# The Need for Ecosystem 4.0 to Support Maintenance 4.0: An Aviation Assembly Line Case
## 1 Introduction
Prescriptive maintenance uses the same knowledge in terms of data from predictive maintenance, integrating it with advice related to the maintenance window of opportunity and the tasks the workers need to perform. It allows generating better overall performance optimizing the allocations of resources, such as work floor surface and assets, for each task at the right moment. Therefore, the output of a prescriptive maintenance process must be a dynamic schedule of preventive maintenance according to the maintenance teams' adversities, spare parts, equipment availability, and tools in the identified maintenance window of opportunity, and the correct maintenance procedure manual. The primary purpose is to keep the machinery's high availability during the expected time of its utilization, given the production demand at the manufacture and assembly lines. The work in Choubey [8] brought the evidence that prescriptive maintenance is still in its infancy and lacks real-world implementations and lessons learned to empower its usage on a large scale. [PERSON] [9] established the Smart Prescriptive Maintenance Framework (SPMF) utilized in the present work to provide a path to prescriptive maintenance implementation in any industry using the ecosystem 4.0 capabilities. Considering that the window of opportunity's prediction is the foremost approach to optimise the entire maintenance process, the present study identified possible techniques to establish this purpose. Several methods are being utilised in literature to predict the window of opportunity based on production planning and product quality. The work in Shamsaei [10] exposed how limiting the problem size could be depending on the solver and algorithmic approach being used. The authors managed to increase the problems' size using a hybrid capacity planning approach based on non-cyclic maintenance (NCMP) and cyclic maintenance (CMP), showing that the solution could have exponential possibilities. The authors utilized a Mixed-Integer Programming (MIP) solver to such problems and could find the solution in a reasonable time, but they did not work with multiple machines. [PERSON] [11] proposed a procedural approach using multivariate data analysis and simulation tools to identify data correlations and real-time failures and implement it in a real-world scenario in the automotive manufacturing industry. The authors considered a scenario of production planning with multiple machines' maintenance planning but did not consider a dynamic production environment. [PERSON] [12] presented an analytical process for prescriptive maintenance based on data analysis and Monte Carlo simulation; however, no discussion was mentioned about ecosystem 4.0 as an enabler. [PERSON] [13] discussed in depth the enablers and main paradigms of the Industry 4.0 remanufacturing and proposed framework but did not present simulations or applications in the prescriptive maintenance field. Similarly, [PERSON] [14] presented a smart maintenance framework, discussed the technological enablers, and asserted that maintenance 4.0 is an uncontestable trend but did not present a simulation or case study. To capture the dynamicity of the environment is necessary to apply the Industrial Internet of Things (IIoT) and understand the patterns in the captured data as already declared in [PERSON]'s work [7]. IIoT comprehends sensors that provide data to support Big Data Analytics and the pattern recognition effort that uses machine learning algorithms as presented in the work of [PERSON] [15]. [PERSON]'s work [16] exposed the main contributions to state-of-the-art descriptive, predictive and prescriptive maintenance using optimization and machine learning algorithms. The authors proposed trends and perspectives about prescriptive maintenance, including metrics variability and conflicting objectives such as productivity and reliability that may affect the design of efficient solvers for problem resolution.