Outlook 2023: 5 Ways PLM Will Foster Innovation and Resilience

Over the past year, various market researchers have predicted that the global PLM market is likely to grow at 4 to 9 percent CAGR between now and 2030. However, it helps to contextualize these predictions with the tough economic times ahead, which are being caused by factors ranging from COVID-19, war, inflation, component shortages and continuing direct and indirect supply chain issues.

To that end, in a recent article “Predictions 2023: Future Fit Tech Leaders Will Seize The Day In Tough Economic Times,” Forrester suggested that “80 percent of companies will pivot innovation efforts from creativity to resilience” in the next year or two. The point is reenforced by Matthew Guarini, VP, Research Director at Forrester, highlighting the technology perspective that sums up the annual prediction: “Companies continue to innovate, but they aren’t always getting the customer experience benefits they seek. In 2023, tech execs will use a pragmatic approach to innovation, focusing initiatives on modernizing business processes, automation, supply risk management and employee experience.”

Tech execs care about technology-related business transformation investments, leveraging tactical tools, enabling technologies and implementing strategic digital enterprise platforms to support and sometimes foster the associated transformation and business value. But it is critical to define how PLM investments contribute to building product innovation and creating new business opportunities. In other words, during difficult economic times tech execs are building the relevant foundations for current resilience and future growth.

In this post, I speculate about PLM trends for 2023. I’m not trying to read a crystal ball about the future of PLM. Rather, I’m elaborating on five hypotheses about how PLM can foster product innovation and contribute to business resilience in times of difficult economic turmoil and global uncertainty.

PLM is not a technology; digital transformation plays a role in enabling PLM—independently of the business scope or industry it relates to. For instance, in Gartner’s “Product Lifecycle Management Primer for 2022,” it defines PLM as “the set of ongoing activities needed to maintain and grow an existing product or service throughout its lifecycle. These activities include continued innovation and evolution while maintaining financial growth.”

Furthermore, Gartner elaborates on the product manager perspective: “Managing innovation and evolution, optimizing lifecycle events, achieving financial objectives and harnessing disruption are key challenges that have transformed in recent years. The modern world of technology, hardware, software and services requires apt attention to market conditions and customer expectations that are changing rapidly. Many challenges of maintaining and growing an in-life product are variations of those challenges in bringing it to initial market launch, but some are unique to in-life products.”

Following from this year, 2023 is set to bring its own lot of challenges. And in so doing, the value delivered by integrated PLM practices can be summed up across the following five 2023 PLM hypotheses towards innovation and sustainable business resilience:

1. Enabling work anywhere-anytime in the post Covid-19 era: PLM processes and associated technologies will contribute to drive forward new enterprise ways of working as the basis of the new normal.
2. Driving supply chain efficiency and product innovation towards operational resilience: PLM process adherence and data standardization will drive better product quality, alternate and substitute management and continuous compliance reporting.
3. Bolstering cross-business integration, including new cross-functional perspectives such as enabling the ESG agenda: PLM will drive further data continuity across functions, product lines, factories, internal and external teams, contributing to better collaboration, better sustainability target tracking and feedback loops.
4. Improving user experience across end-to-end processes, leveraging cross-platform analytics and end-to-end data consolidation: PLM will contribute to drive more informed decision-making, better product innovation governance, and in turn better data standards and quality.
5. Leveraging virtual models and digital twins to drive effective operations and feed product innovation cycles: PLM will foster better virtual representations of the physical world, driving product continuous improvement and optimization.

Let’s dig deeper.

The coronavirus pandemic significantly affected the labor market and how products are defined, developed, manufactured and deployed to markets. There are still debates on whether the world will eventually return to the pre-2020, old normal—seems very unlikely. Remote working arrangements have been widespread, impacting both productivity and locality—translating in different ways for manual (blue-collar) and non-manual work (white-collar) workers.

Remote working influenced a move towards software subscriptions and process redesign to support a work anywhere-anytime model. This ranges from virtual conferencing tools to globally accessible enterprise PLM and ERP platforms. It implies leveraging web-based applications and globally distributed cloud infrastructures to sustain non-functional and accessibility requirements. Beyond technical considerations, core differentiators remain the ability to simplify functional processes and improve user experience, while driving effectiveness, scalability and efficiency. The rise of business analytics is also to support the ability to streamline processes and drive effective decision-making throughout.

Lean product development is becoming the new norm when it comes to open innovation, towards reducing iteration cycles, supplier and production costs due to late issue discovery. It is a matter of improving the flow of information using visual work planning and generating timely actionable operational insights.

The COVID-19 pandemic has amplified the global semiconductor chip shortage through rising demand for electronics amplified by disturbed logistics and bottlenecks as the world emerged from several lockdowns. The ability to manage concurrent product variability and continuously assess impacts from supply chain delivery and logistics is becoming a mandatory requirement when accounting for substitutes and alternates.

Data continuity implies a data thread or, most likely, multiple threads joining the dots across authoring sources and consuming platforms—not only breaking functional silos but also overcoming technical barriers. New integration platforms and service models are emerging to those effects, facilitating how interfaces are developed and maintained.

The sustainability agenda also is clearly in the spotlight, though not only for corporate sustainability reporting purpose. ESG has the potential to drive product innovation and integrated regulatory compliance throughout the business ecosystem and associated engagement model. Furthermore, technologies related to blockchain, and cybersecurity can contribute to better data traceability and product integrity management.

Process simplification, user and consumer experience improvement, and application rationalization are on every organization’s transformation agenda. Pretty much everything nowadays is geared towards better experience; and user experience must be seen as a subset of customer experience towards:

• Finding information quickly, reliably and easily.
• Following a process or complete an activity easily.
• Interacting pleasantly and professionally across boundaries (teams, organizations, product portfolios, etc.)
• Perceiving the value of a product or service; not only from a usability perspective, but from an overall interaction perspective.

A great experience implies contextual data and credible information that one can trust. It is about visibility, consistency and timeliness—and in turn loyalty and brand advocacy. Now customer experience is not only a marketing buzzword. It relates to business collaboration, decision-making, process improvement and continuous product innovation.

Virtual models allow for complexity management through simulation of the physical world, either for a predictive or preventive purpose, scenario planning, impact assessment or driving iterative development. Going forward, digital twins are becoming more realistic, more accurate, more predictive, more dynamic and more timely representations of the real. They are certainly under exploited today and new applications are yet to be discovered.

Effective digital twin management implies robust data collection, consumption and lifecycle management. It also implies the ability to combine analytics at source and cross-model / cross-platform. The ability to combine smart models, digital twins and associated asset information models is likely to accelerate, leveraging technology advances such as AI and machine learning technologies and algorithms.

What are your thoughts?