# Collaborative Networks: A Pillar of Digital Transformation
## 5 Relevant Collaboration Aspects in Industry 4.0
### Collaboration in Vertical Integration  
Under the Industry 4.0 view, smart manufacturing enterprises are organized in multiple layers of networked and collaborative sub-systems. Departing from the traditional multi-layer view as illustrated on the left side of Figure 5, each layer becomes a collaborative network of smart components with increasing levels of intelligence and autonomy. The interactions among these layers lead to an interchange among smart production units, smart logistics, smart products, smart organizational and engineering units, and people. Collaboration among these entities is a requirement to support agile and resilient processes. A clear example of this trend can be seen at shop floor whereas there is a progress from \"control structures\" to \"collaborative structures\". In other words, rather than looking at the shop floor as a collection of machines that (reasonably) obey to control commands, more and more these machines embed high levels of intelligence, being interconnected and represented by digital twins, and thus instead of control it makes more sense to talk about communication, coordination, negotiation, sharing, which leads to a perspective of collaboration. Therefore, we are moving from embedded and cyber-physical systems to some forms of collaborative CPS [15; 55]. Figure 4: Industry 4.0 and collaborative networks: a co-evolution process. Figure 5: Collaboration in vertical integration. ERP: Enterprise Resource Planning, MES: Manufacturing Execution SystemCollaborative activities between robots and humans, represented by the concept of collaborative robotics, is another recent field, although with roots in the original concept of robots [56]. This notion can be generalized to collaboration among multiple machines and humans. Similarly, at higher abstraction layers of the enterprise architecture, the traditional monolithic complex applications tend to be replaced by federations of services provided by smart and even distributed components, each with varying degrees of intelligence. Progress on sensorial systems and IoT facilitates real-time monitoring of processes and fluid interactions (up and downstream) among these multiple layers. The availability of large volumes of data (data-rich environments) and their access in real-time, combined with the development of enterprise-wide analytics and adoption of advanced techniques for data visualization, including virtual reality and augmented reality, also suggest collaboration among the various units of the enterprise. What Can CNs Contribute? Although past work on CNs mostly addressed networks of organizations or people, plenty of concepts and models can be applied to networks of machines or sub-systems, as exemplified in the following:  
* Some earlier works on networks of machines [57] explored the notions of \"virtual organization\" and \"virtual organization breeding environment\" to allow agile reconfiguration of the shop floor. The idea of evolvable manufacturing system [58] also illustrates this possibility. * Similarly, the concepts of \"virtual organization\" and \"virtual organization breeding environment\"/\"business ecosystem\" are being combined with CPS [55], leading to a new generation of collaborative CPS, with potential application in domains as diverse as manufacturing [59], smart buildings [15], and energy virtual power plants [60]. * A few works elaborate on the interplay among CNs, as illustrated in [61] for the case of solar energy plants. * Organizational models for collaboration and negotiation protocols have been adopted from multi-agent systems and applied to engineering, manufacturing, and logistics [62; 63]. Earlier examples can be traced back to applications in agile scheduling [64] and other more recent examples [65]. * The ideas of \"sensing, smart, and sustainable enterprise\", providing an integrated view of the enterprise comprehensively rely on CN concepts and mechanisms [66]. * Self-organizing concepts applied to digital transformation of manufacturing [67].