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Scheduling is a complex task due to the need to optimise multiple competing objectives and react to unpredictable events that may occur during production execution. The strategy of predictive-reactive scheduling can be used to reconcile the conflict between the original schedule and the current shop floor situation. This study seeks to present an inventory data-driven predictive-reactive production scheduling model that supports the evolving concepts of the Industry 4.0. Periodically, a machine learning technique provides predictive scheduling considering a best-case scenario according to an established Key Performance Indicator (KPI). Then, material non-availability causes disruptions in production, which triggers the Simulation-Based Optimization (SBO) method to handle these events. Thus, SBO provides a reactive schedule with the best set of priority rules to sequence jobs on each machine according to the data on the shop floor. This model was validated with a real case study using data collected from a metal-mechanical company. Considering the service level KPI, the results showed that the model is able to find a better solution in the compared scenarios. Therefore, even in a dynamic and stochastic scenario, with machine breakdowns, quality problems, raw material delays, and accuracy issues, the model proved efficient in mitigating these variations' effects. [ABSTRACT FROM AUTHOR]

Production systems are composed of increasingly complex components with unique specifications. Therefore, since holding safety stocks of each component would be prohibitive, maintenance activities rely on the proper delivery of spare parts, making it available at the right time and place. Equipments monitored by sensors as well as the transmission of sensors data to the spare part supply chain represent an interesting venue for dealing with this contemporaneous industrial challenge. In this direction, this paper applies a simulation model derived from a real world scenario to analyze the performance of the collaboration between condition-based maintenance – also known as intelligent maintenance systems – and spare parts supply chains, in comparison with existing maintenance approaches. Obtained results substantiate the potential of monitoring, treating and transmitting equipment condition data to ensure cost-effective maintenance and production systems availability.

This study examines how Industrial Engineering postgraduate programs intend to develop the competencies required for Industry 4.0 in their students. An exploratory and descriptive approach was adopted. Multiple sources of evidence were collected in four of Brazil's best‐ranked and most traditional Industrial Engineering postgraduate programs, involving experienced academics and directors of these programs. Four knowledge dimensions were used to frame our study: factual, conceptual, procedural, and metacognitive. Results indicate that Industrial Engineering postgraduate courses tend to emphasize technical competencies, such as problem and conflict‐solving, decision‐making, and analytical and research skills, rather than competencies related to interpersonal relationships, such as creativity, entrepreneurial thinking, leadership, adaptability, and flexibility. We also found that the development of Industry 4.0 competencies in the examined Industrial Engineering postgraduate programs mostly relies on activities related to procedural knowledge. Our study provides Industrial Engineering instructors and academics arguments to revisit their curricula and better prepare postgraduate students for the challenges imposed by Industry 4.0. [ABSTRACT FROM AUTHOR]

Technological developments along with the emergence of Industry 4.0 allow for new approaches to solve industrial problems, such as the Job-shop Scheduling Problem (JSP). In this sense, embedding Multi-Agent Systems (MAS) into Cyber-Physical Systems (CPS) is a highly promising approach to handle complex and dynamic JSPs. This paper proposes a data exchange framework in order to deal with the JSP considering the state-of-the-art technology regarding MAS, CPS and industrial standards. The proposed framework has self-configuring features to deal with disturbances in the production line. This is possible through the development of an intelligent system based on the use of agents and the Internet of Things (IoT) to achieve real-time data exchange and decision making in the job-shop. The performance of the proposed framework is tested in a simulation study based on a real industrial case. The results substantiate gains in flexibility, scalability and efficiency through the data exchange between factory layers. Finally, the paper presents insights regarding industrial applications in the Industry 4.0 era in general and in particular with regard to the framework implementation in the analyzed industrial case. [ABSTRACT FROM AUTHOR]