# The Need for Ecosystem 4.0 to Support Maintenance 4.0: An Aviation Assembly Line Case
## 1 Introduction  
Large commercial, executive, or defence aircraft assemblers have manufacturing lines composed of many machines for various purposes. Nevertheless, human labour is still responsible for the vast majority of the assembly and finishing phases of different parts. This is partly due to the large size of aeronautical parts that require large and heavy machines, which has hampered the development of automation in the sector. More demanding than the automotive industry, the aeronautical sector also has higher precision and quality requirements, further increasing technological development for manufacturing through autonomous machines [1]. [PERSON] [2] mentioned that robots' use is far more limited in the aerospace industry than in the automotive sector. The most significant development of automation in the sector consists of drilling and riveing panels for wings and fuselage and transporting large parts for the assembly of aircraft wings and fuselage sections [1, 3, 4]. Robotic cells have been used in this sector, with high availability, precision, and quality, also becoming a point of attention for producing the input for several other workshops on the assembly line [3]. The challenge in an aircraft assembly company's production line maintenance is to have different types of machinery at different maintenance paradigms. There are machines built decades ago that only have a maintenance plan (scheduled maintenance interventions) based on obsolete information. In contrast, newer machines already have predictive maintenanceor preventive maintenance approaches in a Total Productive Maintenance (TPM) policy. Adjusting the corrective, preventive, and predictive maintenance plans to be carried out by a small group of maintainers becomes a significant challenge as the machines degrade and the degradation model is not defined. This happens due to a lack of manufacturer's information, ageing of the machinery, the absence of sensors, or a combination of them [4]. However, due to the appearance of corrective maintenance or a change in the demand for the manufacture of new aircraft, there may be a change in parameters and a need to redesign several machines' maintenance plans in the line. Such a situation generates rework and, possibly, a not optimal result for the production line's production and maintenance. The loss of expected productivity and sub-optimal maintenance costs may reduce the company's revenue. A flexible plan is built each year of operation so that there is a statistical base that will support maintenance for decision-makers. Big Data analytics and Digital Twins came to support this approach, but only monitored machines or processes may take advantage of the technique. The Digital Twins' concept is the most comprehensive in terms of using technologies aimed at Industry 4.0. The work of [PERSON] [5] declares that the main difficulty is to establish the right technologies and tools to use the approach appropriately. The result of [PERSON] [6] brings the evidence of acquiring situation awareness from digitized processes, captured data, and performed prognosis throughout the virtualized system, providing timely information to the various actors and decision-makers. [PERSON] [7] pursues the challenge to provide an effective and efficient way to enhance productivity using Big Data Analytics, keeping the economy within the established boundaries and promoting the value-added product. In all these works, the maintenance costs are relevant and should be considered in the total cost. This research paper seeks to identify the Industry 4.0 technologies' current opportunities to generate an \"ecosystem\" that facilitates information acquisition on the processes being executed and the existing assets to obtain the best possible results. It is intended to build a framework to support situational awareness to decision-makers and to suggest the optimal use of the current maintenance workforce being allocated, favouring a smooth and more extensive adoption of the prescriptive maintenance approach.