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Nowadays industries have initiative to reduce a number of production injuries and accidents down to zero. This goal could be achieved only through the proactive identification of accident precursors – such as unsafe conditions (UC) and unsafe acts (UA), whose control could prevent occurrence of accidents. At the same time, smartphones (and mobile applications) are taking dominance over PCs, making mobile devices major interface for human interaction with the web. In this term, application of mobile devices in OSH remains to be more exploited and accepted. Takin into account these trends, the present study aims to propose a novel cloud-based mobile framework for the management of UC/UA reports. Compared to previous studies, we considered the recent trends of mobile, cloud and Safety 4.0 in more details – ending up with the solution that is the combo of JavaScript technologies. Additionally, we provided a publically available use case named the SafE-Tag. Particularly, the SafE-Tag was implemented using the React Native for mobile application, Node.js for web-services and Mongo.db NoSQL cloud database. Beside elaborated computational efficiency, these technologies were found as the most optimal in terms of the cost/complexity to develop and/or adapt in an arbitrary SME. It is concluded that the proposed framework brings benefits for both employees and employers through: (1) intuitive and user friendly identification of key safety performance indicators; (2) digitalization and improvement of safety reporting systems; (3) improvement of safety culture; (4) better understanding and measurement of worker’s contribution to safety; and (5) making the technology affordable.

The article deals with an innovative methodology for risk assessment concerning human, organizational and technical/technological (HOT) factors, based on fuzzy set theory. The aim of this paper is to propose user-friendly prognostic risk assessment tool (PgRA) by obtaining reliable results and supporting further decisions of the safety managers. The HOT factors are introduced with associated sub-factors. The user-friendly interface developed in Matlab environment provides multiple opportunities for further improvement. The settings presented in this article are strictly applied for, but not limited to manufacturing sector. Flexibility of the PgRA tool allows adjustments and customize model regarding the group of the companies. With introduction of fuzzy set theory in the risk assessment process, level of subjectivity is reduced to the minimum. Practical applications: Possibilities of the practical application are modeled to assist in decrease of identified risks during daily work. This is a useful visual management tool, helpful to all safety managers in planning workplace improvements. The safety managers are in position to predict risk level before the real measures are taken. They are able to show the possible realistic results and risk trend behaviour to their supervisor/director, without spending any financial resources.

Industrial working environment demonstrates important advances in terms of working conditions. Nevertheless, there are still present manual tasks with high frequency, namely, repetitive tasks. This kind of working tasks are among the most influential for workers’ psychological and myoskeletal health. The concern for the risk derived by the repetitive task is evident by the fact that there is a well-known technical standard dedicated to this named OCRA (Occupational Repetitive Action). The present study uses OCRA for categorizing the risk produced by a replicated real repetitive task in laboratory conditions, and then compares electrophysiological method for deeper investigating into workers’ psychophysiology. The study combines results from OCRA and Electrodermal Activity (EDA) and offers suggestions for work design experts to consider psychophysiological impact metrics derived from low risk repetitive tasks.

There is an obvious need to redefine approach to provide and develop human resources necessary for modern industrial systems and to find new models that will meet the demands of Lean philosophy and concepts of safety and ergonomics. The authors are suggesting Early Human Resources Management (EHRM) model, inspired by developed pillar structures of Lean based World Class Manufacturing (WCM) and Total Productive Maintenance (TPM) industrial systems and based on proactive approach referring to human resources. The EHRM model is designed through the integration of Early Management and Human Resources development concepts and uses the Vertical start-up (VSU) principle for a drastic reduction of time needed for reaching the full potential and achieving the desired level of knowledge and competencies of human resources in Lean industrial systems. Practical applications: EHRM contributes to reducing the gap between expected and real performances of human resources at an early stage of their professional careers and enables the effective and efficient transition from academic to industrial environment. At the same time, cooperation, communication and knowledge transfer between industry and universities are upgraded, development of educational curricula is facilitated and build around the applicative knowledge skills, and methods.

The evaluation and ranking of resilience factors (RFs) in an uncertain environment has important implications for the management of any enterprise. Determining an improvement strategy of business process resilience is based on the obtained rank of RFs, and it presents a key success factor for an enterprise in dealing with crisis. The complexity and importance of the treated problem calls for analytic methods rather than intuitive decisions. The relative importance of business processes and the relative importance of RFs under each business process are stated by fuzzy pair-wise comparison matrices. The elements of these matrices are triangular fuzzy numbers (TFNs). The fuzzy Analytic Hierarchy Process (FAHP) is used for determination of relative weights of existing variables. The rank of RFs is obtained by using the extent fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS). The real life application on the selection of the management team shows the practical implications in the process industry.

This paper is focused on the problem of compliance of interface between clamping/locating fixture elements and workpiece, under dynamic loads during machining. In contrast to previous investigations, the authors have developed a special device dedicated to testing of physical models which represent clamping/locating elements and workpiece. This device allows optimization of a large number of input parameters which are critical to interface compliance. It was used in experimental investigations to establish the impact that the radius of the spherical tip of a clamping/locating element has on the interface compliance and load capacity. The results of experimental investigation show that, under certain conditions, the clamping/locating elements with larger-radius spherical tips provide significantly lower interface compliance. Future investigations should be aimed at finding optimum macro- and micro-geometries of contact interface, as well as the selection of materials for clamping/locating elements.