The CDM digital twin: a strategic technological asset for high-performance chemical logistics

In the chemical logistics sector, where regulatory compliance, operational precision, and throughput efficiency are non-negotiable, the ability to anticipate scenarios and optimize workflows is a strategic differentiator.
To respond to increasing operational complexity, CDM has implemented an advanced Digital Twin for warehouse operations, developed in partnership with Novalia, to support data-driven decision-making, mitigate risks, and improve overall operational performance.

A Data-Driven virtual warehouse for advanced operational planning

The core of the project is an advanced digital model that accurately reproduces the functional dynamics of CDM’s physical warehouse.
Unlike standard simulation tools, the Digital Twin integrates real operational data including throughput rates, handling times, and space-allocation parameters to create a dynamic, high-fidelity virtual environment.

Within this digital infrastructure, CDM can configure and evaluate variable operating conditions, run scenario-based simulations and assess the impact of decisions on storage capacity, workflows, and resource allocation.

The model enables predictive analysis and supports continuous improvement by allowing operational teams to test alternative configurations without disrupting real-world activities. This analytical approach has facilitated the identification of non-optimal practices, allowing CDM to implement corrective actions while preserving the company’s operational framework.

Predictive bottleneck detection for continuous operational efficiency

One of the Digital Twin’s strongest capabilities is predictive bottleneck analysis, which allows CDM to anticipate and resolve operational constraints before they impact performance.
By modeling throughput, space utilization, and operational load peaks, the system enables CDM to:

• Detect workflow constraints prior to saturation
• Optimize space allocation in compliance with Seveso regulations
• Maintain process continuity during volume fluctuations
• Reduce the risk of operational downtime
• This predictive layer has also been crucial during the design and validation of CDM’s new, larger storage facility, where the Digital Twin served as a secure testing environment.
  Through iterative cycles of simulation, error analysis, and optimization, CDM was able to validate operational layouts, evaluate emergency-handling scenarios, and ensure the scalability of its warehouse processes.

A modular and client-specific operational model

The analytical insights produced by the Digital Twin have enabled CDM to implement hybrid operational models, customized according to:

• Product category characteristics,
• Client-specific service requirements,
• Handling and safety constraints associated with chemical logistics.

Rather than relying on a single standardized workflow, CDM now uses differentiated process logics, ensuring maximum performance and compliance across diverse operational contexts.
This project underscores CDM’s commitment to evidence-based innovation and demonstrates how digital transformation can deliver measurable value in specialized logistics environments.

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