A Dynamic Model of Evolutionary Knowledge and Capabilities Based on Human-Machine Interaction in Smart Manufactures

Abstract

The increasing use of smart machines and devices is not only changing production principles but also reshaping the value of cocreation logic. The interaction between human and smart machine is the enabler of generating augmented intelligence. A system dynamics model is abstracted from smart manufacturing practices to represent the evolutionary processes of inertia, capability, and reliability induced by human-machine interaction. Human-machine interaction is conceptualized into two dimensions: technical and cognitive interaction. Simulation experiments illustrate how the improvement of human-machine interaction can leverage the dynamic capability and reduce the inertia in enterprises through multiple nonlinear feedbacks. There are two pathways to improve reliability and performance in enterprises by human-machine interaction: (1) to promote initiative innovation (change) from endogenous enabler by improving dynamic capability and (2) to promote transformation of knowledge and variation triggered by exogenous environmental changes to improve the dynamic capability for the flexibility and reliability.

Human machine interactions: from past to future- a systematic literature review

“…All three innovation scenarios will prevail in the future (Botha, 2019). Simple machines will transform into “thinking machines” and “innovating machines”, making strategic decisions and building novel creations (Chen et al , 2022). The future envisages the evolution of machines from “tools” to “partners” and thereby to “leaders” in the new cyber–physical system (Wesche and Sonderegger.…”

“…Integrating human (collective) intelligence with AI is propitious: if humans furnish annotated data for AI learning systems, these machines can process the data, learn from them and apply new knowledge to solve problems more efficiently (Brito et al , 2020). They are evolving as new colleagues to cocreate value for their users (Chen et al , 2022). Accentuating innovation in HMIs, cobots, which are collaborative robots, are performing within industrial undertakings alongside human workers (Botha, 2019).…”

“…John McCarthy initially used the phrase “artificial intelligence” at a conference at Dartmouth College in 1956, which marked the official start of the study of AI and served as the basis for many popular AI techniques and algorithms, including ELIZA and global positioning system (GPS). Expert systems and symbolic algorithms, commonly referred to as knowledge-based systems, were at the core of the second paradigm shift from the 1970s (Chen et al , 2022). The previous paradigm was displaced by ML and deep learning due to the greater accessibility of digital data and computer power in the 1990s.…”

An Overview of Approaches and Methods for the Cognitive Workload Estimation in Human–Machine Interaction Scenarios through Wearables Sensors

“…Human-machine interaction (HMI) refers to the dynamic and multidimensional interface between humans and machines, encompassing technical and cognitive dimensions [1,2]. It involves real-time interaction between humans and machines through a human-machine interface [3].…”