Layer Model for the Design of Data-driven Business Models – AI Integration and Industrial Data Fusion Across Hierarchical Levels

Abstract

This paper presents a structured framework for analyzing the role of intelligent sensor systems in enabling data-driven and potentially disruptive business models in manufacturing. Building on a five-level layer model − comprising sensor, machine, shopfloor, plant, and value chain − the study systematically examines each level along five analytical dimensions: data, processes, IT systems, interfaces, and standards. For each level, the current state and expected future developments are exemplarily assessed through literature analysis and industrial case examples. This multi-dimensional approach reveals digitalization potentials and integration barriers at each stage of the value creation process. The findings are then synthesized to explore cross-level fusion strategies, enabling new forms of vertical and horizontal integration. The methodology follows the Zachman framework logic, ensuring structured coverage of each layer and aspect. Real-world use cases − ranging from pay-per-part offerings to cross-company data spaces − illustrate how sensor-based integration supports novel business logics such as Equipment-as-a-Service, predictive quality management, or audit-ready digital twins. The paper contributes to Industry 4.0 discourse by linking sensor fusion architectures with value creation mechanisms, demonstrating how technical infrastructures and business models must co-evolve. The proposed model serves as both a diagnostic tool for digital maturity and a design template for future-ready industrial service models.

Keywords: Data fusion, Artificial intelligence, I4.0, IIoT, Digital twin, Value chain integration, Industrial data spaces

DOI: 10.54941/ahfe1006757