Abstract
Production scheduling is an optimizing problem that can contribute strongly to the competitive capacity of companies producing goods and services. A way to promote the survival and the sustainability of the organizations in this upcoming era of Industry 4.0 (I4.0) is the efficient use of the resources. A complete failure to stage tasks properly can easily lead to a waste of time and resources, which could result in a low level of productivity and high monetary losses. In view of the above, it is essential to analyse and continuously develop new models of production scheduling. This paper intends to present an I4.0 oriented decision support tool to the dynamic scheduling. After a fist solution has been generated, the developed prototype has the ability to create new solutions as tasks leave the system and new ones arrive, in order to minimize a certain measure of performance. Using a single machine environment, the proposed prototype was validated in an in-depth computational study through several instances of dynamic problems with stochastic characteristics. Moreover, a more robust analysis was done, which demonstrated that there is statistical evidence that the proposed prototype performance is better than single method of scheduling and proved the effectiveness of the prototype.

Abstract
This paper describes a solution method that was created with the objective of obtaining a more efficient finished goods distribution process for a food industry company. The finished goods distribution process involves the use of the companys own fleet to serve a specific group of customers, and the use of outsourcing transportation services that can make direct and transshipment customer deliveries. The complexity of the problem is due to the need to decide which customers should be served by each of the outsourcing transportation services, direct or transshipment, and to find cost efficient solutions for the multiple vehicle routing problems created. First, an original clustering method consisting of a logical division of the customer orders using a delivery ratio based on the transportation unit cost, distance and order weight, is used to define customer clusters by service type. Then, an exact method based on a mixed integer programming model, is used to obtain optimal vehicle routing solutions, for each cluster created. The solution method for the company real instances, proved able to reach the initial proposed objectives and obtain promising results that suggest an average reduction of 34% for the operational costs, when compared to the current distribution model of the company. © 2019, Springer Nature Switzerland AG.

Abstract
In an increasingly and globally competitive industry like the automotive sector, the continuous improvement of processes assumes a key role in the enhancement of effectiveness and efficiency. In line with this philosophy, the study undertaken of the work method and its inherent activities has pointed to time measurement as a gemba support tool of great potential in the optimization of the production process and the elimination of mudas. The present case study relates to the improvement of productivity of an assembly line dedicated to the manufacture of brake cables for the automotive industry. The application of the A3 methodology to this improvement project aims to monitor the evolution of key indicators as productivity per hour and the Overall Equipment Effectiveness (OEE) of the assembly line, to define the different improvement actions to be executed, and to achieve the defined target, and to validate the results of its implementation. The measurement of the different activities of the assembly line allowed to identify the major wastes (of material movements, operator movements, among others.) and to identify the most critical workstations that contribute for the unbalancing of the assembly line. The solutions implemented allowed to increase the productivity by 49% and, as a consequence, reduce the cycle time in 33%. The rearrangement and improvement of operations allowed also to increase the efficiency of the assembly line balancing in 11%. As the A3 methodology assumed a key role for this project, allowing the monitoring of the effectiveness of the different improvement actions implemented, it was standardized so that it could be applied to other improvement projects.

Abstract
Production scheduling is a function that can contribute strongly to the competitive capacity of companies producing goods and services. Failure to stagger tasks properly causes enormous waste of time and resources, with a clear decrease in productivity and high monetary losses. The efficient use of internal resources in organizations becomes a competitive advantage and can thus dictate their survival and sustainability. In that sense, it becomes crucial to analyze and develop production scheduling models, which can be simplified as the function of affecting tasks to means of production over time. This report is part of a project to develop a dynamic scheduling tool for decision support in a single machine environment. The system created has the ability, after a first solution has been generated, to trigger a new solution as some tasks leave the system and new ones arrive, allowing the user, at each instant of time, to determine new scheduling solutions, in order to minimize a certain measure of performance. The proposed tool was validated in an in-depth computational study with dynamic task releases and stochastic execution time. The results demonstrate the effectiveness of the model.

Abstract
Nowadays, companies are facing a constant need to develop and increase coordination between operational functions to respond rapidly and accurately to customer requests. Linked with this need, an increasing number of practitioners are resorting to an established and integrated business management methodology, the Sales and Operations Planning (S&OP). The concept of S&OP has gained increased recognition over the years by several authors and companies. This project describes the S&OP implementation in Sogrape Vinhos (wines) S.A., a Portuguese wine producer and distributer. The company was confronted with low accuracy in the establishing the forecast demand plans, especially on a long-term horizon. In order to increase the demand plans accuracy, the company started a S&OP implementation program. This paper describes the company’s current planning process, explains the S&OP’s implementation model presenting the selected parameters adequate to the company’s context, and finally, evaluate the expected outcomes of this project. Preliminary results from the S&OP implementation project at Sogrape indicate significant savings at the operational level and greater effectiveness in developing the company's demand plans. © 2019 The Authors. Published by Elsevier Ltd.

Abstract
This paper proposes the linear frequency cepstral coefficients as highly discriminative features for anomaly detection in ball bearings using vibration sensor data. These features are based on cepstral analysis and are capable of encoding the patterns of a spectral magnitude profile. Incipient damages on bearings can grow rapidly under normal use resulting in vibration and harsh noise. If left undetected, this damage will worsen, leading to high maintenance costs or even injury. Multiple interferences in an industrial environment contaminate the signal, making it a challenge to correctly identify the bearings' condition. Many studies have attempted to overcome this issue at the signal level. However, the discriminative capacity of the current vibration signal features is still vulnerable to interference, which motivates this work. In order to demonstrate the benefits of these features, we (1) show that they are computationally efficient and suitable for real-time incremental training; (2) conduct discriminative analysis by evaluating the separability performance and comparing it with the state of the art; and (3) test the robustness of the proposed features under noise interference, which is ideal for use in the harsh operating conditions of industrial machinery. The data was obtained from a laboratory workbench setting that reproduces bearing fault scenarios. Results show that the proposed features are fast, competitive when compared to state-of-the-art features, and resilient to high levels of interference. Despite the higher performance when using the quadratic model, the proposed features remain highly discriminative when used with several other discriminant function.