Business Development: Will SaaS Offerings Change the PLM Implementation Services Sale?
PLM product software and implementation services come hand-in-hand: acquiring new digital tools, improving new process and operations, rationalizing and integrating enterprise architecture imply a number of implementation services—including:
Aligning and tailoring requirements and platform capabilities (in both directions) based on a gap-fit analysis.
Looking at the big picture across the wider enterprise landscape: governing and integrating data in the neighboring platforms.
Considering SaaS options, there are opportunities to rapidly adopt ‘vanilla’ platforms, clearly contributing to lowering barriers to entry for small and medium enterprises. Though for start-ups, and medium and large organizations, the requirement for adaptation, personalization integration remain. It is important to appreciate that “one size does not fit all” when it comes to enterprise digitalization, should it be PLM, ERP, MES or CRM related. In the manufacturing sector, these practices and platforms are never self-contained, hence they need to be adapted and integrated.
In this post, I explore what it takes to sell SaaS solutions, what questions should be addressed by business development teams, and what criteria should be considered when positioning PLM managed platforms.
Cloud infrastructure has opened the door to new business models, one of which consists of transforming software ownership and rentals into usage-driven models, from IaaS to PaaS, SaaS, etc.
IaaS: cloud-based infrastructure related services, on-demand pay-as-you-go storage, networking, virtualization, elastic compute, databases, etc. e.g., AWS, MS Azure, Google Compute, etc. to be managed by a system / IT administrator.
PaaS: platform, hardware and software tools available over the internet to build new digital product solutions, including the related operating system and associated tools to run development projects—similar to building custom apps on a given PLM platform.
SaaS: software available via a third-party infrastructure provider over the internet, where the end-users
On-premises (in contrast to the above cloud-based services): software installed in the same building of the business operation.
There is a tendency in the PLM industry to assume that SaaS is the “Holy Grail” of all PLM parenthood challenges. There are however many other complementing models from IaaS to PaaS that are equally essential when considering a transition to cloud. As a matter of fact, PLM platforms have apps which can be adapted and tailored to business needs, whereas enterprise architecture and data continuity / integration are also a core source of competitive advantage and potential “digital challenges”. It is important to remember that:
“Cloud is not the cure to all PLM challenges” (virtual+digital, 2016)
Cloud-based solutions offer levels of elasticity and efficiency, with possible trade-offs between capacity, performance, quality and cost; with the possibility to scale up and down per business requirements, while maintaining levels of visibility, adaptability and control of what is “under the hood”. Possible drawbacks include vendor lock-in and migration issues.
Selling PLM SaaS solution means selling off-the-shelf platforms and out-of-the-box software applications that are natively built on this platform. Small and medium enterprises have a need for such flexibility as they lack the IT experience to manage complexity and are possible looking for a self-container solution that is not integrated or personalized to their requirements.
For example, changing business processes to the way the software works may not always be feasibility or optimum, especially when there are multiple platforms in used across the enterprise. There are other things to watch out for, such as how user licenses are managed for each platform and across platforms at the interfaces. Implementation services often go beyond the roll-out of a given platform:
What is the overall enterprise architecture?
What are the gaps and overlaps in terms of master data assumptions?
Which platform will have to be adjusted (a.k.a. customized and configured to align with the wider IT landscape)?
Since not everything in the landscape will remain out-of-the-box, especially in context of complex and holistic PLM or ERP solutions with growing overlapping data scope, how will trade-offs be decided and what will be cost and other implications?
On that basis, is not the PLM solution sale a combination of IaaS, PaaS and SaaS options altogether based on the maturity, scope and scale of the organization?
What will then be the business operations, training and learning implications?