Abstract
We live in a world where companies are shifting to the industry 4.0 paradigm. One of the pillars of Industry 4.0 is the digitalization of physical assets and manufacturing processes, moving toward the Cyber-Physical Production Systems concept (CPPS). In these systems, every component of the production process - machines, tools, workstations, etc. - is equipped with sensors, possesses information about itself, and can interact with each other, allowing the production of smaller batches at lower prices and increase product customization through adaptative processes. Consequently, companies are evolving their information systems to have more visibility and control over their production systems. This change increases both the production system's agility and its vulnerability to communication and information related disruptions. Hence, companies that adhere to Industry 4.0 enabling technologies must adopt new methodologies and tools to become aware of the new risks that arise by the introduction of new digital platforms, their impacts in the production systems, and how they may react to remain resilient. In this paper, disruption events and adequate mitigation strategies are analysed, modelled, and simulated as part of a methodology designed to measure the impacts of disruptive events on the production system.

Abstract
The manufacturing industry faces a new revolution, grounded on the intense digitalization of assets and industrial processes and the increasing computation capabilities imposed by the new data-driven digital architectures. This reality has been promoting the Digital Twin (DT) concept and its importance on the industrial companies' business models. However, with these new opportunities, also new threads may rise, mainly related to industrial data protection and sovereignty. Therefore, this research paper will demonstrate the International Data Spaces (IDS) reference model's application to overcome these limitations. Following a pilot study with a Portuguese machine manufacturing company, this paper will demonstrate the development of a cutting and bending machines DT, leveraged on an IDS infrastructure for interoperability, for the plastic and metal industry and its importance to introduce this machine manufacturing company in a new B2B marketplace from the EU project Market 4.0.

Abstract
This paper presents the approach and results of an improvement project carried on by a company that provides business process outsourcing services based on customised software solutions. The critical objectives considered were to increase the efficiency and effectiveness of the processes involved in developing customised solutions. Thus, the focus of this research was to identify problems in current processes and practices and develop possible solutions to improve. The processes were mapped, the inefficiencies were identified, and suggestions for improvements were presented and analysed. It was concluded that the main problems with the project management and software development processes are related to the lack of visibility of the team load, lack of standardisation and inefficient management processes. These causes result in problems such as IT being unable to plan work and problems associated with quality that negatively influence the lead time of projects. Therefore, suggestions for improvement were formulated and prioritised to address each of these aspects. A more agile approach to software development and redesigning the processes for creating customised solutions were the solutions developed. There was also a need to develop the existing project management software changes to adapt it to these changes. © IEOM Society International.

Abstract
This paper discusses the improvement of product management activities in a fast-growing marketing automation software company. Recently, the company experienced an expansion of its international activities, a fast-growing turnover, and a significant enlargement of the software team. The new needs coming from this situation led the company to move the product development team from a traditional functional organization to a holocratic organization, where self-organized teams are entirely responsible for their product development activities. To design effective management practices and tools for this new organizational model, the company decided to analyze its business processes in-depth. At the first stage, high-level process mapping techniques were employed to comprehensively view the product management activities, their embedding organizational structure, and context. Then, detailed process mapping techniques were employed to build up a detailed visualization of the process workflows. Then, it was possible to conduct a root-cause analysis aiming at identifying the opportunities for improvement. These opportunities were then assessed and prioritized based on their expected impact upon business performance, namely productivity and time-to-market. The actions selected for implementation addressed three main issues: standardization of the planning and monitoring procedures; teams misalignments. Through a systematic process analysis and redesign, it was possible to set up a new set of management tools that cope with these issues, reinforce the workforce commitment and involvement, and ultimately improve business performance. © IEOM Society International.

Abstract
This study investigates the potential of dynamically adjusting WIP cap levels to maximize the throughput (TH) performance and minimize work in process (WIP), according to real-time system state arising from process variability associated with low volume and high-variety production systems. Using an innovative approach based on state-of-the-art deep reinforcement learning (proximal policy optimization algorithm), we attain WIP reductions of up to 50% and 30%, with practically no losses in throughput, against pure-push systems and the statistical throughput control method (STC), respectively. An exploratory study based on simulation experiments was performed to provide support to our research. The reinforcement learning agent's performance was shown to be robust to variability changes within the production systems.

Abstract
Paper aims: This study analyzes the comprehension of production engineering students about the influence of some key variables on the process performance measures in a service process, Originality: This paper points out the need for educators to re-evaluate their approaches to teaching the Operations Management (OM) principles related to process flow measures, Research method: This study used scenario-based role-playing experiments with 2×2×2 between-subject factorial design with three independent variables (variability of activities, capacity utilization, and resource pooling) and four dependent variables related to key internal process performance measures (Flow Time, Overall Quality of service, Quality of service employees, and Queue Size), The sample was composed of 178 undergraduate production engineering students from a large university in Brazil from various institution units, Main findings: These results show that students perceived the use of resource pooling as an impactful practice, However, the students did not correctly identify the effects of increasing resource utilization and the variability on flow time and queue size when activities are pooled, Implications for theory and practice: The teaching of basic concepts of OM requires the support of computational tools, Undergraduate courses that contemplate subjects in the field of OM should work more intensely on simulation-based learning.