Abstract
An unprecedented outbreak pandemic caused disruption around the world. It had a strong impact on economic sector. Although, the pandemic accelerated the growth of e-commerce for specific categories as food retailer. As a result, several companies restructured their structures, in terms of IT and operations. During the first confinement, the operations and the website of SONAE MC were not prepared for the increase that existed due to the pandemic, COVID-19, causing disruption in the supply chain and long lead times. In this paper, it is explained how SONAE MC reduced its dependence on refrigerated vehicles, simplifying operations and reducing the costs of transporting products from online orders in vehicles with cargo space able to transport positive cold food and negative cold. It is also explained how innovation has ensured that products continue to be transported with quality and safety to all customers of the SONAE MC Darkstore. The result was the implementation of the proposed solution which may grow technologically once information and equipment are available.

Abstract
The need of lockdown, due to COVID-19, led many manufacturing companies to accelerate the adoption of digital technologies. Manufacturing companies were strongly affected by workforce shortages associated with the spread of COVID-19 and the lockdown, as well by connectivity losses among business partners. Therefore, these companies are reviewing their strategies to increase productivity, mainly embracing digital manufacturing technologies. Here the adoption of digital technologies aims to improve efficiency and flexibility in their processes, also improving connectivity among business partners. This study investigates how collaborative academia-industry R&D cases accelerated the adoption of digital technologies by manufacturing companies, given the current COVID-19 pandemic situation. Based on multiple case studies, this article reports the challenges and the strategies of three ongoing collaborative industry-academia R&D projects developed during the COVID pandemic situation. The results are presented in four different perspectives derived from industry 4.0 readiness maturity models: interpersonal communication, personal competencies and skills, systems integration, and technological strategy. It highlights the importance of manufacturing companies to have a well-designed digitalization strategy, need of continuous training and development of their workforce, and the support of Research & Technology Organizations (RTO) to bring more maturity to the efforts required during a turbulent situation. The results of this paper can provide relevant decision support for manufacturing companies, and its stakeholders, in face of challenges of the actual pandemic and post-pandemic scenario.

Abstract
The development of the Internet created new technologies that are changing the way of doing business, particularly Industry 4.0 in current days. One challenge of the implementation of new technologies is the change required from companies' business models. However, the literature concerning Industry 4.0 is mainly focused on technological innovations and less in their impact on business models. This paper aims to understand the impacts of the digitalization process inherent to Industry 4.0 on business models. To achieve this purpose, an exploratory multiple-case study based on semi-structured interviews was conducted in two Portuguese medium companies from two different sectors. Findings show that besides companies being able to change to a customer-oriented approach, individualized mass production may not be the only purpose of this transformation. Networking is particularly appealing for small and medium enterprises, once they usually have fewer resources to dedicate to innovation projects. Additionally, in the era of globalization, logistic costs are still an obstacle for serving international markets. Finally, social media are seen as an internal tool of communication for business-to-business companies. Being one of the first empirical studies of the Portuguese context, it aims to diminish the lack of literature concerning this particular topic and enable future researches on the use of business models as a management tool. Secondly, it intends to help managers develop or redesign business models adjusted to a more dynamic and competitive environment. © 2021 IEEE.

Abstract
This work presents a taxonomic proposition that integrates six views of business process (BP), allowing the identification of different BP perspectives as well as their elements used in the BP modelling activity. A literature review (LR) was conducted to identify the theoretical elements of the BP construct. Based on the LR findings, a taxonomic proposition of BP is presented. Finally, interviews were conducted with practitioners to validate it. The taxonomy contributes to the systematisation of knowledge around the theoretical construct BP and offers the practitioner a broad spectrum of points of view for the analysis of a given BP.

Abstract
Zero Defects is one of the ultimate targets for manufacturing quality control and assurance. Such systems are becoming common in advanced manufacturing industries but are at an initial stage in more traditional industrial sectors, such as wood panels, laminates production, pulp and paper processing and composite panels production. This paper proposes the PREFAB framework, applied to the wood based panels industry, to minimize rejected products using AI, machine learning and IoT devices. The framework was built through action research with a Portuguese wood-based panel manufacturing. This framework delivered an innovative decision support system that provides relevant and timely recommendations for shopfloor decision making and to support process/product engineering. Copyright (C) 2021 The Authors.

Abstract
This work addresses the energy-efficient job shop scheduling problem and transport resources with speed scalable machines and vehicles which is a recent extension of the classical job shop problem. In the environment under consideration, the speed with which machines process production operations and the speed with which vehicles transport jobs are also to be decided. Therefore, the scheduler can control both the completion times and the total energy consumption. We propose a mixed-integer linear programming model that can be efficiently solved to optimality for small-sized problem instances.