Anomalies on road pavement cause discomfort to drivers and passengers, and may cause mechanical failure or even accidents. Governments spend millions of Euros every year on road maintenance, often causing traffic jams and congestion on urban roads on a daily basis. This paper analyses the difference between the deployment of a road anomalies detection and identification system in a “conditioned” and a real world setup, where the system performed worse compared to the “conditioned” setup. It also presents a system performance analysis based on the analysis of the training data sets; on the analysis of the attributes complexity, through the application of PCA techniques; and on the analysis of the attributes in the context of each anomaly type, using acceleration standard deviation attributes to observe how different anomalies classes are distributed in the Cartesian coordinates system. Overall, in this paper, we describe the main insights on road anomalies detection challenges to support the design and deployment of a new iteration of our system towards the deployment of a road anomaly detection service to provide information about roads condition to drivers and government entities.

Cyber-physical systems are often misunderstood to be just any embedded systems. The real cyber-physical system should have both physical and digital (computational-communication-control) parts inter-connected in each part and process, and the system itself should have the capacity to change its own behaviour to adapt to changing requirements. This paper presents an architecture of an intelligent cyber-physical system where a reconfigurable manufacturing system is supported by a machine learning algorithm to provide enhanced decision-making to the manufacturing control system. Experiment results are presented showing the machine learning module can help the control system adjust itself with changing requirements provided externally (by a user) in the form of training examples. The result is an architecture of an intelligent cyber-physical system, with physical and digital parts always working in synchronization, enabling change in the system's behaviour in terms of manufacturing process-flow in order to adapt to any change in the production planning.

Purpose – The aim of this paper is to analyze international and national research and development (R&D) programs and roadmaps for the manufacturing sector, presenting how agile and lean manufacturing models are addressed in these programs. Design/methodology/approach – In this review, several manufacturing research and development programs and roadmaps – national programs from the USA and Canada, and international programs from the European Union and from one international organization – are reviewed. Findings – The major finding of this review is that the main concerns in agile manufacturing, as highlighted in these programs, are networks, supply chain and product/service customization, and lean manufacturing's inclination towards achieving better cost efficiency. Although the lean manufacturing approach has been considered in many past and present programs, analysis of the most recent programs shows a greater priority is given to the agile manufacturing approach. The path towards sustainable manufacturing is delineated by pro-active attitude and action towards customers. Research limitations/implications – The study analyzes two national R&D programs from the USA, one international program from the European Union, three international roadmaps from the European Union, one business plan from Canada and one international roadmap from the global organization Intelligent Manufacturing Systems. Practical implications – The findings of this paper are intended to help managers, researchers and practitioners from the manufacturing sector to enhance their understanding and define suitable strategy for their organizations' sustainability and identify suitable manufacturing path with respect to agile and lean philosophies. This study could also help academics in defining course curricula for students more coherent with the R&D policies and/or requirements towards sustainable manufacturing with respect to agile and lean philosophies. Originality/value – There are reviews comparing agile and lean manufacturing paradigms, but there are no reviews about how the two manufacturing concepts are addressed in manufacturing R&D programs and roadmaps.