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.

Classifying a time series is a fundamental task in temporal analysis. This provides valuable insights into the temporal characteristics of data. Although it has been applied to traffic flow and individual-centered accessibility analysis, it has yet to be applied to place-centered accessibility research. In this study, we have proposed an actual isochrone and dynamic time-wrapping distance-based k-medoids method and tested its applicability to a bus accessibility analysis. Using bus floating car data, our method calculated the actual isochrone area as an accessibility measurement and constructs an accessibility time series for each hexagonal geographical unit within the area of interest. We then calculated the dynamic time warp distance between the accessibility time series of pairwise geographical units and used these distances for k-medoid clustering. The optimized class number k was selected by considering the elbow method, silhouette score, and human examination. Our case study in Hefei, China demonstrates the feasibility of our method for accessibility time series classification. We also discovered that the resulting classes follow clear spatial patterns, indicating that different time series classes may be correlated with their spatial location. To our knowledge, this is the first time that such a classification method has been applied to place-centered accessibility time series analysis. Our data-driven method can inform place-centered accessibility in an era in which large quantities of spatiotemporal data like floating car data are available.

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