Reference models play an important role in the knowledge management of the various complex collaboration domains (such as supply chain networks). However, they often show a lack of semantic precision and, they are sometimes incomplete. In this article, we present an approach to overcome semantic inconsistencies and incompleteness of the Supply Chain Operations Reference (SCOR) model and hence improve its usefulness and expand the application domain. First, we describe a literal web ontology language (OWL) specification of SCOR concepts (and related tools) built with the intention to preserve the original approach in the classification of process reference model entities, and hence enable the effectiveness of usage in original contexts. Next, we demonstrate the system for its exploitation, in specific – tools for SCOR framework browsing and rapid supply chain process configuration. Then, we describe the SCOR-Full ontology, its relations with relevant domain ontology and show how it can be exploited for improvement of SCOR ontological framework competence. Finally, we elaborate the potential impact of the presented approach, to interoperability of systems in supply chain networks.

When a human is interacting physically with a robot to accomplish a task, his/her posture is inevitably influenced by the robot movement. Since the human is not controllable, an active robot imposing a collaborative trajectory should predict the most likely human posture. This prediction should consider individual differences and preferences of movement execution, and it is necessary to evaluate the impact of the robot's action from an ergonomics standpoint. Here, we propose a method to predict, in probabilistic terms, the human postures of an individual for a given robot trajectory executed in a collaborative scenario. We formalize the problem as the prediction of the human joints velocity given the current posture and robot end-effector velocity. The key idea of our approach is to learn the distribution of the null space of the Jacobian and the weights of the weighted pseudo-inverse from demonstrated human movements: both carry information about human postural preferences, to leverage redundancy and ensure that the predicted posture will be coherent with the end-effector position. We validate our approach in a simulated toy problem and on two real human-robot interaction experiments where a human is physically interacting with a Franka robot.

The current Internet-of-things (IoT) hype, pushed by the unprecedented rate of the technological enablers’ innovation, is threatening to leave behind some major, not so obvious, unresolved issues. IoT platforms will extend existing enterprise information systems (EIS) infrastructures to encompass cross-domain sensing and actuating capabilities, thus introducing additional complexity and major risks to the implementation. Furthermore, IoT platforms are typically driven by models of the trivial complexity; they support very simple data structures and almost no business logic implementation. Finally, IoT systems are today managed centrally, which often means less openness, less flexibility and greater change management costs. In this article, we provide the overview of the scientific disciplines which could contribute to the resolution of the IoT implementation problem, namely requirements engineering, change management/continuous improvement, model-based systems engineering, system architecture design, interoperability and policy and regulatory aspects. Then, we identify the challenges of these contributions in the context of IoT and finally make an attempt to identify research directions which could have a significant impact. The discussion of the challenges and opportunities is illustrated by the proposed domain framework for implementation of open IoT ecosystems.