Duro

Why You Need Natural Language Search in Your Workflows

dani@durolabs.co — Wed, 25 Jun 2025 23:03:56 +0000

Quick access to accurate product data is critical to hardware engineering. Every decision, from design iterations to supplier selection, relies on knowing precisely what’s in the system: which parts are approved, which assemblies are under revision, what lead times and costs look like, and whether the data is up to date. When that information is hard to find, progress stalls, errors creep in, and teams start working from assumptions instead of facts.

Yet in most PLM (Product Lifecycle Management) systems, the search function is constrained by rigid filters, static views, and field-specific queries. Users need to memorize field names or learn SQL (Structured Query Language) just to perform routine lookups. If you don’t know how the data is structured or which metadata field to search, you’re out of luck.

This extra work adds up, creating unnecessary friction in daily workflows like reviewing parts, checking revision status, and validating supplier information. Time spent navigating product hierarchies, filtering BOMs, or requesting reports from system admins can easily consume hours each week – time that would be better spent on design, sourcing, and decision-making.

Natural language search offers a different model. Users can describe what they’re looking for in plain language, and the system handles the translation. That means no special syntax, no workaround reports, and no digging through admin settings.

Traditional PLM Search Slows Teams Down

Accessing product data should be fast and intuitive, but traditional PLM systems bury this data behind rigid filters, inconsistent field naming, and limited query capabilities. Without knowing exactly how the data is structured, engineers are left guessing.

As a result, tracing how a subassembly rolls into a top-level product requires exporting data and reconstructing the hierarchy offline. Searching for all assemblies that include a specific component involves a series of manual clicks, cross-referencing part numbers across views, or submitting a request for a custom report from a system admin.

These slowdowns compound quickly. They interrupt workflows, obscure critical information, and create unnecessary friction between teams. When engineering, sourcing, and operations can’t access the same product structure in real time, decisions get delayed, and mistakes get made.

What Is Natural Language Search?

Natural language search replaces rigid query inputs with a flexible, intent-driven interface. Instead of requiring users to know specific field names or database structures, it allows them to describe what they need in plain language and get back precise, relevant results.

This is more than a UI update; it fundamentally changes how users interact with PLM data. A sourcing manager might ask, “Show me all components added in the last 30 days over $500,” while an engineer looks for “assemblies waiting for release approval.” The system parses the request, maps it to the appropriate fields and logic, and returns structured data, so no manual filtering or post-processing is required.

By translating everyday language into structured queries, natural language search removes one of the most significant barriers to data access. It allows engineers, program managers, and supply chain teams to get the information they need, when they need it, without interrupting workflows or escalating to PLM admins.

Natural Language Search Examples

The contrast between traditional search methods and natural language search is most obvious in practice. Here’s how common tasks change when intent drives the query:

Use Case

Traditional PLM Search

Natural Language Search

Find recent high-cost parts

Set multiple filters across cost and date fields, sort manually

“Show parts over $1000 added this quarter”

Track release status of assemblies

Manually check approval fields across records

“List assemblies still waiting for release”

Identify where a component is used

Run multi-level BOM reports, cross-reference part numbers

“Where is part 100-00004 used?”

Flag sourcing risk by supplier type

Export BOM, merge with supplier data, filter manually

“List components with overseas suppliers and long lead times”

Powered by LLMs, Tuned for Engineering Context

Natural language search in Duro Design PLM is built on large language models (LLMs) trained to interpret plain language in the context of structured engineering data.

These models recognize intent even when phrasing is imprecise, and they understand common synonyms across roles – “release” vs. “approve,” “component” vs. “part,” “supplier” vs. “vendor.” They also infer relationships between parts, assemblies, revisions, sourcing metadata, and change orders, allowing users to ask complex, multi-dimensional questions without needing to break them into filterable fields.

Unlike bolt-on AI tools that operate outside the PLM, Duro Design’s natural language engine is built into the system itself. Queries run directly against your live product data, so the results reflect real-time status, not an approximate snapshot.

To prevent miscommunication between what a user asks and what the system returns, Duro Design also shows its interpreted query logic alongside the results. For example, if a user types “Show me assemblies pending release,” they will see Duro Design’s interpretation below the search as Status = ‘Pending’ AND Type = ‘Assembly’.

This feedback loop improves clarity, builds trust, and makes it easier to refine queries on the fly, without needing to escalate to a PLM admin or second-guess the data.

As usage grows, the system learns. It adapts to the terminology, phrasing, and patterns unique to your team, continuously improving precision across roles and workflows.

Business Impact: Real-World Use Cases

Natural language search reduces friction across the product lifecycle, and that friction matters. When engineers can’t access the data they need, decisions stall. When sourcing managers have to chase down part details or lead time information, supplier negotiations lose momentum. When teams operate on outdated or inconsistent information, production slows, and errors multiply.

Natural language search avoids bottlenecks at the source by making product data accessible through plain language. The result is faster decisions, better alignment across functions, and fewer preventable delays.

The business outcomes are tangible:

Shorter development cycles: Less time spent hunting for the right information means more time acting on it.
Cross-functional clarity: Engineering, operations, and supply chain teams operate from the same accurate, up-to-date information.
Onboarding without bottlenecks: New team members can ask real questions and get real answers without learning internal systems or asking for help.
Reduced risk: Fewer manual workarounds mean fewer mistakes in critical handoffs or release gates.

PLM Built for the Way Modern Teams Work

In agile engineering environments, clarity and speed drive outcomes. The ability to ask a question and immediately access the correct data can be the difference between momentum and delay. That means natural language search is no longer a nice-to-have; it’s a necessary competitive edge.

Duro Design is the first fully configurable, AI-native PLM built for modern hardware teams. Natural language search is embedded directly into the platform—no setup, no training, and no plug-ins are required. It’s designed to be used by anyone, not just system experts, and it returns consistent, trustworthy results that teams can confidently act on.

This is just one aspect of Duro Design’s focus on empowering hardware engineers. With automated validation, sourcing insights, and predictive change analysis built into the platform, Duro Design helps teams reduce friction, make better decisions, and move faster across the entire product lifecycle.

The Industrial Automation Companies to Follow in 2025

dani@durolabs.co — Tue, 17 Jun 2025 19:52:06 +0000

Labor shortages and rising operational costs are increasingly pushing manufacturers to automate faster than ever. As a result, the industrial automation market is projected to climb from $238.13 billion in 2025 to $449.77 billion by 2032.

Industrial automation redefines what’s possible on factory floors, warehouses, farms, and at sea. The companies leading the charge in industrial automation are building smarter systems that cut downtime, improve precision, and help workers focus on higher-value tasks.

Keep reading to learn about the innovators shaping the future of how things get made, moved, and maintained.

Mirka

Mirka is best known for sanding and surface finishing tools, but they’ve now taken that expertise into automation. Through the acquisition of Flexmill, they’re building robotic polishing systems that improve consistency and reduce manual labor in manufacturing and aerospace settings. Mirka recently announced its first production site outside of Europe, in Indianapolis, to strengthen its presence in the U.S. market and better support its growing customer base.

Dodge Industrial

Dodge Industrial makes the hardware that keeps production lines moving, such as bearings, gearboxes, motors, and mechanical drives. Their products are everywhere behind the scenes, helping factories and facilities run smoothly and safely. Dodge Industrial also offers an intelligent condition monitoring platform called OPTIFY that combines its Industrial Internet of Things (IIoT) and data analytics to reduce machine downtime.

Augury

Augury combines sensors and machine learning to help companies predict when machines are about to fail before they do. The Augury platform listens to equipment, spots issues early, and cuts down on surprise downtime, helping manufacturers avoid costly delays. Augury has seen its revenue increase fivefold in the past three years, and recently raised $75 million in a Series F round.

Blue River Technology

Blue River Technology, by John Deere, builds smart agricultural equipment that uses cameras and machine-learning systems to spot and target only the weeds. This saves chemicals, reduces waste, and gives farmers better control in the field. It’s automation with a real-world sustainability impact. Blue River Technology allows farmers to reduce the amount of herbicides sprayed by 90%.

inVia Robotics

inVia Robotics builds autonomous robots that help warehouses pick and move products. The inVia robots pair with AI-powered software to optimize inventory flow and boost efficiency in e-commerce and logistics—no infrastructure overhaul needed. In 2024, inVia Robotics was named the “Top Supply Chain Project” for its role in transforming Scholastic Canada’s fulfillment operations through intelligent automation.

Rugged Robotics

Rugged Robotics is automating one of construction’s most repetitive tasks: layout marking. Rugged Robotics makes robots that draw precise layout lines directly onto job site floors, so what used to take a team hours with tape measures and chalk now takes minutes, with pinpoint accuracy. This saves time, reduces human error, and addresses a growing workforce shortage in construction.

Durin

Durin, a startup based in El Segundo, is developing autonomous drill rigs for mineral exploration. In early 2025, Durin raised $3.4 million in pre-seed funding, led by 8090 Industries, and they are now planning field testing in Nevada. Mining may be ancient, but Durin’s approach is anything but.

Saronic Technologies

Saronic Technologies is reimagining maritime operations with autonomous surface vessels. Saronic Technologies has two flagship vehicles—Mirage and Cipher—which are built for long-range missions, carrying significant payloads and operating with minimal human oversight. The goal: safer, smarter ocean operations. Last year, Saronic raised $175 million in Series B funding, which placed its valuation at $1 billion.

What’s Next for Industrial Automation Companies?

Automation isn’t just about replacing tasks; it’s about building systems that can adapt, improve, and unlock new possibilities. Innovative industrial automation companies are tackling everything from agriculture to aerospace, construction to biotech, with a mix of robotics, AI, and good engineering.

To keep up with this pace of change, many of these teams are investing in tools like AI-and cloud native PLM software. PLM plays a quiet but critical role behind the scenes, helping companies organize data, reduce errors, and accelerate product development. In an industry where timing and precision matter, the right systems make all the difference.

The Energy Management Companies Driving Innovation in 2025

dani@durolabs.co — Thu, 08 May 2025 18:49:40 +0000

Energy costs are climbing, carbon regulations are tightening, and electrification is accelerating across nearly every industry. In response, the global energy management systems market is on track to nearly double—rising from $60.61 billion in 2025 to $111.86 billion by 2030.

Companies building the next generation of energy infrastructure are at the center of this shift. From rethinking how the grid operates to developing carbon-negative technologies, these energy management innovators are setting the pace for a cleaner, more efficient energy future.

Form Energy

Form Energy is tackling one of renewable energy’s biggest challenges: how to store power once it’s been harnessed. Form Energy is on a mission to create low-cost, multi-day energy storage systems. Their iron-air battery can store electricity for up to 100 hours—far longer than conventional solutions. In 2024, Form Energy announced a $405 million Series F financing round, bringing its total funding to $1.2 billion.

Powell Industries

With over seven decades of experience, Powell Industries designs and manufactures equipment for the distribution and control of electrical energy. The Houston-based company serves various sectors, including oil and gas, petrochemicals, and electric utilities. Powell has recently seen significant growth, driven by increased demand in the electric utility sector and a notable presence in data centers. Powell Industries had a strong start to 2025, beating estimates for second-quarter earnings.

Fluke Corporation

Fluke Corporation has been making dependable test tools and software since 1948—gear that electricians, engineers, and technicians rely on daily. Fluke Corporation has a reputation for accuracy and durability, making it a go-to across industries. In the energy management space, their analyzers and loggers help companies identify where power is being wasted and find smarter ways to use it.

Tantalus Systems

Tantalus Systems helps utilities modernize their power grids by harnessing the power of smart devices, systems, and software to provide better visibility into how energy moves through systems—making grids more efficient, responsive, and ready for the future. Tantalus Systems’ smart grid offerings include intelligent connected devices, communications networks, data management, enterprise applications and analytics.

Emporia Energy

Emporia Energy, based in Littleton, Colorado, develops smart home energy management devices aimed at making homes more energy-efficient and affordable. Their products, including EV chargers and energy monitors, empower homeowners to monitor and reduce their energy consumption, contributing to a more sustainable future. Emporia Energy just released Emporia Pro, a level 2 EV high-speed charger that allows homeowners to charge their vehicles efficiently without overload risks.

Zap Energy

Zap Energy is working on a new approach to fusion power that doesn’t require massive magnets or billion-dollar facilities. Their compact, Z-pinch fusion tech aims to unlock clean, zero-carbon energy at a fraction of today’s cost. A recent Zap Energy study showed their method produces stable thermal fusion.

Lunar Energy

Lunar Energy aims to make solar power more practical at home. The Lunar Energy system combines rooftop solar and battery storage in one package, so homeowners can use their own energy when it’s needed most, like during outages or peak-rate hours. This innovative approach could transform residential energy consumption.

Arbor Energy

Founded by a former SpaceX engineer, Arbor Energy developed a modular system that converts organic waste into carbon-negative electricity and clean water. Arbor Energy’s compact power stations—each one capable of powering thousands of homes—offer a promising step toward carbon-negative energy production. Arbor reports it’s saved customers $7.5 million since launching in 2022.

Enertiv

Enertiv brings real-time data insights to commercial buildings. Enertiv’s software continuously monitors equipment and environmental conditions to track how equipment is performing and where energy is being wasted. Enertiv helps building operators reduce costs, cut emissions, and keep systems running smoothly.

WattTime

WattTime is an environmental tech nonprofit that provides Automated Emissions Reduction (AER) software. WattTime technology allows IoT devices and buildings to automatically prioritize energy from cleaner sources, enabling users to reduce emissions and support cleaner energy grids. WattTime recently partnered with REsurety to launch a free platform offering access to marginal emissions data, aimed at helping organizations take more impactful climate action.

GridPoint

GridPoint gives commercial buildings the tools to use energy smarter. GridPoint’s platform collects and analyzes data to fine-tune energy use—cutting emissions and saving customers money. With over 7.5 billion kWh of energy saved, GridPoint is leading the way in building efficiency. GridPoint recently raised $45 million in strategic funding to support international expansion and accelerate its product roadmap.

What’s next for energy management companies?

These companies are pushing the energy sector forward with fresh ideas and practical solutions, making energy cleaner, more reliable, and easier to manage.

As pressure builds to cut emissions and overhaul aging infrastructure, tools like electrification, real-time data, and decentralized energy systems are becoming essential. But solving these complex challenges takes more than good ideas—it takes systems that can scale. That’s why many energy management companies are turning to PLM software. It’s not just about staying organized; it’s about bringing new products to market faster, reducing costly missteps, and keeping teams aligned as things grow more complex.

By building smarter operations from the inside out, energy management companies are better equipped to turn bold innovation into lasting impact—for the grid, for consumers, and for the planet.

What is a Digital Thread? Why it Matters for Modern Manufacturing

dani@durolabs.co — Wed, 02 Apr 2025 13:37:54 +0000

Manufacturers are moving toward smarter, more connected ways of working. As the industry undergoes a digital transformation, the term “digital thread” is often heard in conversations about product data management. But what is a digital thread?

A digital thread provides real-time data so teams can catch issues earlier, make faster decisions, and keep development on track. Digital threads also help teams adapt quickly to change—whether it’s a design revision, a disruption in the supply chain, or a new regulatory requirement.

As hardware cycles accelerate and external conditions shift rapidly, responsiveness has become essential to staying competitive. This article explains what a digital thread is and its benefits, how it differs from the concept of a digital twin, and why it matters for hardware companies looking to stay ahead in Industry 4.0.

What is Digital Transformation?

Digital threads are part of a shift toward digital transformation in manufacturing. Manual processes are being traded in for fully connected systems, replacing paper-based tracking, siloed software, or handoffs over email with integrated tools that share data across teams.

Cloud platforms, automation, and real-time analytics are quickly improving how products are designed, built, and supported. The result is a more efficient and accurate flow of information between hardware design teams, suppliers, and customers.

For hardware companies, the payoff for digital transformation is big: faster development cycles, fewer handoff errors, better visibility into every stage of the process, and the ability to adapt when things change.

What is a Digital Thread?

A digital thread is the full timeline of a product’s data, connecting everything that happens to a product from the moment it’s designed to the day it’s retired. It keeps everything connected: CAD models, revisions, supplier data, part history, and engineering change orders (ECOs). Instead of that information living in separate systems or teams, the digital thread brings it all together so it’s traceable and usable throughout the product’s life.

A digital thread allows engineers to track decisions back to their source. This means they can spot issues early and ensure products meet compliance requirements without the usual back-and-forth. Manufacturers need a complete digital thread for their products, not just from an organizational perspective, but because they’re highly effective for reducing mistakes, facilitating teamwork and efficiency, and strengthening the foundation for AI-driven automation.

Digital Thread vs. Digital Twin

Digital threads and digital twins get mentioned in the same conversations, but they’re not the same thing. A digital twin is, in theory, a working model of the physical product or system that pulls in real-world data from sensors, logs, and simulations.

Engineers use digital twins to study how a product behaves under real conditions, test changes, and predict failures before they happen. It’s especially useful for systems in the field that can’t be taken offline, like factory equipment, aircraft, or power systems.

Both tools rely on the same underlying product data. But while the digital thread is about organizing and connecting that data across the lifecycle, the digital twin is about using it to analyze behavior and make better decisions during operation and support.

Digital Thread Examples

Aerospace

Jet engines have thousands of parts and extremely strict safety standards. Today’s aerospace companies use digital threads to track every component from design to production. If a turbine blade wears down faster than expected, engineers can trace it back to the exact batch of materials, the supplier, and how it was made. This makes it easier to find the root cause—whether a material defect or manufacturing issue—so problems can be addressed without grounding an entire fleet.

Automotive

Today’s cars have many software-driven systems, from electronic brakes to adaptive cruise control. If a flaw is found in a braking module, a digital thread helps track exactly which vehicles are affected by linking production records to supplier data. Leading robotics companies driving innovation in automotive manufacturing use digital threads to enhance traceability and streamline recall processes, allowing manufacturers to pinpoint and address issues efficiently.

Medical Devices

Medical devices are highly regulated products that require careful documentation of even the smallest design changes. With a digital thread, if a pacemaker’s circuit board is updated and that change affects battery life, engineers can trace when the update was made, who approved it, and which devices are affected. This helps manufacturers comply with FDA regulations and make necessary fixes without interrupting patient care.

Digital Twin Examples

Factory Operations

Digital twins of factory production lines allow manufacturers to model workflow changes before touching the physical setup. For example, an automotive manufacturer might create a digital twin of its assembly area to test how adding a new model to the line would affect turnaround time and throughput. Engineers can simulate workstation timing, equipment loads, and worker movement to identify slow points before making physical changes. This helps avoid downtime and disrupting active operations.

Energy Systems

Digital twins are used in the energy sector to maximize the value of wind farms. Wind turbine operators can use a digital twin of a specific turbine to track how blade pitch, wind speed, and temperature affect power output. If a drop in efficiency is detected, they can simulate different conditions to pinpoint the cause—whether it’s wear on a gearbox or changing wind patterns at the site. This helps schedule maintenance before a failure occurs and ensures each unit stays productive without pulling it offline unnecessarily.

Product Performance

PLM and Digital Threads

Today’s hardware development demands speed, accuracy, and alignment across the product lifecycle. To meet these demands, manufacturers need a digital thread—a continuous flow of product data that connects design, engineering, production, and supply chain teams.

Legacy PLM systems weren’t built to support that level of connectivity. They’re rigid, siloed, and often get in the way of collaboration. That’s why we’re seeing the rise of PLM 4.0 – an approach to PLM that replaces outdated tools with API-first, AI-enabled platforms built for speed, flexibility, and integration across teams and systems.

Duro is introducing an AI-powered PLM designed to provide a complete digital thread from day one. It keeps all product data in one place, so teams can easily manage part revisions, share information with suppliers, and handle change orders without chasing down files or updates.

With everything connected, engineers and operations teams have the visibility to move quickly and make informed decisions. As digital transformation continues to reshape manufacturing, companies investing in a strong digital strategy will be ahead.

PLM 4.0 – The Rise of AI and the Digital Thread

Shaun Kennedy — Thu, 13 Mar 2025 19:01:21 +0000

In 2024, the global PLM (Product Lifecycle Management) market hit $26.24 billion, driven by aerospace, robotics, and industrial automation usage. As more industries move toward PLM 4.0 — AI, IoT, and big data will significantly improve how products are designed, manufactured, and maintained.

PLM 4.0 is deeply intertwined with Industry 4.0 and the digital transformation of manufacturing. AI-driven analytics and intelligent automation are improving efficiency, predictive insights, and real-time decision-making, helping manufacturers navigate supply chain instability, anticipate risks, and maintain production continuity.

This article explores how AI-enabled PLM and Industry 4.0 are evolving together, shaping the future of manufacturing and driving smarter, more connected product lifecycles.

What is Industry 4.0?

Industry 4.0, also known as the Fourth Industrial Revolution (4IR), is driving digital transformation in manufacturing through AI, IoT, and real-time data analytics. By integrating these technologies, companies can optimize production, reduce inefficiencies, and enhance adaptability to supply chain demands and market fluctuations.

With billions of IoT devices connected across industries like robotics and industrial automation, manufacturers gain real-time visibility into operations. This enables predictive maintenance, improved uptime, and smarter decision-making. AI accelerates these advancements, making product development and production faster and more efficient.

“What’s exciting about 4IR is the sheer magnitude and pace of new technologies empowering hardware design and manufacturing companies. It allows them to move faster, taking products from a paper-napkin sketch to mass production more efficiently than ever before.”

Michael Corr, Duro CEO

What is PLM & PLM 4.0?

PLM (Product Lifecycle Management) systems manage a product from conception through design, manufacturing, and retirement. Manufacturers rely on PLM software to streamline collaboration, maintain a single source of truth, and integrate supply chain data—improving efficiency, reducing costs, ensuring compliance, and accelerating innovation cycles.

PLM 4.0 represents the next evolution, incorporating AI, IoT, and big data to create a more connected, intelligent, and automated product development process. AI-driven automation reduces manual tasks, while digital twins and digital threads enable real-time simulation, predictive maintenance, and improved traceability. Enhanced data integration supports smarter decision-making at every stage of the product lifecycle.

AI-Powered PLM

Integrating AI into PLM software will redefine how products are designed, tested, and brought to market. AI-driven analytics can now detect risks in product development, helping manufacturers reduce failures and streamline compliance monitoring. Automating these processes ensures regulatory standards are met and minimizes costly production errors.

AI-powered PLM changes how today’s engineers interact with vast data repositories by enabling natural language search capabilities and eliminating the need for complex key-value pair queries. Generative AI tools enhance BOM analysis, optimizing material selection for cost, lead time, or weight, ensuring more efficient product development.

AI significantly advances overall product lifecycle management by generating new product iterations based on existing design data, supply chain information, and customer feedback. Advanced demand forecasting allows manufacturers to align development processes with market needs. AI-driven supply chain monitoring can identify potential disruptions, allowing companies to make proactive decisions about component sourcing and production adjustments.

“As companies embrace Industry 4.0, they need solutions beyond data storage to enhance decision-making, streamline supply chain management, and accelerate innovation. PLM 4.0, powered by AI-driven automation, predictive analytics, and digital threads, enables engineering teams to move faster while reducing complexity. Duro is delivering an AI-enabled PLM that is accessible and helps manufacturers optimize every stage of the product lifecycle—from concept to production.”

Jon Stevenson, Former GrabCAD VP

IoT and PLM

The Internet of Things (IoT) is a network of connected devices that collect and exchange real-time data, driving automation and smarter decision-making across industries. IoT improves efficiency, reduces downtime, and optimizes workflows by providing continuous performance insights from connected machinery.

When integrated with PLM, IoT data enhances reliability, productivity, and operational efficiency. Companies like Rapid Robotics use AI-driven automation with PLM to connect IoT-enabled robotics, eliminate repetitive tasks, and improve efficiency. As modern machinery incorporates IoT capabilities, manufacturers can analyze performance data within PLM, applying insights to streamline operations and enhance product reliability.

One of the biggest shifts in manufacturing is the growing connectivity of physical systems through IoT. While modern machines generate valuable operational data, much of it remains siloed in dashboards or disconnected from critical decision-making, limiting its full impact.

PLM goes beyond data collection—it actively integrates information into the product lifecycle. If a testing plan requires a container’s pressure to stay below a set threshold or an engine’s temperature within safe limits, IoT and PLM can automatically validate, record, and apply this data, improving quality control, predictive maintenance, and overall performance.

Digital Twins

Digital twins are virtual models of physical products that provide real-time insights for performance optimization and predictive maintenance. These models allow manufacturers to simulate conditions, test improvements, and identify potential issues before they impact production. This level of insight reduces downtime and improves overall operational efficiency.

By integrating digital twins into PLM, manufacturers can proactively address failures before they occur. Virtual testing environments allow continuous innovation while cutting costs and accelerating development cycles. This digital-first approach significantly enhances how companies approach product testing and refinement.

Digital Threads

Digital threads ensure data flows across the entire product lifecycle, providing end-to-end traceability for product changes. They give every department access to up-to-date product information, reducing errors and miscommunication.

Digital threads improve decision-making by structuring unstructured data into actionable insights. By integrating these capabilities into PLM software, manufacturers create a more transparent and efficient development process, enabling greater agility in responding to market changes.

Digital threads reduce reliance on third-party suppliers and streamline version control. Engineers often spend too much time rechecking values, an issue digital threads help resolve by centralizing knowledge and improving data accessibility. By ensuring critical insights aren’t lost in scattered spreadsheets or undocumented workflows, digital threads free engineers to focus on innovation and efficiency.

AI-enhanced Data Management

A Hexagon survey found that 98% of manufacturers face data challenges hindering innovation and time to market. Poor data quality and availability slow the adoption of AI, automation, and digital twins, impacting collaboration and productivity. The same study found that nearly 40% of manufacturers lag in automation, falling behind competitors that leverage data-driven efficiency.

These challenges contribute to supply chain disruptions, where global supply networks remain vulnerable to external factors like political instability, weather conditions, and fluctuating market demands. Predicting part availability, pricing, and delivery timelines accurately has long been a struggle for manufacturers. AI-driven analytics can now accurately aggregate and interpret disparate data sources—many of which may be inconsistent or unreconciled.

This is where AI significantly outperforms its predecessors, like machine learning and advanced algorithms, by processing massive datasets, identifying patterns, and generating predictive insights. With the ability to analyze historical and real-time data, AI helps manufacturers anticipate part availability, forecast delivery timelines, and optimize pricing strategies, keeping them competitive in volatile markets.

PLM 4.0 — The Future of Manufacturing

Traditional PLM tools weren’t built for fast-paced product development. PLM 4.0 introduces faster agile workflows that let engineers focus on innovation instead of outdated, manual processes. Hardware teams now demand the same speed, flexibility, and collaboration that software teams have long enjoyed—there’s a reason we hear hardware is the new software so often.

Large manufacturers still rely on spreadsheets and legacy software to track programs and manage data—systems unfit for modern hardware development. PLM 4.0 eliminates these inefficiencies with an AI-driven, digital-first approach, enabling real-time collaboration, automation, and smarter decision-making. As the manufacturing industry continues its digital transformation, hardware engineering will increasingly resemble software development.

PLM 4.0 integrates AI-powered analytics, digital threads, and IoT for secure, efficient data management. These technologies redefine how manufacturers develop, produce, and maintain products, keeping them competitive in an industry that evolves daily. The companies that embrace AI-enabled PLM will drive the next wave of industrial innovation, building faster, more resilient supply chains.

The AI in Manufacturing Trends to Watch in 2025

dani@durolabs.co — Tue, 25 Feb 2025 19:02:49 +0000

The manufacturing industry’s AI market is expected to grow from $3.2 billion in 2023 to $20.8 billion by 2028, or 45.6% annually.

This rapid expansion is driven by the shift toward Industry 4.0, where AI-driven automation and data connectivity play increasing roles in product development. By some estimates, AI can deliver 30% cost savings and increase production output by 15%, making it a critical driver of efficiency in this new industrial era.

In this article, we’ll look at the most important AI trends in manufacturing for 2025, from the growing role of generative AI to smarter supply chain management and PLM advancements. We’ll also highlight real-world applications and explore how tools like AI-enabled PLM can help hardware companies adapt and succeed in this quickly evolving landscape.

Generative AI for Usability & Sustainability

Generative AI (GenAI) is making industrial technology easier to use and more accessible. Instead of requiring engineers to write complex search queries or manually sift through data, AI-powered tools can now intuitively provide clear insights, design recommendations, and process optimizations.

GenAI is helping manufacturers reduce waste and make more sustainable choices. By simulating and testing product variations before they are physically produced, AI design tools help companies minimize material waste and improve energy efficiency. These systems can also factor in key sustainability considerations—such as carbon footprint, recyclability, and material usage—to guide smarter decision-making in product development.

Looking ahead, we can expect more intuitive AI interfaces, allowing engineers to interact with design software using natural language and visual prompts. Sustainability will remain a driving force, with AI helping to optimize material selection and reduce emissions.

Supply Chain Management

Supply chains involve mountains of complex, disparate datasets, which AI is excellent at processing. AI can quickly analyze data and look for warning signs so that instead of reacting to problems, businesses see them coming. That means fewer bottlenecks and unforeseen disruptions that delay product launches and inventory management.

AI systems can help manufacturers adjust production based on real-time market data. They’re so effective at factory floor quality control and predictive maintenance that this year, over half of all manufacturers are projected to use AI for these processes.

We expect AI-driven supply chains to become even more autonomous soon, with self-correcting logistics systems that automatically adjust procurement, delivery routes, and inventory.

Metadata Enrichment

Manufacturing data is notoriously complex. It contains data like technical specs, production logs, drawings, and sensor readings, which are challenging to organize and cross-analyze.

Metadata is the “data about data” that provides context to this information and makes it usable. AI-powered tools can automate metadata enrichment, taking technical data and automatically pulling key details from different sources, putting them into an easy-to-read format, and suggesting helpful tags and categories.

With better metadata, manufacturers can unlock the potential of their data. Finding what they need is easier, collaboration gets smoother, and everyone can make better decisions. Soon enough, we expect to see proactive data management systems that anticipate user needs and surface relevant data before it’s even searched for.

AI and Digital Twins

Digital twins are digital models of real-world systems. They allow manufacturers to digitally simulate scenarios to make better, more efficient product decisions. With the advancement of GenAI, digital twins are evolving into more dynamic, self-improving systems that are even more effective for optimizing production.

Creating and maintaining digital twins used to require significant time and expertise. AI makes this much more manageable, generating detailed models faster and updating them with real-time production environment data. This allows manufacturers to experiment with different designs, workflows, and supply chain scenarios without interrupting operations.

We expect AI-enhanced digital twins will play a growing role in smart factories, with real-time sensor data continuously feeding into AI models. This will help manufacturers fine-tune production for better quality and efficiency. Industries like automotive, aerospace, and industrial manufacturing are already seeing the benefits, and as AI improves, digital twins will become an even more valuable tool for optimizing production.

Component Procurement

While it may not be as flashy as assembly line robotics or digital twins, component procurement is a crucial part of manufacturing success. Disruptions in the supply chain, shifting prices, and unexpected delays can cause issues that slow production.

AI-powered procurement tools can analyze supplier data to help manufacturers identify the best sources for parts based on availability, lead times, and cost. Instead of manually searching supplier catalogs, AI can automate vendor selection, compare pricing trends, and even predict shortages. This means manufacturers can source components faster and more efficiently.

AI will continue improving procurement by integrating directly with PLM (Product Lifecycle Management) and ERP (Enterprise Resource Planning) systems. This will allow manufacturers to automate decisions based on live production data, ensuring that the right parts are ordered at the right time—without human intervention.

AI-Enabled PLM

AI has been making waves in manufacturing, yet PLM platforms have been slow to evolve. A lot of legacy systems like Oracle Agile PLM still rely on rigid processes that make it difficult to find information, optimize designs, and adapt to supply chain shifts.

That’s where Duro sets itself apart–as the only PLM platform built with AI, Duro is at the forefront of optimizing hardware companies’ operations. Duro’s PLM helps manufacturers establish a digital thread, which ensures design data, supply chain information, and production records are always accessible and aligned.

Duro’s AI-enabled PLM platform is designed to give hardware teams unprecedented control over product development, supply chain data, and design optimization—without the complexity of legacy systems.

Why Embrace AI in Manufacturing?

The industry is rapidly evolving, from GenAI enhancing data usability to AI-powered supply chains, digital twins, and automated procurement. These technologies are making manufacturing more agile, ensuring companies can adapt to disruptions before they happen.

As technology evolves, so do demands on manufacturers–and legacy systems, with their rigid structures, can’t keep up. Companies need modern, AI-driven tools that adapt to real-time changes and optimize decision-making. That’s where Duro comes in. Built to enhance product development and supply chain management, Duro is an AI-enabled PLM that gives hardware teams fast, reliable access to data while maintaining a digital thread across every production stage.

Investing in AI today isn’t just about keeping up—it’s about staying ahead. Manufacturers that integrate AI into their workflows will work faster, make better decisions, and build more resilient operations. With tools like Duro PLM, companies can turn AI’s potential into a real competitive advantage, ensuring they’re ready for whatever comes next.

The Aerospace Companies Driving Innovation in 2025

Shaun Kennedy — Thu, 13 Feb 2025 14:42:24 +0000

The global aerospace industry is tipped to surpass $1 trillion by 2030, driven by innovative aerospace companies advancing next-generation spacecraft and propulsion systems.

Aerospace industry trends indicate a shift toward private investment, miniaturized satellites, and new launch technologies, shaping the future of space exploration and commercial flight.

This article highlights some of the top aerospace companies leading this transformation and how their cutting-edge solutions expand access to space, optimize defense systems, and advance commercial aerospace.

ispace

ispace is redefining lunar exploration with its “Hakuto-R” lander program, aiming to establish sustainable commercial activity on the Moon. ispace successfully launched its first lunar mission aboard a SpaceX Falcon 9, laying the groundwork for future resource utilization and long-term habitation. With plans for a second mission and NASA partnerships, ispace is a key player to keep an eye on in the lunar economy.

ABL Space

After initially developing the “RS1″ rocket and a mobile launch system, ABL Space Systems is now pivoting from commercial launch to missile defense, citing a shifting market and national security needs. Despite setbacks in RS1 test flights, ABL Space has raised nearly $500 million, including a $372 million Series B round, to develop its technology. With the DoD allocating $13.5 billion to missile defense in FY 2025, ABL Space aims to apply its expertise to responsive launch and advanced defense solutions.

Turion Space

Turion Space is advancing orbital debris removal, satellite servicing, and space sustainability, developing solutions to track space objects, remove defunct satellites, and extend the life of active assets. Turion Space is now expanding its mission to support national security and space domain awareness. Turion Space recently secured a contract to build multi-payload satellites for the U.S. Space Force, enhancing orbital monitoring and defense capabilities.

EOI Space

EOI Space is advancing Very Low Earth Orbit (VLEO) imaging with its “Stingray Constellation”, a network of high-resolution satellites providing real-time geospatial intelligence for the defense, insurance, and emergency response sectors. EOI Space’s technology will enhance natural disaster response by delivering instant, high-resolution imagery to assess damage, coordinate relief operations, and improve situational awareness. EOI Space has successfully tested its flight propulsion system, bringing it closer to deploying its advanced Earth observation capabilities.

Loft Orbital

Loft Orbital is re-thinking satellite deployment with its satellite-as-a-service model, enabling companies to launch payloads without building custom satellites. Its “YAM” series satellites support defense, climate monitoring, and Earth observation missions. Loft Orbital recently raised $170 million in Series C, bringing its total funding to $325 million to expand manufacturing, integrate AI into satellite operations, and scale its space infrastructure services.

AstroForge

AstroForge is advancing space mining by extracting critical metals from asteroids, making off-world resources more accessible and cost-effective than traditional mining. AstroForge’s early 2025 “Odin” mission will test deep-space refining technologies and gather crucial data for future asteroid mining operations. Backed by $55 million in funding, AstroForge is scaling its capabilities, refining its extraction processes, and moving closer to making asteroid mining a commercially viable industry. AstroForge named its target asteroid “2022 OB5” and secured a multi-launch deal with Stoke Space to support future deep-space mining missions.

True Anomaly

True Anomaly enhances space security and sustainability by developing advanced hardware and software solutions at the intersection of spacecraft, software, and artificial intelligence. Its Jackal Autonomous Orbital Vehicle (AOV) is designed for versatile on-orbit missions, including space domain awareness and satellite servicing. In December 2024, True Anomaly successfully launched and controlled its Jackal AOV during Mission X-2, showing how it can support real-time threat monitoring, maneuverability testing, and autonomous space operations.

Varda Space

Varda Space Industries is advancing in space manufacturing, leveraging microgravity to produce high-value pharmaceuticals and advanced materials. Varda Space completed its first re-entry mission, proving the feasibility of space-based production and Earth return logistics. With growing partnerships in the biotech and semiconductor industries, Varda Space is set to commercialize space manufacturing. Rocket Lab recently delivered its third in-orbit manufacturing spacecraft for Varda, supporting its efforts to scale in-space production.

Inversion Space

Inversion Space develops orbital cargo delivery solutions with a capsule-based reentry system to quickly return materials from space. Its technology has applications in biomedical research, national security, and supply chain resilience. With $71 million in funding from Space Force, Inversion Space is advancing its “Arc” autonomous reentry capsule to deliver high-precision cargo to remote and strategic locations. Inversion Space launched its subscale pathfinder capsule, “Ray,” on SpaceX’s Transporter-12 rideshare mission, demonstrating its precision landing capabilities for future defense and commercial applications.

Vaya Space

Vaya Space develops orbital launch vehicles, in-space propulsion systems, and advanced missile engines, combining liquid bipropellant engines’ performance with solid rocket motors’ simplicity and reliability. Vaya’s propulsion system uses liquid oxidizer, solid fuel, and non-explosive, non-toxic propellants. Vaya Space uses 7.8 metric tons of recycled post-industrial plastic per launch to reduce waste and lower environmental impact. Vaya Space recently announced a multi-launch contract with Space Telecommunications to deploy up to 250 satellites using its “Dauntless” rocket, with launches set to begin in 2027.

Firefly Aerospace

Firefly Aerospace is expanding access to low-cost, high-performance space launches with its “Alpha” and “Beta” rockets. It has also developed “Blue Ghost,” a lunar lander for NASA missions. Blue Ghost recently completed a huge milestone — firing up its engines to leave Earth orbit and begin its journey to the moon. This marked a significant step in Firefly’s role in lunar exploration and is definitely one to keep an eye on.

Howmet Aerospace

Howmet Aerospace is a global provider of advanced aerospace materials, supplying engine components, structural parts, and defense solutions. Howmet Aerospace specializes in high-performance alloys, coatings, and 3D-printed aerospace components. With growing demand for next-gen aircraft and hypersonic systems, Howmet Aerospace is positioned to continue advancing aerospace engineering and recently reported strong financial performance, with Q4 profits rising to $314 million from $236 million last year, surpassing market expectations.

What’s next for aerospace companies?

The aerospace industry is advancing quickly, with aerospace companies pushing forward in lunar exploration, asteroid mining, in-space manufacturing, and satellite deployment. Innovations in next-generation propulsion, reusable launch systems, and deep-space resource extraction are expanding access to space and redefining how materials and technology are developed beyond Earth.

Satellite and cargo deployment are becoming more flexible and efficient, improving space sustainability, national security, and responsive delivery systems. Missile defense is also emerging as a key priority, with companies shifting their focus to meet growing defense and security demands. With strong government contracts and commercial partnerships, aerospace companies are shaping the future of space and defense.

As these companies scale operations and tackle complex engineering challenges, many are adopting PLM software to streamline product development, improve collaboration, and enhance data management. By integrating PLM software into their workflows, aerospace companies can accelerate innovation to keep a competitive edge in an industry that demands both precision and speed every minute of the day.

5 Ways AI Enhances Metadata in Manufacturing

Shaun Kennedy — Wed, 05 Feb 2025 16:42:23 +0000

By 2030, the global manufacturing industry is projected to generate 4.4 zettabytes of data—a massive volume that requires advanced metadata strategies to remain manageable and useful. Properly leveraging this data will determine manufacturing companies’ ability to stay competitive in the new era of massive data streams.

Metadata, often described as ‘data about data,‘ gives raw information meaning—making it easier to find, organize, and use. Yet, in manufacturing, where complex supply chains and outdated systems are the norm, metadata is often overlooked or underutilized.

This article will explain five key ways AI can enrich metadata, help manufacturers drive innovation, and accelerate time to market. We will also look at the benefits of pairing AI-enriched metadata with agile PLM (product lifecycle management) software.

What is Metadata & Metadata Enrichment?”

Metadata provides the “who,” “what,” “where,” and “why” of data, giving structure and meaning to raw information. Using this information, metadata enrichment is a process that refines technical data into contextually meaningful assets, describing roles, formats, and relationships to improve usability and accessibility.

In the manufacturing industry, metadata enrichment ensures that everything from CAD files to scanned documents and assembly line logs can be efficiently located, interpreted, and used. It refines data by adding details like tags and keywords that make it easier to find, analyze, and use effectively.

AI is crucial in automating this process, reducing manual effort while ensuring consistency, accuracy, and depth. AI can analyze a CAD file and automatically extract part dimensions, materials, and compliance standards.

AI makes it easier for engineering and procurement teams to quickly find and apply relevant data. AI-powered metadata enrichment allows organizations to unify large amounts of data, bridge engineering, and business silos, and make data-driven decisions at every stage of design, production, and operations.

Manufacturing Data Challenges

Manufacturing data is uniquely difficult to manage due to its complexity and deep integration with technical processes. Unlike standard business data, it often includes intricate engineering drawings, CAD models, scanned legacy documents with handwritten annotations, and unstructured data from IoT sensors and supply chains.

AI-powered solutions help address these challenges by automating metadata enrichment at scale, ensuring consistency across disparate sources, and uncovering insights from previously buried, unstructured formats.

Ongoing supply chain disruptions don’t make data management any easier. Nine in ten manufacturers reported disruptions in 2024, reinforcing the need for more adaptive, AI-driven data solutions. As global supply chains grow more complex, manufacturers are turning to AI to manage and utilize their data, ensuring greater efficiency, accuracy, and resilience.

How AI Enriches Metadata in Manufacturing

High-quality metadata is crucial for searching, retrieving, and analyzing data, but manual curation is inefficient, given the volume of data generated by manufacturing processes. AI-powered tools can help companies overcome these limitations and efficiently enrich their metadata. Here’s five ways it achieves this:

1 - Improved Context and Discoverability

AI can convert complex technical information into formats that are easier to interpret. By scanning a bill of materials (BOM), AI can rename fields labeled “PLN_END_DT” to “Planned End Date,” allowing team members to track and manage key deadlines. AI links these standardized data points to related information such as project milestones, company timelines, and supply chain details.

AI-enhanced metadata makes data more searchable by extracting keywords and generating related terms. AI can process a CAD file and produce associated terms like “assembly design” or “load capacity,” making the file easier to find. AI can proactively suggest required components, maintenance schedules, or deadlines related to the file being searched for.

2 - Legacy System Integration

AI bridges the gap between legacy systems and modern data strategies, turning outdated paperwork, drawings, and production logs into digital, searchable assets.

Advanced Optical Character Recognition (OCR) trained on engineering symbols (e.g., GD&T symbols) can extract actionable data from technical documentation, blueprints, and scanned records. AI can quickly analyze engineering diagrams, extracting geometric tolerances and part specifications for quick and easy integration into modern workflows.

With AI enhancing systems like agile PLM (product lifecycle management) software and making decades of physical records digitally accessible, manufacturers can ensure that no critical data is lost in translation.

3 - Automated Metadata Validation

Bad metadata slows everything down—files get lost, parts are mislabeled, and mistakes slip through the cracks. AI can fix this by automatically checking metadata for missing details, outdated information, and errors before they cause real problems.

If an engineer forgets to add a material spec to a CAD file, AI immediately flags the issue, preventing costly delays or errors during production. AI can also cross-check part numbers, compliance details, and supplier data to ensure accuracy before they move to procurement or manufacturing.

4 - Predictive Maintenance and Operational Efficiency

Metadata is crucial for predictive maintenance and factory operations. By analyzing data from IoT sensors and machine logs, AI can catch small performance issues before they turn into bigger problems, allowing teams to fix equipment before it fails. That means fewer breakdowns and longer lifespans for machinery.

But maintenance is just one part of the process. Metadata also helps manufacturers track energy consumption, which can reveal inefficiencies and underused equipment. Manufacturing companies can use these insights to fine-tune production and reduce waste.

5 - Assembly Line Insights

AI-driven metadata tracking helps manufacturers pinpoint bottlenecks, fine-tune production flow, and get real-time alerts when performance deviates from the norm. If setup delays or material shortages are slowing things down, AI can flag these issues before they escalate, allowing managers to take quick action.

Metadata makes it easier to track key production metrics—like cycle times and throughput—helping teams spot inefficiencies and optimize workflows. With real-time data analysis, manufacturers can make on-the-fly adjustments to keep operations running at peak efficiency.

Why AI is key for Manufacturing Data Management

Managing vast amounts of manufacturing data is a growing challenge, and outdated systems make it even harder for companies to stay efficient. AI-powered metadata tools help organize and enrich data, allowing teams to work smarter, reduce costs, and make faster, more informed decisions.

The impact is even greater when AI-enriched metadata is combined with an AI-enabled PLM system. These advancements create a centralized hub for product and supply chain data, giving teams real-time insights to drive efficiency, foster innovation, and accelerate time to market.

As supply chains grow more complex and production demands rise, the sheer volume of unstructured data makes AI-driven metadata essential. Companies that embrace the benefits of AI in supply chain management will gain a competitive edge, while those that hesitate risk falling behind in an industry where data-driven efficiency is no longer optional.

Duro vs Oracle Agile PLM

Shaun Kennedy — Wed, 29 Jan 2025 16:16:17 +0000

Duro and Oracle Agile PLM have each played a significant role in the PLM industry, though with different approaches. Oracle PLM is widely seen as a traditional “legacy” system, while Duro is viewed as a more modern, cloud-native alternative.

While Oracle PLM was once marketed as “the agile” solution, it has struggled to keep pace with SaaS growth and the shift toward cloud-native platforms. In contrast, Duro has been described as the PLM bringing hardware development into the agile era.

Today, these two PLM systems stand at a crossroads—one is reaching the end of its lifecycle, with support ending in 2027 and users encouraged to migrate, while the other continues to evolve with AI-driven capabilities and natural language functionality to meet the changing demands of manufacturing.

This guide breaks down the key differences between these PLM solutions to help you determine which best aligns with your organization’s needs. If you’re already considering a transition away from Oracle Agile PLM, check out our guide on how to make the switch and avoid a lengthy migration.

Duro PLM Overview

Duro is an AI and cloud-native PLM platform that accelerates time to market, helping engineering teams develop products up to three times faster. Its agile, lightweight architecture streamlines bill of materials, change orders, and sourcing management. Duro ensures smooth and instant connectivity with out-of-the-box integrations for CAD tools like SolidWorks, NX, and Altium, as well as ERP systems like NetSuite. Duro offers self-hosted PDM for tailored data management and is a trusted, ITAR-compliant solution for aerospace, robotics, industrial automation, and energy management companies.

Oracle Agile PLM Overview

Oracle Agile PLM has been a cornerstone of the PLM industry, known for its deep integration with Oracle’s ERP and SCM systems, enabling unified workflows and data management. Its highly configurable platform allows extensive customization through workflows and business rules, making it adaptable across industries. With advanced change management, document control, product costing, supplier collaboration, and quality management, Oracle PLM supports regulated industries. Its on-premises and hybrid deployment options suit organizations needing local infrastructure. Backed by a long history and a large installed base, it remains a proven choice for managing complex product lifecycles.

Duro PLM Differentiators

Instant onboarding

Duro’s streamlined implementation gets companies operational in hours. With optimized workflows and plug-and-play integrations, users can quickly adopt the PLM without disrupting projects. The intuitive UI eliminates the need for extensive training or consultants, making it ideal for agile teams.

User-friendly interface

Duro’s clean, modular design ensures easy navigation, even for non-technical users. Focusing on essential features reduces onboarding time and boosts efficiency, optimizing it for growth and performance without unnecessary complexity.

Lightweight design

Duro’s flexible, lightweight platform adapts to company needs, providing a streamlined alternative to feature-heavy solutions like Oracle PLM and Teamcenter. Its plug-and-play capabilities and native integrations eliminate the need for third-party services, simplifying implementation and improving efficiency.

Best-of-breed tech stack

Duro empowers modern engineering teams to craft their ideal hardware tech stack. With integrations for leading tools like SolidWorks, Siemens NX, and Altium 365, Duro ensures efficient data synchronization across platforms. This flexibility allows teams to use their preferred tools without being locked into a single ecosystem, driving collaboration and productivity.

Change orders approved in seconds

Duro streamlines approval processes with customizable change order workflows, approval templates, and Slack notifications for rapid feedback and expedited design approvals. Duro users can tailor forms and workflows using YAML files to meet their organization’s specific needs. The platform supports Engineering Change Orders (ECO), Manufacturing Change Orders (MCO), and Documentation Change Orders (DCO), ensuring review and approval processes align with internal standards and terminology.

Data management and automation

Duro emphasizes automation to minimize manual data entry, ensuring data accuracy and accelerating product development. Duro reduces errors by automatically retrieving datasheets, part specifications, and pricing from leading part distributors and keeps all team members aligned with the most current information. Companies like Rapid Robotics rely on Duro’s PLM to centralize product data and design revisions, enabling efficient collaboration and faster workflows.

ITAR-Compliant

Duro is fully ITAR compliant and offers a robust ITAR compliance checklist to support companies navigating strict regulatory requirements. Its cloud-native architecture safeguards sensitive data while enabling the agility needed to accelerate time to market. Today’s top aerospace companies choose Duro to build their ITAR compliant tech stacks to ensure regulatory compliance.

Product performance & upgrades

Duro’s flexible, lightweight design enables quick updates with minimal downtime, keeping teams productive and focused. Its efficient infrastructure allows instant upgrades, giving users access to the latest features without delays or disruptions. Duro’s recent NX add-in was rolled out to all users, and it is ready to install and configure within minutes.

AI Search functionality

Duro’s AI-powered search, leveraging natural language processing, differentiates it from Oracle PLM and other traditional PLMs like Teamcenter. This feature allows users to search intuitively using everyday language, such as ‘locating all 10K ohm resistors’ or identifying ‘BOMs with lead times over ten weeks.’ With quick and accurate access to data, Duro enhances productivity and simplifies the management of large datasets.

Who is Duro not suited for?

Duro may not be ideal for organizations that prefer on-premises or hybrid solutions requiring complex integrations they may not fully utilize. It is designed for teams seeking a streamlined, cloud-native approach, offering essential features without unnecessary complexity. Duro may not suit companies that are not yet ready to embrace AI-driven capabilities to meet emerging trends in manufacturing.

Oracle Agile PLM Differentiators

Oracle Agile PLM stands out for its robust enterprise integrations, reconfigurability, and comprehensive feature set. Oracle PLM has been a reliable choice for regulated industries and large-scale deployments because of its scalability and long-standing market presence. Here’s a breakdown of its key strengths:

Deep Oracle integration

Oracle PLM integrates effectively with Oracle’s suite of enterprise systems, including ERP and SCM. Its unified data model ensures consistent information across Oracle applications, enabling efficient workflows between PLM and other business processes.

Configurability and flexibility

Oracle PLM is highly configurable, offering extensive customization without the need for coding. Its adaptable workflows and business rules make it suitable for various industries and complex business needs. Duro is better suited for those seeking advanced customization options as it provides an API and a low-code interface, enabling tailored solutions to meet specific requirements.

Comprehensive feature set

With robust change management, document management, product costing, supplier collaboration, and quality management capabilities, Oracle PLM provides a broad feature set to support end-to-end product lifecycle management. However, companies transitioning from Agile PLM to Oracle Fusion may struggle with costly, custom integrations that are time-consuming to maintain.

Enterprise scale

Built for large-scale deployments, Oracle PLM is designed to handle extensive datasets with ease. Its on-premises foundation and support for hybrid workflows make it suitable for organizations requiring local infrastructure while managing multi-site, global implementations. Additionally, it maintains enterprise-grade security and performance to meet the demands of large, complex operations.

Oracle Agile PLM Limitations

User experience

Oracle PLM’s interface reflects traditional PLM solutions, with an older design that can feel outdated compared to modern PLMs. Its more complex navigation structure and steeper learning curve can pose challenges for new users, often requiring extensive training to achieve proficiency.

Cloud transition

Originally built for on-premise deployments, Oracle PLM’s transition to the cloud has lagged behind cloud-native competitors. Its cloud offerings are less mature, and hybrid deployments can be more complex to manage, limiting its appeal to teams seeking fully cloud-based solutions.

Modern integration

Oracle PLM takes a less API-first approach, which can complicate integration with modern tools. Its limited support for low-code and no-code capabilities adds to the challenges, making it harder for companies to adapt and extend the platform to meet evolving needs.

Lack of AI features

Oracle PLM’s lack of AI-driven capabilities hinders its ability to provide faster and smarter features like advanced search and reporting. Without AI enhancements, it will continue to struggle to deliver real-time insights or predictive analytics, resulting in slower workflows and limited efficiency compared to modern platforms like Duro.

The inevitable migration

With Oracle Agile PLM ending Premier Support in 2027, users will lose access to updates, security patches, and technical assistance, leaving only limited Sustaining Support. This creates significant risks, including security vulnerabilities and outdated functionality, forcing customers to migrate. We get asked daily about the scale of this migration.

Who is Oracle PLM not suited for?

Oracle Agile PLM is likely not a fit for companies seeking a more lightweight, flexible, cloud-native solution. Its on-premises roots and heavy infrastructure make it better for organizations that are comfortable with managing extra tools and vendor lock-in. Businesses prioritizing change, AI-driven efficiency, and streamlined integrations may find it limiting, especially if they prefer to avoid unnecessary complexity or reliance on contractors.

Choosing the Right Agile PLM Software

Whether selecting a traditional system like Oracle PLM or a modern platform like Duro, choosing software that aligns with your team’s specific needs and workflows is critical. A strong PLM should centralize product data, streamline management processes, and empower teams with tools that add real value. A lightweight, flexible solution that avoids unnecessary complexity allows you to focus on driving innovation and growth.

Equally important is selecting a PLM system with agile workflows that adapt to your processes—not the other way around. Rigid, outdated systems can hinder progress, stifle innovation, and increase costs. A flexible PLM lets you use only the tools you need, avoid vendor lock-in, and stay competitive across evolving industries.

The rise of AI in manufacturing and supply chain operations has positioned cloud-native PLMs like Duro at the forefront of innovation. An AI-enabled PLM drives faster decision-making, predictive analytics, and improved collaboration—key for companies aiming to stay ahead. We see this with aerospace companies leading in PLM adoption, embracing agile workflows and advanced technologies to drive innovation and meet compliance.

Not all businesses are ready to move beyond traditional on-premises or hybrid systems. However, for those prepared to adapt, Duro’s AI-powered platform provides the scalability and agility needed to thrive in the future of PLM, manufacturing, and the Fourth Industrial Revolution (4IR).

What are the benefits of AI in Supply Chain?

Shaun Kennedy — Tue, 14 Jan 2025 20:32:16 +0000

The modern supply chain often includes multiple suppliers, manufacturers, and distributors networks, where even minor disruptions can lead to significant delays or shortages. As global markets become more complex, businesses are adopting artificial intelligence (AI) to tackle issues and avoid all too common supply chain disruptions.

This change is happening fast, with 95% of data-driven decisions set to be partially automated. AI is quickly becoming a standard tool for hardware and manufacturing companies looking to predict potential disruptions, optimize logistics networks, and respond to unforeseen events with agility.

This article examines AI’s role in reshaping supply chains and how manufacturers can leverage AI-powered systems, such as generative AI and predictive analytics, to optimize production, improve logistics, and build more resilient operations. We also examine how companies use PLM software to simplify and automate supply chain workflows.

What is AI’s role in the Supply Chain?

In the global market, supply chains are no longer just operational necessities; they are pivotal to achieving broader business goals like cost efficiency, competitive differentiation, and customer satisfaction. Hardware and manufacturing companies must take a proactive and integrated approach to their supply chains to thrive.

Using AI systems to process supply chain data allows companies to optimize their supply chains. AI can process endless information and continuously improve its ability to detect patterns and predict outcomes. It’s extremely useful for identifying inefficiencies, mitigating risks, and suggesting practical solutions.

AI can analyze logistics data to anticipate delays, such as port closures or weather events, and recommend alternate shipping routes to ensure on-time deliveries. It can also help companies quickly identify alternative suppliers by analyzing supplier finances, customer ratings, sustainability metrics, and real-time news alerts. AI is now being used to negotiate with suppliers, which is useful for large enterprises that devote significant resources to supplier communications.

How Does AI affect Supply Chain Performance?

AI helps hardware and manufacturing companies streamline operations and tackle challenges more efficiently and accurately. Generative AI, in particular, allows hardware companies to model possible supply chain issues to preempt them. AI can analyze data to predict supply chain disruptions, accurately forecast demand, adjust production schedules for peak efficiency, and monitor machine performance.

Generative AI in the supply chain

Generative AI (GenAI) is an emerging technology that is reshaping supply chain management. By leveraging large language models and other generative tools, companies can simulate complex supply chain scenarios, identify risks, and test strategies before implementing them. Generative AI can help manufacturers model the impact of supplier shutdowns or material shortages and develop contingency plans in advance.

GenAI facilitates better decision-making in the supply chain by processing large amounts of data and presenting actionable insights in an easily digestible format. Whether suggesting optimal inventory levels or identifying alternative suppliers, GenAI enables businesses to remain agile in the face of uncertainty. As generative AI advances, its role in enhancing supply chain resilience and efficiency will continue to grow.

Mitigating Disruptions with AI

By analyzing historical and real-time data, AI can predict disruptions in supply chains, such as supplier delays or extreme weather events, and recommend contingency plans. AI in procurement plays a critical role here by identifying alternative suppliers, optimizing purchasing decisions, and ensuring the timely delivery of materials.

Manufacturers can use AI to anticipate delays in raw material shipments or factory shutdowns, allowing them to adjust sourcing or production schedules proactively. AI models can analyze weather patterns or logistical bottlenecks to recommend alternate sourcing options or adjust production schedules, minimizing the operational impact.

Demand Forecasting and Planning

Enhanced demand forecasting helps manufacturers align production with market demand. For example, robotics companies can use AI to analyze order trends, component availability, and production capacity. This enables them to adjust production schedules, ensuring they meet customer demand without overproducing or running into supply shortages. This precision allows for smoother operations and better use of resources.

Logistics Optimization in Manufacturing

AI supports manufacturing operations by closely monitoring production processes and equipment health, identifying potential issues, and alerting teams before problems escalate. Manufacturers use AI-driven predictive maintenance to track machinery performance across their factories. When a machine shows signs of wear, AI alerts the team to schedule repairs, preventing expensive breakdowns and reducing downtime.

Predictive analytics also help minimize unexpected expenses by identifying potential bottlenecks early. AI systems can flag when a part will run out of stock, preventing costly rush shipments or unscheduled production halts for repairs.

AI in Supply Chain Examples

Real-world applications of AI in the supply chain are already delivering impressive results. Manufacturing companies are enhancing their supply chain management by integrating PLM (product lifecycle management) powered by AI to optimize production planning, control inventory, and improve overall supply chain visibility.

Supply chain workflows

Companies like Rapid Robotics use AI to streamline workflows by automating repetitive manufacturing tasks. By integrating AI with production lines, they eliminate bottlenecks caused by manual intervention, enabling faster production timelines and more reliable delivery schedules for their customers. Rapid Robotics recently introduced Rapid ID, a robotic solution for the supply chain powered by Gen AI and 3D vision, further enhancing precision and efficiency.

Smart warehouses

Large e-commerce platforms use AI to anticipate shopping trends and adjust inventory levels, ensuring their supply chains remain responsive and well-stocked. By leveraging these insights, manufacturers and suppliers can effectively align their operations to meet consumer demand.

Retail forecasting

AI-powered robots handle picking, packing, and sorting tasks with unmatched accuracy and speed, cutting down on human error and labor costs. AI ensures supply chain efficiency by accelerating order fulfillment times and reducing errors that could delay shipments. This capability enables manufacturers and distributors to meet customer demand better while optimizing their supply chain operations.

Shipping logistics

Shipping companies are employing AI to navigate severe weather and other local disruptions. AI monitors weather patterns, dynamically reassigns delivery vehicles, and forecasts issues such as road closures, thereby maintaining delivery schedules.

The Future of AI in Supply Chain

The use of AI in supply chain management marks a new era of enhanced agility, sustainability, and efficiency as technology reshapes traditional processes and delivers enhanced solutions. With tools like predictive analytics to anticipate disruptions and generative AI for modeling complex scenarios, AI fundamentally enhances production planning, logistics, and operations across manufacturing sectors.

In 2024, aerospace and robotics industry trends demonstrated that these sectors greatly benefit from adopting AI and PLM software to enhance operations and reduce disruptions. This integration enhances real-time visibility and proactive decision-making capabilities, enabling these industries to predict and manage potential supply chain issues more effectively, including part shortages and shipping delays.

As AI technologies evolve, hardware solutions like PLM become essential for integrating these innovations into everyday supply chain management. By leveraging AI and PLM, companies can improve visibility and collaboration and streamline operations, setting new standards in efficiency. Adopting PLM software helps businesses maintain a competitive edge and future-proof their supply chains against upcoming industry shifts, ensuring resilience and adaptability.