Concrete benefits

The adoption of these advanced tools, whether in single or integrated mode, leads to the following concrete benefits

1. Cost reduction: the use of simulation eliminates the need for physical prototypes and optimizes production, reducing costs associated with materials, labor and machinery. In addition, simulations allow virtual testing of production configurations and flows, avoiding waste and inefficiencies throughout the production cycle.
   
2. Accelerated time-to-market: the ability to run virtual tests simultaneously with production can dramatically reduce the time to develop and put new products into production, improving the speed of response to market demands and shortening product life cycles.
   
3. Improved quality and productivity: Simulation, without continuous integration with real data, enables the optimization of production processes in real time by analyzing different operating scenarios and working conditions. This leads to the creation not only of more reliable products, but also the reduction of defects in production, improving quality and reducing costs due to errors and rejects.
   
4. Environmental sustainability: digital modeling with digital twins and simulation help reduce material consumption and energy requirements, improving operational efficiency. In addition, by reducing waste and emissions associated with the physical production of prototypes, these technologies support companies in their sustainability strategy, with a positive impact on the environment.
   
5. Customization and innovation in production: with the ability to virtually test and optimize production in real time, companies respond more nimbly to market needs by customizing production processes and products to meet specific customer requirements. This stimulates innovation, increasing competitiveness and the ability to adapt to market changes.
   

These benefits not only improve efficiency in the design and manufacturing processes, but also optimize the entire product life cycle, increasing the company’s resilience in an increasingly complex and competitive industrial environment.

How much does it cost to build a Simulation System or Digital Model.

The cost of developing a simulation system or digital model varies widely depending on the complexity, the area of application, and the technologies used. Here are some concrete examples of investment costs to get down to the operations of the sectors under consideration

Simulation for mechanical components (mechanics)

• Estimated cost: $200,000-$500,000 for optimization of a complex system.

• Description: simulation of wear, friction, and mechanical stress to improve the life and performance of industrial components.

• Cost factors

• • Mechanical simulation software (e.g., Abaqus or Altair HyperWorks).
  • Creating detailed models of components, such as gears or motors.

• • Testing to optimize energy consumption and reduce maintenance costs.

• • Upgrades for new generations of machinery.

Welding process modeling (mechanical)

• Estimated cost: 30,000 to 70,000 USD to optimize the quality of a weld.
• Description: simulation of the welding process to predict and reduce defects such as cracks or deformations in welded joints.
• Cost factors
  • Welding simulation software (e.g., Simufact Welding or SYSWELD).
  • Modeling of materials and process parameters.
  • Virtual testing to identify and correct problems before production.
  • Creation of detailed reports for product quality improvement.

Injection molding process modeling (plastic)

• Estimated cost: US$100,000 to US$300,000 to optimize a production line.
• Description: simulation of the injection molding process to reduce defects in parts, improve quality, and optimize material use.
• Cost factors
  • Software for molding simulation (e.g., Moldflow or Moldex3D).
  • Detailed modeling of molding parameters (temperature, pressure, time).
  • Virtual testing to identify defects such as porosity or deformation.
  • Collaboration with production process experts to improve results.

Aerodynamic and structural analysis of aircraft (aerospace)

• Estimated cost: 1 – 3 million USD for an advanced study of a new aircraft model
• Description: simulation of aircraft aerodynamic and structural behavior to ensure safety, efficiency, and regulatory compliance
• Cost factors
  • Purchase of aerodynamic simulation software (e.g., ANSYS Fluent or STAR-CCM+)
  • High-performance hardware for CFD (Computational Fluid Dynamics) simulations.
  • Integration of complex physical models, such as structural deformation during flight
  • Collaboration with aerospace engineers and updates according to new regulations.

Thermal simulation of electronic circuits (electronics)

• Estimated cost: US$100,000 to US$300,000 for a study on an advanced electronic device.
• Description: Analysis of thermal behavior and performance of electronic circuits to avoid overheating problems and improve reliability.
• Cost factors
  • Software for thermal and circuit analysis (e.g., COMSOL Multiphysics or ANSYS Electronics).
  • Detailed 3D modeling of electronic boards and components.
  • Thermal performance testing in extreme scenarios.
  • Upgrades for new circuit configurations or components.

Simulation for wind and photovoltaic plants (energy)

• Estimated cost: 1 – 2 million USD for a medium-sized wind or solar farm.
• Description: Optimization of the performance of renewable energy systems by simulating the behavior of systems under different climatic conditions.
• Cost factors
  • Specific software for wind and photovoltaic energy analysis (e.g., WindPRO or PVSyst).
  • Development of customized turbine models and solar panels.
  • Sensor integration for monitoring and predictive maintenance.
  • Processing of local climate data and advanced simulations to maximize efficiency.

Simulation of indoor airflow (HVAC)

• Estimated cost: US$50,000 to US$80,000 for an average commercial building.
• Description: Optimization of ventilation, heating and air conditioning (HVAC) systems to ensure thermal comfort and energy efficiency.
• Cost factors
  • Software for airflow simulation (e.g., ANSYS Fluent or OpenFOAM).
  • Creation of 3D models of interior environments.
  • Analysis of airflow behavior in different configurations.
  • Training of technicians in the use and interpretation of results.

General considerations

• Initial investment costs can be reduced by using open-source solutions, but this can result in higher customization and training costs.
• Complex projects require significant computational resources (often in the cloud) and interdisciplinary expertise.
• The benefits in terms of reduced cost and development time make the investment highly worthwhile in the long run.

Four concrete examples of uses of simulation systems or digital models for an Italian SME

1) Virtual prototype design: sector production of machinery

An SME that develops industrial machinery can use software such as ANSYS or SolidWorks Simulation to virtually design and test prototypes. Simulation enables

• • Evaluate material strength and durability.
  • Perform stress tests without building physical models.
  • Reduce costs associated with traditional prototypes.

Required skills

• • Experience using CAD/CAE software such as ANSYS, SolidWorks Simulation or equivalent.
  • Engineering skills (mechanics, thermodynamics, etc.).
  • Ability to validate results with physical comparison tests.

Investment needed

• • Simulation software: €10,000-30,000 for annual licenses.
  • Hardware: advanced workstation (€4,000-8,000).
  • Training: from €1,500-3,000 per technician/engineer.
  • Estimated total: €15,000-40,000.

• • Advantage: reduced prototyping costs by 30-40% and shortened development time.

2) Production process optimization: manufacturing sector

A company that manufactures mechanical components can conduct a simulation of production flows to optimize factory layout. Using tools such as FlexSim or AnyLogic, the company can

• • Reduce product throughput time.
  • Identify bottlenecks and improve resource efficiency.
  • Test changes without interrupting actual production.

• • Advantage: 15-20% improvement in operational efficiency and reduced lead time.

Required skills

• • Knowledge of process modeling (e.g., workflows, internal logistics).
  • Ability to use simulation software (such as FlexSim, AnyLogic or similar).
  • Skills in data analysis and interpretation of results.

Investment needed

• • Software license: €5,000 to €50,000 per year, depending on complexity and number of users.
  • Training: about €2,000-5,000 per course dedicated to one or more operators.
  • Hardware: a high-performance workstation (about €3,000-10,000).
  • Estimated total: €10,000-65,000 to start the project.

3) Predictive maintenance with digital twins: industrial plants sector

An SME that operates production facilities can implement a digital twin to monitor machinery in real time. Using platforms such as PTC ThingWorx o Siemens Mindsphere, you can

• • Predicting impending failures.
  • Optimize maintenance cycles.
  • Reduce downtime.
    Advantage: 20-30% reduction in maintenance costs and increased productivity.

Required skills

• • Experience in integrating IoT sensors to collect data.
  • Knowledge of digital twin platforms (e.g., PTC ThingWorx, Siemens Mindsphere).
  • Data analysis skills and application of machine learning techniques.

Investment needed

• • IoT hardware: sensors and actuators (€2,000-10,000 per plant).
  • Software and platform: licenses from €10,000-50,000 per year.
  • Training: advanced courses in the use of IoT platforms (€3,000-5,000).
  • Estimated total: €15,000-65,000.

4) Environmental simulations for green certifications: renewable energy sector

An SME developing photovoltaic panels can simulate the energy efficiency of new designs under different environmental conditions, using software such as HOMER o PVsyst. With these simulations, the company can

• • Optimize panel design to maximize energy yield.
  • Obtain environmental certifications faster.
  • Improve the sustainability of the product.
    Advantage: Faster access to regulated markets and increased competitiveness.

Required skills

• • Knowledge of energy simulation software such as PVsyst, HOMER, or EnergyPlus.
  • Experience in calculating energy performance and environmental regulations.
  • Ability to interpret and present results to obtain certifications.

Investment needed

• • Software licenses: €2,000-15,000 (depending on the software chosen).
  • Training: specialized courses (€1,000-3,000).
  • Hardware: computer with GPU for complex processing (about €3,000).
  • Estimated total: €6,000-21,000.

These examples show how the adoption of digital simulation and modeling systems can help an Italian SME improve efficiency, quality and sustainability while maintaining a competitive advantage.

Challenges and future prospects

Despite the many benefits, the adoption of digital simulation and modeling systems presents some challenges. Creating accurate models requires advanced technical skills and a deep understanding of physical phenomena. In addition, processing complex simulations can require significant computational resources.

With the evolution of artificial intelligence (AI) and machine learning (ML), however, these barriers are diminishing. Indeed, machine learning algorithms can improve the accuracy of models, while cloud computing makes unlimited computational resources accessible. In the near future, the combination of simulation, digital modeling, and AI could open up new opportunities for the development of even more innovative and sustainable products.

Conclusions

The use of digital simulation and modeling systems represents a paradigm shift in the way companies design, test and produce. With these technologies, it is possible to anticipate the results of physical testing, reduce costs and speed up development time. Although there are still some technical and organizational challenges to be solved, the benefits far outweigh the obstacles, making this approach an essential choice for companies aiming to remain competitive in an ever-changing marketplace.