Product complexity was the elephant in the room in 2017, as companies across aerospace and defense,
automotive and industrial manufacturing looked to differentiate their offerings and create disruptive business models. These challenges exposed significant gaps in their existing processes. As a result, product lifecycle management (PLM) found a resurgence as manufacturers recognized the need for a new generation of PLM to manage their product processes—one that legacy systems have failed to provide.
Industry analysts took notice of this new thinking. CIMdata released a formal definition for a “Product Innovation Platform”—an innovation-enabling business platform that would support all product-related disciplines and users through the entire product lifecycle.
It was a big year for resetting old conceptions of PLM and defining what next-generation PLM can accomplish. So what’s on deck for 2018? We have four predictions that could significantly shape manufacturers’ businesses in 2018.
Prediction #1: PLM becomes an enterprise platform for innovation
Manufacturers will find even more ways to create value using next-generation PLM product platforms, primarily by addressing the disconnected processes that have arisen from legacy PLM. Today’s world is about the business of engineering—the development of smart, connected products that require complex, cross-discipline processes and data exchange. Design disciplines across software, electrical and mechanical systems have been siloed for too long. Next-generation PLM is addressing these issues by connecting functions and domains for improved collaboration. We expect leading-edge companies to implement deeper connections to enterprise systems such as ERP and CRM, both internally and with suppliers. Ultimately, the result will be digital transformation that yields a competitive advantage.
Prediction #2: Increasing product complexity will impact shareholder value
Manufacturers’ ability to master product complexity will begin to impact shareholder value in 2018. That is, companies that deliver on their promises to develop next-gen products will be rewarded, and those who have underestimated what it takes to get there will be held accountable. Already, auto manufacturers have put stakes in the ground to deliver Level 4 autonomous cars at scale by 2021. Shareholders have bought into these bold goals. Companies’ ability to execute will be tested. We’ve already seen how this has played out with a company like Tesla.
These organizations that have set high investor expectations tied to next-generation products need to make sure they have the systems and IT infrastructure required to get them there. Today, many of these companies rely heavily on spreadsheets, and they need to ask themselves how successful a path that is to creating shareholder value.
Prediction #3: Digital twin moves from marketing term to manufacturing reality
Will 2018 finally be the year digital twin gets some teeth? We’ve heard a lot of hype at many events about the promise of the digital twin. Every organization seems to have a different viewpoint about what it is and how it can create value. Frankly…it’s very confusing and hard to discern exactly how digital twin can be of value to an organization.
We expect this all to come to a head in 2018 as a formal definition of what the digital twin is—and isn’t—gets established, as well as the use cases that create value. Digital twin must ultimately convey a range of data sets and context that describe the product at a point in time. The question we believe manufacturers will focus on is “Does the digital twin tell me the exact configuration of the asset I am designing, manufacturing and maintaining?”
Prediction #4: MBSE momentum creates shortage of systems-engineering talent
Manufacturers who are building complex products are increasingly turning to Model-Based Systems Engineering (MBSE) to accelerate early design. Pioneered by the aerospace and defense industries, MBSE is gaining momentum in automotive and industrial manufacturing. The good news: MBSE enables systems-level design and improves cross-discipline collaboration. The bad news: the lack of experienced systems engineers may hinder initial progress.
Why? We have seen previous examples where disciplines gained popularity because of the improvements they brought to manufacturing. Six Sigma is a prime example where organizations quickly found a shortage of “Black Belts” able to manage the processes. The solution was looking from within for people who could be retrained to support the new strategic direction. We expect the same for MBSE. Within the existing major disciplines, we expect software or electronics engineers to be strong candidates for systems-engineering training, particularly as software becomes a larger driver of product functionality.
