Five Digital Transformation Failures and How to Avoid Them
Up to 70 percent of digital transformations fail: Don’t be one of them
Technology innovations for products and services are advancing rapidly. But manufacturers increasingly find their product development systems are unable to support what it takes to bring these innovations to market.
The 2019 Product Life Cycle Management Market Analysis Report from consulting firm CIMdata states that digitalization and complexity are driving several major trends in the product engineering discipline, including product innovation platforms, modeling and simulation platforms, model-based systems engineering, and democratization of modeling and simulation.
These critical business strategies will drive rapid and significant changes in business processes, and other changes will only continue to accelerate with the next generation of PLM software. Specifically, the success of any product development software will not only be based on the quality of the applications, but on the end-to-end platform’s ability to adapt to changing business processes and evolve with technical trends.
According to market research firm International Data Corp., digital transformation spending will reach $2.3 trillion by 2023. This is not surprising. Businesses must either produce next-generation products with next-generation capabilities, or they will go out of business. But, the challenge companies face is that up to 70 percent of digital transformations fail. What’s more, digital transformation is, ironically, more difficult for larger companies than smaller ones. In fact, according to a 2019 study by consulting firm Gartner Inc., “Through 2021, digital transformation initiatives will take large traditional enterprises, on average, twice as long and cost twice as much as anticipated.”
How can your organization avoid a digital transformation failure? Here’s a look at the five most common reasons for digital transformation failure.
Failure 1: Glad That’s Over With!
Assuming that “digital transformation” is a one-time event doesn’t address the ever-accelerating pace of innovation. That leaves companies with a rigid product development ecosystem that can’t handle the next wave of innovation in engineering, manufacturing and service.
The goal is to reduce technical debt—to eradicate systems that cost more in maintenance and upkeep than they deliver in value. New resilient platform technologies can improve that calculus.
Technical debt occurs when an organization deploys and customizes an application based on a rigid technology stack and schema. This creates instant legacy—an inability to quickly adapt and upgrade when the business needs it to. If the majority of your IT budget is consumed maintaining legacy systems that are keeping your organization running at status quo while the rest of the market pulls ahead, then your ability to transform is greatly diminished.
The number of companies suffering from mounting technical debt is verging on an epidemic. According to survey by Accenture Strategy, 85 percent of executives stated that legacy technology hampers their digital transformation efforts. To sustain transformation and continue evolving their products and business strategies, manufacturers must have flexible product development systems.
Failure 2: The Right Vendor Will Solve All Our Problems!
Aiming to “standardize across the organization” on one vendor’s tool set is a surefire way for engineers to start developing workarounds. Domain experts are the lifeblood of product development, and their ability to innovate relies on their freedom to choose the best tools for the job.
Product development technologies that are open to work with existing tools and can integrate seamlessly with new tools in the future will help ease the burden of digital transformation on domain experts and eradicate the workarounds that thwart transformation. This openness can scale digital transformations across internal stakeholders as well as value chain partners, where standardization on one tool set is even less likely.
Failure 3: It’s Still in Pilot.
Digital transformation often begins with a pilot project, but it shouldn’t end there. Technologies that create new islands of information don’t accelerate change: They impede it. Information that resides in a system no one can access, except those who put it in there, doesn’t deliver value to other teams.
Likewise, technology that only applies to the one or two limited use cases in pilot and can’t grow beyond its scope won’t be able to expand digital transformation across the organization. Limiting reuse, impeding information flow and obscuring traceability sets digital transformation back for all but a few people, who are now more disconnected from the technology ecosystem than before.
Failure 4: It’s Someone Else’s Job.
There are always people problems when it comes to change, but sometimes organizational leadership creates them. Assigning change to one department, pilot or vendor belies the central challenge of change: owning it as a company.
Engineers need more, not less, agility with, and access to, their information—which is why manufacturers are increasingly ‘in-sourcing’ their IT. Engineers should be able to freely change, expand and extend the company’s product development technologies—via integrations, configurations, and customizations or even by authoring new applications within it. The ease and level of control with which teams can manage your digital transformation relies on selecting tools with open architectures, connectors, application program interfaces and authoring environments.
Failure 5: If Only We Had More Data.
Transformation needs more knowledge—not more data. Servers full of data your teams can’t make sense of are not useful. Value resides in the knowledge acquired from that data and delivered to internal and external teams who can use it to improve your products and business strategies.
Analyzing product data to develop knowledge and insights relies on context. Context is found in the product life cycle, starting upstream with the requirements products were designed to meet; continuing throughout their evolution across engineering domains, supply chains, manufacturing and service; and extending to how customers experienced those products—the ultimate success or failure in meeting those initial requirements. Analyzing product data requires access to this context, and an understanding of the connections between processes, and the data they generate, across the end-to-end product life cycle.
Getting Transformation Right.
Acknowledging that “change is the only constant” will help companies choose product development systems that can keep up with the fast pace of innovation. Platform technologies are increasingly popular options. To support changing processes, they should offer agile deployment, allowing for flexibility not just during deployment but throughout their use, as well. To support changing tools and domains, they should offer a wide range of integrations and connections among tools internal and external teams already use, or want to add in the future.
Aras Innovator PLM software has been designed from the ground up with resiliency in mind. The purpose is to give companies the scalability and flexibility required while ensuring upgradeability even when applications are heavily customized. The underlying Aras PLM platform technology makes this possible. The combination of Aras Innovator’s unique model-based approach and service-oriented architecture (SOA) ensures flexibility and scalability whether running PLM applications out-of-the-box or highly customized.
To provide engineers with control over your data, analyses and business processes, platforms should be open and authorable, supporting configurability, customizability and easy ways to create new applications that share data seamlessly with existing applications. Platforms to support manufacturers must enforce connectivity and change-management throughout the data and processes across the entire product life cycle.
For more information on PLM software, call Aras at 978-806-9400 and www.aras.com.