###### Abstract
Exponential technology, along with advanced processes, high-performance computing, and disruptive technologies (e.g., automation and robotics, autonomous and collaborative robots, advanced mechatronics, micro- and nano-manufacturing, and supercomputing) are key enablers for proper M&M in 14.0 [6; 7; 12; 32; 33]. Moreover, advanced interfaces, virtual, augmented, and mixed reality technology, along with simulation and digital twins, further promote and enhance CollM&M among collaborating entities. These technologies enable advanced and integrated decision-support systems (DSS) and databases (DB), along with knowledge engineering and knowledge bases (KB), automatic data acquisition, and a semantic web for enhancing collaboration [7]. In addition, data science, along with business intelligence, big data processing, and data analytics, are essential pillars of I4.0 supporting CollM&M practices [6; 7]. All these issues are crucial for enabling advanced, integrated, and intelligent supply networks, projects, businesses, and their interoperable and fully supported implementation, to reach M&M while ensuring high-quality product development and M&M practices based on appropriate standards, means, and communication devices and protocols, to fully ensure appropriate extended supply network management strategies [5; 8; 10; 15; 32; 33; 34]. This Special Issue aims to provide new insights regarding CollM&M models and practices, aligned with the contemporary needs regarding the capability of co-creation actions supported by I4.0 technology [7]. In this regard, each of the six selected papers of this Special Issue makes a novel contribution to this purpose. [PERSON] et al. present expectations and limitations of cyber-physical systems (CPS) for advanced manufacturing in the scope of the grinding industry. In their work, the authors refer to the importance of grinding technology in the manufacturing of high-added-value precision parts, accounting for approximately 20 to 25% of the whole machining costs in the industrialized world and relying heavily on the experience and knowledge of the operatives. Thus, the authors conclude that suitable approaches are needed to overcome these issues, and digital twin technology is promising in this regard by contributing to the reduction and possibly even the elimination of unnecessary trial-and-error strategies through intensive collaboration between all the involved agents from the university to the industry. The authors highlight the successful implementation of this technique in developing new and more realistic models for predicting wheel wear. [PERSON] et al. propose a system model for offline seismic event detection in Colombia. The authors put forward an integrated model that includes five sub-models and is based on a machine-learning approach, and they highlight its suitability for identifying P-wave windows in historical records that permit detect seismic events. Their proposed model permits seeking, gathering, and storing seismic data, along with data reading, analysis, sampling, and classification. The authors further provide some recommendations regarding their model's implementation in developing a seismic-event detection system. [PERSON] et al. put forward a systematic literature review (SLR) about investigating degradation and upgradation models for flexible unit systems. In their work, the authors research the so-called flexible unit systems (FUS) in the current I4.0 era and the context of descriptive, predictive, and prescriptive analysis, aiming at integrating distributed and digitalized systems. The authors highlight that the existing literature mostly focuses individually on the descriptive, predictive, and prescriptive analysis paradigms. Moreover, the authors also claim that the literature is unclear about the integration of degradation and upgradation models for FUS.