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Revolutionizing Aerospace and Defense Data: The Right Approach

Jason Kasper — Tue, 02 Apr 2024 09:21:00 GMT

How can Aerospace and Defense (A&D) companies best meet the needs of product development and manufacturing teams and their unique view of the authoritative source for product information—the bill of materials (BOM)? In other words, how do engineering and manufacturing teams unlock their own data to maximize its value in light of the functionality their PLM software offers (or, perhaps, the limitations placed on them)?

The challenges of EBOM/MBOM management

On the one hand, BOM (Bill of Materials) information must be an accurate, up-to-date, change-managed, and trustworthy source for the product's definition. On the other hand, different teams in the organization each need a view of the BOM that represents their domain-specific data, including different ways to organize existing information and new information that doesn't impact other teams. The ability of a software solution to successfully balance both the structure and the flexibility required from the BOM can impact a company's productivity, compliance, and product quality.

A&D companies typically use one of two approaches to manage these diverse needs. The first, a “Single BOM,” includes engineering and manufacturing data and uses filtering and calculation to generate different views of the same information depending on the user’s role. To accommodate this approach, the two organizations must first agree on how the product structure will be broken down, how information will be added and organized, and how changes will be managed. Then, for consistency’s sake, they can’t deviate from agreed-upon structures and processes throughout the lifetime of the product.

The second approach, a "Dual BOM," leverages two separate BOM structures for engineering and manufacturing. Both organizations can manage unique information and data structures, meeting engineering's need to facilitate design reuse and manufacturing's need to optimize factory operations. However, for the sake of product quality and compliance, every part in the EBOM must be continuously reconciled with parts in the MBOM to be sure the design will be accurately realized in production. When data is replicated between two separate structures, managing traceability and change between them involves a cumbersome process that is not easily modified or upgraded in most PLM software tools.

For A&D companies pursuing either approach, effectivity management is key—that is, assigning a set of information that is accurate based on engineering and/or manufacturing conditions. So, in addition to managing engineering information and data structures alongside their manufacturing counterparts, the complexity of when each set of information is effective—or, accurate for the produced product—must be accounted for, easily understood, and flexible enough to accommodate change.

The Aras approach

Aras believes the best practice approach is allowing for multiple views of the same information, with each view enabling new information and data structures to be managed by individual teams. The two views should be reconciled by logic, rather than existing as duplicate data managed in disconnected tools or systems, so that changes affecting downstream views are flagged for resolution. The Aras platform offers shared services for change management and effectivity across engineering and manufacturing applications, enabling seamless change management that includes both teams’ data and processes, and complex effectivity calculations to account for both teams’ decisions.

Standard functionality in Aras Innovator^® allows companies to realize either of the two most common approaches to EBOM / MBOM management, as well as effectively evolve their current approach to match the recommended Multi-View BOM best practice. And while not required as part of this benchmark, the Aras platform offers standard capabilities to uniquely integrate manufacturing process planning with manufacturing process quality—including control plans, process flow diagrams, and risk management—to keep quality and process planning synchronized.

Finally, to take full advantage of the complex business processes required by the benchmark use cases, the Aras approach leverages built-in platform capabilities for creating low-code customizations without compromising upgradability. While some PLM users still shudder when they hear the word “customization,” Aras subscribers know that the platform is built to be customized, ensuring that a company’s data and processes are not “locked in” by the tool being used. Instead, the Aras platform is flexible enough to meet the needs of companies’ ever-evolving business processes—however complex. With each software upgrade, which Aras performs for its subscribers, custom functionality introduced by the company is retained and available to users again, as soon as the upgrade is complete, alongside new platform features.

What else is top of mind for Aerospace & Defense? The Digital Thread!

Recently CIMdata Research’s Aerospace and Defense PLM Action Group did some significant research on the state of the Digital Thread. The findings indicate that digital thread investment within the ecosystem of industrial users, their customers, suppliers, and solution providers has reached an inflection point. Initial implementations of targeted digital thread solutions have provided value proof points and essential learnings. Now expanded investment rounds are ramping up, guided by these early achievements and with expectations driven by the value potential revealed. Read more here.

Taking Flight: Building Resilience in Aerospace Companies

Jason Kasper — Thu, 14 Mar 2024 09:26:00 GMT

The aerospace industry's legacy approach to product data management is broken. To fix it, the sector needs to prioritize three things: transparency, flexibility, and adaptability.

When an aerospace company launches a major new product development effort, it must make many important strategic decisions outside the core engineering challenges. How will work be divided up between project partners, suppliers, and subcontractors? How will the activities of multiple stakeholders be coordinated? What design tools and data management systems will be used?

With ten years or more between product generations, the answers to these questions tend to differ for every project. The aircraft, engines, and systems under development today use new materials, new technologies, and new architectures. Projects involve different partners with varied responsibilities and expertise. In addition, design tools, simulation packages, and collaboration systems continue to evolve rapidly.

The industry's usual approach is to use each significant new product introduction as an opportunity to upgrade its supply chain, engineering processes, and collaboration approach. Companies pick the best combination of solutions available today, expecting that projects will benefit from the cost, quality, and productivity improvements that a decade's improvement in technology can deliver.

A legacy of complexity

This approach works, but only up to a point. A key weakness is the issue of in-service support. Aerospace products can stay in production for many years and in operation for several decades. Manufacturers must maintain access to product data throughout that time. When data is intimately linked to the tools used to create it, companies must support legacy engineering and data management systems long after they have been retired from use in new projects.

Today, the strain is beginning to show. Keeping tens or even hundreds of different systems up and running is a drain on time, money, and talent. And that's before things go wrong. Suppliers and technology vendors don't always stick around for the full lifecycle of a product. If they abandon a market, or disappear altogether, companies can find themselves struggling with obsolete, unsupported technology.

Products change, too. A single aircraft type may go through multiple upgraded cycles in its lifetime as the customer needs change and new technologies become available. Such upgrades are made harder and more costly if, as becomes increasingly likely, the product’s original data platform wasn’t designed with them in mind.

The age of the adaptable data platform

How can the aerospace sector break out of this cycle of rising costs, complexity, and obsolescence? The answer is a shift in the way it addresses product and process-related data. Companies should move beyond today's project-by-project perspective, and demand systems are designed with flexibility and agility in mind. At Aras, we call this approach the open and adaptable PLM and digital thread data platform.

An open and adaptable platform is one that supports the unique needs of an organization and evolves over time, changing to fit business requirements while never locking users into a specific technology. It is a platform made to be upgraded while maintaining all previous customizations and one in which the data is fully transparent.

Any truly open and adaptable data platform should be able to pass three key tests:

1. Eliminating data dependency on the application:

In many PLM systems, it is virtually impossible to understand the data layer of the application due to the complexity of the model or proprietary encryption. Working with complex data structures in the application may be fine in today’s application, but if this data is needed years from now, without the application, it may no longer be accessible. An open and adaptable platform allows the owner to control their data— it’s not held hostage by a software vendor or an aging technology.

2. Evolving to meet new business requirements

Change is certain. Business processes change often, quickly, and without concern for their supporting systems' ability to change with them. An adaptable PLM platform is never locked into a single technology. If a platform is built with hard dependencies on its technology, everything built on top of the platform also becomes dependent. The ability to adopt new technology in a strategic manner will set the stage, allowing for growth and change regardless of how customized and complex the eventual technical landscape becomes.

Meet today’s challenges, while preparing for tomorrow’s

A resilient solution openly accommodates whatever is needed to meet today's and tomorrow's business and technology requirements without disruption. The Aras platform has been architected from the ground up with that kind of resiliency in mind.

Aras’ unique model-based approach ensures that its users have the performance that is required now and, in the future, regardless of what challenges may lie ahead.

Check out the CIMdata eBook, Adapting the Digital Thread to learn more.

What is Digital Transformation?

Jason Kasper — Tue, 14 Nov 2023 14:18:00 GMT

Examples and key metrics to transform your product lifecycle

What is digital transformation?

Digital Transformation is the integration of digital technologies into all areas of a business, fundamentally changing how that business operates and delivers value to customers. This transformation involves adopting new digital technologies such as cloud computing, big data, and artificial intelligence, as well as connections across your enterprise ecosystem to automate, streamline, and optimize business processes and products. It requires a shift in mindset and culture, focusing on agility, innovation, and continuous improvement.

Creating the right set of iterative projects can impact all aspects of a business. Take, for example, improving consumer interaction with your internal operations. Understanding how to streamline and simplify how your customers work with you by taking their perspective will allow you to gain insights into customer needs and improve collaboration across the entire organization while improving your customer’s experience with your current products and ensuring loyalty to purchase the next generation of products. This results in increased efficiency, reduced costs, and improved customer satisfaction.

In addition, these projects enable businesses to figure out what is holding them back from responding quickly to changing market conditions, adapting to new technologies, and remaining competitive in an increasingly digital and global marketplace. Companies that embrace Digital Transformation are better positioned to drive innovation, seize new opportunities, and achieve sustainable growth.

This all sounds great, but where do you start? It’s simple, really; it starts with your most valuable intellectual property – your product lifecycle data. Read on to gain insights on measuring value and where you can start to help digitally transform your product lifecycle.

What does digital transformation mean in the context of the Product Lifecycle?

It all boils down to re-inventing traditional product lifecycle management processes and accessing and understanding product data to improve efficiency, speed up time-to-market, reduce costs, and enhance collaboration across the entire product development ecosystem. This includes incorporating technologies such as cloud computing, big data analytics, artificial intelligence, and the Internet of Things (IoT) to drive innovation, streamline processes, and improve product quality.

Integrating the above-mentioned technologies with your PLM processes is essential for supporting digital transformation initiatives such as implementing cloud-based PLM systems, creating and leveraging Digital Twins, folding in IoT data to your product data, and using AI and machine learning to provide insights and directions you may not have even considered. This all can result in a new realm of businesses optimized product design, a reduction in time-to-market, next-level improvements to product quality, and enhanced customer experiences and satisfaction.

The use of digital technologies in PLM can enable businesses to gain greater visibility into the entire product development process, improve collaboration among cross-functional teams, and enable better decision-making based on real-time data. This will streamline operations and allow organizations to keep a step ahead of the competition in today’s software-focused digital age.

Three examples of Digital Transformation projects in PLM:

Here are three examples of PLM projects to support Digital Transformation:

A Digital Thread of the Product Development Process: To digitally transform, Kendrion sought a single product lifecycle management (PLM) environment to implement a digital thread to facilitate traceability throughout the product development process. This will enable the mapping of requirements to engineering, increase the reuse of information, reduce dependency on manual spreadsheet-based processes, and enhance the visibility of all product information.

Transitioning Business Model to Green Energy: Technip Energies’ digital-by-design approach aims to achieve full project lifecycle traceability and optimization from concept through design, execution, and operations. This collaboration will increase efficiency and create new opportunities in the energy industry.

Changing business model to Product as a Service: Tamturbo evolved one product line to a unique selling point—in addition to purchasing and owning, it is also possible to implement an Air-as-a-Service business model—the customer only pays for the compressed air they use with no need for capital investment.

What are the business metrics to measure the effectiveness of Digital Transformation in PLM?

Several business metrics can be used to measure the effectiveness of Digital Transformation in PLM:

Time-to-Market: The time it takes to bring a new product to market from the initial concept to the final product launch. By leveraging digital technologies such as cloud-based PLM systems, businesses can accelerate product development cycles, reduce time-to-market, and gain a competitive advantage.

Product Quality: Optimize product design, reduce defects, and improve manufacturing processes. Measuring product quality can be done through metrics such as product failure rates, customer complaints, and returns. By improving product quality, businesses can enhance customer satisfaction, reduce costs, and increase revenue.

Cost Savings: Measuring cost savings can be done through metrics such as reduced material costs, improved manufacturing efficiency, and reduced product development costs. By reducing costs, businesses can improve profitability and reinvest in new digital technologies to further enhance their PLM processes, creating a pathway for continuous improvement.

Customer Satisfaction: Enhance customer experience, tailor products to customer needs, and provide personalized services. Measuring customer satisfaction can be done through metrics such as customer satisfaction scores, Net Promoter Score (NPS), and customer retention rates.

Overall, measuring effectiveness requires a holistic approach considering a range of business and PLM-specific metrics. In doing so, businesses can assess the impact of digital technologies on their PLM processes, optimize their operations, and achieve their business objectives.

Why Aras Innovator is the PLM platform for Digital Transformation

Although not an all-encompassing list, Aras Innovator should be considered as your PLM platform to support Digital Transformation:

Open Architecture: Allows for customization, extension, and integration with other systems and applications. The platform's flexible architecture enables businesses to adapt quickly to changing market conditions, integrate with new technologies, and extend functionality to meet evolving business needs.
Scalability: Enables businesses to support small teams or enterprise-level deployments. This makes it an ideal choice for businesses that are looking to expand or scale their operations without having to worry about limitations in their PLM system.
Cloud-based: Provides businesses with the agility, scalability, accessibility, and connectivity required for digital transformation initiatives supporting enterprise-level business change.
Advanced Functionality: Support for Digital Thread, Digital Twin, and Enterprise Integration are just some of the capabilities that enable businesses to optimize product design, reduce costs, and improve customer satisfaction.
Community-driven: We have a strong community of users, developers, and partners who collaborate to develop new features and functionalities, share best practices, and provide support. This community-driven approach fosters innovation and enables businesses to stay ahead of the curve by getting hands-on with solutions delivered by their peers.

The next step in your PLM Digital Transformation is your checklist

Are you ready to solve your most complex use cases? The only way to get started is to understand if you have the right solutions in place. Get started by evaluating your business through these six questions:

Is your platform customizable? Once it is, can you upgrade easily without data loss?
Does your platform enable the creation of an end-to-end graphically visible Digital Thread?
Does your platform give you the flexibility to meet the process requirements that exist today?
Does your platform give you TOTAL data transparency?
Does your platform provider try to make you fit into their deployment model, sacrificing features?
Can your platform evolve technically without disrupting business processes and integrations?

This will provide an initial level of understanding as well as put you on a path to a better way to transform digitally.

Learn more:

Creating a Resilient Enterprise Ecosystem

The Power of Aras Enterprise SaaS: How Aras Enterprise SaaS Enriches Cloud Deployments

The Digital Twin: Everything, Everywhere, All at Once – Part 3

Jason Kasper — Tue, 11 Jul 2023 12:21:00 GMT

This blog is the third in a three-part primer on the Digital Twin. Part 1 reviewed what a Digital Twin is, and Part 2 presented the digital twin in various industrial contexts. Part 3 focuses on use cases.

What Industries use Digital Twins?

Most industries can pursue opportunities to leverage Digital Twins in their specific industries. Here are a few industry examples for inspiration:

Nuclear

NuScale Power’s mission is to improve the quality of life for people worldwide by providing scalable advanced nuclear technology for producing electricity, heat, and clean water. This case study follows NuScale's journey as they implement the Aras platform. NuScale will be the first nuclear power plant to be designed and managed with PLM as the backbone for its single source of data. This will enable them to provide an as-built Digital Twin configuration of the reactors at the time of delivery with traceability to all product data created during the engineering and manufacturing of the product.

Oil and Gas

For Caltex Oil Tools, a change in ownership and corporate leadership led to a strategic shift in focus to highly specialized engineering capabilities and solutions. The team recognized the importance of PLM in developing improved engineering processes—and Aras was the solution to drive their digital transformation. In the future, they will deliver an as-built Digital Twin configuration with the physical product, enabling them to develop field service support.

Industrial Manufacturing

Many industrial companies are still managing their product-related data in spreadsheets, which leads to a loss of visibility, traceability, and the risk of incorrect information. This is because the volume of product data is constantly increasing. As a consequence, these companies find themselves struggling with data silos. This was the case for the fast-growing Finnish compressor manufacturer Tamturbo. The company decided to switch to Aras Innovator® to create "a central station for all of our product-related data,” enabling them to create a Product-as-a-Service (PaaS) business model.

Is Digital Twin IoT?

Getting the latest as-running condition of the asset operating in the field using Internet of Things (IoT) sensors is of great importance to aircraft designers and OEMs in identifying potential design flaws and facilitating design changes. Comparing real-time data against the as-running Digital Twin configuration allows for the ability to apply various simulation models to predict the behavior of the physical asset and create different operating scenarios to decide when to plan or defer maintenance in order to keep the asset from failing. This is a significant step forward in the industry’s use of predictive maintenance.

Maintenance is usually performed based on the OEM’s recommendation on a fixed schedule, resulting in increased maintenance costs. Predictive maintenance provides a flexible schedule-based approach to predict the probability of failure. Maintenance can be scheduled to prevent the asset from failing before it happens. This decreases the cost of maintenance cost while also increasing the effective utilization of the asset. This is called prescriptive maintenance.

Promising technologies such as IoT, predictive analytics, and simulations all have value, but only when used in the context of the individual asset. This means building and maintaining a digital record of the configuration of products as they are manufactured, maintained, and upgraded. This is the key to keeping the asset in the field, and it's Digital Twin synchronized.

The Digital Twin is updated whenever a significant change happens to the asset. For example, if electric motor serial number #001 is replaced with electric motor serial number #002, the corresponding change is made to the Digital Twin configuration. The electric motor, serial number #001, becomes part of the Digital Twin’s history and is forever linked with the Digital Thread.

With a constantly up-to-date Digital Twin configuration, simulation models can be built to the specific characteristics of a particular asset and coupled with Time Series and IoT data generated from the physical asset to predict potential failures.

What is the difference between a model and a Digital Twin?

One emerging trend is to use simulation models, or CAD models created during the engineering phase of the product lifecycle, as the Digital Twin of an asset. The concept is that comparing these digital models with operational data while running different simulation models may result in the identification of failures. See our Digital Twin Primer Part 2 for more information.

The Digital Twin in aircraft design

Building a Digital Twin configuration creates an opportunity to bring modeling and simulation techniques to the operations phase of an asset. These capabilities can be used to compare operational data, simulate the behavior of the asset now, as well as the future state of the asset if the behaviors continue. This allows organizations to improve accuracy of operations and maintenance decisions to optimize their aircraft fleets and can be used by OEMs to support future product improvements in next-generation aircraft.

Modeling and simulation capabilities are separate from a Digital Twin

They are often used in the design phase and referred to as digital models, not twins. To improve accuracy, a Digital Twin configuration is required so that you are modeling and simulating the exact representation of the asset. Connecting the Digital Twin configuration to the Digital Thread of product data allows operations and maintenance to access original models and simulations created during the design phase for use as a comparison.

What technology can create a Digital Twin?

You cannot rip and replace legacy software systems to accelerate the building of Digital Twins. There can be hundreds of systems and related data globally supporting the various aspects of the product lifecycle, encompassing different disciplines. The answer is a platform approach.

Platforms can enable organizations to move quickly and build flexible individual Digital Twin configurations. They can adapt and change as your business, or the environment they operate in, changes, or the profile of the product itself changes―as in the case of upgrades.

If you don’t use an overlay approach, the other issue you may encounter is building Digital Twin configurations with static metadata models. Organizations are quickly finding that when they build an individual Digital Twin working with a static data model, they have trouble changing it to meet new needs. The data model needs to be flexible to keep the Digital Twin up to speed.

Existing PLM systems can trap valuable information, or worse, and users stop capturing some of the data and decisions within them. Important information can be trapped in existing systems because they are closed. This makes it difficult to create meaningful relationship connections between all of a product’s digital assets and their revisions across the lifecycle― –bill of material(s), parts, software, electronics, CAD models, documents, requirements, process plans, service manuals, and maintenance history.

Do Digital Twins predict the future?

With context and traceability in place, it’s time to predict the future. There is an emerging opportunity to do this with the many predictive analytics tools out there today, but those become more relevant when you use them to predict behaviors based on individual contact with a product or system of products.

As simulation moves out to the field, it becomes operational simulation, which is about trying to understand what a product’s been through in its lifetime that might affect its operation once we apply an update. Based on an individual configuration of an individual asset and use simulations, you then can predict what will happen if we replace a part, or update software.

The other opportunity here is simulating what will happen to a currently operating product. Let’s say that we start to see failures in the product once we reach a certain temperature. We can then use that data to predict what will happen if the temperature goes up even further. It's simulating not only the current version of the Digital Twin but also the future based on real-time product feedback.

Are Digital Twins the future?

Many articles and analyst reports describe the proliferation and growth opportunities related to Digital Twin technology. The bottom line…it has taken off. This means organizations in a variety of industries have expressed or are testing specific technologies related to the Digital Twin. A multitude of solution providers have flooded the market with messages about their technology, which creates confusion as to where to start, and answer the ultimate question, where is the business value? The current consensus is that although many projects have been implemented or are in the process of being implemented, the business value has been limited.

Why? Most pilot projects lack the ability to scale. They see success initially because they pick a specific asset or location (manufacturing site) to create a Digital Twin view, then start visualizing and gathering data to these physical spaces digitally. When they try to scale across the enterprise, it becomes very difficult because they are thinking about one side of the equation – its performance. Many Digital Twin pilots started here before understanding the total picture. Implementing Digital Twin solutions focused on understanding the generated data, how to add more sensors to gain additional data, then analyzing this information using advanced analytics, artificial intelligence, and machine learning capabilities.

Scaling your Digital Twin success

This is all for naught if you want to scale your Digital Twin success. There is an equally important side of the digital twin market – the Digital Twin configuration. This is the as-built and as-maintained asset model and creates the context required for the Digital Twin performance. It is necessary to accurately represent analytics, visualizations, and simulations to support specific use cases across any industry. When existing pilots fail to scale, they haven’t thought about how to sustain the Digital Twin configuration of the physical asset as it changes over time. This makes analysis increasingly inaccurate, creating a loss in the initial business value they were attaining during the pilot/proof of concept phase.

Only when organizations get the total picture and begin to implement the Digital Twin configuration technologies in conjunction with digital twin performance will accurate and sustainable business value emerge.

What to learn more?

Here is a list of recommended reading:

The Digital Twin Time Machine

Putting Failure into Context with the Digital Twin

Digital Twins: The Landing Pad, the Toolkit, and the Palette

The Digital Twin: Everything, Everywhere, All at Once (Part 1)

The Digital Twin: Everything, Everywhere, All at Once - Part 2

How to improve maintenance planning and diagnosis with digital twins

Predict and simulate the future with digital twins

Ships, trains, and airplanes; using the digital twin to support product as a service

Mid-Year Update! Becoming a Manufacturing Leader During Recessionary Times

Jason Kasper — Thu, 15 Jun 2023 15:46:00 GMT

Is your digital transformation strategy turning changing business conditions into competitive advantages?

In January, we held a webinar to review “Transformative Business Opportunities During Recessionary Times.” This was an opportunity to set the stage for 2023 expectations and create a framework for organizations potentially heading into recessionary times layering into this elevated energy costs and supply chain disruptions.

While the impact of these trends varies by industry or even by company, let's take a look at the areas we targeted for 2023 and how they may continue to affect our foreseeable future.

Are you responding to trends by Digitally Transforming?

The best way to digitally transform is through iterative projects to support growth; we call this Iterative Digital Transformation - an agile digital transformation strategy which involves investing in smaller, proactive projects to improve the organization continually. The old “big bang” transformation approach does not align with the current economic climate, so it’s time to change. Innovation is still critical to remain competitive and investing in the right projects gives you the competitive edge to become a leader in your space or maintain your leadership position by growing in turbulent times.

Emerging Trend Alert: Artificial Intelligence (AI) for PLM, specifically using AI for Generative Design. This is a great example of the next hot thing that organizations may feel inclined to focus on right now because everyone is talking about it! But don’t spend too many cycles on AI just yet; it’s a bit of hype until we consider the best approach to create value for Generative AI in PLM for organizations.

Solution: Define expectations of Generative AI and align to PLM, to iteratively explore its use and value over time. This is how Aras is discussing the relationship of Generative AI to PLM with our customers:

The what (as in design data). In the past, PLM was a system of record for mechanical BOM – it provided the answer to what – a system of record in configuration management.
The why (as in design intent). Today, PLM is including all product representations on the left side of the engineering V model in the digital thread – and therefore, it’s beginning to provide the answer to why for the what. Why is the BOM the way it is? It may include some AI for intelligent search and pattern recognition to interpret the data, but that’s about it.
The how (as in tell me what to do). Tomorrow Generative AI will allow PLM to become generative all by itself; telling the engineers how in the context of system/requirements/performance targets. In other words, it will first start guiding engineers on what incremental decisions to make (a set of incremental choices), and eventually, it will tell engineers the best solution. It will be able to do that because AI would learn from the past what and why retained in PLM and traceable via digital thread.

Keep in mind, however your approach, it should be iterative. A great example of Iterative Digital Transformation can be found in our recent case study showing how Tamturbo evolved their product offerings into an Air as a Service model, creating a stronger connection and more visibility into their customers’ needs.

Building business resilience is the only path forward:

How well-equipped is your organization’s ability to respond to business disruption? Can you adapt to changing compliance and evolving business conditions? The reality is that we now all work with a distributed workforce, and keeping up with security standards and business expectations is difficult. The goal of business resilience is ensuring your business systems are compliant AND connected, which is internally focused. But you also have to look at addressing and conforming quickly to outside pressures, for example, sustainability policies and government regulations. This includes emerging technologies that you may not even know you need yet but should be prepared to leverage when the time comes.

Emerging Business Disruption Alert: Sustainability. This can range from organizational goals of becoming carbon neutral to designing your products to reduce their environmental footprint. Emerging regulations and compliance activities will continue to evolve, and organizations will need flexibility in their systems and processes to adapt quickly; otherwise, the cost implications, and competitiveness of their solutions could be dramatic. To learn more about leveraging PLM in sustainable product design, read this blog from Patrick Willemsen.

Solution: Avoid adding more tools to address gaps. It creates a high-risk, temporary solution while increasing technical debt. The correct answer is looking for evergreen solutions that mold to your current needs and change – at low cost – to emerging business requirements.

A great customer example is Ottobock, who use PLM to quickly adapt to evolving needs.

Mitigating supply disruptions will continue

Supply chain! There is no indication supply chain issues are going away anytime soon. The past two years have shown us that we rested on our laurels when it came to our supply chain, thinking of it as a one-way street where we press our suppliers for cost-effective parts, fast, on-time delivery, and blame them when it doesn’t happen. Keep the supply chain top of mind and look for creative ways to address the many aspects of supply chain mitigation.

Emerging Supply Chain Trend Alert: There are simmering tensions on the west coast, a work slowdown that could evolve into a full strike. Are you ready for it? Have you considered the effect on your business when 22,000 longshoreman strike at ports bringing 42% of our imported goods?

There are myriad opportunities to move static supply chains to dynamic ones while creating a competitive advantage for the product. It starts with increasing visibility by connecting your suppliers and customers to your supply chain processes – across the entire product lifecycle.

Solution: Bolster your supply chain through a systems engineering approach to identify multiple ways to implement the functions of a product. Get the proper visibility to alternate sources of components EARLY in the engineering cycle – all too often, we are reactionary here, costing time and delays in product manufacturing and delivery.

But don’t take my word for it; a great example of harnessing the power of connectivity is told by our customer Microsoft.

Continue to evaluate your position against your peers

It’s important to keep taking stock of what is possible based on comparing your position vs. your peers. Here is a good framework to consider when evaluating yourself and others and what actions to take during difficult times.

Many manufacturers use this framework to determine the focus of resources, what their organization is good at, and more importantly, how to invest in becoming more competitive and set up the organization to emerge as a leader as economic times improve.

It’s important to be mindful that no matter the disruption or changing business requirements, it can be treated as an opportunity for discrete manufacturers to invest in the right things, leading to future success.

Do you want to see what else Aras has to say on current market trends? Watch the on-demand version of our “Hot Takes” webinar, where Aras gives our spin on the latest topics in the news and what they mean for PLM.

The Digital Twin: Everything, Everywhere, All at Once - Part 2

Jason Kasper — Tue, 16 May 2023 18:38:00 GMT

This blog is the second in a three-part Aras Primer on the Digital Twin. Part 1 reviewed what a Digital Twin is, Part 2 will present the Digital Twin in various industrial contexts, and Part 3 will bring the information back around and focus on use cases.

The Digital Twin provides significant benefits to manufacturers and others in asset-intensive industries. In Part 1 of this series, we defined the Digital Twin. This second blog will share insights into how the Digital Twin is used in various industrial contexts, including Digital Twin in AI, predictive maintenance, and a section on how to build a Digital Twin.

Although there are unlimited possibilities on how to utilize Digital Twins to improve business outcomes, we thought we would provide a few examples for inspiration:

Product as a Service (PaaS)
Many industrial companies are still managing product-related data in spreadsheets, which leads to a loss of visibility, traceability, and the risk of incorrect information. The volume of product data is constantly increasing, and companies like The Finnish compressor manufacturer Tamturbo find themselves struggling with data silos. The company switched to Aras Innovator® to create "a central station” for all our product-related data. “Now, we can’t imagine being without it,” said CEO Igor Nagaev. Read the case study or watch the demo for more information about Tamturbo’s “Air as a Service” initiative.

Over the Air (OTA) updates
An excellent example of where the opportunity lies is with the software running on products today. The Digital Twin configuration creates the context; otherwise, your simulations of an over-the-air (OTA) software's impact on products is just an estimation or a guess, which is not good for customer relations. When simulation moves out to the field, it becomes operational simulation. It is about trying to understand what a product’s been through in its lifetime that might affect its operation once we apply an update. Simulation can have a more significant presence in the physical world if the context is there. You then can predict based on an individual configuration of an individual asset and use simulations to predict what will happen if we replace a part or update software.

Predictive Maintenance
Another opportunity is the simulation of what will happen if I change something in a Digital Twin configuration and simulate behaviors based on what is happening to the Digital Twin now. For example, let’s say that we start to see failures in the products once we reach a specific temperature; we can then use that data to predict what will happen if the temperature goes up even further to see those failures. It's simulating not only the current version of the Digital Twin but also simulating based on the real-time feedback that we're getting from the product.

What is a Digital Twin in manufacturing?
There are many use cases for the Digital Twin across the product lifecycle in manufacturing a product. For the manufacturer, technology is available now to link product data as it is created in the design, engineering, and manufacturing stages and to capture it as it changes. We call this the Digital Thread. Many manufacturers realize the importance of creating digital threads between departments. At the final stage of the product manufacture, there is a need to connect everything to the final product. This Digital Twin Configuration displays the as-built configuration of a product – and links all product data history, decisions, who made them, and why, including the CAD models, simulations, and requirements.

Combining the digital thread with each Digital Twin configuration can also help by capturing reliable data from the operating asset in the field that can help OEMs validate design intent versus operational context and make appropriate design changes. Creating the Digital Twin configuration allows many individuals or groups access to this information that has been traditionally siloed and, for the most part, unavailable once the asset is in operation.

What is a Digital Twin in AI?
Predictive Maintenance is the ability to determine when maintenance should be performed based on the asset’s operating conditions in the field. Technology advancements in sensors, analytics, and simulation can be the solution to predicting maintenance problems, but only when a Digital Twin configuration approach is considered. An individual asset’s context must be used as a baseline to predict failure with data generated from IoT sensors and validated with simulation models. The key is to develop corresponding business processes to support asset configuration changes, the ability to collect IoT data specific to individual configurations, and the use of simulation to build a digital model of the specific asset configuration. Predictive maintenance will yield significant results to organizations' maintenance effectiveness using the above framework.

How do you build a Digital Twin?
This may all sound great, but how do you get started? If you are a manufacturer, you need to ensure your “as-built” configuration is as accurate and detailed as possible. This creates a digital thread by capturing all serialized electrical, electronic, mechanical, and software components
and linking them to engineering parts and their related history. CAD models, simulations, requirements, change orders, and so on are considered based on the value they can provide in supporting your required use cases.

If you are in operations and maintenance, you can start by keeping accurate records of your inspections—track what a product’s configuration looks like when it arrives and ensure this information is digital, not paper-based. It should be in a searchable database, inspectable, and maintained. Whatever the business case, the Digital Twin configuration should be able to extend out to installation, commissioning, and operation at a customer site, allowing it to update the configuration as things change. This will enable you to start capturing additional data from the field where your product is maintained.

The ultimate goal is for product manufacturers to have a Digital Twin configuration from when the product is built and to continue that configuration until that individual physical product is retired and recycled. If you can achieve that vision, you are going to significantly increase the long-term value delivered by the complex products you maintain and operate.

Stay tuned for Part 3 coming soon. In the meantime, if you would like to learn more about the Digital Twin, visit our solutions page.

The Digital Twin: Everything, Everywhere, All at Once

Jason Kasper — Tue, 11 Apr 2023 12:49:00 GMT

What is a digital twin?

This blog is the first in a three-part Aras Primer on the digital twin. Part 1 will review what a digital twin is, Part 2 will present the digital twin in various industrial contexts, and Part 3 will bring the information back around and focus on use cases.

A digital twin is the virtual representation of a physical product or asset that reflects the real-time configuration and can trace maintenance activities and operational performance that have occurred to the product over time, including decisions made during the engineering and manufacturing processes.

It's important to note that a digital twin differs from a digital model. A digital model reflects a view of a product to support design and engineering scenarios. It does not reflect the final product's reality or how it evolves once it is in service. For example, during the engineering and manufacturing of the product, exceptions, part replacements, supplier changes, and engineering design changes occur, creating a significant gap between the digital model used in the design phase and the real-world product.

Two distinct elements are essential to understanding the digital twin. The performance side, which includes time series data streaming back from sensors where predictive analytics capabilities are used, is called the Digital Twin Performance. Today, much focus and investment have been placed on the performance side of the digital twin solution through pilots and deployments for a combination of capabilities, like cloud storage, data lakes, gateways, artificial intelligence and machine learning, analytics, and dashboards. But often, these pilots don’t scale, or the value degrades over time when deployed.

The current as-running asset configuration—called the Digital Twin Configuration—is equally important and provides the context for interpreting, analyzing, and simulating time series data. Without this, you lose the individual story each product has to tell. The configuration of every product in operation can differ, from vehicle to vehicle, machine to machine, plane to plane, and ship to ship. The longer something has been out in the field, the more it changes. To effectively analyze the sensor data, you need accurate context. Digital Twin Performance cannot happen in isolation; instead, it requires the individual Digital Twin Configuration of the managed asset.

Your Digital Twin Configuration data will be quite different depending on what business value you are trying to deliver to customers. It is different because the use case requirements are very diverse—predictive maintenance, performance optimization, and over-the-air software updates require vastly different Digital Twin Configurations. Therefore, the tools built in the past will not effectively support your digital twin's future.

In a perfect world, the Digital Twin is created when the physical asset is completed with its manufacturing processes and serialized information recorded. If it needs capturing during another phase, there are plenty of opportunities to catch and manage changes during the commissioning and operation of an asset. The key is to capture and manage it; this is the context you need for practical predictive analytics implementations.

What data is needed for a digital twin?

Everything must be connected through to the final manufactured product’s digital twin. This digital twin configuration has the ability to link all product data history, decisions made, who made them and why, including CAD models, simulations, and requirements. The initial version of the digital twin can be updated as changes to the asset are made so organizations have a complete historical record of each individual asset.

Once the product is in service, additional data can be collected and leveraged for analysis. This process includes operations data based on sensors placed on intelligent connected products or the Internet of Things (IoT). Other information can be collected from the assets' surroundings, including temperature, humidity, weather, etc.

Finally, maintenance records, service manuals, CAD and simulation models, and job plans can be connected to the digital twin configuration to continue to build each product's unique history to support failure and predictive maintenance requirements and operational decisions.

Stay tuned for Part 2 in this series. In the meantime, if you’re interested in learning more about the Digital Twin, check out our solutions page.

The Meaning of Product Lifecycle Management

Jason Kasper — Tue, 21 Mar 2023 14:25:00 GMT

PLM and the Transition to SaaS

This blog is a part of our new series of “Aras Primers” that provide simple definitions and basic information on concepts and applications relevant to the PLM industry. This blog will review the basics of PLM and why transitioning to a Software as a Service (SaaS) model can provide higher-quality data and collaboration at every process stage, reducing time-to-market and costs.

What is PLM?

Product Lifecycle Management (PLM) is a process that involves managing the entire lifecycle of a product from conception, design, development, and manufacturing to service and disposal.

PLM uses software and processes to manage product-related data throughout the product's lifecycle. The goal of PLM is to streamline the product development process, reduce costs, improve product quality, and enhance communication and collaboration among different teams involved in the product development process.

PLM can encompass various aspects of product development, such as design, engineering, procurement, manufacturing, quality control, and maintenance. It can also involve collaboration with suppliers, customers, and other stakeholders.

PLM software provides features such as data management, product data visualization, document management, workflow management, and project management. PLM systems are essential to various industries, including automotive, aerospace, consumer goods, and electronics.

What does PLM do?

Product Lifecycle Management, commonly referred to as PLM, is a comprehensive approach companies use to manage the entire lifecycle of a product, from ideation and design to development, manufacturing, and end-of-life disposal. The goal of PLM is to improve the quality of products, reduce time-to-market, increase efficiency, and lower costs.

PLM provides a centralized platform for managing data related to a product throughout its lifecycle. The system enables collaboration among various stakeholders, including product designers, engineers, manufacturers, suppliers, and customers, to ensure everyone is on the same page and working towards common requirements.

PLM software captures and manages product data, including CAD files, engineering and manufacturing change orders, engineering, manufacturing and service bills of materials (BOMs), specifications, and other related information. This information can be accessed and updated by authorized users from many different locations, including the design office, manufacturing plant, and supply chain partners.

PLM also facilitates better decision-making by providing real-time visibility into the product development process. With accurate data, businesses can quickly identify and address issues, prevent costly errors, and make informed decisions about product design, production, and distribution.

One of the significant benefits of PLM is that it allows businesses to reduce product development time and cost. By streamlining processes and eliminating unnecessary steps, companies can bring products to market faster and at a lower cost. Additionally, PLM reduces the risk of costly errors and rework by ensuring that product data is accurate and up-to-date.

PLM’s role in compliance and the supply chain

PLM also plays a vital role in ensuring compliance with regulatory requirements, such as safety and environmental standards. The software can track and manage compliance data, including material and substance information, certifications, and approvals. This information can be easily accessed and shared with stakeholders, including regulatory bodies, to ensure that products meet all necessary requirements.

Another key benefit of PLM is that it enables companies to optimize supply chain management. By integrating with supplier systems, PLM can ensure that suppliers meet quality standards and deliver materials on time. It’s essential to look for solutions that provide a centralized platform for collecting data related to the product, facilitate collaboration among stakeholders, improve decision-making, reduce product development time and cost, ensure regulatory compliance, and optimize supply chain management. A platform approach to PLM will help businesses improve the quality of their products, increase efficiency, and remain competitive in the global marketplace.

How does Aras support PLM?

The Aras PLM platform, Aras Innovator, supports product development and provides a flexible and scalable foundation that allows organizations to manage their product-related data and processes efficiently.

Aras Innovator includes a set of applications and platform services that can be customized to meet an organization’s specific needs. Some of the capabilities and applications include:

Product Data Management (PDM): Connect, manage, and access product data, including bills of materials (BOMs), CAD models, and other related documents.
Change Management (CM): Manage changes to product designs and related processes, including change requests, change orders, and engineering change notices.
Quality Management (QM): Supports quality control and assurance processes, including inspection planning, non-conformance management, and corrective and preventive actions.
Manufacturing Process Planning (MPP): Helps organizations plan and manage their manufacturing processes, including work instructions, routing, and process flows.
Requirements Management (RM): Enables organizations to capture and manage product requirements, traceability, and impact analysis.
Variant Management (VM): Allows companies to efficiently handle complex product portfolios by defining and tracking product variants, options, and configurations.
Digital Twin Core (DTC): Captures the as-manufactured and as-maintained configuration of products to support next generation product design and maintenance processes.

Aras' PLM platform is based on an open architecture that allows organizations to easily integrate with other systems and data sources to support the Digital Thread. Organizations can “Build with Aras” to customize out-of-the box applications and build new ones to support evolving business requirements. It supports collaboration between different teams, suppliers, and contractors involved in product development.

Why is Software as a Service (SaaS) important to PLM?

SaaS based PLM solutions enable opportunities to improve:

Accessibility: Access anywhere with an internet connection, making it easier for team members to access product data and collaborate remotely. This is especially important for companies with distributed teams or partners in different locations.
Scalability: Scale up or down depending on the size and needs of an organization. This is particularly beneficial for small and medium-sized businesses that may not have the resources to invest in an on-premise PLM system.
Cost-effectiveness: Do not require hardware or infrastructure investments. Additionally, SaaS solutions typically have lower ongoing maintenance costs, as software updates and support are included in the subscription fee.
Security: Include robust security measures, including encryption, backups, and disaster recovery. This can provide organizations with greater confidence in the integrity of their product data.
Continuous improvement: Updated regularly, providing access to the latest features and capabilities. This can help organizations to stay up-to-date with the latest trends and best practices in product development.

These benefits can help organizations streamline product development processes and improve collaboration, ultimately leading to better products, differentiated processes, and increased competitiveness.

How does Aras Enterprise SaaS support PLM?

The Aras Enterprise SaaS solution enables organizations to manage their product-related data and processes efficiently. Here are some of the ways Aras Enterprise SaaS supports PLM:

Accessibility: Aras Enterprise SaaS is accessible from anywhere with an internet connection, making it easier for team members to access product data and collaborate remotely. This is particularly beneficial for companies with distributed teams or partners in different locations.
Scalability: Aras Enterprise SaaS can be easily scaled up or down depending on the size and needs of an organization. This allows organizations to pay only for the resources they need, making it more cost-effective than traditional on-premise PLM solutions.
Security: Aras Enterprise SaaS has robust security measures in place to protect product data, including encryption, backups, and disaster recovery. This provides organizations with greater confidence in the security and integrity of their product data.
Continuous improvement: Aras Enterprise SaaS is regularly updated with new features and capabilities as part of the subscription, allowing organizations to stay up-to-date with the latest trends and best practices in product development. Unlike other SaaS PLM solutions the upgrade can happen on your schedule and retains all customizations. This can help organizations to improve their product development processes and stay competitive.
Customizability: Aras Enterprise SaaS is highly customizable because of its low-code nature, allowing organizations to tailor the platform to meet their specific needs. This can help organizations to optimize their product development processes and improve collaboration among different teams.

In summary, Aras Enterprise SaaS supports PLM by providing a cloud-based platform that is accessible, scalable, secure, continuously improving, and highly customizable. These benefits can help organizations to streamline product development processes, improve collaboration, and ultimately deliver better, and more profitable products.

The Meaning of Product Lifecycle Management

Jason Kasper — Tue, 21 Mar 2023 14:25:00 GMT

PLM and the Transition to SaaS

What is PLM?

Product Lifecycle Management (PLM) is a process that involves managing the entire lifecycle of a product from conception, design, development, and manufacturing to service and disposal.

What does PLM do?

PLM’s role in compliance and the supply chain

How does Aras support PLM?

Aras Innovator includes a set of applications and platform services that can be customized to meet an organization’s specific needs. Some of the capabilities and applications include:

Product Data Management (PDM): Connect, manage, and access product data, including bills of materials (BOMs), CAD models, and other related documents.
Change Management (CM): Manage changes to product designs and related processes, including change requests, change orders, and engineering change notices.
Quality Management (QM): Supports quality control and assurance processes, including inspection planning, non-conformance management, and corrective and preventive actions.
Manufacturing Process Planning (MPP): Helps organizations plan and manage their manufacturing processes, including work instructions, routing, and process flows.
Requirements Management (RM): Enables organizations to capture and manage product requirements, traceability, and impact analysis.
Variant Management (VM): Allows companies to efficiently handle complex product portfolios by defining and tracking product variants, options, and configurations.
Digital Twin Core (DTC): Captures the as-manufactured and as-maintained configuration of products to support next generation product design and maintenance processes.

Why is Software as a Service (SaaS) important to PLM?

SaaS based PLM solutions enable opportunities to improve:

Accessibility: Access anywhere with an internet connection, making it easier for team members to access product data and collaborate remotely. This is especially important for companies with distributed teams or partners in different locations.
Scalability: Scale up or down depending on the size and needs of an organization. This is particularly beneficial for small and medium-sized businesses that may not have the resources to invest in an on-premise PLM system.
Cost-effectiveness: Do not require hardware or infrastructure investments. Additionally, SaaS solutions typically have lower ongoing maintenance costs, as software updates and support are included in the subscription fee.
Security: Include robust security measures, including encryption, backups, and disaster recovery. This can provide organizations with greater confidence in the integrity of their product data.
Continuous improvement: Updated regularly, providing access to the latest features and capabilities. This can help organizations to stay up-to-date with the latest trends and best practices in product development.

How does Aras Enterprise SaaS support PLM?

The Aras Enterprise SaaS solution enables organizations to manage their product-related data and processes efficiently. Here are some of the ways Aras Enterprise SaaS supports PLM:

Accessibility: Aras Enterprise SaaS is accessible from anywhere with an internet connection, making it easier for team members to access product data and collaborate remotely. This is particularly beneficial for companies with distributed teams or partners in different locations.
Scalability: Aras Enterprise SaaS can be easily scaled up or down depending on the size and needs of an organization. This allows organizations to pay only for the resources they need, making it more cost-effective than traditional on-premise PLM solutions.
Security: Aras Enterprise SaaS has robust security measures in place to protect product data, including encryption, backups, and disaster recovery. This provides organizations with greater confidence in the security and integrity of their product data.
Continuous improvement: Aras Enterprise SaaS is regularly updated with new features and capabilities as part of the subscription, allowing organizations to stay up-to-date with the latest trends and best practices in product development. Unlike other SaaS PLM solutions the upgrade can happen on your schedule and retains all customizations. This can help organizations to improve their product development processes and stay competitive.
Customizability: Aras Enterprise SaaS is highly customizable because of its low-code nature, allowing organizations to tailor the platform to meet their specific needs. This can help organizations to optimize their product development processes and improve collaboration among different teams.

Becoming a Manufacturing Leader During Recessionary Times

Jason Kasper — Thu, 02 Feb 2023 17:45:00 GMT

The right digital transformation strategy turns recessionary pressures into competitive advantages

Businesses continued to respond to new, challenging and ongoing changes in 2022, and to no one’s surprise, this won’t slow down in 2023. While still addressing challenges such as elevated energy costs, supply chain disruptions, and others, a new question is top of mind for discrete manufacturers – the recession – will we or won’t we, are we or aren’t we?

Source: Bloomberg

We could speculate about the recession's length, and interesting data is available to help understand the current thinking. According to IDC’s Market Perspective, “Implications of Inflation and Potential Recession on the Manufacturing Industry,” IT executives were surveyed in September 2022 and there is a majority sentiment (72%) that a recession is likely to occur within the next year, and a significant minority in North America (40%) believe that we're already there. At the same time, a majority also expect the recession to be both shallow (24% say mild; 55% say moderate) and relatively short lived, as illustrated:

How can you respond?

In our recent webinar, “Transformative Business Opportunities During Recessionary Trends” we polled the attendees on how they intend to address global challenges:

Interestingly, 50% are focusing on small, targeted investments for growth. This is good news and aligns with a key trend integral to our customers’ success: Iterative Digital Transformation, an agile digital transformation strategy. This involves investing in smaller, proactive projects to improve the organization continually. The old approach of “big bang” transformation does not align with the current economic climate, so it’s time to change. Innovation is still critical to remain competitive and investing in the right projects can lead you to become a leader in your space or maintain your leadership while growing in turbulent times.

A great example of Iterative Digital Transformation can be found here – learn how Tamturbo evolved their product offerings into an Air as a Service model, creating a stronger connection and more visibility into their customers’ needs.

Investing for business resilience

How well-equipped is your organization’s ability to respond to business disruption? Can you adapt to changing compliance and evolving business conditions? The reality is that we now all work with a distributed workforce and keeping up with security standards and business expectations is difficult. The goal of business resilience is ensuring your business systems are compliant AND connected, which is internally focused. But you also have to look at addressing and conforming quickly to outside pressures, for example sustainability policies, and government regulations. This includes emerging technologies that you may not even know you need yet but should be prepared to leverage when the time comes.

Our second attendee poll question asked how organizations are doing this today:

Clearly, the answers span the gamut of how to approach this. But adding more tools to address gaps should be avoided, especially in lean times. It creates a high risk, temporary solution, while increasing technical debt. The right answer is looking for evergreen solutions that mold to your current needs and change – at low cost – to emerging business requirements.

A great customer example of this is Ottobock, who use PLM to quickly adapt to evolving needs.

Continue mitigating supply chain disruptions

Supply chain! There is no indication supply chain issues are going away anytime soon, in fact, it could be a good thing if we keep supply chain top of mind and look for creative ways to address the many aspects of supply chain mitigation. The past two years have shown us that we rested on our laurels when it came to our supply chain, thinking of it as a one-way street where we press our suppliers for cost effective parts, fast, on-time delivery, and blame them when it doesn’t happen.

There are myriad opportunities to move static supply chains to dynamic ones, while creating a competitive advantage for the product. It starts with increasing the visibility by connecting your suppliers and customers to your supply chain processes – across the full product lifecycle.

Our last poll question asked just how connected discrete manufacturing networks are:

It’s not a surprise that this is a work in progress, but the good news is how much of an opportunity there is for improvement! And that this opportunity can directly result in improved product availability and competitive advantage. One way to bolster your supply chain is through a systems engineering approach to identify multiple ways to implement the functions of a product. Another is getting the right visibility to alternate sources of components EARLY in the engineering cycle – all too often we are reactionary here, costing time and delay in product manufacturing and delivery.

But don’t take our word for it, a great example of harnessing the power of connectivity is told by our customer Microsoft.

To get started, it’s time to evaluate your position against your peers

When you are under recessionary pressure, it’s important to take stock as to what is possible based on the position you are in when comparing yourself to your peers. Here is a good framework to use in thinking about evaluating yourself and others, and what actions to take during difficult times.

Many manufacturers use this framework to determine the focus of resources, what their organization is good at, and more importantly how to invest to become more competitive and set up the organization to emerge as a leader as economic times improve.

The case has been made to prepare for a slowdown – no matter where you reside. In these stressful times it’s important to be mindful that this is an opportunity for discrete manufacturers to invest in the right things, leading to future success.

Learn more about trends and opportunities during recessionary times – watch the webinar.

Becoming a Manufacturing Leader During Recessionary Times

Jason Kasper — Thu, 02 Feb 2023 17:45:00 GMT

The right digital transformation strategy turns recessionary pressures into competitive advantages

Source: Bloomberg

How can you respond?

In our recent webinar, “Transformative Business Opportunities During Recessionary Trends” we polled the attendees on how they intend to address global challenges:

Investing for business resilience

Our second attendee poll question asked how organizations are doing this today:

A great customer example of this is Ottobock, who use PLM to quickly adapt to evolving needs.

Continue mitigating supply chain disruptions

Our last poll question asked just how connected discrete manufacturing networks are:

But don’t take our word for it, a great example of harnessing the power of connectivity is told by our customer Microsoft.

To get started, it’s time to evaluate your position against your peers

Learn more about trends and opportunities during recessionary times – watch the webinar.

The Digital Twin and Digital Thread in Aircraft Design and Maintenance

Jason Kasper — Thu, 25 Aug 2022 15:59:00 GMT

From Product Configuration to Predictive or Condition Based Maintenance (CBM+)

There are many use cases for digital twins across the product lifecycle in the Aerospace industry. For the manufacturer there is Product Lifecycle Management (PLM) technology available now to link product data in the design, engineering, and manufacturing stages, and to capture changes as the product evolves. We call this connection the digital thread. Many manufacturers are realizing the importance of creating digital threads between departments.

There is a need to connect everything through to the final manufactured product’s digital twin. We refer to this as the digital twin configuration which displays the as-built configuration of a product – with the ability to link to all product data history, decisions made, who made them and why, including CAD models, simulations, and requirements. The initial version of the digital twin can then be updated as changes to the asset are made so that organizations have a complete historical record of each individual asset, in this case, airplanes, drones, etc.

This information can be used to help the aerospace industry move closer to Predictive or Condition Based Maintenance (CBM+) and away from the current preventive maintenance schedules recommended by OEM vendors – creating greater efficiencies and lowering the cost of maintenance.

Simulation and modeling with the digital twin

Building a digital twin configuration creates an opportunity to bring modeling and simulation techniques out to the operations phase of an asset. These capabilities can be used to compare operational data, simulate the behavior of the asset now, as well as the future state of the asset if the behaviors continue. This allows organizations to improve accuracy of operations and maintenance decisions to optimize their aircraft fleets and can be used by OEMs to support future product improvements in next generation aircraft.

Modeling and simulation capabilities are separate from a digital twin. These capabilities are used most often in the design phase and are referred to as digital models, not digital twins. There are emerging opportunities that surface when assets are in operation, for example, leveraging modeling and simulation technologies in the field. To do this correctly and improve accuracy, a digital twin configuration is required so that you are modeling and simulating the exact representation of the asset. Connecting the digital twin configuration to the digital thread of product data allows operations and maintenance to access original models and simulations created during the design phase for use as a comparison.

Leveraging performance data for sustainability and aircraft design

Aras defines the digital twin space differently than most vendors, seeing two parts of a digital twin. First, the digital twin configuration defines what the asset and its components are, which is then connected to product, maintenance, and operational data. Second, there is performance data that takes information generated from assets and compares it to the individual configuration of the asset to make operational and maintenance decisions to meet their business needs.

Getting the latest as-running condition of the asset operating in the field using Internet of Things (IoT) sensors is of great importance to aircraft designers and OEMs in identifying potential design flaws and facilitating design changes. Comparing real-time data against the as-running digital twin configuration allows for the ability to apply various simulation models to predict behavior of the physical asset and create different operating scenarios to decide when to plan or defer maintenance in order to keep the asset from failing. This is a significant step forward in the industry’s use of predictive maintenance.

Maintenance is historically performed based on OEM’s recommendation on a fixed schedule which results in increased maintenance costs. Predictive maintenance provides a flexible schedule-based approach to the ability to predict the probability of failure. Maintenance can be scheduled to prevent the asset from failing before it happens. This decreases the cost of maintenance cost while also increasing the effective utilization of the asset.

Interested to know more? Visit our digital twin core solution page.

What is the Future of PLM in the Metaverse?

Jason Kasper — Thu, 14 Jul 2022 16:45:00 GMT

The Metaverse is personal, will it become all business?

IDC defines the Metaverse as an evolution of today's internet that leverages mobile devices, augmented and virtual reality headsets, and next-generation networks to create a persistent and continuous user experience with a strong sense of presence. Are we at the level of Ready Player One yet? No, but we are already more than halfway there! If you think about IDC’s definition, all the technology exists today – including the haptic gaming chair that simulates realistic force and tactile feedback, creating a strong virtual sense of presence. As the technology continues to advance, it will only be a matter of time until personal use of the metaverse explodes. With all these technologies and seemingly farfetched concepts the question is - what will its role and VALUE be to industry and your daily work life? Your first product review meeting is about to start in the Metaverse, so strap into your haptic chair and let’s get started!

A little reality check

We need context for this meeting to make sense and the technology to support it. The Metaverse is BIG, and for it to be successful, it will require technologies not typically imagined for the success of a global team. There is significant infrastructure required. But there are many similarities to what is used today. For instance, Microsoft Teams or Google Meet are designed to meet virtually now, but in the Metaverse it will be immersive and through the haptic chair you will feel and experience the same space together. This could improve the human connection that is missing from today’s technology, but this is more than meeting together in a meeting space. It could be at your headquarters, or on Mars – that’s right, Mars. We’ll let the IT teams of the future figure that out.

What better way to use the Metaverse than to meet at the place your complex product will operate, experience the product as the user would, and understand the issues “firsthand” through virtual collaboration. To be successful requires an accurate representation of the complex product about which you are meeting. Conveying and manipulating it is the centerpiece of your meeting. Let’s move on to what is important in creating the context and traceability for an effective Metaverse collaboration and the role product lifecycle management plays.

It won’t work without product data

The meeting topic requires access to product data; the good news is this isn’t any different than meetings today, except that with an outdated PLM solution, you are bringing a spreadsheet to the meeting. (Wow that is fun, you’re on the Mars surface with your team and you display a spreadsheet from your desktop, wanh wanh.)

The good news is, a SaaS-based platform approach to product data implemented today will create the context for your meetings when you enter the Metaverse – whatever the location may be. Everyone will have access to the information, everyone will know what decisions have been made, and who is responsible for next steps. Accessing this data and navigating its path will make your Metaverse meeting more efficient because everyone will have the right data at the right time. A lack of accurate product data will result in poor collaborative discussions. Since seeing is believing, there is no sense of logging in and going to Mars to look at a spreadsheet. The meeting should be immersive.

A digital twin for product context is key

Not only does the meeting space need to be accurate to convey context of where the product will operate, but the complex product you are discussing needs to have accurate context too. This requires managing the virtual product’s configuration and keeping it up to date as things change. There is no point in looking at a configuration three generations behind, you need the latest configuration to create the context for the meeting. This needs to be navigable, not only as a 3D representation, but augmentable so you can access related information to engage in the collaborative discussion. This requires connectivity to information throughout the enterprise including customers, suppliers, and within the organization.

Product data requires connectivity, aka the digital thread

To navigate product data in the Metaverse won’t be much different than it is today, albeit a much more incredible, customizable interface based on a preloaded user profile. The digital thread will be just as important in the Metaverse as it is today. Preparing for that now will set the stage for the flexibility to adapt to a Metaverse future.

By connecting silos of information with context, this digital thread is traceable. While in the Metaverse you can explore information to build a baseline for the next generation product. You can also trace issues in a product from the customer’s point of view to the team or engineer that designed it. This insight enables organizations to move quickly to the resolution of problems and to make adjustments to support customer needs –that is, the digital thread tells a story in the Metaverse.

Conclusion

As fantastic as the Metaverse for business may seem, we have most of the tools available now to help set the stage and prepare for its impending future. This means taking actions now such as:

Implementing a SaaS based PLM solution that allows for customization to support your evolving needs now, and again when you enter the Metaverse.
Creating traceability with a digital thread, looking for opportunities that provide the integrations needed to sustain the connection with context across the enterprise ecosystem.
Maintaining context visually through the digital twin. Look for options that allow you to keep pace with product configuration changes not only during the development of products but once they are manufactured and in service.

Are you heading to the Metaverse? Let us know how by commenting on this blog. We look forward to future conversations with you here, and someday in a collaborative Metaverse, with a view of Mars.

Can Your App Do That?

Jason Kasper — Thu, 30 Jun 2022 14:01:00 GMT

Simplifying and synchronizing the transition from EBOM to MBOM

Managing complex business processes globally that extend through product development into manufacturing is effortless with the Aras PLM Platform Application—Aras Manufacturing Process Planning (MPP). MPP possesses capabilities that allow multiple, editable views of the same data for the Manufacturing Process Plan, Work Instructions, and Manufacturing Bill of Materials (MBOM). Utilizing the MPP, they can be automatically synchronized as changes occur—can your application do that?

Using MPP, engineers can manage their entire process by concurrently defining manufacturing processes, work instructions, and MBOMs while avoiding EBOM/MBOM conflicts using intuitive, visual cues with immediate feedback to reconcile the two. You are not running reports after you did all your hard work only to learn some inaccuracies that need resolution—can your app do that?

Authoring your process plan

Products that combine complex mechanical designs, electronics, software, and firmware require detailed process plans. These process plans include operations, equipment, and tooling illustrated by models, drawings, pictures, graphics, videos, and text. With MPP multiple users can edit different levels of the MBOM at the same time, apply conflict resolution, and create visually rich electronic work instructions concurrently with the process Plan!

MPP provides the ability to add phantoms, consumables, etc. to the MBOM. In addition, MPP has drag and drop editing to restructure the MBOM while maintaining the digital thread along with real-time automatic reconciliation indicators between the EBOM and MBOM, and from the as-designed to the as-planned configurations.

Create process plans with (re)purpose

We all believe our situation is unique at a plant, on a specific line, etc. This doesn’t mean your process plan has to be. Since there is a digital thread created when process plans are made, they become searchable. This enables engineers to repurpose completed process plans that are similar to your plant processes and allows engineers to modify existing plans to improve productivity and efficiency.

Push data your way

The Aras PLM platform is open by design. The data was created by you and should be easily accessed so that other systems and groups can use that data to get their jobs done. Your ERP systems need the MBOM to develop supply chain requirements for materials. The process plan needs to be sent to the MES system to start the process of building the product. Once your process plans are complete, they can be shared to your MES system. Most other systems have separate tools to do MBOM to ERP and process plan to MES. With MPP its one in the same – can your app do that?

Seeing is believing

With MPP engineers can transform your company’s EBOM to MBOM while at the same time building work instructions and process plans. This results in increased productivity for the team building the MBOM while keeping everything up to date in real-time.

MPP provides an intuitive user interface with full traceability of a digital thread across the product lifecycle. Most other solutions create a gap in traceability because these processes are siloed, making it very difficult to keep data in sync. The result is increased product cost, reduced quality, and slower time to market. This also means your existing solutions are not up to the task of supporting todays and tomorrow’s product complexity challenges.