought to strengthen its ability to assess and manage operational risks in complex industrial infrastructures — starting with a pilot on an automotive plant. Their goal was to offer their clients advanced risk prevention services, while helping both insurers and policyholders to better understand, quantify, and mitigate the consequences of rare but high-impact events.
The goal: ​Evaluate how disruptions (such as disasters, load surges, supply fragmentation) would affect an industrial plant’s performance and resilience — and identify the most effective preventive investments to limit operational and financial impacts.

Our Contribution

We conducted a detailed business analysis and built a systemic digital twin of an automotive plant stressed under various feared events (fire, flooding, cyber-attack) — integrating real-world data and representing logistics flows, asset utilization, and failure scenarios. We then used our systemic model to perform both interactive and stochastic simulations to test resilience under various conditions and to identify prevention schemes.​

Key Results

  • Quantified the operational impacts of different disaster scenarios and plant industrial configurations.​
  • Identified optimal asset sizing and staffing strategies to maintain resilience while controlling costs.​
  • Demonstrated how prevention investments could effectively reduce risk exposure.​

Client Impact

“The approach proposed by Systemic Intelligence allowed us to identify the most effective preventive investments that we could recommend to our customer for mitigating a number of feared events and to calculate the associated optimal insurance premium.“

Project Snapshots – Railways

Performance analysis of Signaling on high-speed train line​

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Client Need

Faced with growing demand for high-speed rail services, a national rail infrastructure manager needed to improve the throughput and reliability of a key high-speed rail line. A critical question was how to select and configure signaling technologies to safely accommodate denser traffic patterns — without excessive infrastructure investment or operational risk.​
The goal: Determine the optimal configuration of the signaling system to support traffic densification, taking into account technical constraints, safety requirements, operational hazards, and return on investment.​

Our Contribution

We developed a systemic model of the high-speed railway line of interest, integrating infrastructure, traffic flow dynamics, and signaling technologies. Using this model, we ran scenario-based simulations to compare various signaling options and quantify their operational performance under realistic traffic increase and disruption scenarios.​

Key Results

  • Identification of the optimal trade-offs between capacity gains, safety performance, and cost for the new signaling configurations.​
  • Quantification of the impact of disruptions (maintenance, breakdowns, seasonal traffic) on the line performance under each scenario.​
  • Delivery of an operational analysis tool enabling the client to make well-informed architectural decisions.​

Client Impact

“We now have a clear, data-backed perspective on our signaling strategies’ real-world performance. This was crucial for making confident investment decisions and future-proofing our rail system against rising demand.“