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
Kidney exchange programs aim at matching end-stage renal disease patients who have a willing but incompatible kidney donor with another donor. The programs comprise a pool of such incompatible patient-donor pairs and, whenever a donor from one pair is compatible with the patient of another pair, and vice versa, the pairs may be matched and exchange kidneys. This is typically a two-step process in which, first, a set of pairs is matched based on preliminary compatibility tests and, second, the matched pairs are notified and more accurate compatibility tests are performed to verify that actual transplantation can take place. These additional tests may reveal incompatibilities not previously detected. When that happens, the planned exchange will not proceed. Furthermore, pairs may drop out before the transplant, and thus the planned exchange is canceled. In this paper, we study the case in which a new set of pairs may be matched if incompatibilities are discovered or a pair withdraws from the program. The new set should be as close as possible to the initial set in order to minimize the material and emotional costs of the changes. Various recourse policies that determine the admissible second-stage actions are investigated. For each recourse policy, we propose a novel adjustable robust integer programming model. Wealso propose solution approaches to solve this model exactly. The approaches are validated through thorough computational experiments. Summary of Contribution: In the paper, we present an original work related to the modeling and optimization approaches for Kidney Exchange Programs (KEPs). Currently, KEPs represent an alternative way for patients suffering from renal failure to find a compatible (living) donor. The problem of determining an assignment of patients to (compatible) donors that maximizes the number of transplants in a KEP can be seen as a vertex-disjoint cycle packing problem. Thus, KEPs have been extensively studied in the literature of integer programming. In practice, the assignment determined to a KEP might not be implemented due to withdraws from the program (e.g., a more accurate compatible test shows a new incompatibility or a patient health condition unable him/her to participate on the KEP). In our paper, we model the problem of determining a robust solution to the KEP, i.e., a solution that minimizes the material and emotional costs of changing an assignment. In this way, we propose and design solution approaches for three recourse policies that anticipate withdraws. Through computational experiments we compare the three recourse policies and validate the practical interest of robust solutions.

Abstract
Nowadays there are several countries running independent kidney exchange programmes (KEPs). These programmes allow a patient with kidney failure, having a willing healthy but incompatible donor, to receive a transplant from a similar pair where the donor is compatible with him. Since in general larger patient-donor pools allow for more patients to be matched, this prompts independent programmes (agents) to merge their pools and collaborate in order to increase the overall number of transplants. Such collaboration does however raise a problem: how to assign transplants to agents so that there is a balance between the contribution each agent brings to the merged pool and the benefit it gets from the collaboration. In this paper we propose a new Integer Programming model for multi-agent kidney exchange programmes (mKEPs). It considers the possible existence of multiple optimal solutions in each matching period of a KEP and, in consecutive matching periods, selects the optimal solution among the set of alternative ones in such a way that in the long-term the benefit each agent gets from participating in the mKEP is balanced accordingly to a given criterion. This is done by use of a memory mechanism. Extensive computational tests show the benefit of mKEPs, when compared to independent KEPs, in terms of potential increase in the number of transplants. Furthermore, they show that, under different policies, the number of additional transplants each agent receives can vary significantly. More importantly, results show that the proposed methodology consistently obtains more stable results than methodologies that do not use memory.