Abstract
Currently, performance analysis on complex manufacturing systems is performed in an ad hoc way since the main objective is to verify if the strategy designed has been helping companies achieve their targets following a reactive approach. However, more and more companies are performing in competitive markets, forcing them to become more proactive than reactive. This way, a simple approach is no longer suitable for this type of companies, and a stronger and effective interaction between the strategic and operational layers is key. Therefore, this research proposes a framework composed of both qualitative and quantitative methods that allow decision-makers to better understand their production system. Moreover, using key leading indicators as reference, the idea is to provide companies with the ability to anticipate future performance behaviors based not only on the knowledge acquired, but also on a mathematical tool that will synthesize this knowledge and infer future performance behaviors. This paper explores a critical issue for contemporary industrial organizations and sustainability issues concerning energy consumption. In this scope, an important research was performed aiming at modeling and understanding the normal behavior of electricity consumption, as well as the factors affecting energy consumption in the painting line of an automotive plant. © Springer International Publishing Switzerland 2013.

Abstract
Complexity in manufacturing systems appears under a variety of aspects, namely product, processes and operations and systems. Considering that the manufacturing environment is rapidly and constantly changing, with higher levels of customization and complexity, there is higher demand for flexibility and adaptability from companies. In this context, it seems essential to explore new approaches that can support decision-makers to take better decisions concerning the action plans that they need to launch to achieve the expected strategic and operational performance and alignment goals. Companies should become able to analyse their performance drivers, understand their meaning and the feedback loops that affect them. Therefore, decision makers can look into the future, and act even before these causes affect the transformation systems efficiency and effectiveness. This paper presents an approach oriented to multi-performance measurement in complex manufacturing environments. With this approach it is expected to overcome the gap between the operational and strategic layers of a manufacturing system, in order to reduce time when measuring performance and reacting to unexpected behaviours, as well as reduce errors when taking decisions. Moreover, it is expected to decrease the time necessary to calculate an indicator or to introduce a new one into performance management process, reducing the operational costs.

Abstract
Sustainable development is a widely spread concept nowadays, especially due to external pressure related to environmental and social issues, affecting all players of the supply chain. Sustainable policies must be adopted, such as improving process performance and reducing waste. With sustainability as a driver of operational excellence, this study is focused on the improvement of the production process of a company by reducing variability. A variability analysis was done to understand its root causes and act upon them, as well as a quantification of waste in the process. Finally, an improvement plan was delineated to mitigate the problems identified. Copyright © 2020 Inderscience Enterprises Ltd.

Abstract
The modern digital era is characterized by a plethora of emerging technologies, methodologies and techniques that are employed in the manufacturing industries with intent to improve productivity, to optimize processes and to reduce operational costs. Yet, algorithms and methodological approaches for improvement of energy consumption and environmental impact are not integrated with the current operational and planning tools used by manufacturing companies. One possible reason for this is the difficulty in bridging the gap between the most advanced energy related ICT tools, developed within the scope of the industry 4.0 era, and the legacy systems that support most manufacturing operational and planning processes. Consequently, this paper proposes a conceptual architecture model for a digital energy management platform, which is comprised of an IIoT-based platform, strongly supported by energy digital twin for interoperability and integrated with AI-based energy data-driven services. This conceptual architecture model enables companies to analyse their energy consumption behaviour, which allows for the understanding of the synergies among the variables that affect the energy demand, and to integrate this energy intelligence with their legacy systems in order to achieve a more sustainable energy demand. © 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the FAIM 2021.

Abstract
The COVID-19 crisis exposed the vulnerability and poor resilience of the global supply chains. The objective of this research is to reflect on the possible impacts of the Coronavirus crisis in the global supply chains and provide some recommendations to overcome the present situation, offering suggestions for future research: (1) What are the contingency factors affecting Supply Chains in the complex COVID-19 operating environment? (2) How do these factors affect post-COVID-19 operating performance? After a contextualization of the COVID-19 pandemic crisis and its impacts, theoretical background on Supply Chains and Supply Chain Management are presented, and a summary of the main scenarios for the post-COVID-19 crisis are discussed. The propositions regarding the contingency factors and their impact on the Supply Chain operating performance in post-COVID-19 suggest that successful companies will focus on creating a new kind of operational performance and minimize risks. To that end, companies will aim to improve their operations' resilience (ability to resist, hold on, and recover from shocks) and accelerate the end-to-end digital transformation. Consumers will have to adapt to the contact-free economy, less low-cost supply chains, and put additional emphasis on service levels. Governments will reinforce the focus in the health sector supply chain and increase spending in the health and social care sectors. Furthermore, the longer, the more concentrated, the less transparent, and the more price sensitivity is the supply chain, the more challenging the adaptation to the new pos pandemic realities. Suggestions for future research are also provided.

Abstract
Build to Order or Make to Order is a common approach for highly configured products such as special vehicles (vehicles that are adapted and altered to suit a specific purpose). Examples of such vehicles are special ambulances as well as vehicles adapted for the support and transport of passengers with less mobility. In this type of business, operations are scheduled in response to a confirmed order received from a final customer. Thus, the variability and the uncertainty characterizing what is project based, generate a complexity that requires specifically tailored managerial approaches to handle all the involved processes - from design and engineering to production and delivery. Hence, in this accentuated complexity, it is extremely important to guarantee that both the material and information flows are efficient and effective. The present study, framed in a program of operational improvement in a manufacturer of special vehicles, aims to address some concrete improvement opportunities related to the significant number of raw materials stockouts and to the high number of changes made by the client after production has started. In fact, during the manufacturing and assembly process, there are constant changes that delay and difficult planning and consequently decreases the overall efficiency and effectiveness. Strategies to address all these matters are to be identified and applied. © IEOM Society International.