João Pedro Basto

Abstract
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.

Abstract
The industry faces more and more the challenge of deploying and taking advantage of evidence-based strategic decisions to enhance profit gain. In this research, the possibility of having a fully integrated system composed by a simulator and an IoT platform with the capability of collecting real-time data from the shop floor and returning performance indicators to support decision making is evaluated. The suggested approach involves a Manufacturing Executing System (MES) producing a production schedule, an IoT Platform composed by a message broker and a real-time database, a Simulator including simulation software and a wrapper, and a user application serving as an interface between the user and the IoT Platform and Simulator integrated system. A detailed analysis of the functionalities and integration of the Simulator and the IoT Platform will also be explored. To evaluate the approach, one use case of a production line in the automotive industry is used. The application of the integrated IoT Simulation system permits its validation and consequent future work. © 2019, IEOM Society International.

Abstract
Additive Manufacturing (AM) is one of the most trending production technologies, with a growing number of companies looking forward to implementing it in their processes. Producing through AM not only means that there are no supplier lead times needed to account for, but also enables production closer to the end customer, reducing then the delivery time. This is especially true for companies with a wide range of low and variable demand products. This paper proposes a mixed integer linear programming (MILP) model for the optimal design of supply chains facing the introduction of AM processes. In the addressed problem, the 3D printers allocation to distribution centers (DC), that will make or customize parts, and the Suppliers-DC-Customers connections for each product need to be defined. The model aims at minimizing the supply chain costs, exploring the trade-offs between safety stock and stockout costs, and between buying and 3D printing a part. The main relevant characteristics of this model are the introduction of stock service levels as decision variables and the use of a linearization of the cumulative distribution function to account for demand uncertainty. A real-world problem from a maintenance provider is solved, showing the applicability of the model. © 2019, IEOM Society International.

Abstract
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.

Abstract
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.