The continuous evolution of digital technologies applied to the more traditional world of industrial automation led to Industry 4.0, which envisions production processes subject to continuous monitoring and able to dynamically respond to changes that can affect the production at any stage (resilient factory). The concept of agility, which is a core element of Industry 4.0, is defined as the ability to quickly react to breaks and quickly adapt to changes. Accurate approaches should be implemented aiming at managing, optimizing and improving production processes. In this vision paper, we show how process management (BPM) can benefit from the availability of raw data from the industrial internet of things to obtain agile processes by using a top-down approach based on automated synthesis and a bottom-up approach based on mining.

In the era of Industry 4.0, the New Space Economy, often called Space 4.0, has taken center stage in the satellite industry. The advent of mega‐constellations, which entails mass satellite production, necessitates state‐of‐the‐art manufacturing methods. Leveraging Industry 4.0 technologies like the Internet of Things (IoT) and Big Data analytics show great potential for improving the manufacturing, assembly, integration, and testing (MAIT) cycle. This paper focuses on how Industry 4.0 principles enhance data analysis and automation in space manufacturing, illustrated through a case study at an aerospace company, with a focus on the composite sandwich panel manufacturing line. We introduce two key contributions. First, an interactive dashboard is proposed to enhance data analytics capabilities, offering real‐time access to insights and key performance indicators (KPIs) for operators and data analysts, and enabling the exploration of customized metrics. This facilitates comprehensive analysis of the entire MAIT process, supporting trend detection, anomaly identification, and areas for improvement to facilitate data‐driven decision‐making. Second, we present two strategies to tackle the challenges posed by the constrained number of attempts to insert installations on sandwich panels. These strategies are founded on a proposed data analytics approach rooted in Markov chain principles. This approach aids operators in making informed decisions on whether to proceed with additional attempts or discard the insert. By calculating the probability of successful insertions in future attempts, our approach can suitably enhance resource usage and production timelines. The proposed approach is evaluated through stress testing, where three processes insert 212,000 sensor records into Kafka queues at varying throughputs, monitored via Metricbeat for system resource usage. Results show low CPU usage (below 20%), consistent network throughput, and stable average data insertion times after initial peaks, demonstrating the architecture's scalability and efficiency.