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A new paper, Intelligent Environments in Manufacturing Ecosystems: Improving Innovation Performance through Digital Platforms and Connected Intelligence, has been published in the journal DIGITAL.
The paper examines how manufacturing sectors and ecosystems can improve their innovation performance by combining digital platforms, organisational collaboration, and connected intelligence distributed across humans, organisations,communities, and AI agents. At the core of the paper is a transformation engine, which describes how capabilities and interactions among human intelligence (HI), collective intelligence (CI), and machine intelligence (MI) can modify activity routines and improve the innovation performance of a manufacturing ecosystem.
The paper follows a three-stage methodology. First, changes in ecosystem’s innovation performance are formalised using a vector autoregressive model, in which the state of the ecosystem is represented by a set of innovation input, output, and impact KPIs. The transformation engine, changing the ecosystem’s state, is represented by a weighted matrix of directed binary couplings among the three forms of intelligence, as illustrated in the figure.
Second, it presents the SmartGreenEcos experiment, which translates this abstract architecture into an operational intelligent environment for a manufacturing ecosystem. Digital platforms, e-services, expert knowledge, inter-company collaboration mechanisms, and AI agents are combined to support experimentation, learning, collaboration, and innovation. SmartGreenEcos illustrates how the transformation engine can be embedded within an actual industry ecosystem.
Third, simulations are used to investigate the behaviour of the transformation engine. Analysis of eigenvalues and eigenvectors of the weighted interaction matrix makes it possible to identify critical parameters, dominant patterns of interaction, and thresholds at which connected intelligence generates significant changes in the ecosystem innovation performance.
A main implication of this perspective is that intelligent environments are understood as ecosystems of capabilities and interactions among human and organisational actors, digital technologies, and AI applications, which propel innovation. Their transformative capacity depends on how different forms of intelligence—human, collective, and machine—are organised and mobilise capabilities. Digital platforms and AI agents become more valuable when they strengthen binary couplings between people, organisations, communities, and machines.
The methodology presented is also relevant to other types of ecosystems in industry, services, utilities, mobility, and innovation. This paper contributes to a better understanding of the broader perspective presented in the book Evolving Intelligent Environments: Smart Ecosystems and Transformative Innovations in the 21st Century, which addresses a research agenda on smart ecosystems, connected intelligence, and transformative innovations. A central question for intelligent environments, therefore, across their evolution, is how human, collective, and machine capabiliies can be effectively connected to modify activity routines and generate innovation.
Here is the link to the pdf of the paper