At a Glance:
- Executives at organizations that experimented with new technologies during the pandemic and those that invested more in digital tech were twice as likely to report “outsize revenue growth” relative to their peers, according to McKinsey analysts.
- Digital transformation can be achieved in different ways, but it starts with digital resources that can reformulate strategies for developing products or processes.
- Taking a practical approach to PLM, manufacturers may connect business processes to the right data to facilitate better decision-making.
- Today’s smart connected products drive interactions and require ongoing coordination across multiple functions.
The fact that supply chain interactions are inextricably linked to internal operations has never been more apparent than during the past pandemic-riddled year. More than ever, integrating digital technology into all aspects of the business profoundly changed the way manufacturers operate and deliver value to their customers.
Research has borne this out: A McKinsey Global Survey conducted in 2020 reports that companies have not only seen a four-fold acceleration of digitization, but also that the share of their digitally enabled products accelerated by a whopping seven years.
What’s more, McKinsey analysts noted that executives at organizations that experimented with new technologies during the crisis, and those that invested more in digital tech, were twice as likely to report “outsize revenue growth” than their peers did.
As a business strategy, digital transformation can be achieved in different ways in different industrial settings, but it boils down to tapping into digital resources that can reformulate strategies for developing products or processes. And if digital transformation is the end goal, product lifecycle management software is the platform that orients an enterprise toward a direction that will help it achieve specific outcomes, argued Mark Reisig, VP of Product Marketing for Aras Corp., Andover, Mass.
The following pointers on PLM may be helpful for manufacturing decision-makers aiming to turn product data into assets that complement operations.
Connect business processes to decision-making data. Product lifecycle management (PLM) is the process of managing the entire lifecycle of a product from inception; through engineering, design and manufacturing; to service and disposal of manufactured products. It assists manufacturing companies in their search for innovation and added value across the extended enterprise. PLM is as much about the people, methods and processes as it is about harnessing software solutions for coordinating the development of products.
Taking a practical approach to PLM, manufacturers may connect business processes to the right data to facilitate better decision-making and enhance operational efficiency, explained Reisig. Instead of opting for a solution that relies on a monolithic platform, he contends that the PLM platform should be open, adaptive and sustainable.
Kawasaki Heavy Industries (KHI), which uses Aras PLM software platform-as-a-service, is an excellent example. A global manufacturer of equipment for the aerospace, energy, industrial equipment, power, rolling stock and shipping industries, KHI was challenged to respond to design and development requirements unique to each of its industry segments. KHI elected to leverage PLM digital technologies for product development and cost optimization across the group.
Adopting the Aras PLM platform enabled efficient sharing of process innovations across core areas such as data management, bill of materials management, document management, sales information management, design data management, infrastructure system collaboration, visual collaboration, self-service reporting and mobile applications. KHI reported that using a unified platform significantly enhanced levels of standardization and process improvements between companies.
Foster a culture of innovation by collaborating across disciplines. Connecting users across engineering, manufacturing or maintenance disciplines is the backbone to successful collaboration, said Reisig. Successful PLM implementations for industrial environments hinge on the extent to which users can trace component parts across their lifecycles, he added.
“Consider a wind turbine: Using the PLM, I can look at a part, query and say, ‘show me all the parts that have had a failure in the past year,’” he explained. “I can narrow down one part and see all the related items [such as service bulletins and who’s worked on it]. I can literally graphically trace back into engineering and see the latest part that’s been used, I can see the simulations that have been run against it, I can go all the way back to the various requirements for software, hardware, or attributes used in the software that are in the requirements. This traceability is an inherent digital thread.”
When engineers design a product from multiple CAD sources, said Reisig, that data can be converted to show the product in a “lightweight” 3D visual. “All of the data, no matter where you are in the product lifecycle, can be used in any of the applications,” he noted.
“The digital query or graphical representation of the wind turbine in this case is no longer a CAD visualization, because it didn’t come from just one CAD vendor or one group of CAD users,” explained Reisig. “Instead, it came from CAD users who worked on that engine, some of which stemmed from third parties and who may have worked in SolidWorks versus Annex—and all of the data were converted so users could use their own terminology.”
Greater access to data facilitates collaboration, so engineers may communicate efficiently with systems engineers or quality control. Instead of top-down decision-making, data sharing enables problem solving across organizations. “And that leads to innovation,” said Reisig.
Unlock a digital supplier interface with secure permissions capabilities. By any measure, data protection makes data synchronization a complex task. Along with ensuring secure web access, permissions and compliance with ITAR (International Traffic in Arms Regulations), making decisions on who a company will collaborate with in an open system can be challenging.
Interacting with third parties, password guessing, rogue employees who steal data and phishing (luring employees to click a link that leads to malware) are examples of common breaches. The risk is even greater for companies that work with aerospace and defense due to the sensitive nature of the industry.
Aras works with companies in aerospace (Airbus), defense (Kawasaki Heavy Industries), automotive (Audi, Honda, GM, Denso) and electronics (Microsoft), and has placed a heavy focus on balancing between security permissions to allow openness and flexibility for each company to work at its convenience. Sharing data with suppliers is managed through a supplier portal capability, which includes permission capabilities for need-to-know scenarios and setting boundaries for restricted access based on geography.
“A wide range of supplier collaboration use cases allows multiple suppliers to either exchange information or collaborate in common space,” said Reisig.
Pave the way by avoiding siloes. Product lifecycle management has a storied past that can be traced as far back as the 1930s. For several decades the concept remained more theory than a practical managerial instrument. But it wasn’t until 1985, when automaker American Motors Company (AMC) took a hard look at ways to speed up its product development processes, that industries fully acknowledged the PLM’s practical role as a life-giving competitive power.
Two technologies are credited for enhancing the automaker’s capabilities for producing its prime SUV products (Jeep Cherokee) at the time. The first was to bolster engineering productivity with the use of computer-aided design software systems. The second was to co-opt a centralized database—a PLM system—for communicating engineering changes and resolving conflicts quickly.
AMC’s product data management system was effective and was subsequently adopted by Chrysler after acquiring AMC. PLM software is cited as the crucial technology that steered Chrysler to becoming the auto industry’s lowest-cost producer by the mid-1990s.
Throughout the 2000s, PLM was popularized as companies looked for ways to organize increasingly complex products. Reisig explained that as requirements grew more sophisticated and software such as asset lifecycle management (ALM) was developed, various software modules would be employed to manage the software systems separately from the product (such as a car). Disparate modules for requirements, quality and project management would be managed by separate business lines.
Acquiring software systems was common practise for asset intensive organizations, noted Reisig. “If you look back at a Siemens, PTC or Dassault, billions of dollars were spent on acquiring companies to build up portfolios of PLM products,” he said.
But the problem with acquiring different technologies and performing integrations, according to Reisig, was that it instilled a culture and protocols that lacked a defined feedback mechanism to the PLM system.
“An average Fortune 500 company has somewhere between 500 and 2,000 different applications for a product used across them,” pointed out Reisig. This underpins the historical truth that different groups are managed by different people with different budgets and the likelihood that software modules are not built on the same technology stack. “If your supplier or your software vendor has multiple products, the ability for the software department, hardware engineers, electronics and systems engineers to collaborate [will be stunted],” he said.
What the evolution of PLM has taught us is that different disciplines have to talk to each other, asserts Reisig. Today’s smart connected products drive interactions and require ongoing coordination across multiple functions, from design, engineering and operations through IT, sales and service. And although many PLM systems are designed as out-of-the-box solutions, he said they are not useful for sophisticated companies, as they are neither equipped to anticipate their future needs (stemming from a silo effect), nor are they designed to be customized.
Most mature software providers that started with on-premises solutions are adopting cloud-native software-as-a-service (SaaS) business models, according to CIMdata, a consulting firm that focuses exclusively on PLM and how it enables digital transformation. What’s notable about the SaaS approach is that the systems are already set up to allow for regular upgrades and ensure updated capabilities are available.
Yet, digital initiatives invariably hit a roadblock when the tools are not compatible or unable to support an integrated end-to-end digital thread. “In the PLM industry, once you’ve customized something it will cost roughly a million dollars to attempt to upgrade it and people tend not to upgrade them,” said Reisig. “Among the three major competitors, customers average between eight and 12 years for an upgrade.”
This is where Aras was able to differentiate. The company’s founder, Peter Schrorer, realized early on that “everything is going to change” and had the foresight to build an open PLM platform, explained Reisig. The platform allows new functionality to be added to its application services, as opposed to the product, and a modeling engine allows users to develop or download an application for free.
What’s Ahead
As product companies weigh their options, the question to be answered is neither whether their PLM suite is a high-risk or a high-reward solution, nor what kind of return on investment it can show during optimal cycles. Rather, the trend—and the pressure—for vendors over the course of the next phase of PLM evolution is to demonstrate whether shuttling data from complex machines that blend mechanical, electronic and software engineering disciplines can be implemented with minimum disruption to current workflows and whether the solution has the resilience to withstand trying times.
Reisig expects manufacturers will look toward model-based systems engineering, artificial intelligence (AI), machine learning and simulation capabilities that provide both agility and opportunity. Having analytics that inform real decisions on how to improve products is game-changing, he said.