Abstract
The selection of the resources system (SRS) is an important element in the integration/project of Agile/Virtual Enterprises (A/V E) because its performance is dependent of this selection, and even of its creation. However, it remains a difficult matter to solve because is still a very complex and uncertain problem. We propose that using Value Analysis (VA) in the pre-selection of resources phase represents a significant improvement of the SRS process. The current literature fails to formally address the pre-selection phase and none of the resource selection models incorporate the resources value and its consequence in the complexity of the selection process. Whereby, ours developed model with VA constitutes an innovative approach towards greater sustainability in the configuration of A/V E in the context of Industry 4.0, where a massive interconnection among enterprises is expected and consequently the increase of the selection process complexity. After the construction of a demonstrator tool for a set of the problem formulations, this paper verifies by computational results the thesis regarding the benefits of applying VA to the SRS process: VA reduces the complexity of the SRS process, even ensuring that the final system of resources achieve higher quality/value grade.

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 in the presence of real-time events is of great importance for the successful implementation of real-world scheduling systems. Most manufacturing systems operate in dynamic environments vulnerable to various stochastic real-time events which continuously forces reconsideration and revision of pre-established schedules. In an uncertain environment, efficient ways to adapt current solutions to unexpected events, are preferable to solutions that soon become obsolete. This reality motivated us to develop a tool that attempts to start filling the gap between scheduling theory and practice. The developed prototype is connected to the MRP software and uses meta heuristics to generate a predictive schedule. Then, whenever disruptions happen, like arrival of new tasks or cancelation of others, the tool starts rescheduling through a dynamic-event module that combines dispatching rules that best fit the performance measures pre-classified by Kano’s model. The proposed tool was tested in an in-depth computational study with dynamic task releases and stochastic execution time. The results demonstrate the effectiveness of the model. © 2020, Springer Nature Switzerland AG.

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
Time, cost, and quality are the three indispensable factors for the realization and success of a project. In this context, we propose a framework composed of a multi-objective approach and multi-criteria decision-making methods (MCDM) to solve time-cost-quality trade-off optimization problems. A multi-objective Simulated Annealing (MOSA) algorithm is used to compute an approximation to the Pareto optimal set. The concept of the exploratory grid is introduced in the MOSA to improve its performance. MCDM are used to assist the decision-making process. The Shannon entropy and AHP methods assign weights to criteria. The first methodology is for the inexperienced decision-makers, and the second concedes a personal and flexible weighting of the criteria weights, based on the project manager's assessment. The TOPSIS and VIKOR methods are considered to rank the solutions. Although they have the same purpose, the rankings achieved are different. A tool is implemented to solve a time-cost-quality trade-off problem on a project activities network. The computational experiments are analyzed and the results with the exploratory grid in Simulated Annealing (SA) are promising. Despite the framework aims to solve multi-objective trade-off optimization problems, supporting the decisions of the project manager, the methodologies used can also be applied in other areas.