Search

Distributed manufacturing systems represent a new paradigm in the industrial context, supported by new technologies provided by industry 4.0. In this paper, a model for dynamic allocation of Production Orders (PO) in the context of distributed additive manufacturing systems is proposed. The scheduling model performs a local optimization of PO allocation considering a production times forecasting model, fed by system state data obtained by means of an IoT platform, and transportation real-time data. A simulation-based experiment was conducted in a test case with real and simulated data collected from an elevator spare parts provider in Brazil. A significant reduction of 77.94% of the Average Waiting Time (AWT) was obtained, allowing for an increased efficiency of the additive manufacturing system, which supports the forthcoming pilot application.

In the current industrial revolution, additive manufacturing (AM) embodies a promising technology that can enhance the effectiveness, adaptability, and competitiveness of supply chains (SCs). Moreover, it facilitates the development of distributed SCs, thereby enhancing product availability, inventory levels, and lead time. However, the wide adoption of AM in industrial SCs creates various challenges, leading to new difficulties for SC design. In this context, this paper proposes a new design approach to AM SCs using optimization methods. More specifically, the proposed approach, comprising the p-median and mixed-integer linear programming models, considers the decision of deploying productive resources (3D printers) in specific locations of generic spare part SCs. The approach was evaluated in a real-world use case of an elevator maintenance service provider. The obtained results demonstrated the promising capabilities of the proposed design approach in managing the challenges arising from the forthcoming widespread use of 3D printers in manufacturing SCs., Comment: 9 pages, 8 figures. Published to Published to IEEE Xplore at Oct 07 2020. Periodical Title: IEEE Transactions on Industrial Informatics

The continuous adoption of Additive Manufacturing (AM) can enhance Supply Chain’s (SC) effectiveness, adaptability and competitiveness. AM allows for a decentralized SC, bringing production centres nearer to customers, increasing products availability and decreasing inventory level and lead time. However, the integration of SC and AM brings difficulties, leading to the need of a completely new SC design. This paper proposes an optimization model supporting the design of spare parts SCs operating under a Make-To-Order (MTO) strategy. The proposed approach considers the decision of deploying productive resources (3D printers) in locations of a spare parts SC. The problem is represented as a combination of the p-median and location-allocation optimization models, which are solved using a Mixed Integer Linear Programming (MILP). The approach is tested in two scenarios from a real-world use case of an elevator maintenance service provider. Obtained results demonstrated the promising capabilities of the proposed approach for handling the new design challenges arising from the forthcoming widespread use of 3D printers in manufacturing SCs.

The amount of data extracted from production processes has increased exponentially due to the proliferation of sensing technologies. When processed and analyzed, data can bring out valuable information and knowledge from manufacturing process, production system and equipment. In industries, equipment maintenance is an important key, and affects the operation time of equipment and its efficiency. Thus, equipment faults need to be identified and solved, avoiding shutdown in the production processes. Machine Learning (ML) methods have been emerged as a promising tool in Predictive Maintenance (PdM) applications to prevent failures in equipment that make up the production lines in the factory floor. However, the performance of PdM applications depends on the appropriate choice of the ML method. The aim of this paper is to present a systematic literature review of ML methods applied to PdM, showing which are being explored in this field and the performance of the current state-of-the-art ML techniques. This review focuses on two scientific databases and provides a useful foundation on the ML techniques, their main results, challenges and opportunities, as well as it supports new research works in the PdM field.

The Industry 4.0 movement is driving innovation in manufacturing through the application of digital technologies, leading to solid performance improvements. In this context, this paper introduces a real-time analytical framework based on predictive, simulation and optimization technologies applied to decision support in manufacturing systems, enabled by an underlying reference implementation of an open Industrial Internet of Things (IIoT) platform. This architecture integrates critical equipment, manufacturing and corporate systems through a Unified IIoT Cloud Platform. A real case study on the aeronautic industry demonstrates the proposal feasibility of this architecture to enhance productivity, predict equipment failures and bring agility to react to unexpected events. In this case study, the monitoring tool displays the current status of the critical resources and the predictive tool calculates a probability of failure. When this probability reaches a certain threshold, the simulation tool is triggered to evaluate the impact of the disruption in the system’s productivity. Results from the tools are displayed online through an alert system so that each stakeholder is informed timely and in a contextualized way.

O trabalho desenvolvido nesta dissertação tem como objetivo a criação de um novo método de sequenciamento das operações para uma fábrica de sapatos real. O objetivo é que as soluções de sequenciamento geradas pelo sistema a ser desenvolvido permitam à fábrica minimizar o tempo total de produção (também conhecido como makespan) para um conjunto de encomendas, aumentando assim a sua produtividade e a capacidade de resposta rápida.