Just How Clear was our 2018 Crystal Ball?

Update! Tackling Complexity in 2018: PLM Predictions follow up for Manufacturers

The major theme in 2018 was how manufacturers of complex products can adapt and differentiate their offerings by creating innovative products and business models that stand out from the crowd. In addition, as many of our customers found, addressing the gaps required to support product complexity can uncover serious challenges, such as broken processes, missing solution capability, and resource gaps. As a result, reinvigorating product lifecycle management (PLM) technology and processes is finding a resurgence as manufacturers recognize the need for a new generation of PLM to manage their business—a promise legacy PLM providers failed to provide.

OK, now down to business. It is time to review our predictions for 2018 in order to take stock of how things progressed in the realm of product complexity. Use this update as a measuring stick to see what you thought of some larger topics surrounding the product lifecycle and where your organization stands in terms of how you approach product complexity. Let us review the four predictions for manufacturers we offered up at the beginning of the year to see how they shaped our customers’ approaches to the product lifecycle in 2018.

Prediction #1: PLM Becomes an Enterprise Platform for Innovation

We expect leading edge companies to implement deeper connections to enterprise systems such as ERP, CRM, both internally and with suppliers. Ultimately, the results will be digital transformation that yields a competitive advantage.

We were about 25% right—this prediction is coming to fruition, but more time and momentum is required for PLM to become an Enterprise Platform. We are definitely trending in the right direction.  Why? The Big 4 PLM vendors—Aras, Dassault, PTC, and Siemens have all taken strides this year to invest in capabilities through acquisitions and product development to leverage valuable data created during the Engineering, Manufacturing, and Service stages of the product lifecycle. 

First, Siemens, purchased low-code application developer Mendix. The product strategy of Mendix is to allow the ENTIRE workforce to participate in the application delivery process in order to speed application development—allowing for an increased velocity of applications for the enterprise.   

Another example of platforms dedicated to engineering expanding the enterprise is Dassault’s purchase of IQMS—a manufacturing ERP company. With this acquisition, Dassault extends their platform to small and medium sized manufacturing companies to transform business operations—managing engineering, manufacturing, and business ecosystems.

The Aras PLM Platform reaches out and shares information to our customers’ supplier networks. This secure connection extends PLM capabilities out to a manufacturer’s partners and suppliers via a solution named Secure External Access. This connects a manufacturer’s ecosystem to PLM information, while providing highly secure access controls to manage intellectual property.  

As the value of accessing data surrounding complex products increases, we expect to see more expansion of PLM into other solution areas, such as Customer Resource Management and Enterprise Resource Planning solutions.

Prediction #2: Increasing Product Complexity Will Impact Shareholder Value

Manufacturers’ ability to master product complexity will begin to impact shareholder value. 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.

100% correct in our prediction, although when we wrote this we were assuming that organizations might be caught unprepared for product complexity and that would have a negative effect on shareholder value. However, there were also cases where organizations already recognized the value of data emerging from their products in the field and endeavored to preserve their shareholder value by making big strides in protecting it. 

For example, John Deere has been moving fast and furious, state by state, to protect intellectual property and services they want to provide to their customers based on data generated from their products. Recently they have won some key battles in states like California—essentially locking out anyone but John Deere from having the ability to diagnose and recommend maintenance fixes. 

In the automotive industry, transformation is occurring at a rapid pace as they prepare for an autonomous future. Announcements this year from the big players show the product complexity transformation has begun for an autonomous, connected, and electric future. Addressing this new product mix has affected shareholder value negatively, but long-term value will hold true as they take strides to disrupt their traditional business model and product mix in order to prepare for a future where large OEMs can build product complexity at scale. 

Prediction #3: Digital Twin Moves from Marketing Term to Manufacturing Reality

A formal definition of what the Digital Twin is—and is not—is finally 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?”

Not correct! There is still way too much confusion and less clarity as to what a Digital Twin is and how it can be used to create value across the product lifecycle. This has not become manufacturing reality yet. Why? The vendor community continues to put a spin on it, based on pre-existing solutions and capabilities, to try to sell a sexy vision of the future. Unfortunately, most Digital Twin projects are pointing to limited value, on pilots only.

A good example of this is the thought that a simulation model, created during the engineering phase, is the Digital Twin. This is incorrect, there is no way a Digital Model will remain relevant as an exact comparison to the physical product after it goes through the manufacturing process, let alone as it operates in the field. Too many changes occur in the configuration for the simulation model to provide the context needed to reflect what its performance is in the field.

Why? Products are not manufactured and maintained in the same way. Configurations of physical products change as they are built, when they are shipped, as they are installed. They can change after testing and configurations change through continuous operations and maintenance activity. Therefore, the first priority is to get your configuration house in order.

Stay tuned in 2019 for more information on our view on Digital Twins and their capabilities!

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 & Defense industry, 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.

First, a little fun fact. We were so right about the demand for Systems Engineers that “Indeed,” a worldwide employment related search engine for job listings, made sure to put “System Engineer” in its search bar for its commercials! See it here:  https://www.ispot.tv/ad/w0hm/indeed-work-is-changing

In addition, as witnessed at Aras’ customer conferences, ACE and ACE Europe this year, our customers presented more “V-Diagrams” than any time previously. Why? As product complexity continues to increase, the traditional tools used to engineer a product just will not cut it anymore. In order to manage a system of systems and compete, you need to verify system behavior and design throughout the product lifecycle. All of this leads to the increasing need for Systems Engineering talent this year and it will undoubtedly continue into the near future.

To learn more about the challenges of Systems Engineering, check out our new eBook.

2018 is coming to an end, but product complexity will continue to accelerate

Coming up with, and then grading predictions, is a fun and engaging intellectual activity. However, there is a value in looking to the year ahead. These predictions tee up and provide context for our customers on projects they are looking into as they tackle product complexity. It is important to use these predictions as a comparison to the projects in which you are investing. They can provide a perspective on whether the projects you are engaging in are high risk and if you are considering the big picture regarding the value they may provide.  

This year was all about the emergence of product companies changing the playing field with their offerings. To do this, they recognized that a renewed focus on PLM was required to control the many disciplines of engineering required to create market-leading solutions.

New challenges will emerge in 2019. It appears that global economic growth may be in jeopardy and the potential to settle into an era of cost cutting may emerge. Therefore, products and their related quality and innovation will determine who continues to be relevant, as times get tough. Those willing to address the gaps in process and technology and double down on the promise of PLM, with vendors who truly understand what it takes to get there, will be successful in their product complexity endeavors. 

We look forward to monitoring the PLM landscape to see what innovations emerge from our customers using a Product Innovation Platform approach to PLM. Wishing you the very best holiday season and a Happy New Year!

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