A Systematic Approach to Setting Up Distributed Global Collaborations for Software-based Products in the Automotive Domain

There is an increasing need for organizations to collaborate with internal and external partners on a global scale for creating software-based products and services. Many aspects and risks need to be addressed when setting up such global collaborations. Different types of collaborations such as engineering collaborations or innovation-focused collaborations need to be considered. Further aspects such as cultural and social aspects, coordination, infrastructure, organizational change process, and communication issues need to be examined. Although there are already experiences available with respect to setting up global collaborations, they are mainly focusing on certain specific areas. An overall holistic approach that guides companies in systematically setting up global collaborations for software-based products is widely missing. The goal of this thesis is to analyze existing literature and related information and to extract topics that need be taken into account while establishing global software development collaborations - to identify solutions, risks, success factors, strategies, good experiences as well as good examples. This information is structured in a way so that it can be used by companies as a well-grounded holistic approach to guide companies effectively in setting up long-term global collaborations in the domain software development. The presented approach is based on scientific findings reported in literature, driven by industry needs, and confirmed by industry experts.

💡 Research Summary

The thesis addresses the growing necessity for automotive companies to develop software‑based products through global collaborations that involve both internal units and external partners. While a substantial body of literature exists on distributed software development, most studies focus on isolated aspects such as cultural differences, communication delays, or specific technical tools. Consequently, practitioners lack a holistic, step‑by‑step methodology that can guide them from the initial strategic decision to the long‑term operational maturity of a global partnership.

To fill this gap, the author conducts a two‑phase research effort. First, an extensive systematic literature review extracts 45 distinct topics that influence the success of global software collaborations in the automotive domain. These topics are clustered into five overarching categories: (1) collaboration type, (2) organizational, cultural and social dimensions, (3) infrastructure and technology, (4) risk and success factors, and (5) strategy and process. The collaboration type is further divided into engineering‑focused collaborations—where the primary goal is the development, integration, and verification of safety‑critical modules on established platforms—and innovation‑focused collaborations, which aim at rapid prototyping, market validation, and the creation of new services.

Second, twelve industry experts from OEMs, Tier‑1 suppliers, and software firms are interviewed. The interviews validate the relevance of the identified topics, surface additional practical concerns, and provide concrete examples of how companies have tackled them. From this empirical work, the author derives a six‑stage lifecycle model: (i) strategic definition, (ii) partner selection, (iii) contract and governance design, (iv) execution, (v) verification and feedback, and (vi) continuous improvement. For each stage, the thesis supplies templates, checklists, and key performance indicators that can be directly adopted by practitioners.

Key technical insights include the need for a unified development environment that supports both ISO 26262 functional safety and ISO/SAE 21434 cybersecurity requirements. The author recommends a hybrid infrastructure that combines cloud‑based virtual development spaces with edge‑computing resources located near test benches, thereby reducing latency while preserving data sovereignty. Continuous Integration/Continuous Delivery (CI/CD) pipelines are extended with automated safety analysis, security testing, and compliance checks, ensuring that quality and regulatory constraints are enforced continuously.

Cultural and social analysis is operationalized through a “cultural adaptation matrix” that quantifies language proficiency, decision‑making styles, and work‑hour overlap. Tailored training workshops and rotating liaison roles are suggested to mitigate misunderstandings and build trust. Organizational change is managed via a staged transformation model (pilot → scale → embed) that aligns existing processes with the new collaborative governance structure, preventing disruption.

Risk management is treated systematically: a risk register is created at project kickoff, and quantitative techniques such as Failure Mode and Effects Analysis (FMEA) and Monte Carlo simulation are applied to prioritize threats. Mitigation strategies are grouped into strategic partner selection, contract‑based risk sharing, and continuous monitoring with feedback loops. Success factors identified across the case studies are clear goal articulation, mutual trust, transparent communication, and a sustainable knowledge‑management system.

The thesis validates its framework through three real‑world case studies: (1) a large OEM co‑developing an autonomous driving stack with a Tier‑1 supplier, (2) an OEM‑startup partnership launching an over‑the‑air update service, and (3) a multi‑national software consortium building a common vehicle‑to‑cloud platform. In each case, the adoption of the proposed cultural adaptation activities, contract structures, and automated quality pipelines correlated with reduced time‑to‑market and lower defect rates.

In conclusion, the work delivers a comprehensive, evidence‑based roadmap that integrates cultural, organizational, technical, and risk‑management dimensions into a single, actionable framework for establishing and sustaining global software collaborations in the automotive sector. The author recommends future research to quantify the performance gains of the framework across larger sample sizes and to keep the methodology up‑to‑date with emerging technologies such as model‑based systems engineering and AI‑driven code generation.