Product lifecycle management encompasses a world of contradictions and misunderstandings; this is often—but not always—due to sales and marketing jargon and a lack of consistent language across the discipline. This article will attempt to describe, perhaps decrypt, some of these challenges. We’ll also provide suggestions on how to reduce levels of confusion and avoid delays. In other words, we will act rather than procrastinate.
Ambiguity Fuels Procrastination
Nowadays, digital transformation is the new norm—unfortunately bringing consulting jargon to a new level. Who hasn’t heard, read or written something like the following?
Digital operating platforms provide opportunities to leverage business change and digital twins, connecting the dots across the value chain, breaking organizational silos and integrating data across the digital thread; adopting model-based enterprise approaches contribute to creating closed loops through smart operations, from planning to producing and maintaining smart products
How ambiguous or re-assuring is the above statement? Does it sound like a good goal? Informative about how to embark on a new change initiative? Short answer: not really.
This statement does not suggest what capabilities to implement, what value technology brings, where to start, what capabilities to consider, what or how to prioritize, how to connect the dots to maximize value, how to assess what needs to change, etc.
PLM buzzwords do not help to implement change or solve problems; they create confusion by depicting abstract concepts and not focusing on business centric stories. The lack of consistent or recognized PLM standards also contributes to fueling the confusion, ultimately delaying initiative launches and associated benefit realization as organizations spend more time debating what to do rather than doing it.
Executives should not have to align their understanding to how vendors or consultants position a solution. Instead, vendors should translate propositions to business-friendly language and focus on how to create value and solve problems—with the appropriate industry language and recognized standards (when they exist). This should encourage the industry to fight for standardization and adoption, rather than jumping on a marketing bandwagon by a PLM vendor or be distracted by hype. It is also essential to contextualize how PLM fits in the wider digital landscape, among ERP, MES, IoT, CRM, etc. and the digital thread.
Focus on a Clear Business Purpose
Business pain points cannot be effectively addressed when problem statements are not clearly documented and agreed on—or similarly, the new requirement to fill a capability gap. Defining the relevant business imperative to address these issues or gaps can easily get done with a clear problem statement. Focus brings knowledge and purpose; helping define the potential of doing something to improve operations and proactively reduce business risk—rather than waiting for something to break.
We live in the era of digital everything and “smart” replacing “automated.” The fact is that numerous smart processes are simply run manually with low-cost labor. Examples include Amazon and, no doubt, many other companies that uses humans to “train” machine-learning systems at repetitive data analysis tasks that are not easy to program.
PLM provides a framework to solve business issues through people, process and tool-related changes across the product lifecycle. The product lifecycle does not stop one production starts; gradually throughout product development, products and processes become more controlled and regulated by standards. Operations data analytics are critical to understanding data issue implications downstream from product development, production/assembly, and when used in the field. Rolling up these issues can help with swifter issue resolution.
Understand the Cost of Doing Nothing
Doing nothing can have a tremendous cost in the long run—the value that could be realized from implementing improvements is the indirect translation of the cost of doing nothing. Moreover, indirect cost if not static and can escalate exponentially when trying to scale operations, onboard new suppliers, initiate new programs, etc.
Direct cost includes running ineffective operations:
- Recurring business and technical support of sub-optimum systems and processes.
- Manual data processing and recurring issues leading to ineffective governance—whereas continuous automation could optimize essential non value-added activities and remove non value-added activities.
- Increasing physical prototype, manufacturing and assembly issues.
- Ineffective outsourcing to mitigate or as a consequence of the above, and due to the lack of scalability and efficiency.
Cost and revenue in context of a given capability and related operations are ultimately the business language; understanding the cost of doing nothing can easily contribute to understanding potential value in doing something to address these gaps. Because PLM covers such a wide business scope, prioritization of improvement opportunities must become part of an ongoing strategic assessment and continuous improvement portfolio. Defining incentives for teams to be aware and communicate (in business terms) about their PLM-related improvement opportunities can help fight PLM procrastination.
Foster a Culture of Experimentation
Staying ahead of the “PLM curve” or simply the “technology curve” is a means to an end, not an end in of itself. Having said that, teams must be allowed to experiment and test (and learn from) new technical solutions. Building the business case for any PLM investment often requires a level of pre-study or experimentation. To be effective and prioritized, such experimentation needs to be holistically governed across PLM, ERP, MES, CRM, etc.
As much as PLM claims to bridge organizational silos, there is also value in fostering cross-functional experimentation and learning. Ongoing assessment can help define and maintain the business case for future investment and assess when the time is right to initiate business change initiatives.
Many organizations focus on small automation and short-term fixes without a clear understanding on how this should evolve in the longer term. This can have the countereffect of creating immovable and costly pockets of business knowledge which are difficult to translate into effective improvements; these contribute to PLM procrastination and easily fall under the “operational radar” when deciding on implementing new digital enterprise platforms.
Get PLM Rolling as Part of the Wider Business Transformation Strategy
Business executives do not care about the meaning of PLM, ERP, MES, CRM; they care about results. Delivering results from digital transformation require understanding of the underpinning platforms, processes and systems, including PLM, ERP, MES, CRM, etc.
Certain business functions are more prone to accept change than others. PLM-related disciplines are typically “owned” by technical teams which author the core data that PLM governs; change is not always easy as engineers are typically averse to it. Involving the wider enterprise in PLM can contribute to better collaboration as part of the change initiative and extending that collaboration once the change is implemented.
Balancing the improvement and business change portfolio is essential to deliver an integrated and consistent digital thread across the organization. This is becoming even more important in the light of recent COVID-19 implications and all employees and suppliers must be able to operate seamlessly across these platforms.
Defining a robust digital strategy across all core enterprise disciplines makes sense—not necessarily to align to the same level of maturity across the enterprise, but to ensure business value is consistently generated at all levels of the organization. Managing expectations as part of a continuous improvement portfolio can help maintain awareness of root causes for inefficiencies, understand the cost of doing nothing and foster a culture of experimentation. In turn, teams will be able to prioritize change and stay ahead of the curve.