Abstract
Small and medium-sized enterprises (SMEs) in Europe risk their competitiveness if they fail to embrace digitalization. Indeed, SMEs are aware of the need to digitalize-more than one in two SMEs are concerned that they may lose competitiveness if they do not adopt new digital technologies. However, a key obstacle is related with decision-makers' lack of awareness concerning digital technologies potential and implications. Some decision-makers renounce digital transition simply because they do not understand how it can be incorporated into the business. Take into account this common reality, especially among SMEs, this research project intends to identify the skills and subjects that need to be addressed and suggests the educational methodology and implementation strategy capable of maximizing its success. Therefore, and supported by a focused group research methodology, an innovative training program, oriented to decision-makers, was designed and implemented. The program was conceived based on a self-directed learning methodology, combining both asynchronous lecture/expositive and active training methodologies, strongly based on state-of-the-art knowledge and supported by reference cases and real applications. It is intended that the trainees/participants become familiar with a comprehensive set of concepts, principles, methodologies, and tools, capable of significantly enhancing decision-making capability at both strategic and tactical level. The proposed programme with a multidisciplinary scope explores different thematic chapters (self-contained) as well as cross-cutting thematic disciplines, oriented to the Industry 4.0 and digital transformation paradigm. Topics related with Digital Maturity Assessment, Smart Factories and Flexible Production Systems, Big Data, and Artificial Intelligence for Smarter Decision-Making in Industry and Smart Materials and Products, as well as new production processes for new business models. Each thematic chapter in turn is structured around a variable set of elementary modules and includes examples and case studies to illustrate the selected topics. A teaching-learning methodology centered on an online platform is proposed, having as a central element, a collection of videos complemented by a set of handouts that organize the set of key messages and take-ways associated with each module. In this paper, we present the design and practice of this training course specifically oriented to decision-makers in SME.

Abstract
Small and medium-sized enterprises (SMEs) in Europe are conscious that their competitive position depends on their success to embrace digitalisation challenges. However, some decision-makers in companies discard digital transformation because they do not understand how it can be incorporated into their businesses. Therefore, academia, research centres, and technological clusters are responsible for building the infrastructures and providing the support and the training that will progressively change this mindset. This paper aims to report an experience on designing a training program to train the trainers under the digital transformation topic. To define strategies to understand better the companies (and professionals) needs and motivations and the requisites to deliver the training course, the focus group methodology was applied. In this paper, we present a training program methodology and structure that intend to respond to industrial requests and, in this way to accelerate the digital transformation of companies, especially SMEs.

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 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
Collaboration between companies in transportation problems seeks to reduce empty running of vehicles and to increase the use of vehicles' capacity. Motivated by a case study in the food supply chain, this paper examines a lateral collaboration between a leading retailer (LR), a third party logistics provider (3 PL) and different producers. Three collaborative strategies may be implemented simultaneously, namely pickup-delivery, collection and cross-docking. The collaborative pickup-delivery allows an entity to serve customers of another in the backhaul trips of the vehicles. The collaborative collection allows loads to be picked up at the producers in the backhauling routes of the LR and the 3 PL, instead of the traditional outsourcing. The collaborative cross-docking allows the producers to cross-dock their cargo at the depot of another entity, which is then consolidated and shipped with other loads, either in linehaul or backhaul routes. The collaborative problem is formulated with three different objective functions: minimizing total operational costs, minimizing total fuel consumption and minimizing operational and CO2 emissions costs. The synergy value of collaborative solutions is assessed in terms of costs and environmental impact. Three proportional allocation methods from the literature are used to distribute the collaborative gains among the entities, and their limitations and capabilities to attend fairness criteria are analyzed. Collaboration is able to reduce the global fuel consumption in 26% and the global operational costs in 28%, independently of the objective function used to model the problem. The collaborative pickup-delivery strategy outperforms the other two in the majority of instances under different objectives and parameter settings. The collaborative collection is favoured when the ordering loads from producers increase. The collaborative cross-docking tends to be implemented when the producers are located close to the depot of the 3 PL.

Abstract
Distribution warehouses are a critical part of supply chains, representing a nonnegligible share of the operating costs. This is especially true for unautomated, labor-intensive warehouses, partially due to time-consuming activities such as picking up items or traveling. Inventory categorization techniques, as well as zone storage assignment policies, may help in improving operations, but may also be short-sighted. This work presents a three-step methodology that uses probabilistic simulation, optimization, and event-based simulation (SOS) to analyze and experiment with layout and storage assignment policies to improve the picking performance. In the first stage, picking performance is estimated under different storage assignment policies and zone configurations using a probabilistic model. In the second stage, a mixed integer optimization model defines the overall warehouse layout by selecting the configuration and storage assignment policy for each zone. Finally, the optimized layout solution is tested under demand uncertainty in the third, final simulation phase, through a discrete-event simulation model. The SOS methodology was validated with three months of operational data from a large retailer's warehouse, successfully illustrating how it may be successfully used for improving the performance of a distribution warehouse.