The presentation explored how a leading automotive supplier confronted a growing challenge: after the start of production, software-driven products continued to generate new feature requests, configuration variants, and compatibility updates, resulting in a “never-ending project” scenario. Traditional project management structures—based on the V-model and a strict separation between development and series production—could no longer support the constant influx of post-launch software demands.

To address this, the organization evaluated multiple frameworks, including Product Lifecycle Management, Software Development Lifecycle, and Service Management, but found that none fully aligned with the hybrid hardware–software nature of their systems. This led them to explore Application Lifecycle Management (ALM) as a comprehensive approach covering the entire lifecycle from concept to retirement.

The speaker outlined how ALM introduces continuous development, maintenance, and operational oversight beyond SOP, and how its principles required reinterpretation in the company’s project-oriented environment. Key expectations included full lifecycle coverage, cost optimization, and improved transparency and quality tracking.

A comparative simulation of traditional, agile, and hybrid methodologies showed that agile and hybrid approaches outperform classic project management in environments with frequent change and high uncertainty. This finding guided the company toward a hybrid setup:
– maintaining the overarching traditional PM structure,
– but embedding agile practices (Scrum teams, CI/CD, iterative development, DevOps tools).
Vendor-side teams were also transitioning toward agile or hybrid models, enabling smoother collaboration.

The introduction of tools such as Jira, Confluence, and Azure DevOps significantly improved version control, collaboration, incident and problem management, and integration between development and operations. Early adoption of AI and machine-learning applications—particularly in linking requirements with test cases—further enhanced efficiency.

The transition was not without challenge: the learning curve spanned 1–2 years, some initial solutions had to be discarded, and organizational alignment required continuous refinement. Still, the shift toward ALM ultimately delivered greater flexibility, better lifecycle visibility, and more sustainable handling of ongoing feature and variant requests. The presenter emphasized that adopting ALM isn’t about applying a ready-made method—since no mature standard exists yet—but about iterating, learning, and adjusting in true agile fashion.