• Artificial Intelligence
• Circular Economy
• Digital Transformation
• Manufacturing
• Multi-Level Perspective
• Outcome-as-a-Service
• Servitization
• Sustainability Transition

Manufacturing industry is shaped by rising sustainability demands and rapid
technological innovation. As a sector responsible for nearly a third of global
carbon emissions, it faces growing pressure to reconcile productivity with
sustainability. Circular economy strategies and digital technologies particularly artificial intelligence (AI) offer a promising path forward.
This research explores how manufacturers can transition from linear, product-based
systems to circular, service-oriented business models such as Outcome-as-a-Service
(OaaS), enabled by digital innovation. Grounded in Sustainability Transition Theory and
the Multi-Level Perspective (MLP), the study investigates how digital technologies act as
mechanisms to overcome systemic barriers and enable sustainable transformation.

Background and Purpose
The transition toward sustainable manufacturing has become increasingly urgent due to
environmental pressures, regulatory change and shifting customer expectations. While
circular economy (CE) strategies and servitization have gained momentum, the
mechanisms through which digital technologies enable these transitions remain
underexplored.

This thesis addresses two key research questions:
1. What mechanisms enable digital technologies to support manufacturers’
transitions to circular OaaS models across niche and regime levels?
2. How can manufacturers leverage digital technologies to transform barriers into
opportunities for circular OaaS models?

Research Methods
The study adopts a qualitative multiple case study approach, guided by an enhanced MLP
framework. Secondary data were collected and triangulated from three leading
manufacturers: Rolls-Royce, Signify, and Caterpillar - each exemplifying different stages
of digital maturity and circular service model innovation. A theory-informed analytical
framework was used to explore the interaction between digital technologies, circularity
goals, and servitization strategies across MLP levels.

Findings
The research identifies three distinct AI-enabled transformation mechanisms:
• Barrier Digitization: Converts physical limitations into digital visibility and
prediction

• Barrier Reconfiguration: Shifts business logic from product-centric to outcome-
based models

• Barrier Elimination: Removes systemic frictions through full-spectrum digital
integration

These mechanisms enable firms to overcome multilevel transition barriers and
reconfigure value creation through data-driven, circular service models.

Contributions and Implications
Theoretical Contributions:
• A mechanism-based extension of the MLP tailored to digital innovation in
manufacturing
• Clarification of AI’s role as a catalyst for system-level transformation
• Integration of perspectives from Servitization, Service-Dominant Logic (SDL)
and Disruptive Innovation
Practical Implications:
• A roadmap to help manufacturers transform barriers into enablers of circular value
• Strategic insights for technology providers, educators and policymakers
• Emphasis on scalable digital models balancing economic performance with
sustainability