A business team reviews the SAP PLM add-ons strategy and roadmap for 2025/2026
In SAP and PLM implementations for automotive Tier 1 suppliers, cloud ERP based on a Fit-to-Standard approach is becoming the norm. In such projects, the most critical responsibility for the project manager is defining where to align with standard processes and where to retain differentiation through add-ons.
Conference Room Pilot (CRP) serves as a prototype phase where this boundary is determined not through theoretical requirements, but through hands-on system validation.
For automotive Tier 1 companies, processes often exceed standard ERP assumptions. These include PLM integration, part number and drawing management, prototype and tooling processes, and supplier collaboration. As a result, add-on decisions made after CRP significantly influence project success.
Most ERP methodologies define a process where, after multiple CRP cycles, add-on requirements are documented and development effort is estimated.
In automotive Tier 1 environments, where manufacturing, quality, engineering, and sales (OEM-facing) functions are all involved, controlling this timing becomes a key responsibility of the project manager.
The objective during the prototype phase includes:
A critical point is to avoid continuously expanding a “wish list” of new features after each CRP. Instead, teams should consistently return to Fit-to-Standard principles and retain only truly necessary add-ons.
The first evaluation point is whether a process contributes directly to competitive advantage.
In automotive Tier 1 companies, the following areas are often differentiators and potential candidates for add-ons:
If a process directly drives competitive differentiation, it justifies consideration for add-ons.
In contrast, internal workflows such as approval processes or administrative procedures should prioritize standardization and maintainability over customization.
Many add-on requirements arise from PLM integration, engineering change management, and drawing/version control.
However, SAP’s standard PLM integration capabilities already enable:
This means that many integration scenarios can be implemented without additional custom development.
Project managers should avoid jumping into add-on discussions when engineering teams request replication of current PLM processes. Instead, CRP should validate:
Only when process or rule changes cannot address the requirement should it be classified as an add-on.
Even in automotive Tier 1, adoption of SAP S/4HANA Cloud (especially Public Edition) is increasing under Fit-to-Standard principles.
For example, JTEKT Gear Systems adopted a strategy where:
Project managers should present both options during CRP:
These should be reviewed with executive stakeholders to ensure alignment with Fit-to-Standard policy. Establishing a shared principle of avoiding unnecessary customization is especially critical in multi-site, global deployments.
While some level of add-on development is inevitable in ERP projects, it is essential to manage risks and costs across the full lifecycle.
Many unnecessary add-ons stem from insufficient evaluation of standard capabilities during CRP.
In automotive Tier 1, systems often support long-term mass production contracts spanning 15 years or more. Therefore, add-on decisions must consider:
The key question is whether long-term maintainability and scalability outweigh short-term convenience.
To support decision-making after CRP, the following framework can be used across PLM, engineering, manufacturing, quality, and costing teams:
Processes with more “Yes” answers should be considered add-on candidates. Others should be addressed through standardization or existing integration capabilities.
In automotive Tier 1 SAP and PLM implementations, add-on decisions after CRP are not merely technical choices—they are strategic decisions that shape long-term competitiveness, scalability, and system sustainability.
Project managers must lead these decisions by balancing Fit-to-Standard principles with business differentiation, ensuring that every add-on delivers measurable value over the system lifecycle.
Parts of this article were developed with reference to generative AI suggestions and were reviewed, refined, and supplemented based on the author’s professional expertise and judgment.
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