• What advantages do graph databases offer over widely implemented relational databases?
• How can graph databases advance digital transformation initiatives?
• How do graph databases enhance engineering applications?
• What business and technical problems do graph databases handle better than the alternatives?
• How can graph databases increase the value engineers can derive from their data?

What is a graph database?

A graph database (GDB) uses graph structures to represent and store data. Graph structures consist of data tables for entities, called vertices, and relationships, called edges. Graph databases physically store pointers for the relationships between the entity values. This feature differentiates graph databases from relational databases, which establish relationships by performing joins based on foreign key values.

Engineers are attracted to graph databases to meet big, complex data challenges that traditional relational and more recent NoSQL databases cannot conquer. High-volume data challenges arise frequently in organizations with a high degree of digital transformation.

Graph database advantages

Graph databases offer advantages over the more widely used relational or specialty NoSQL databases based on their ability to persist relationships among entity values. This technical feature leads to the following benefits:

• Superior query speed, which is especially noticeable as query complexity increases.
• Ability to describe real-world relationships accurately. This accuracy improves developers’ understanding of the database and simplifies the implementation of schema changes.
• Flexibility in revising data structures. This flexibility reduces the cost and disruption of modifying the database and application programs as changes are required.

How graph databases advance digital transformation

Graph databases’ advantages are realized when digital transformations use them for their digital datastores.

Here’s how. Digital transformation typically requires the following tactical steps after the strategy has been defined:

• Transforming and integrating data from disparate data sources.
• Scanning and digital conversion of paper documents.
• Structuring unstructured digital data.
• Cleaning and augmenting digital data.
• Integrating digital data from external data sources.
• Implementing data analytics and visualization software.

Engineers who employ graph databases can perform these digital transformation steps to improve digital data accessibility faster and more cheaply.

Graph databases enhance engineering applications

The benefits of graph databases apply to many of the applications engineers work with routinely. Whenever engineers can better understand relationships among parts, components, subassemblies, tasks, and work processes throughout the engineering life cycle, they can:

• Improve quality.
• Identify and plan to mitigate risks.
• Reduce elapsed time and delays.
• Reduce costs or at least minimize expected increases.
• Anticipate future problems and identify alternative courses of action.

Managing these relationships at a detailed level and making them easily visible to engineers is precisely the value that graph databases offer.

Graph database opportunities

Data volume explosions and increasing application sophistication trigger the use of graph databases. The many DBMS advances of the past, plus considerable improvements in computing infrastructure performance introduced over many decades, are nevertheless straining or failing to handle these increasing demands.

Applications that access graph databases can solve these lack-of-scale problems that create frustrations. Examples of applications which engineers use regularly and benefit from graph databases include:

• Artificial intelligence.
• Asset management.
• Automation and computer-aided manufacturing.
• Computer vision.
• Computer-aided design.
• Construction management.
• Defect tracking.
• Digital twin and simulation.
• Inventory and warehouse management.
• Maintenance management.
• Manufacturing process optimization.
• Product lifecycle management.
• Quality control.

These applications benefit from using graph databases because they can:

• Deliver excellent performance for complex data analytics.
• Simplify data ingestion and integration from diverse data sources.
• Manage vast data volumes reliably.
• Offer high application availability,
• Support open standards for query and update syntax.

When is a graph database superior?

Graph databases are suited to specific application characteristics. They are not a substitute or an alternative to relational or other databases. The various types of databases fulfill different data processing and application requirements.

Graph databases are superior when working with less structured data, many relationships and processing modest numbers of large transactions with reasonable response times. Large transactions typically access multiple tables for many or even all entity values. Relational databases are superior when working with highly structured data, few relationships and processing large numbers of small transactions with fast response times. Small transactions typically access a small number of tables for a single entity value.

Graph databases are superior when the data and structure are not well understood at database design time and are expected to change. Relational databases are superior when the data and structure are well understood at database design time and are not likely to change much. The difference is that the database schema of graph database applications can be revised without taking the application offline. That’s not possible with relational databases.

Graph databases increase the value of data

Organizations increasingly understand that their data contains more value than was previously recognized. Graph databases allow engineers to extract that value when they:

• Examine dynamic, complex or unusual relationships between data. This capability is particularly useful for analyzing root causes, understanding component interactions and building visibility of supplier performance.
• Can understand context and relevance more easily. Context describes the relationship among individual pieces of data. Relevance defines the usefulness of pieces of data to making specific decisions. This capability is advantageous for evaluating design alternatives, avoiding distractions and confusion in decision-making and responding to supply chain disruptions.
• Can easily search their organization’s intellectual property, typically stored as unstructured data in documents. This capability is helpful for opportunity discovery or problem analysis.

Graph databases add value for engineering applications when they manage large volumes of data with significant numbers of relationships among entities. Examples include product and component codes, material types and finish codes or performance specifications.

The post How graph databases are a valuable tool to advance digital transformation appeared first on Engineering.com.

Infrastructure engineering software company Bentley Systems, Inc., based in Exton, Pa., has developed new asset analytics capabilities that apply AI to crowdsourced imagery for automated roadway asset detection and inspection.

“The collaboration between Bentley’s Blyncsy offering and Google’s expansive mapping and imagery databases has the ability to disrupt the tedious process of monitoring infrastructure conditions and damage assessments [after] a natural disaster,” says James Lee, chief operating officer for Bentley Systems. “Together, we can help infrastructure professionals better forecast maintenance needs long before they escalate into costly or hazardous safety problems, and respond intelligently and instantly to crises—all through the use of AI-generated insights pulled from constantly updated datasets and historical records of infrastructure.”

Unveiled at Google Cloud Next 2025, the new capabilities in Bentley’s Blyncsy software leverages Imagery Insights from the Google Maps Platform to rapidly detect and analyze roadway conditions.

Acquired by Bentley in August 2023, Blyncsy applies computer vision and artificial intelligence to analyze commonly available imagery to identify maintenance issues on roadway networks.

Bentley and Google partnered up in October 2024 to integrate Google’s high-quality geospatial content with Bentley’s infrastructure engineering software to improve the way infrastructure is designed, built, and operated.

“We have a history of leadership in applying repurposed imagery for roadway maintenance, and the addition of Google’s 360-degree imagery and AI will further enhance the value Bentley provides to transportation departments and engineering firms globally,” said Mark Pittman, director of transportation AI at Bentley. “The expansion of our relationship with Google will enable us to further develop our growing infrastructure asset analytics capabilities—initially in the transportation sector with other industries to follow.”

The combination of Imagery Insights from Google Street View, Vertex AI, and Blyncsy will make it easier for departments of transportation—and the engineering firms and consultants supporting them—to identify areas of concern and analyze changes in the condition of roadway and transportation assets over time.

“As our strategic partner, Bentley combines industry-leading infrastructure solutions with Google’s leading AI and mapping technologies, like Vertex AI and Street View, to bring powerful analytics to public and private sector leaders who need mobility insights for making more informed decisions,” said Yael Maguire, Google’s vice president and general manager for Google Maps Platform and Google Earth.

Google Street View’s global panoramic imagery gives Bentley highly detailed analysis of assets, along with visual references. Google’s Vertex AI builds and maintains models to alert transportation agencies of changes to infrastructure assets before they become safety hazards. In addition to supporting roadway maintenance activities, these capabilities can also aid in disaster recovery efforts, providing a cost-effective solution for conducting rapid damage assessments, which can help rebuild faster.

Bentley says Google’s Imagery Insights “will be generally available in Blyncsy in 2025.”

The post Disaster recovery is just the start for this Bentley-Google collab appeared first on Engineering.com.

In this article I’m going back to the basics of product lifecycle management (PLM) by exploring two fundamental questions: Who uses PLM?  And what are some of its challenges?  

This is my third article on PLM basics for Engineering.com. Previously, I wrote Answering 3 Top PLM Questions and Why Every Enterprise Needs Its Own Digital Twins.  

A common thread across all these articles is the importance of collaboration and innovation.  PLM supports these vital enterprise processes as no other technology can do.  Without a well-defined PLM strategy and associated enabling technologies, the gathering and management of data, insights, and inspiration is never reliable.  Only PLM-enabled collaboration and innovation can ensure the long-term sustainability of an enterprise.

Since the first two articles were written, the importance of PLM to enterprise-scale digital transformation and vice versa has become more widely recognized. But PLM’s message is often lost in noisy and cluttered marketplaces that change at ever increasing rates.  To keep pace, PLM solution providers come up with elaborate new tools and add-on capabilities that make digital twins more powerful; digital threads more all-encompassing; end-to-end-end connectivity reach further and deeper because both the ends and the beginnings of products’ lifecycles grow hazier amid expanded capabilities.  At the same time, PLM is being extended into new areas of the enterprise, some of which have only tenuous links to the everyday understanding of “product.”   In these uncertain times, going back to basics has historically proven to be a wise policy. 

All three of these back-to-basics articles drew inspiration from Answerthepublic.com, a marketing-focused platform that unearths user questions commonly entered into search engines.

Who Should be Using PLM?

This question addresses why PLM is so widely used and appreciated, even without deeper understanding of it. PLM is probably the most widely implemented among the myriad tools engineers rely on—standalone CAD included.  New-product developers in all industries turn to PLM at every stage of their work:

•  Creating, redeveloping, and enhancing the enterprise’s products, services, assets, or systems, both physical and digital.

•  Creating, developing, and enhancing processes.

•  Enhancing and extending enterprise connectivity.

•  Delving into supply chain management to cope with the variants in all of the above.

•  Everyone, technically trained or not, who supports these engineers.

If you are using PLM, the aim should be extending the use of it from product development throughout the extended enterprise, including the enablement of:

Digital twins, which are virtual representations—digital surrogates—of physical assets (or services, or even manufacturing systems and the organization itself) that exploit data flows into and out of that asset.  A digital twin of a product typically holds geometry and representations of materials, components, and behavior through the asset’s multiple iterations—as-designed, as-produced, and as-maintained.

Digital threads, which are webs of decisions and myriad links that reach all the data, decisions, and processes that create, maintain, and leverage digital twins from design engineering through production, sales, service, support, and warranties.

End-to-end (E-2-E) lifecycle connectivity, which reaches and joins everything relevant to each digital twin and its digital threads from initial ideation through end-of-life and disposal or remanufacturing and repurposing.

Digital transformation, which is rendering/converting all the enterprise’s data to get rid of bothersome formats and silos that prevent data and information from being freely accessed, used, shared, and reused; the digital transformation of an organization’s product lifecycle is thus a powerful enabler of collaboration and innovation.

What are some challenges of PLM enablement?

As with any transformational technology, implementers and project managers must overcome multiple challenges.  Because PLM’s broad capabilities and toolsets reach deep into the enterprise’s data, these challenges sometimes overwhelm the resources allocated to the implementation; start-up dates are missed, and deadlines are blown.

Like any large-impact technology implementation, some of the challenging aspects of PLM include:

•  Significant investment in money, time, and resources that requires careful monitoring and control.

•  Intensive planning, system-to-system accommodations, and staff retraining across the enterprise.

•  Tightly focused efforts for digital transformation to deal with unformatted data and information while facilitating access to departmental data silos.

•  Similarly, a tight focus on dealing with unexpected job complexity that can frustrate new users.

•  Persistence during a sometimes tedious implementation with a nonstop focus on priorities.

•  Continuous updates for top management so they aren’t tempted to reassign resources and reallocate funds.

•  Continuous evaluation and improvement throughout the continued usage of the new digital PLM-enabling technologies, commonly called “staying the course.”

Every marketplace is constantly reconfiguring itself, driven by countless innovations. This can disrupt enterprise-scale collaboration and thwart innovation. Gathering and managing data and insights can become so complicated that their use becomes unreliable—or worse.

Among the consequences of these reconfigurations is the fragmentation of PLM.  PLM implementation challenges have motivated a handful of software startups to offer toolkits for digital twins, digital threads, and even enhanced connectivity as stand-alone capabilities.  Marketed as sufficient in themselves for everyday user tasks, these toolkits are being tacked on to information technology, operational technology, engineering technology, and other top-of-the-enterprise platforms and systems. 

I must caution that none of these narrow offerings have PLM’s powerful and widely used capabilities to foster true innovation and collaboration, or at least not to the extent necessary.  And only collaboration and innovation can ensure the long-term sustainability of the enterprise.

The underlying theme running through all three of these articles is enabling and enhancing collaboration with PLM, and, as noted, no other technology can do this. Gathering and managing data, insights, and inspiration can never be reliable without PLM and the digital transformation it enables. In turn, only with the digital enablement of PLM can the long-term sustainability of the enterprise be assured.

And this is why understanding the basics of PLM is so critical.

The post Understanding PLM: who uses it, why they use it and its challenges appeared first on Engineering.com.

On April 2, 2025, the U.S. government announced a sweeping new tariff regime that impacts products produced from virtually every country in the world. In an official release, the White House says the move is meant to reprioritize manufacturing in the US. While many economists believe the new tariffs will not produce this result, it doesn’t change the fact that most US manufacturers will have some significant math in their immediate future.

If you buy finished goods, this is likely not a complicated feat. But if you buy raw materials and components from suppliers in other countries and then use those materials and components to make your products, you have a much heavier lift.

Hopefully your company has maintained a reasonable digital transformation investment strategy and you have one or even several digital assets that will make this process much easier and far more accurate than any manual process—especially once you have to deduce if you can absorb some of the costs or have to pass them on to customers.

If you are stuck scrolling through spreadsheets to figure out your exposure, have fun and good luck.

Here is a list of digital solutions commonly found in manufacturing, and how you can use them to find your tariff exposure, calculate your additional spend and decide if it’s worth eating any increase:

Enterprise Resource Planning (ERP)
Purpose: Centralized financial, inventory, and operational data management

Role in Tariffs:

• Provides financial visibility into product and material costs
• Tracks landed costs for imported components
• Supports decision-making on pricing adjustments
• Inventory tracking, but (near) real-time supply chain risk assessments may require additional risk management tools

Supply Chain Management (SCM)
Purpose: Optimizes procurement, logistics, and supplier management

Role in Tariffs:

• Models tariff impact on supply chain flows (e.g., supplier costs, lead times)
• Optimizes sourcing strategies to minimize cost increases
• Supports trade route and logistics adjustments to avoid tariff-heavy regions
• Tariff-specific impact simulations may require integration with ERP or specialized tariff analysis tools

Trade Compliance and Tariff Management Tools
Purpose: Ensures compliance with international trade laws and updates tariff classifications

Role in Tariffs:

• Tracks and updates tariff changes in response to regulatory updates
• Automates classification of goods under the correct Harmonized System (HS) codes
• Supports compliance audits and documentation for trade regulations
• Forecasting financial impact of tariff changes requires integration with ERP or BI systems

Business Intelligence (BI) and Predictive Analytics
Purpose: Data visualization and financial impact analysis

Role in Tariffs:

• Analyzes historical and real-time cost impacts of tariffs
• Models financial scenarios to predict margin impacts
• Integrates with ERP and trade compliance data to provide actionable insights
• Supports strategic decision-making on pricing adjustments and supplier shifts

Pricing Optimization Software
Purpose: Adjusts product pricing based on market conditions and cost fluctuations

Role in Tariffs:

• Determines whether tariff costs should be absorbed or passed on to customers
• Optimizes pricing strategies based on competitive market data
• Prevents margin erosion by aligning pricing with demand sensitivity

Product Lifecycle Management (PLM)
Purpose: Manages product design, BOMs, and supplier data

Role in Tariffs:

• Identifies which materials and components are subject to tariffs
• Supports product redesign efforts to reduce reliance on high-tariff materials
• Stores country-of-origin and trade compliance documentation
• Can be involved in redesigning products to mitigate tariff impacts

Digital Twins and Scenario Planning
Purpose: Virtual simulation of manufacturing operations for efficiency and resilience

Role in Tariffs:

• Simulates supply chain resilience strategies in response to tariff disruptions
• Models operational efficiencies to offset increased costs
• Tests alternative sourcing and logistics adjustments before implementation
• Less directly involved in product redesign than PLM tools

The post Technology rundown for analyzing tariff impact in manufacturing appeared first on Engineering.com.

By now, you probably already know that digital transformation is a manufacturing strategy that integrates advanced digital technologies to enhance efficiency, reduce waste and optimize resource use. But one angle that’s not always talked about is that the right application of these digital technologies can significantly improve sustainability.

Below is an examination into key areas where digital transformation drives environmental and operational benefits. It includes foundational steps for beginners and a few advanced techniques for more experienced engineers further down the digital transformation road.

Data-driven decision-making for sustainable operations         

At the foundation of digital transformation is data collection and analytics, which enable real-time tracking of key sustainability metrics such as energy consumption, material waste and emissions. IoT (Internet of Things) sensors, SCADA (Supervisory Control and Data Acquisition) systems and AI-driven analytics help manufacturers make informed decisions that optimize production efficiency while minimizing waste.

Beginners can start with IoT-enabled sensors to monitor machine performance, energy usage and material waste. From there, use basic dashboards to visualize trends and identify inefficiencies. The next step is to implement predictive maintenance using these insights to reduce unexpected breakdowns and extend machine lifespan.

Advanced users may have already deployed digital twins to create a virtual model of production systems, allowing their engineers to test optimizations before making real-world changes. AI-powered anomaly detection can automatically adjust machine parameters and reduce energy waste while integrating machine learning (ML) algorithms to analyze historical data to improve production scheduling and minimize resource-intensive downtime.

Energy efficiency and carbon footprint reduction

Manufacturing facilities are obviously energy-intensive, but energy management systems can significantly lower power consumption and carbon emissions without disrupting production.

For beginners, smart meters and IoT sensors can track energy consumption at different production stages. Once you have this data, implement automated power-down schedules for non-essential equipment during off-peak hours.

More advanced users can integrate AI-driven load balancing to redistribute energy usage across equipment dynamically. They may decide to explore microgrid solutions that combine renewable energy sources (solar, wind) with energy storage for more sustainable operations. Carbon footprint tracking software will make it easier comply with environmental, social and governance (ESG) standards and improve sustainability reporting.

Optimization for sustainable sourcing and logistics

A sustainable supply chain reduces emissions, optimizes material use and ensures responsible sourcing throughout a manufacturer’s network. Digital tools help companies improve inventory management, optimize transport routes and reduce overproduction.

If you haven’t already, implement cloud-based inventory management systems to track raw materials, reducing excess stock and waste. PLM and ERP software are the gold standard for this, but smaller manufacturers may not need all of the functionality these platforms provide and might decide to piece together the functionality they want using smaller software platforms that require less investment and cause less disruption during start-up. The goal is to gather enough data to use demand forecasting to avoid overproduction and prevent obsolete inventory or costly overstock. Next, implement a supply chain platform to ensure ethical sourcing and reduce supplier-related inefficiencies.

Advanced users are likely at least considering AI-driven dynamic routing systems for delivery fleets, optimizing transportation routes to reducing fuel consumption. RFID and GPS tracking will monitor product movement and optimize storage conditions, reducing spoilage. Next, establish closed-loop supply chains, where returned or defective materials are reintegrated into production rather than wasted.

Waste reduction and circularity

Everyone knows minimizing waste is critical for sustainable manufacturing. Advanced digital tools help manufacturers keep a handle on waste by tracking, sorting and helping repurpose materials efficiently.

Start with robust defect detection to reduce waste caused by faulty production runs. Introducing 3D printing (additive manufacturing) to minimize material waste and create precise, on-demand parts could make sense for a growing number of manufacturers. A basic data-fuelled recycling programs for metal, plastic and other byproducts can keep waste under control.

For advanced users, AI-powered sorting systems to automatically separate and classify waste materials for recycling can improve results of any recycling program. Digital product lifecycle tracking will accommodate customer product returns for disassembly and reuse, potentially taking the edge of raw material costs as digital and smart advanced remanufacturing strategies will help refurbish returned components and reintroduce them into production lines.

Smart manufacturing for sustainable production

Industry 4.0 technologies like automation, robotics, cloud computing and AR (augmented reality) can significantly reduce resource waste and improve efficiency in manufacturing environments.

Beginners can start by implementing basic robotics for repetitive tasks to improve precision and reduce material waste. Cloud-based collaboration tools will reduce paperwork and streamline production planning. Adopting AR-based training modules allows employees to learn new skills without exhausting physical materials.

Advanced users might look to deploy AI-powered collaborative robots (cobots) to enhance precision manufacturing and minimize errors, all while collecting valuable data. Edge computing from devices on the line analyzes machine data locally (rather than in the cloud). This reduces energy consumption for data processing and gives the impetus to implement real-time digital simulation models that predict potential disruptions and adjust production accordingly.

Key takeaways for manufacturing engineers

For those just starting, begin by implementing IoT sensors, analytics and basic automation to monitor and improve sustainability.

For experienced engineers: Use advanced AI, blockchain and digital twins to optimize energy, supply chains and circularity.

Whether you are just starting your digital journey or are an advanced user of the latest digital technologies, it’s important to understand efficiency is just one piece of the payback delivered by digital transformation—it’s about future-proofing operations which includes reducing environmental impact.

The post How digital transformation boosts sustainability in manufacturing appeared first on Engineering.com.

Bruce Mark is an independent project management consultant who has been asked to lead a project to introduce artificial intelligence to improve scheduling of one of the automotive manufacturing plants of a leading parts manufacturer. The company insisted that Bruce see the project as not just installing the technology but also making sure that the technology adoption would deliver business benefits: shorter lead times, more deliveries made on time, lower inventories, greater scheduling flexibility and continuous improvement in scheduling activity.

In previous projects, Bruce’s role only included the technical installation and the cutover to the new system. Now, the operational contribution of the new technology was the main objective – ensuring that digital transformation achieved business strategic objectives. Bruce considered what this meant for management of the project in a way that would ensure the radical changes desired would be achieved.

We know that most organisations have never managed radical change. Their business success depends on reliable, repeatable activity that effectively meets their customers’ needs. This requires significantly different skills. For project managers, skills that worked in the past, may not be enough for the future.

The PMI Talent Triangle

The Project Management Institute is the leading global organisation of project managers. They provide professional designations that are internationally accepted as proof of project management capability. As technology has enabled change in organisations in recent years, the PMI has tried to ensure that their designation requirements change in response.

The ‘ideal’ skills that the Project Management Institute believes are required for most projects are described in their Talent Triangle which has three elements:

Ways of Working: There are now various ways of managing projects, including predictive, agile and hybrids of these. Project managers need to understand them and be capable of their application when needed.

Power Skills: People skills including communications and empathy that can be applied in managing project teams and stakeholders.

Business Acumen: Understanding the organisation that the project is taking place within and how the project contributes to its strategic objectives.

All projects require these project manager capabilities to be applied in ways that are appropriate for that specific project. All projects are unique and the project manager needs to select and apply the appropriate skills from their skills toolbox.

Why Are Digital Transformation Projects Different?

Significant digital transformation projects require super project managers. The PMI’s Talent Triangle provides a good broad categorisation of the skills that project managers need for most projects. Digital transformation projects require a high level of capability in their application.

Digital transformation projects that are intended to achieve radical change often involve new work processes, new products and services, new business models and new business eco-systems. Projects will vary in the extent to which they involve each of these areas but managing projects that involve substantial change in business activities, in human resources (new skills, people and ways of working), operational processes etc. requires a high level of skill in a range of disparate areas. A technology project in a more slowly changing organisation is usually much less complex.

The PMI Talent Triangle provides a useful framework for understanding how radical digital transformation requires higher project management skill levels.

Ways of Working: There are a wide range of tools and techniques available to project managers today from predictive and agile methodologies. Complex digital transformation projects require a high level of skill in selecting and applying appropriate tools to each part of the project. For example, agile tools may be used for technology development activity while a predictive approach may be better for managing data migration.

Power Skills: The impact of digital transformation on people in the organisation can be substantial and difficult to manage. Establishment of new roles and skills and elimination of others, new processes and working practices and changes in management practices and organisational culture require skills in communications and managing change, along with knowledge of good job design and modern approaches to management.

Business Acumen: Digital transformation is undertaken in pursuit of business strategic goals which today often involve fundamental changes in operations, products and services, business models and business eco-systems. The change that is being made is not just technical but involves the whole business. The digital transformation project must be managed to ensure that it is fully aligned with the business transformation. The project manager needs to have both a good understanding of the business and the changes it is making, along with how the technology contributes to this. Systems thinking to understand the integration of activity throughout the project is critical.

The project manager in a digital transformation project requires a high level of skill that is not usually required to the same extent in many other projects. Their knowledge of and ability to apply a wide range of project management tools, their ability to work with people and their business knowledge and skills are critical to project success.

Project managers who have this combination of skills are rare, so it is important that the skills in the project team complement those of the project manager where needed.

Assessing DX PM Skills

We have developed a tool to assess the capability of a project manager for a digital transformation project, for the participants in our University of Waterloo, Watspeed online Digital Transformation Certificate Program. It can be used to consider candidates for project manager roles, to understand the capabilities of existing project managers and where support may be needed to ensure project success.

In each of the sections below, first determine the extent to which each capability is ‘Needed’ in your digital transformation project by rating these on a scale of 1 (not needed) to 10 (expert capability). Next consider the capabilities of your project manager in the ‘Assessed’ column on a scale of 1 (no capability) to 10 (expert capability). The final column allows you to record any action you wish to take, based on the assessment. In each category, the final row allows you to total your assessment scores and develop a summary of capability in each of the PMI Talent Triangle elements.

The Digital Transformation Project Management Assessment Tool

Managing a significant digital transformation project requires capabilities in a range of areas that are often not sufficiently considered in project manager and project team selection. This simple tool is intended to help you focus on and improve the project management of your digital transformation projects.

The post Improve your digital transformation project management appeared first on Engineering.com.

Dassault Systèmes has unveiled 3D Universes (styled as 3D UNIV+RSES for branding), a bold step toward reimagining how industries engage with digital and physical realities. This is not just another 3D modeling update. It represents a fundamental shift from static digital twins to an AI-powered, generative intelligence ecosystem. The branding itself—3D UNIVERSES instead of “3D Universes”—signals a new paradigm where virtual and real (V+R) are seamlessly integrated, enabling continuous learning, automation, and adaptability across product lifecycles.

But with this shift comes a set of key challenges: What does this mean for legacy users? How will intellectual property be managed in an AI-driven world? And can Dassault Systèmes convince the market that this is more than just another buzzword-laden evolution?

Virtual + real: more than just digital twins

The concept of V+R (Virtual + Real) is not new to Dassault Systèmes. It has been a central theme in the company’s Virtual Twin Experience, where digital twins are no longer mere representations but are continuously evolving with real-world inputs.

In 3D Universes, this vision is taken further:

• AI-powered models learn from real-world behaviors and adjust accordingly
• Virtual companions provide intelligent assistance in decision-making
• Generative AI and sense computing optimize designs and simulations in real-time

This moves beyond the traditional “digital twin” approach. Rather than acting as a static mirror of the physical world, 3D Universes enables a dynamic, self-improving system that continuously integrates, analyzes, and adapts. The idea is not new. For instance, Siemens and other ‘PLM software’ providers are actively exploring opportunities for AI to add an intelligent layer to the PLM data backbone.

From static to generative intelligence

Dassault Systèmes has long been a leader in 3D modeling, PDM/PLM, and simulation, though 3D Universes marks a significant departure from traditional software functionality. It introduces an AI-driven, generative framework that transforms how products are designed, validated, and maintained.

Key differentiators from this new positioning include:

• AI-assisted workflows that automatically refine and evolve designs.
• Predictive simulations that adapt based on real-world sensor data.
• A “living” knowledge platform that evolves with industry trends and user inputs.

You get the idea. Rather than designing a product in isolation, cross-functional teams, from Product Development, Engineering, Quality, Procurement, and supply chains can now co-create with AI, allowing for an iterative, automated process that reduces risk, enhances efficiency, and accelerates innovation cycles.

Beyond software—a living digital ecosystem

The shift to 3D Universes also seems to represent a move away from traditional licensing-based software models toward a consumption-based, Experience-as-a-Service (XaaS) framework—a similar commercial model per the approach recently described as “AI-as-a-Service” by Microsoft CEO Satya Nadella. This aligns with broader industry trends where companies are transitioning from one-time software purchases to continuous value-driven digital services.

What does this mean in practical terms?

• Customers will consume intelligence rather than static software.
• Real-time virtual twins will become decision-making hubs, constantly updating based on real-world inputs.
• AI-generated designs will automate engineering iterations, dramatically reducing manual effort.

This is a major shift for legacy customers who are accustomed to on-premises, private cloud hosting, and transactional software ownership. Dassault Systèmes will need to provide a clear roadmap to help these organizations transition without disrupting their existing workflows and wider integration landscape.

IP, trust and the generative economy

One of the most critical challenges in this transformation is intellectual property (IP) ownership and data security. In an AI-driven, generative economy, where does human ingenuity end and machine-driven design begin? If AI generates a product variation based on learning from past designs, who owns the output?

Some key concerns include:

• Ensuring IP integrity when AI continuously iterates on existing designs.
• Managing security risks as real-world data feeds into digital models.
• Addressing industry adoption barriers for companies that have built their entire business around traditional IP protection frameworks.

Dassault Systèmes, and other enterprise solution provider in this space, will need to provide strong governance mechanisms to help customers navigate these complexities and build trust in the generative AI-powered design process.

Dassault Systèmes issued a YouTube video presentation as a teaser to outline the core ambitions of 3D Universes, reinforcing its role in shaping a new generative economy—elaborating on key messages:

• Virtual-Plus-Real Integration: A seamless blend of digital and physical data enhances accuracy and applicability in simulations.
• Generative AI Integration: AI-driven processes enable more adaptable and intelligent design iterations.
• Secure Industry Environment: A trusted space for integrating and cross-simulating virtual twins while ensuring IP protection.
• Training Multi-AI Engines: Supports the development of AI models within a unified framework, promoting more sophisticated AI applications.

While the video presents a compelling vision and sets timeline expectations towards an aspirational 15-year journey by 2040, it introduces complex terminology that might not be easily digestible for a broad audience. The use of “Universes” as branding adds an extra layer of abstraction that could benefit from clearer explanations and, in due time, a gradual transition roadmap for legacy users.

Additionally, the practical implementation and real-world applications remain vague, leaving some unanswered questions about industry adoption and integration. How will companies transition to this model? What are the concrete steps beyond the conceptual framework? The challenge will be ensuring that this does not become another overcooked marketing push that confuses rather than inspires potential adopters. Users demand clarity and pragmatism in linking solutions to problem statements and practical value realization.

A bold leap into the future

The potential of 3D Universes is enormous, but its success hinges on several key factors:

• Market Education: Dassault Systèmes must articulate the value proposition beyond buzzwords, demonstrating tangible ROI for both new and legacy users.
• Seamless Transition Strategy: Organizations need a clear pathway to adopt 3D Universes without disrupting their current operations.
• AI Governance & IP Assurance: Addressing industry concerns around AI-generated designs, IP ownership, ethical AI, and data security will be crucial for widespread adoption.

If 3D Universes delivers on its promise, it has the potential to redefine how industries design, simulate, and optimize products across their entire lifecycle. By truly integrating Virtual + Real intelligence, Dassault Systèmes is making a bold statement about the next frontier of digital transformation.

The question now is: Are industries ready to embrace this generative future, or will skepticism slow its adoption? Furthermore, where should organizations start on this journey? Can solution providers be bold enough to share a pragmatic roadmap towards this goal, and keep us posted on their learnings in this space? Will 3D Universes bring us one step closer to the “Industry Renaissance” previously advocated by Dassault Systèmes Chairman Bernard Charles? Time will tell, but one thing is certain—Dassault Systèmes is positioning itself at the forefront of the next industrial/digital revolution.

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This progress, while mandated by some policies, has been driven through business action. After all, the shift is not only beneficial to the planet, but also for people and profitability. Research from Gartner underscores this – in 2024, it found that 69% of CEOs view sustainability as a growth opportunity. Consequently, two-thirds of Fortune’s Global 500 companies have now made substantial commitments to tackling climate change across their total value chains.

However, we cannot afford to lose momentum. To reach our sustainability pledges, we must turn our attention to circularity. Alarmingly, the share of global circularity fell from 9.1% in 2018 to 7.2% in 2023. Yet, during this period, resource consumption continued to accelerate, and we have consumed a staggering 500 gigatonnes of resources – 28% of all materials used by humanity since 1900, concentrated in just the last five years.

If circularity is an indicator of environmental progress, we must take bold action to achieve our sustainability targets and continue driving meaningful change.

Discarding the ‘take-make-waste’ economy

Circularity is an economic model that aims to eliminate waste, preserve resources, and reuse materials. A stark contrast to the linear ‘take-make-waste’ model that our current economy is built upon.

Take the life of a bottle of water for example: oil is drilled from the earth and refined into polyethylene terephthalate – take. Then this is moulded into bottles that are filled with water – make. Finally, they are consumed once and then discarded – waste. Just 12% of the world’s plastic bottles are recycled, and meanwhile, the 88% of remaining bottles are tossed into a landfill, where they can take up to 450 years to decompose.

This unsustainable pattern of consumption significantly contributes to greenhouse gas emissions and biodiversity loss. Transitioning to a circular economy could drastically reduce the materials we use by 70%, creating better resource utilization. So, how exactly can a business become more circular?

It all starts with the design

According to the Schneider Electric Circularity framework, the first consideration for achieving circularity is to adopt Eco-Design which directly impacts our product development and innovation. This means creating products for reliability and longevity, rather than quick disposal. Designing and innovating for circularity enables our products to be used better, used longer and used again.

Building on this foundation, businesses must evolve from a traditional and transactional sales approach to an as-a-service model. To support this shift, companies should consider supplementing sales with rental, repair, and support services. This simultaneously extends product lifecycles, reduces waste and creates new growth opportunities. By offering second-hand tech and trade-in programs, companies can meet demand for affordable, eco-friendly options. This model reduces waste, boosts customer loyalty, and drives long-term profits.

Optimizing resources through responsible sourcing

The first principle is use better, which calls for the responsible sourcing of materials to optimize manufacturing and minimize waste. For instance, sourcing best-in-class materials ensures reduced environmental and social impact throughout the supply chain. Schneider Electric products are currently made with 32% recycled materials with the aim of reaching 50% by the end of 2025. In addition, more than half of our sites recover 99% of waste, demonstrating our commitment to using better.

We are also expanding our smart factory network, with two recent facilities in Monterrey and Shanghai. Both sites utilize machine learning-enabled prototyping, smart planning, and GenAI-driven maintenance to boost productivity while driving down resource consumption. This commitment to sustainability and operational efficiency has been recognized by the World Economic Forum who have designated both factories Lighthouse status.

Circularity demands companies to address sustainability in their total value chains. Sustainable supply chain programs have already been launched in various sectors, such as global healthcare, the semiconductor industry, as well as mining, materials, and minerals. These programs help companies responsibly source materials, reduce emissions, and eliminate waste at every link.

Extending product lifecycles

The second principle is to extend the lifespan of products and use longer.Businesses must anticipate the need for equipment by opting for condition-based repair, digitally enabled maintenance, and equipment modernization. Reparability and circularity services, such as Schneider Electric’s EcoFit, can extend asset life by up to 25%.

In fact, our customer ArcelorMittal reconditioned 13 medium voltage switchgears, avoiding the reprocessing of 26 tonnes of material, saving the equivalent of 170 metric tonnes of CO2. Refurbishing existing equipment instead of discarding it reduces waste and emissions while conserving resources.

The third and final principle is to use again. This concept encourages businesses to recirculate products, parts, and materials within the economy; effectively refurbishing and reselling assets that have reached the end of their initial use.  Schneider Electric’s Altivar drive sends damaged modules to our repair centres for testing. Once refurbished, products come away with the same warranty as new ones, achieving up to 80% savings on resources, energy, and emissions.  

Building a circular business growth framework

Adopting circularity is not just about protecting the planet – it’s a powerful business tool that drives cost savings and growth. By embracing circularity, businesses can differentiate themselves, create new value streams, and stay ahead of an increasingly competitive market.

Moreover, global climate and environment regulations will make circularity a necessity, rather than a choice. The EU, for instance, has introduced some of the world’s most comprehensive policies, such as the Circular Economy Action Plan, the Ecodesign for Sustainable Products Regulation (ESPR), the Corporate Sustainability Reporting Directive (CSRD).

Businesses that adopt circularity now will not only proactively prepare to comply with future regulations but also position themselves as leaders, and build a competitive advantage. So you see, circularity isn’t just the right thing to do; it’s the smart thing to do for any forward-thinking organization.

Frédéric Godemel is the Executive Vice-President for Energy Management and member of Schneider Electric’s Executive Committee. 

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The VARLab specializes in harnessing advanced simulation and modeling methods to tackle business challenges. Magnetic 3D’s 100-in. glasses-free 3D displays empower researchers to visualize and refine complex layouts, such as manufacturing facilities, in a fully immersive environment. By integrating real-world data, they can test configurations and simulate business processes to optimize efficiency. This smart manufacturing approach enables companies to streamline operations, reduce costs, and refine design, development, and engineering workflows before committing to physical construction. Additionally, linking the digital twin to real-world sensors provides real-time insights for ongoing improvements.

One of the core advantages of digital twins is their ability to provide dynamic 3D simulations of real-world processes. Magnetic 3D’s technology enhances this experience by adding depth perception, allowing stakeholders to step inside the simulation for a more intuitive understanding. This capability is particularly valuable in early-stage concept development, where engineers can use immersive 3D visualization to present their ideas and secure stakeholder buy-in more effectively.

Dr. Carolina Cruz-Neira, a trailblazer in virtual reality and co-inventor of the CAVE (Cave Automatic Virtual Environment), serves as co-director of the VARLab and holds the Agere Chair Professorship at UCF. Her expertise in immersive technologies will help drive innovative applications of holographic 3D displays in digital twin research, further bridging the gap between virtual simulations and real-world implementation.

“We are excited about our partnership with Magnetic 3D because their glasses-free 3D displays have incredible benefits and a lot of utility for many applications in our field,” she said in a press release. “Our research group primarily focuses on modeling and simulation in 3D environments, so we appreciate Magnetic 3D’s platform, which allows us to visualize and collaborate in 3D as a group without having to wear 3D glasses or headsets.”

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Survey results highlighted escalating trade conflicts, rising global tensions, and persistent supply chain disruptions are placing unprecedented pressure on manufacturing and supply chain leaders.

Despite these challenges, Fictiv says the report also shows momentum in onshoring, AI adoption, and increasing reliance on digital manufacturing platforms.

“Concerns about tariffs and trade wars are clearly top of mind for manufacturing and supply chain leaders,” says Dave Evans, co-founder and CEO of Fictiv. “We’re seeing a level of global uncertainty and supply chain disruption we haven’t seen since 2020. However, the report also shows that companies are embracing new technologies and strategies to build more resilient and agile supply chains.”

Key Findings

• Global Uncertainty on the Rise: 96% are concerned about the impact of current trade policies, and 93% believe trade wars will escalate in 2025.
• Supply Chain Disruptions Accelerating: 77% report a lack of resources limits their ability to manage the supply chain effectively, and 68% prioritize onshoring as a key strategy.
• Scaling Production More Difficult: 91% face barriers to product innovation, and 86% report sourcing parts takes time away from new product introduction. However, 90% see digital manufacturing platforms as essential.
• Sustainability Takes Hold: 95% report that weather and climate events impact their supply chain strategy, and 91% have sustainability initiatives and governance in place.
• AI Advances: 87% report advanced levels of AI maturity, and 94% use AI for manufacturing and supply chain operations.

Fictiv says its report underscores the need for manufacturers to embrace innovation and adaptability by building more resilient supply chains, leveraging digital manufacturing, embracing AI to transform operations from inventory management to product design, and prioritizing sustainability.

This is the tenth year Fictiv has commissioned the report.

Download the full 2025 State of Manufacturing & Supply Chain Report here.

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