## 5 Current Limitations and Future Trends
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Core Subjects** & **References** & **Critical Features** \\ \hline Blockchain and Data Security & [2, 13, 18, 21, 24, 28, 30, 32, 35, 41, 46, 55, 60, 72, 73, 74] & Enhances MaaS data security using blockchain, but economic feasibility remains unassessed Cloud and edge computing support real-time decision-making, though multi-cloud integration challenges persist AI models improve efficiency but need real-world validation across industries Cybersecurity and Fault Tolerance & [29, 30, 32, 35, 37, 38, 41, 46, 49] \\ IIoT and Smart Manufacturing Analytics Data-Driven Supply Chain Management & [55, 57, 59, 60, 72, 73, 75, 38, 20, 24, 28, 29, 30, 32, 35, 37, 38] & Cloud and edge computing support real-time decision-making, though multi-cloud integration challenges persist AI models improve efficiency but need real-world validation across industries Cybersecurity and Fault Tolerance & \\ IIoT and Smart Manufacturing Analytics Data-Driven Supply Chain Management & [55, 57, 59, 60, 72, 73, 75, 38, 20, 24, 28, 29, 30, 32, 35, 37, 38] & All models improve efficiency but need real-world validation across industries Cybersecurity solutions address fault tolerance but require industry-specific case studies IIoT strengthens data-driven manufacturing, though integration with legacy systems presents challenges Optimizes supply chain decisions but lacks standardized implementation frameworks \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Data innovations. overcome these challenges, strategical policy measures are required: assistance for digital transformation, training to upskill the employees, and the design of safe, user-friendly platforms based on the needs of SMEs. These actions are necessary for MaaS to become accessible and beneficial to smaller organizations. Overall, the vision Industry 4.0 represents consists of achieving a more efficient and accessible personalized production process [65], which will be supported by cyber-physical systems [78], cloud manufacturing [79; 80], deep learning methods [61], or machine-learning-based feature extraction methods [81]. Additionally, the more MaaS is integrated into the reality of the industries, the more technical developments will also emerge in the future. Virtual reality assists digitalization by providing the capability of off-site operation of the machinery by the employees [82]. The lack of trust in the protection of the data in all the technologies consists of one of the most crucial challenges of the contemporary manufacturing methods [41], and to be encountered, there should be integrated evidence-based and transparency-based trust mechanisms that utilize IoT and blockchain technologies as well [83]. The MaaS ecosystem introduced by [PERSON] et al. [84] could be further developed with the addition of capabilities such as process derivation service or a search engine combined with its supplier knowledge base and an assisting software. Task-service-matching optimization problems could be enriched with the capability of the negotiation in the pricing procedure in the future [85]. As [PERSON] et al. [48] suggest, crowdsourcing task derivation could be even more useful, by involving planning of the material flows, or mixing tasks on the shop floor in real-time. Regarding now MaaS supply chains, the challenges currently encountered include larger data volumes, node counts [86], and a complexity of managing a distributed and decentralized system [87]. To achieve a truly efficient MaaS supply chain transparency in the future, [PERSON]. [86] suggests that the classification of stored data should be conducted by employing specific encrypting methods for private data.