Category: Expertise

98% of industrial companies launched projects based on digital technologies in 2022. At the same time, only 44% of them have seen results (a figure that has been steadily declining since 2019). This gap highlights the increasingly stringent demands of companies with regard to digital transformation and a growing pragmatism towards Industry 4.0.

Industrial digitization is a given, but in the face of numerous constraints and uncertainties in the global context, manufacturers carefully evaluate the relevance of investments in digital technologies and must ensure the profitability of a project before implementing it. Focus on the main benefits of digital technologies for industry throughout the project lifecycle.

Surmonter le mur invisible entre le concept et la réalité

The most profitable digital technologies are those that provide industrialists with solutions to the most costly problems. These include errors, delays, document synchronization issues, and the resulting inconsistencies, as well as communication difficulties among stakeholders. These problems create an invisible wall between concept and reality. The good news is that this wall is not insurmountable.

Visualize the entire project through multidimensional modeling

Traditional 2D or 3D plans have their limitations. They certainly offer a spatial perspective of industrial equipment, but often fail to capture its complexity, its interaction with the environment, and the project in which it is involved. This is where multidimensional modeling comes into play.

Today’s digital tools allow for the integration of all dimensions of an industrial project. Not only do they take into account the spatial dimensions (X, Y, Z) of the equipment, but they also include time, budget, materials, thickness, among others. With these multiple dimensions, project management and task planning become much more efficient, which results in:

• Time savings,
• a reduction in errors and inconsistencies
• an improvement in the overall project design.

Multidimensional modeling is not limited to providing a static representation of project data. It also allows for dynamic visualization, where the user has the freedom to adapt the display according to the specific task at hand and its unique features. This is a major asset for deep understanding and agile manipulation of complex projects.

Furthermore, immersive visualization represents another major advancement made possible by augmented reality and virtual reality. This technology makes information easily accessible, particularly for on-site operators who benefit from assistance in their tasks. In this way, they can navigate and interact with project data in an intuitive manner, thereby enhancing their engagement and efficiency.

Promote the dissemination of information and collaborative work

Traditional methods of industrial project management present significant challenges in terms of information dissemination and collaborative work. Information is often fragmented, versions can become desynchronized, and redundancies and inconsistencies are common, not to mention issues of compatibility and interoperability.

In this context, digital tools offer the possibility of simplifying project management by centralizing information and fostering better communication and smooth coordination. The digital twin, in particular, represents a major innovation in information management. By unifying all data sources and providing real-time visualization of changes, it facilitates information sharing, rapid iteration, and a better understanding of the project. This helps to avoid costly mistakes and delays, thereby promoting faster project completion.

These technologies prove to be extremely cost-effective in the long term: they contribute to better productivity, cost reduction, and faster project completion, offering a significant return on investment.

Automate the production processes

The automation of production processes using digital technologies, particularly AI and digital twins, is a cost-effective investment due to its optimization potential. For example, in aeronautics, AI applied to a digital twin can model and optimize the entire manufacturing process, creating an iterative loop of improvement.

Firstly, technical information is integrated into the digital twin. Sensors installed on the production line then collect real-time data, which is incorporated into the digital twin to enrich a machine learning algorithm. This AI is capable of predicting production performance, identifying potential problems, and suggesting improvements. These recommendations are implemented, performances are evaluated, and the results are used to improve the accuracy of the AI.

This cycle of continuous improvement, enabled by automation, leads to an increase in production efficiency and a decrease in errors, offering a significant return on investment through cost reduction and improved productivity.

Facilitate the MRO of industrial equipment

Beyond design, digital tools are relevant during the construction and operation phases of industrial equipment in that they help prevent risks and enhance safety on construction sites. For example, AI can analyze real-time data to identify potential hazards and suggest preventive measures, benefiting both employees and the longevity of the industrial equipment.

Digital technology is revolutionizing the operational readiness (MCO) of industrial equipment by generalizing automated preventive maintenance. The fundamental technology of this revolution is the digital twin. A virtual replica of an industrial equipment, it continuously collects operational data from its real-life counterpart. This data can include measurements such as temperature, pressure, operating speed, and other critical variables.

Coupled with AI algorithms, the digital twin can analyze this data to detect trends or anomalies that might indicate a potential failure. For example, a sudden rise in temperature or an unusual fluctuation in pressure could be early warning signs of a breakdown. AI can then flag these issues upstream, allowing maintenance teams to intervene and resolve the problem before a failure occurs.

By anticipating problems before they become costly breakdowns, these digital tools reduce operational maintenance costs. The savings achieved through reduced downtime and repair costs make it a long-term profitable investment.

The search for profitability guides the orientations of industrialists in the field of digital transformation. In this regard, investing in Industry 4.0 digital technologies brings tangible benefits. The improvement of project visualization, collaboration, process automation, and predictive maintenance contribute to increased efficiency, cost reduction, and a better return on investment.

*Source: Industry 4.0 Barometer, Wavestone, Bpifrance, France Industrie, 2022 Edition

Digital tools offer businesses in the industrial sector opportunities for optimizing business processes, which are essential to integrate in order to increase the profitability of projects and remain competitive.

However, the organizational changes brought about by digital transformation present challenges if the specificities and constraints of the industry are not properly taken into account. That’s why it’s essential to address the organizational challenges of digital transformation to successfully combine industrial heritage with innovative solutions.

Addressing the organizational challenges of digital transformation.

The mistake made by many companies is to reduce digital transformation to a technological issue. It is primarily an organizational challenge that can be addressed by first modeling business processes.

Digital transformation: much more than just a technological issue.

• Digital transformation is not just about adding new technological tools; it’s primarily about deeply evolving business processes and the organization of the company as a whole. It’s a complex process that can cause frictions if not steered in the right direction and properly implemented. In this regard, the human factor and the desire to spread a culture of innovation are at the heart of any digital transformation project.The frictions that can arise from integrating digital technology often come from the fact that traditional industrial processes have unique specificities and constraints:
• Strict regulations: Safety, quality, and environmental standards can hinder the adoption of new technologies.
• Precision and reliability: Technological failures can result in significant costs and safety risks, making the adoption of unproven new technologies challenging.
• Data security and protection: The integration of new digital technologies increases the attack surface for companies, thus leading to potential security and data protection issues. This challenge is especially significant for companies dealing with sensitive information. Therefore, any digital technology must be carefully assessed and secured to minimize these risks.
• Process complexity: Industrial processes can be extremely complex, involving many steps, machines, and employees. This complexity requires careful planning and change management.
• Capital invested in existing equipment: Industrial companies have often invested significant amounts in their existing equipment and infrastructure. Therefore, the return on investment for modernizing equipment and infrastructure must be demonstrated.

Business process modeling in support of organizational changes.

In order for the integration of digital technologies to improve the profitability of your industrial company, it is essential to consider how you can transform your business processes. To do this, the first step is to have a clear vision of these processes by modeling them accurately and comprehensively. This process modeling (or workflow) allows for the representation and understanding of all tasks, stages, and resources (human, financial, and material) that make up each process.

Take the example of a maintenance process in the aerospace sector: traditionally, regular inspections, often based on predefined time intervals or flight hours, may require the aircraft to be taken out of service, resulting in downtime costs. By modeling this workflow, optimization opportunities can be identified. A digital twin (an exact virtual replica of the plane) allows for real-time monitoring of the performance of all the aircraft’s systems. If a deviation from expected performance is detected, an alert is generated, thus accurately targeting maintenance operations. By avoiding unnecessary inspections and anticipating potential failures, the use of the digital twin can improve maintenance efficiency, reduce downtime costs, and enhance profitability.

Successfully bridging industrial heritage and digital transformation.

The pursuit of profitability is the driving force behind any digital transformation project. To ensure this profitability, it is essential to target the processes to be optimized, to include transformation actions in a roadmap, and to provide the means for this transformation by forging partnerships.

Target the business processes to optimize.

To improve the profitability of a digital technology industrial project, the first step is to identify the friction points in business processes: loss of time, errors, inconsistencies, duplicates, communication difficulties, etc. Digital technology is justified as soon as it solves a clearly identified problem and this optimization is profitable. In other words, digital transformation should be driven by the desire to solve problems in business processes and by a constant search for return on investment.

The digital twin technology can be used to optimize several business processes throughout the lifecycle of an industrial product. In the aerospace sector, for example, the digital twin transforms the processes at work during the design phase, replacing tests with simulations, especially to anticipate the behavior of components and the system as a whole under various scenarios. The digital twin also optimizes processes related to manufacturing, assisting in decision-making based on the characteristics of the project (time/budget constraints, availability of stakeholders or materials, regulatory constraints, etc.). Similarly, a digital twin transforms the processes related to keeping operational (MCO) by integrating preventive maintenance. These organizational changes reduce costs and improve the profitability of the industrial project.

Create a roadmap for digitalization.

Once these application areas are identified, it is crucial to develop a roadmap to guide digital transformation. This roadmap should define the key steps of the transformation, the technologies to implement, the necessary resources, and the performance indicators to monitor in order to measure the effectiveness and impact of the digital transformation in the company.

The development of this roadmap is based on the analysis of existing industrial processes and the expected effects within the context of integrating digital technologies. For example, in the case of a nuclear power plant, the roadmap for the implementation of a digital twin would include the following points :

• Initial Assessment: Identifying business processes altered by technology (monitoring reactors, maintenance of cooling systems, etc.)
• Objectives: The project should target SMART (specific, measurable, achievable, relevant, time-bound) objectives. This could be a reduction in unplanned downtime or an extension of equipment lifespan.
• Design of the Digital Twin: This phase depends on the specifics of the project because the deployed tool must be customized. In any case, it is essential to model the business processes to optimize and connect to all the project’s data sources.
• Implementation: Integrating the digital twin into the project involves tasks such as deploying sensors, establishing data connections, or training staff on the tool’s use.
• Continuous Evaluation: After implementation, monitoring of KPIs is necessary to ensure that the digital twin operates as expected and delivers the anticipated benefits.

Establish strategic partnerships to accelerate innovation

Forging partnerships with digital solution providers is a wise decision to successfully merge your industrial legacy with the digital world. Such collaborations can grant access to invaluable technical expertise, offer co-innovation opportunities, and help overcome the inherent challenges of digitalization. It’s also a way to foster a culture of innovation within the company, stimulating idea sharing and mutual learning. In this light, it’s crucial to choose partners who share your vision of innovation and have a deep understanding of digital technologies and their applications in the industrial sector.

The digital transformation of industrial processes is essential for improving the profitability of projects. Through process optimization, digitalization brings greater efficiency, better anticipation of problems, and opportunities for innovation. However, to successfully merge industrial heritage with digital innovation, one must follow a clearly defined strategy and skillfully combine business and IT expertise.

Succeeding in your multi-stakeholder industrial projects: the key role of information sharing.

At the heart of multi-stakeholder industrial projects lies a key challenge: information sharing. Navigating the ocean of big data is a titanic challenge, but it is necessary to meet it to avoid the unfortunate consequences of fragmented information: redundancies, inconsistencies, errors, interoperability issues… and the inevitable time losses and additional costs that result.

Fortunately, there are solutions to address these challenges and implement sound and efficient information management. In this perspective, digital solutions – with the digital twin at the forefront, offer immense potential to support industrial project managers.

The challenges of information management.

The complexity of information sharing in industrial projects

The success of a complex industrial project relies on effective information sharing among countless stakeholders. From the project manager to the subcontractor, through design offices, classification bodies, and suppliers… These actors generate multiple flows of document and information exchanges (2D and 3D plans, technical specifications, spreadsheets, test results, quality reports, etc).

Furthermore, actors in these types of projects often operate in silos, each using their own tools and data formats. From spreadsheets to 3D design tools, through specialized document management systems, each stakeholder has their preferences, adding an additional layer of complexity.

In summary, the complexity of information sharing is proportional to the number of stakeholders involved and the diversity of data produced. However, the complexity of information flows is a major problem that, if not seriously addressed, can lead to potentially serious errors.

The pitfalls of poor information management.

A poor management of information in the context of an industrial project results in a lack of fluidity in exchanges between stakeholders. We have identified the typical pitfalls encountered in this type of project:

Data silos and information fragmentation

If we reduce an industrial project to the sum of the data produced by each participant, one could say that no one fully knows the project. Each participant holds a piece of the puzzle in their information system, but the compartmentalization of data hinders the effective dissemination and use of data, so no participant has a complete view of the project.

This fragmentation of information is exacerbated by the diversity of systems and software used, as well as the variety of file formats. It gives the impression that not one, but several parallel projects are being built. In summary, although the information produced is more comprehensive and accurate than ever, its utilization proves particularly challenging.

Redundancies and inconsistencies

As the project progresses, documents, especially drawings and designs, are constantly revised. This proliferation of documents and versions can lead to a lack of synchronization, leaving stakeholders working “blindly,” unaware of the progress of other teams.

This situation is conducive to mistakes and inconsistencies. For instance, if a design change is not properly communicated, it can lead to delays and cost overruns, such as ordering unsuitable materials or the need for costly and time-consuming adjustments. Proper management of these issues of duplication, redundancy, and version synchronization thus becomes essential for the successful completion of the project.

Compatibility and interoperability issues.

Interoperability is a crucial issue in information management. However, it is challenged by the diversity of tools and data formats used. For example, an engineering team might work with specific 3D modeling tools, while another team might prefer a particular task management software. These tools can generate files in different formats, not recognized by other systems.

This lack of interoperability creates a barrier to the smooth exchange of information. If a team cannot open an important file because they don’t have the right software, it can slow down the project. Likewise, if data cannot be integrated from one system to another due to incompatibilities, it can lead to a loss of crucial information, misunderstandings, and delays in completing the project.

Improving information management through digital means.

Now that we have identified the problem and its consequences, let’s explore how digital tools, especially the digital twin, improve information management.

The benefits of optimized information management.

Digital transformation solves problems and creates new ones. The good news is that it can also provide solutions to these new problems. Here’s a brief overview of the benefits that digital technology can offer:
Smooth exchanges and mutual understanding: Collaborative platforms such as Microsoft Teams are useful for allowing stakeholders to share files, chat in real time, and organize virtual meetings, thus promoting better coordination.

Visualization of the overall project progress: project management software such as Jira or Asana offer interactive dashboards and task tracking features, allowing each stakeholder to visualize the progress of the project in real time.

Anticipating problems: Data analysis tools like PowerBI or Tableau are efficient. These tools allow for extracting relevant insights from project data and generating preventive alerts to signal potential issues.

More effective decision-making: Business Intelligence tools like QlikView or SAP BI centralize information and present it in an understandable form, thereby facilitating data-based decision-making. For instance, detecting a cost increase might prompt a review of certain procedures to optimize spending.
All these tools provide concrete solutions to the problems posed by information management within an industrial project context.
However, one might be tempted to think that multiplying tools equates to feeding the problem by adding complexity. But this overlooks the digital twin technology, which allows for the unification of all tools and data sources.

The digital twin: a revolutionary tool for information management in industrial projects.

The digital twin technology represents a significant leap in information management within the industry. A digital twin is much more than a simple 3D replica. It includes the physical aspects of a product, as well as all project variables (time, budget, materials, thicknesses, etc.).
Unlike traditional methods of information sharing which can suffer from access and synchronization issues, the digital twin centralizes all project information on a single platform. When a participant adds information to the digital twin, all other stakeholders are instantly informed. This approach eliminates concerns about document versions and ensures constant updating of the model.

The collaborative potential of the digital twin goes beyond simple coordination. It facilitates exchanges between all parties involved and allows for effortless iterations. By providing a complete view of the project in all its aspects, the digital twin promotes a better understanding and more informed decision-making. At each stage, it provides stakeholders with all the necessary information to make the most judicious decisions and actively participate in the project’s progression.

Multi-stakeholder industrial projects require optimized information management to avoid fragmentation, redundancies, inconsistencies, and interoperability issues that arise from the complexity of such projects.

The adoption of the digital twin centralizes and simplifies this information sharing, promoting smooth collaboration, a better understanding of the project, and informed decision-making.

Technical progress inexorably transforms industrial professions: some emerge while others reinvent themselves or disappear. This process has been amplifying and accelerating since the end of the 20th century with the development of industrial computing. With digital transformation, we are shifting into high gear.

Among all the digital technologies being deployed in the industrial sector, digital twins and artificial intelligence (AI) hold a predominant place. Enhanced design, predictive maintenance, process automation, decision-making support… the benefits of these innovations are numerous, provided they are integrated into professions. Through this article, we provide you with some insights on how to reconcile traditional professions with technological innovations.

Technologies that redefine industrial professions.

The digital transformation and Industry 4.0 invoke a myriad of technologies: Internet of Things (IoT), robotics, 3D printing… Among them, the digital twin and AI hold a special place: these technologies cover all industrial projects and transform many professions.

The digital twin, a new tool for industrial professions.

The digital twin plays a crucial role in the evolution of industrial professions. It is a virtual representation of an object, process, or system, capable of simulating its physical counterpart in real time. The digital twin acts as a bridge between the physical and digital worlds. This technology radically transforms the management of an industrial product throughout its lifecycle, from its design to its operation, including its maintenance in operational condition.

The use of a digital twin is revolutionizing design and manufacturing by allowing numerous iterations at no additional cost. This improves the product by reducing design errors and development time.

The professions involved during these phases had already undergone significant changes with CAD/CAM (Computer-Aided Design and Computer-Aided Manufacturing). The digital twin amplifies these changes. Some examples include:

• The design engineer can test and optimize a product in a virtual environment. For example, they can simulate the behavior of an aircraft under flying conditions, the energy efficiency of a moving ship, or the performance of a wind turbine facing different winds, without having to construct a costly physical prototype.
• The draftsman can visualize the design in a realistic and interactive 3D environment, exploring every detail of the design.
• The CAD technician can test different designs to detect potential problems before manufacturing. They can also use the digital twin to plan the manufacturing process, for example by determining the optimal order of machining operations.
• The process engineer can use the digital twin to optimize production processes. For example, it is possible to simulate different production scenarios to determine the most efficient one.

The digital twin is also a valuable ally during the exploitation phase of an industrial product. It profoundly transforms professions related to maintenance, achieving a real paradigm shift: whereas maintenance was mainly reactive (repairing failures when they occur), the digital twin generalizes predictive maintenance.

Thanks to the data collected on the physical object, it is possible to predict failures before they occur. As a result, maintenance professions are increasingly focused on continuously monitoring the condition of the product and analyzing the collected data to solve problems before they lead to costly breakdowns.

Artificial intelligence, an accelerator of operational efficiency.

Artificial intelligence also promises to transform industrial professions as the promises carried by this technology are significant. AI is, above all, a relevant answer to process and analyze massive data (big data) and to extract actionable information: by helping to decipher the complexity of reality, AI assists humans in making strategic decisions (risk management, investment, business processes, etc.). As a result, AI impacts several professions across the entire value chain.

AI is also the cornerstone of automation for industrial companies and induces changes at all levels. Examples:

• Production automation: AI allows the automation of complex production processes. The role of operators then shifts towards supervision, coordination, and optimization of automated systems.
• Predictive maintenance: Maintenance technicians benefit from AI, which allows them to intervene before a failure occurs. This is one of the many synergies between AI and digital twin.
• Supply chain automation: Managers can rely on AI to manage inventory and automate procurement. Here too, AI allows them to anticipate more.

Integrating digital innovations into industrial professions: towards a culture of innovation.

To take advantage of new digital technologies rather than being overwhelmed by them, industrial companies must identify opportunities for integration and adopt a culture of innovation to promote the internal dissemination of these innovations.

Identify the opportunities for integrating digital technologies.

To determine whether it is relevant to deploy a digital twin or AI in an industrial context, it is essential to analyze existing processes beforehand to identify integration opportunities and pinpoint the points of convergence between these technologies and traditional trades.

For example, to analyze a maintenance process, one must understand the current process (inspection, repair, maintenance…) and take into account the resources mobilized. The next step is to identify improvement areas (recurring failures, prolonged downtimes, challenges faced, etc.) and set clear objectives to enhance this process.

Then arises the question of means: would a digital twin help achieve these objectives? Would virtual reality be relevant for remote diagnostics? Ultimately, the analysis must show whether the cost and effort required to implement a digital twin would be justified by the potential improvements in the maintenance process.

Adopt a culture of innovation.

The success of a digital transformation relies primarily on the human factor. The most powerful technologies will be of no use if employees do not support the project or are not properly trained. For this reason, it is essential to encourage exploration and experimentation to spread a culture of innovation within the company.

With this in mind, it is desirable to raise awareness and train employees on new technologies, through workshops, seminars, demonstrations, etc. For instance, it is possible to set up training in digital twins and AI. The largest industrial companies have understood this well: for example, Bosch with its “Bosch Innovation Lab”, a space dedicated to experimentation and the discovery of new technologies, where employees can collaborate on innovative projects.

Digital transformation is a lever for growth and innovation for the industry. Digital twins and AI, to name just these technologies, have a clear impact on industrial professions, and this across the entire value chain and throughout the product lifecycle.

It is crucial for companies to be proactive and seize the opportunities offered by these technological advances. Reconciling historical industrial professions with digital technologies relies on adopting a culture of innovation at all levels of the company.

Coordinating an industrial project is a tremendous challenge: throughout the project, stakeholders come and go, each using their own tools and producing large amounts of data, disseminated in paper or digital format. This results in such complexity that traditional methods of collaboration and information sharing show their limitations.

To meet the human and technical challenge of coordinating industrial projects, let’s first understand the difficulties faced and the potential negative impacts they can have on project management. We will then see that these challenges are not insurmountable: new digital tools are emerging to improve collaboration on industrial projects.

The challenges of coordinating industrial projects.

1. Synchronisation des versions : comment rester dans le bon tempo ?

2. Data access: the limitations of siloed organization

Having relevant data is crucial in an industrial project. However, this need is hindered not only by the colossal amount of data generated, but also because of their dispersion across different systems and file formats. This siloed organization results in data fragmentation, which becomes difficult to access for stakeholders.
Imagine, for example, that the drawings and designs of an airplane are located in the CAD software of a design office, while the performance data of the materials are in the database of a laboratory, and safety specifications in the documents of the classification body. An individual wanting to verify the compatibility of a material with the specifications will need to access these three distinct sources, which is time-consuming and sub-optimal.

3. Decision-making: deciding in an uncertain environment.

The siloed organization and the proliferation of document versions make an industrial project an uncertain environment, in which no stakeholder has a comprehensive view. In the absence of accurate and up-to-date information, decision-makers must rely on assumptions or guesses, which can lead to mistakes, and therefore delays and budget overruns.
Take the example of a shipyard: if a design change is not properly communicated, the logistics manager might order unsuitable materials, resulting in additional costs and delays. Similarly, engineers could work based on outdated specifications, which might require costly and time-consuming adjustments.

Strengthening collaboration through digital tools.

The industrial sector is not immune to the digital transformation and the surge of tools designed to enhance collaborative work and improve productivity. Document sharing, messaging, video conferencing, project management… there’s a plethora of tools that meet these needs, which project managers in the industrial sector can implement to improve information management and communication between stakeholders.

Although these tools offer real advantages for improving the coordination of industrial projects, they do not meet all the needs of companies in this sector, particularly because they do not provide an overview of the project in all its dimensions. This is precisely what a collaborative digital twin allows.

The collaborative digital twin: a revolutionary tool for industrial project coordination.

The technology of the digital twin, which is defined as a virtual representation of an object, process, or system, represents a major advancement for project coordination and collaborative work in the industrial sector.

Let’s clarify right away that a digital twin is not just a 3D model. It is a model that incorporates not only the physical dimensions of the product but also all dimensions of the project (time, budget, materials, thicknesses, etc.). In the operational phase, sensors placed on the product transmit data to the digital twin, which then behaves like its counterpart in the physical world.

Where traditional methods of information sharing pose access and synchronization difficulties, the digital twin centralizes all project data on a platform. When a stakeholder enriches the digital twin, all involved parties are informed in real-time. This mechanism eliminates the problems of document version synchronization and guarantees continuous updating of the model.

The collaborative aspect of the digital twin goes further in that it allows direct exchanges between stakeholders and offers the opportunity to easily make iterations. By presenting the project as a whole and in all its dimensions, the digital twin promotes a better understanding and more informed decision-making. At each step, it provides stakeholders with all the information they need to make the best choices and contribute to the project’s evolution.

Coordinating industrial projects poses significant human and technical challenges. Difficulties in synchronizing versions, fragmented access to data, and decision-making in an uncertain environment are all obstacles to the success of projects.

However, the emergence of digital twin technology offers new prospects for the industry: centralization of data, an overview, real-time collaboration… The digital twin represents a major advancement for the coordination of industrial projects.

At the heart of the digital transformation of the industrial sector, digital twins and artificial intelligence (AI) are innovations that hold immense promises for businesses.

A true virtual replica of a physical object, the digital twin allows it to be modeled and simulates its functioning. This technology radically transforms the way we interact with an industrial product throughout its lifecycle, from its design to its retirement, through its construction and operation.

AI, on the other hand, handles the astronomical volume of data from an industrial project. Paired with a digital twin, AI offers the possibility to conduct advanced analyses, automate processes, and improve operational efficiency, and even customize services and customer experience. Ultimately, AI also holds the promise of a simplified human/machine interaction. This extends the boundaries of the digital twin…

A spotlight on two complementary digital technologies that are revolutionizing industrial projects.

The digital twin: a virtual representation of reality.

A “digital twin” is a precise virtual representation of an object, process, or system. This model can simulate, predict, and optimize its physical counterpart in real-time. The data collected and analyzed provides a 360-degree view of the twinned entity throughout the product’s lifecycle.

Improve the design through simulation.

The first advantage of the digital twin is the ability to conduct a potentially infinite number of tests during the design phase. The virtual dimension of the digital twin allows one to operate in a secure environment and to explore all conceivable avenues at a marginal cost.

Take the aerospace industry as an example: designing an aircraft, from its structure to its onboard systems, is a complex process involving many iterations. Thanks to the digital twin, engineers can virtually test all design variations, model extreme situations, and simulate the behavior of the aircraft in various scenarios. This approach reduces design errors (and therefore associated costs) and enhances the safety and efficiency of the final product.

Optimize maintenance operations.

When the product enters its operational phase, its digital twin plays a crucial role in optimizing maintenance operations. The myriad of sensors equipping the object in the physical world transmit all the operating data to the digital twin in real-time.

Imagine a ship loaded with sensors: accelerometers to measure vibrations, thermocouples for temperatures, pressure gauges for pressures, pH sensors to detect corrosion, etc. These sensors constantly send data in real-time to the digital twin, which analyzes them to find solutions to potential malfunctions, predict likely failures, optimize maintenance plans, and so on.

The benefits are tangible: unexpected downtimes are reduced, resulting in significant savings.

AI to decipher a complex reality.

AI involves mimicking human intelligence (neural networks) to solve problems. Given the massive volume of data generated by digital twins, this technology emerges as a valuable ally. It allows for the processing and analysis of this data to aid in decision-making, automate processes, and personalize the customer experience. An overview of the synergies between digital twin and AI.

Analyzing massive data: a strategic lever.

The phenomenal amount of data generated by a digital twin can be challenging to process. That’s where AI comes into play with its advanced analytical capabilities: paired with the digital twin, it assists operators in making informed strategic decisions.

Taking the example of predictive maintenance for ships, the digital twin should be able to analyze the data and make relevant deductions. For AI to correctly interpret data, such as an increase in vibrations or temperature, it must be able to contextualize them within the digital twin. In this way, it’s possible to intervene to resolve issues before they result in a breakdown.

Let’s take another example, in the context of a nuclear power plant, the combination of the digital twin and AI would be extremely useful for modeling and monitoring reactor corrosion in real-time, thanks to sensors measuring temperature, pressure, or even the chemical composition of the environment. Operators could then better plan maintenance operations to enhance the safety, efficiency, and longevity of the facility.

Automate to enhance operational efficiency.

AI, with its learning and adaptation capabilities, is a major asset for automation. When applied to a digital twin, AI can analyze workflows, identify bottlenecks, and suggest improvements to optimize the efficiency of a process.

For example, in the context of aerospace production, the digital twin/AI pairing can optimize the entire manufacturing process. Let’s focus on this iterative loop in 4 steps:

Engineers integrate into the twin all the plans, technical specifications, and assembly procedures associated with each component of the aircraft.

Sensors are installed throughout the production line to collect real-time data such as temperature, pressure, assembly speed, error rates, and so on. This data is then incorporated into the digital twin.

The collected data enhances a machine learning algorithm embedded in the digital twin. The AI is then able to model and predict the performance of the production line. It can identify errors before they become problematic and offer recommendations to enhance production efficiency (adjusting machine parameters, changes in the assembly sequence, etc.).

The AI’s recommendations are then implemented on the production line. Performance is assessed and compared to the digital twin’s predictions, allowing the AI’s accuracy to be refined over time.

Customize and enhance the customer experience.

Finally, the digital twin/AI pairing can play a key role in enhancing customer experience. Within the aerospace sector, these technologies enable manufacturers to customize the services provided to airlines. Indeed, it’s possible to integrate into the digital twins the flight data of aircraft during their operational phase. This allows for an understanding of the specifics of each airline (for example, preferred routes, common weather conditions, etc.) and to offer customized improvements for each plane (adjusting engine parameters to reduce fuel consumption, maintenance scheduling to minimize downtime, etc.).

Interacting naturally with the digital twin: the promises of conversational AI.

Integrating AI into a digital twin opens up new possibilities in terms of human/machine interaction. Indeed, conversational agents (chatbots) have the potential to simplify the use of the tool, particularly allowing non-technical profiles to query the digital twin and access valuable information.

For instance, a factory operator might ask the conversational agent, “What is the likelihood that machine X will break down in the coming days?”. The conversational agent can then consult the digital twin, analyze the data, and provide a comprehensible answer to the operator, thus facilitating operational decision-making.

This use of AI becomes even more relevant as the digital twin becomes more complex over time. The addition of increasingly rich information leads to the creation of menus and sub-menus which increase the number of clicks and thus the time to access information. An integrated conversational AI allows us to bypass this tree structure. Furthermore, it can harness all relevant data based on the request, where a tool without AI would only display the requested elements without trying to contextualize the need. In this regard, AI enhances access to information and magnifies the capabilities of the digital twin.

The digital twin and AI are the new technological cornerstones of the industry and pave the way for efficiency gains across the entire value chain, from design to operation of industrial products. This synergy is the key to a profound transformation of industrial processes: there is no doubt that companies in the sector should integrate these technologies to remain competitive.

Are 2D/3D plans truly essential for designing an industrial product? This seemingly odd question warrants in-depth reflection at a time when complexity is becoming a clear hindrance to the management of industrial projects: imprecise and partial representation, lack of context, limited simulation, difficulties in sharing and updating… to name but a few challenges linked to the use of plans.

The emergence of digital technologies such as collaborative twins could assist industrial companies in overcoming these challenges. By providing an accurate, interactive, and dynamic representation of industrial products, these technologies not only allow a better understanding of the product but also a more efficient and collaborative management of information. A spotlight on a paradigm shift in the industry.

2D/3D blueprints: an imprecise and partial representation.

2D/3D plans provide an imprecise and partial representation of reality: imprecise because they don’t capture the product in all its complexity, and partial because they don’t contextualize the product in its environment.

Grasp the complexity and dynamics of an industrial product.

Representing an industrial product in all its complexity is a significant challenge. Despite the undeniable utility of 2D/3D plans, they often fail to accurately convey the many details of a product, whether it’s about its various components, how they interact, or their operation.

Take the example of an airplane engine: this complex system consists of about 20,000 components. On a 2D plan, it’s almost impossible to represent all the parts of the engine and their relative positions to each other. Under such conditions, grasping the overall structure and operation of the engine becomes difficult.

3D modeling offers a more “realistic” view of the product but fails to illustrate how the parts interact with each other, the airflow through the compressors, the aerodynamic and thermal phenomena occurring in the combustion chamber, etc. Even though 3D plans represent a significant advancement for industrial design, they do not capture the complexity and dynamics of a product.

Furthermore, 2D/3D plans do not provide a spatial perception or immersion comparable to what physical models or virtual reality offer. Even with a 3D plan, one cannot “walk around” inside the product, see it from all angles, or intuitively understand its operation.

For instance, in the case of maintenance operations on an energy production site, technicians cannot solely rely on 2D/3D plans to plan their intervention. Indeed, these tools do not allow for spatial projection and visualization of access or assembly constraints, unlike a digital twin or virtual reality immersion.

Contextualize the industrial product in its environment.

The operational reality of an industrial product is not limited to its physical structure alone. It is essential to consider the environment in which the product will operate, that is to say the real conditions of use and the interactions with other systems. However, 2D/3D blueprints do not provide the information to contextualize the product.

Having only 2D/3D blueprints without contextual information is like knowing the map, but not the territory: the industrial product is designed without a real grasp of reality. This partial blindness can lead to consequences on the product’s performance and reliability, and result in delays (and therefore, budget overruns).

Take the example of a merchant ship. On a 2D/3D blueprint, this ship is usually represented in isolation, without taking into account the environmental conditions in which it must navigate. Yet, a merchant ship does not sail in a vacuum. It has to face waves, wind, and ocean currents. It also has to interact with other systems: ports, navigation channels, maritime traffic, and so on.

Without this contextual information, a 2D/3D blueprint of the ship provides only a partial and potentially misleading image of its operational reality. How will the ship behave in rough seas? How will its navigation system interact with the maritime traffic control system? What challenges will it face when docking at a particular port? All these questions remain unanswered with just a 2D/3D blueprint.

Industrial information management: a productivity factor not to be overlooked.

2D/3D plans: a source of risks and inefficiency

Information management is a critical factor for the success of an industrial project. However, 2D/3D plans make this process particularly challenging and lead to a significant increase in the risk of errors, inconsistencies, and information loss.

Saving time in information processing.

Managing industrial information based on 2D/3D plans involves many manual tasks: integrating changes, exporting and transmitting files, copying information, etc. These tasks are time-consuming and affect the overall project: indeed, the time spent on them is not dedicated to more productive activities, such as design, production, or problem-solving. Moreover, changes made to a plan can lead to manual adjustments on many other associated plans, thereby increasing the processing time and the risk of errors.

Reduce the risk of error or misinterpretation.

One of the major pitfalls of using 2D/3D plans is the risk of error or misinterpretation of information during the transmission and copying of files. Whether it’s dimensions, materials, or assembly procedures, information that is not conveyed or misunderstood can compromise the product’s performance or even its safety, not to mention the costly adjustments in time and resources that this can entail.

Eliminate the risk of version desynchronization.

An industrial project leads to countless revisions of 2D/3D plans, which requires good synchronization of versions to ensure stakeholders are working on the most up-to-date version. Indeed, an outdated version of a plan can easily result in serious consequences for the progress and final cost of a project.

For example, if a design team makes a change to a plan but this modification is not properly communicated to the production team, the latter might continue to produce parts based on the old version of the plan. When the error is detected, the correction can lead to significant delays and additional costs, not to mention the deterioration of the relationship between the teams.

The collaborative digital twin: an alternative to blueprints.

The collaborative digital twin provides concrete solutions to the problems posed by traditional 2D/3D plans. A virtual representation of a product or industrial system, the digital twin not only incorporates its physical complexity but also its operational dynamics and environment.

With a digital twin, it’s possible to grasp an industrial product in all its complexity. The smallest details are accessible and observable from different angles, making it easier to understand the interactions between each element and their behavior in the overall functioning of the product.

Moreover, the digital twin facilitates immersion into the product or system and thus provides a deep understanding of it, far beyond what 2D/3D plans can offer. Indeed, beyond the physical dimensions of the product, the digital twin integrates crucial contextual data to model the product’s interactions with other systems and simulate its operation under specific conditions.

Furthermore, a collaborative digital twin is a valuable asset for information management. Stakeholders make their additions and enrichments, which are then visible to everyone. No more need to worry about synchronizing plans: the digital twin is always up-to-date. There’s no longer a need to communicate countless versions of the plans to teams; the digital twin serves as a single persistent reference, thus reducing the time spent on manual information processing and the risk of resulting errors.

En conclusion, malgré leur rôle fondamental dans la conception industrielle, les plans 2D/3D présentent des limites significatives : ils offrent une représentation imprécise et partielle des produits, engendrent des risques d’erreur, d’incohérences et de perte d’information. De plus, ils nécessitent un travail manuel considérable pour leur mise à jour et leur synchronisation. 

Les jumeaux numériques collaboratifs émergent comme une solution innovante, en intégrant la complexité, la dynamique et le contexte des produits industriels. Ils offrent une gestion de l’information plus efficace et favorisent une meilleure collaboration entre les parties prenantes. Leur adoption généralisée pourrait révolutionner la manière dont nous concevons, fabriquons et exploitons les produits industriels, rendant l’industrie plus performante, plus sûre et plus innovante.

SmartShape: Shaping the Future Work Environment

Today’s industrial world challenges

The modern industry faces a mountain of complex data. Interpreting this information to make informed decisions can prove to be a major challenge. This is where SmartShape comes in. It is a digital twin software solution that aims to simplify industrial data and make it accessible and understandable.

SmartShape: A Revolution in Data Visualization

The innovation of SmartShape is based on the fusion of shapes (2D mock-ups, 2D plans, photogrammetry…), business data (spreadsheets, APIs, sensors, ERP, PLM, MES…) and code (processes, instrumentation, data mining, machine learning, AI). All of this is orchestrated in a collaborative environment, available online and offline, in real-time and responsive. The goal is to enhance existing data without changing tools.

SmartShape is compatible with all CAD software and all data sources or models. Moreover, the application is universal, available on web browsers, tablets, or smartphones.

To learn more about the SmartShape concept, check out this article on our blog.

SmartShape: a solution to combat digital duplicates

One of the main advantages of SmartShape is its ability to combat digital duplicates which lead to fragmentation of dead data and sequential silos. SmartShape provides a single source of truth by creating a digital twin that centralizes all information. For more information on digital twins, check out this post on our blog.

Sovereignty and Security with SmartShape.

Security and sovereignty are two major concerns for SmartShape. The software and data are hosted on dedicated servers provided by the clients, isolated from the internet, and with military-grade security. For more information about our security approach, please refer to our technical documentation.

Concrete examples of SmartShape application.

1. Chantiers de l’Atlantique: SmartShape has enabled the replacement of 2D paper blueprints with a collaborative 3D digital twin. For more details, please refer to this article.
2. Suez: The Suez company used SmartShape to create a digital twin of its sewer network using photogrammetry, thereby reducing the time technicians spend in a hazardous environment.
3. Bureau Veritas: SmartShape assisted Bureau Veritas in replacing 2D PDF blueprints with a collaborative 4D digital twin connected to a comment management platform.

Frequently Asked Questions (FAQ)

What is SmartShape?

SmartShape is a digital twin software solution that consolidates all available data into one collaborative space, making the data understandable because it’s visual. For more information, check out this article.

What are the benefits of SmartShape?

SmartShape offers numerous benefits such as collaborative mode, offline mode, integration of conversational artificial intelligence, and combating digital duplicates.

How does SmartShape ensure sovereignty and security?
SmartShape hosts the software and data on dedicated servers provided by the clients, isolated from the internet, and with military-grade security. For more details, please refer to our technical documentation.

Is SmartShape compatible with all CAD software?
Yes, SmartShape is compatible with all CAD software like Catia, SolidWorks, Revit, Microstation, and others.

How can I contact SmartShape?
You can contact us through this page.

SmartShape, as an industrial metaverse, is a real game-changer for industries such as naval, military, energy, water treatment, aerospace, and many more. To learn more about our achievements and expertise, feel free to visit our website or our LinkedIn page.