## 4 Literature Review
Correspondingly, [PERSON], [PERSON], and [PERSON] [35] performed simulations and experiments in the laboratory to examine an on-demand service system in terms of stability, readiness, etc. Moreover, other methodologies among the papers include the formulation of a statistical model [16], solution of objective functions [44], and optimization algorithms [19,58]. The reviewed literature aligns with the overarching objectives of MaaS by emphasizing digital transformation, resource optimization, and decentralized production, yet certain aspects diverge in their scope and implementation. For instance, [PERSON] [55] highlights the significance of data collection and processing by installing sensors in manufacturing machines, reinforcing MaaS's goal of data-driven decision-making. Similarly, blockchain architectures [38] and cloud manufacturing solutions [19,20,59] support transparency and interoperability, which are essential for MaaS adoption. Case studies further validate MaaS applications, such as [PERSON] and [PERSON] [20], who examined six different manufacturing platforms, and [PERSON], [PERSON], and [PERSON] [44], who implemented a low-carbon product-family-planning model in a custom kitchen construction company. While these studies contribute to key MaaS principles, some diverge by focusing on specialized applications rather than holistic MaaS integration. For example, while blockchain and cloud computing enhance security and scalability, challenges like SME adoption and regulatory compliance remain underexplored. These findings indicate that while MaaS research largely aligns with its core objectives, gaps persist in achieving full-scale, industry-wide implementation. Regarding the practical MaaS implementation, it is proven by case studies from multiple industries, for example, the automotive sector. MaaS gave the manufacturers the opportunity to utilize decentralized production systems, which reduce lead times and costs through on-demand part production. A similar example of the healthcare sector, MaaS has empowered the rapid prototyping and production of medical devices, a useful contribution especially during periods of high demand, such as the COVID-19 pandemic. The mentioned paradigms validate the transformative perspective of MaaS across various segments, proving its role in improving efficiency and adjustability while minimizing the need for heavy capital investments. The literature under examination reveals both alignment and divergence across different studies, highlighting key trends, contradictions, and unresolved challenges. While [PERSON] and [PERSON] [2] and [PERSON] et al. [38] emphasize blockchain-based architectures to enhance security and trust in MaaS platforms, other studies, such as [PERSON] and [PERSON] [37], prioritize interoperability without focusing on data security concerns. Similarly, [PERSON] et al. [44] explore MaaS from a sustainability perspective, advocating for low-carbonproduct family planning, whereas [PERSON] and [PERSON] [20] focus on engineering-to-order business models, revealing a gap in standardized implementation strategies. Additionally, while [PERSON] [55] and [PERSON] [60] stress the importance of real-time data collection for smart manufacturing, [PERSON] et al. [24] and [PERSON] et al. [19] highlight the need for dynamic scheduling and optimization, exposing a lack of integrated frameworks to balance flexibility with efficiency. Furthermore, the role of AI in MaaS is widely discussed, with [PERSON] et al. [48] exploring crowdsourced manufacturing, yet challenges remain in ensuring scalability and demand forecasting [49]. These variations in focus and methodologies indicate an evolving MaaS landscape, underscoring the need for further research in trust mechanisms, SME adoption, cost-effectiveness, and large-scale interoperability.