Abstract
The digitization of manufacturing industry has led to leaner and more efficient production, under the Industry 4.0 concept. Nowadays, datasets collected from shop floor assets and information technology (IT) systems are used in data-driven analytics efforts to support more informed business intelligence decisions. However, these results are currently only used in isolated and dispersed parts of the production process. At the same time, full integration of artificial intelligence (AI) in all parts of manufacturing systems is currently lacking. In this context, the goal of this manuscript is to present a more holistic integration of AI by promoting collaboration. To this end, collaboration is understood as a multi-dimensional conceptual term that covers all important enablers for AI adoption in manufacturing contexts and is promoted in terms of business intelligence optimization, human-in-the-loop and secure federation across manufacturing sites. To address these challenges, the proposed architectural approach builds on three technical pillars: (1) components that extend the functionality of the existing layers in the Reference Architectural Model for Industry 4.0; (2) definition of new layers for collaboration by means of human-in-the-loop and federation; (3) security concerns with AI-powered mechanisms. In addition, system implementation aspects are discussed and potential applications in industrial environments, as well as business impacts, are presented.
Highlights
The adoption of innovative digital technologies, referred to as Industry 4.0, is progressively leading to improved product quality, work safety, fault predictions and efficiency in energy use and production [1,2,3]
Autonomous Mobile Robots (AMR) downtime is undesirable and should be reduced as much as possible. This scenario assumes that a company has implemented a fleet of AMRs connected to a network infrastructure that allows for monitoring by company administrators for business and safety, and by the AMR supplier for maintenance
At the core of this approach is the provision of components for timely data collection, processing and curation, relying on the dynamic instantiation of data pipelines, while addressing security, privacy and confidentiality concerns across the physical and virtual entities
Summary
The adoption of innovative digital technologies, referred to as Industry 4.0, is progressively leading to improved product quality, work safety, fault predictions and efficiency in energy use and production [1,2,3]. The adoption and integration of AI-based innovation in the manufacturing domain comes with a few hurdles and caveats that must be properly addressed in order to take advantage of its full potential, without jeopardizing the irreplaceable role of humans and the protection of sensitive data and procedures [22,23]. In this context, the goal of the study presented in this manuscript is to analyze an AI-based collaboration approach in industrial IoT manufacturing.
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