Managing PLM Projects: How Do They Compare to ERP Projects?
PLM projects, like other digital transformation initiatives, can be deemed successful when they deliver upon the promise of business change and capabilities as scoped in their business case. At a macro level, success can be derived in many ways: proactive business engagement to final functional acceptance, timely deployment, accurate data migration, effective training and knowledge transfer; most of which are typical deliverables of the implementation team.
Typical critical success factors (CSFs) include the validation that the selected platform, software vendor and implementation partner(s) are fit-for-purpose. These are important factors as implementing PLM is often referred as a ‘journey of discovery’ coupled with ongoing change leadership. When compared with ERP projects, a number of common and unique challenges often arise when dealing with PLM—especially in terms of technical, business engagement and change risk mitigation. This is not to say that ERP implementations are more predictable than PLM implementations; they are however more mainstream compared to the wide domain scope covered by PLM.
In this post, I elaborate on CSFs for PLM projects and how they compare to CSFs for ERP projects.
“One simple difference is that ERP optimizes what you have (existing and physical products), while PLM optimizes what you do not have (new products, simulations, virtual representations, intellectual property and knowledge).”
Elaborating on the types of business benefit that PLM and ERP bring help understand what to expect from successful enterprise platform and alike implementations. Defining digitalization success includes looking at business change, the adoption of new working models and practices, levels of automation, team redesign, collaboration intensity, data access and reusability, etc.
Defining PLM success: 5 High-Level CSFs
Success can typically be defined in terms of business adoption, IT / technical solution stability, robustness, future-readiness, etc. and also in terms of business value. With PLM, such value is hard to measure as it often implies cost avoidance, risk mitigation or benefit realization that are not directly or accurately quantified.
Typically, critical success factors (CSFs) illustrate how to define success—not to confuse with key performance indicators (KPIs) which reflect how to measure success. For example:
CSF (define success): sell more products
KPIs (measure performance):
Number of products sold this month, by product line, by region (unit: number)
Monthly product sales revenue and profit for the above (unit: financial currency)
Time series of the above for the last 12 months, from one month to the next (unit: number or financial currency)
Incremental sales compared to budgeted, quarterly forecast and latest execution plan, compared with results from last financial year (unit: %age)
Broadly speaking, PLM success can typically be measured by a combination of 5 criteriums across people, data, process and tool dimensions:
Key user and change lead sign-off, post UAT and deployment / data migration is the obvious generic success criteria and indicator a good business engagement in defining and testing the solution; arguably, that however does not imply value realization.
Platform adoption, learning engagement and usability: adoption of the new working practices / tools, once the solution is ‘live’ and transition from the ‘old ways’ is complete; this is mostly relevant as digital change often come with a dip in performance and time to adapt.
Data accuracy, quality, reusability (often part of the PLM business case): a measure of how new data quality is improved, reduction of number of issues, of time and cost it takes to resolve them, of business implications from these issues, etc.
Platform and data accessibility, i.e. the ability to find information, share with other, collaborate more effectively, and many other measures which relates to effective operations.
Sustainable platform for growth, fostering scalable and repeatable processes across the value chain (holistically, not only pin-point solutions).
Scope clarity: by design, ERP projects and solutions are often more modular than PLM; perhaps this is due to greater process and stakeholder ownership clarity with most business capability, whereas the PLM scope is often more nebulous and subjects of misalignment between functional groups; PLM requirements can rapidly span out-of-control is the scope is not sufficiently narrowed and clarified during project inceptions.
Measurable business value: PLM-driven benefits are often derived in terms of process or resource efficiency and cost avoidance, they span across many disconnected stakeholders, rather than focusing on siloed transformational value; ERP solutions have the potential to derive clearer, more specific, yet more isolated, short-term transformational value, thanks to their modular logical transaction-driven data models and processes.
Effective data integration: per the Digital Thread principle, PLM—the discipline, not just the system(s)—aims at connecting people and data, through shared libraries, operating standards, system automations and interfaces which drive how transition complexity and how much change can be deployed at once or in a number of iterative phases; in contrast, ERP is rooted in IT systems with clear input / output, mostly defined as ‘static views’ of a product at a given time.
In a subsequent post, I will discuss how to measure PLM success and elaborate on a number of key performance indicators (KPIs) used when implementing vs using PLM solutions.