Effective, transparent, and reliable data exchange are the most important points for fostering sustainability, resilience, and energy efficiency in the manufacturing industry. However, over the past years, various challenges have come to the forefront within this sector.
Supply Chain Disruptions: The COVID-19 pandemic highlighted existing vulnerabilities in global supply chains, leading to disruptions in the flow of materials and components. Issues such as raw material shortages, transportation bottlenecks, and labor shortages have persisted, impacting manufacturing operations worldwide.
Cybersecurity Risks: With the increasing digitization of manufacturing processes through technologies like the Internet of Things (IoT) and Industry 4.0, cybersecurity threats have become a significant concern. Manufacturing facilities are increasingly vulnerable to cyberattacks that can disrupt operations, steal sensitive data, or compromise product quality and safety.
Data Silos: Manufacturing organizations often operate with fragmented data systems, leading to isolated data silos across departments or functions. This fragmentation inhibits seamless data interoperability and hampers comprehensive insights that could drive operational efficiency and innovation.
Lack of Standards: The absence of standardized data formats and protocols complicates data exchange and integration efforts within and across manufacturing enterprises. Without universally accepted standards, interoperability becomes a significant challenge, impeding the flow of data between different systems and stakeholders.
Data Privacy Concerns: With the proliferation of data collection and sharing practices in manufacturing, ensuring data privacy and protection is paramount. Manufacturers must navigate complex regulatory landscapes, safeguarding sensitive information from unauthorized access or misuse while balancing the need for data-driven decision-making.
Ownership and Control: Determining ownership rights and control over manufacturing data can be contentious, especially in collaborative environments or supply chain networks. Disputes may arise regarding data ownership, usage rights, and intellectual property, complicating data sharing agreements and hindering collaborative initiatives.
Legacy Systems Integration: Many manufacturing facilities still rely on legacy systems that were not designed with interoperability in mind. Integrating these outdated systems with modern data platforms and technologies poses significant challenges, requiring extensive customization, retrofitting, and investments in interoperability solutions.
DMaaST aims to enhance manufacturing ecosystem resilience and adaptability by employing a Smart Manufacturing Platform comprising four layers. The data layer establishes a foundation for real-time data integration across organizations using ontologies and OriginTrail Decentralized Knowledge Graph. Following this, a two-level cognitive digital twin is deployed to model both manufacturing services production lines and value chain stages. It incorporates human expertise, data-driven algorithms, and physical modeling. An algorithm for multi-objective distributed decision support systems leverages this data to facilitate optimal production decisions. Outcomes will be communicated via user-friendly interfaces and timely scoreboards, assessing circularity, sustainability, and product traceability. Over the four-year period, DMaaST ensures scalability and innovation by providing insights for replicating and improving manufacturing processes, advancing technologies in aerospace and electronics sectors.