Nowadays, reliability of sensors is one of the most important challenges for widespread application of Internet-of-things data in key emerging fields such as the automotive and manufacturing sectors. This paper presents a brief review of the main research and innovation actions at the European level, as well as some on-going research related to sensor reliability in cyber-physical systems (CPS). The research reported in this paper is also focused on the design of a procedure for evaluating the reliability of Internet-of-Things sensors in a cyber-physical system. The results of a case study of sensor reliability assessment in an autonomous driving scenario for the automotive sector are also shown. A co-simulation framework is designed in order to enable real-time interaction between virtual and real sensors. The case study consists of an IoT LiDAR-based collaborative map in order to assess the CPS-based co-simulation framework. Specifically, the sensor chosen is the Ibeo Lux 4-layer LiDAR sensor with IoT added capabilities. The modeling library for predicting error with machine learning methods is implemented at a local level, and a self-learning-procedure for decision-making based on Q-learning runs at a global level. The study supporting the experimental evaluation of the co-simulation framework is presented using simulated and real data. The results demonstrate the effectiveness of the proposed method for increasing sensor reliability in cyber-physical systems using Internet-of-Things data.

Production Internet as an eco-systemic Web-based infrastructure goes beyond the traditional setups of industrial cooperation, as well as existing peer-to-peer services for economic exchange, like e-tailing, e-sharing or crowd-funding. This paper investigates the feasibly functional arrangements of Production Internet. Throughout reflection on the state-of-art, supplemented by a foresight research, the needs, requirements and benefits are identified then composed into functional setting, which was validated using a prototype implementation. The discussed conceptualization is expected to improve operational effectiveness, efficiency of resources, and reduce transaction costs.

Semantic technologies are recognized as crucial in those domains, which intensively exploit information and communication technologies (ICT) and automation technologies (AT). Ontology engineering means can facilitate new functionalities, organizational structures and processes. Intra- and inter-organizational integration of different layers, functions, domains and processes may be simplified or enabled. Knowledge-driven solutions can be also facilitated. This paper investigates functional aspects of ontology-aided manufacturing and supply chain management by a literature review. The purpose is to assess potential for further research and to suggest its future key directions, aiming at novel solutions in terms of structures, controls, processes and functionalities.

The side negative effects of Lean Management implementations are well known among practitioners. Recently they became a subject of systematic research. This paper presents conclusions from a causal analysis of the unlikely results, which were identified along case studies. The research was conducted in the machine building industries in EU. The insights from interviews and expert-panels were systemized by cause-and-effect models, thus suggesting the root causes of the negative effects. The synthetized results are considered herein as an input for constructing a method for multi-perspective assessment, to enable better planning and control of the Lean Management implementations.

(2019). Supply Chain Configuration: Concepts, Solutions, and Applications. Production Planning & Control. Ahead of Print.