Worldwide competitiveness poses enormous challenges on managers, demanding a continuous quest to increase rationality in the use of resources. As a management philosophy, Lean Manufacturing focuses on the elimination of activities that do not create any type of value and therefore are considered waste. For companies to successfully implement the Lean Manufacturing philosophy it is crucial that the human resources of the organization have the necessary training, for which proper tools are required. At the same time, higher education institutions need innovative tools to increase the attractiveness of engineering curricula and develop a higher level of knowledge among students, improving their employability. This paper describes how Lean Learning Academy, an international collaboration project between five EU universities and five companies, from SME to Multinational/Global companies, developed and applied an innovative training programme for Engineers on Lean Manufacturing, a successful alternative to the traditional teaching methods in engineering courses.

Production scheduling is an optimizing problem that can contribute strongly to the competitive capacity of companies producing goods and services. A way to promote the survival and the sustainability of the organizations in this upcoming era of Industry 4.0 (I4.0) is the efficient use of the resources. A complete failure to stage tasks properly can easily lead to a waste of time and resources, which could result in a low level of productivity and high monetary losses. In view of the above, it is essential to analyse and continuously develop new models of production scheduling. This paper intends to present an I4.0 oriented decision support tool to the dynamic scheduling. After a fist solution has been generated, the developed prototype has the ability to create new solutions as tasks leave the system and new ones arrive, in order to minimize a certain measure of performance. Using a single machine environment, the proposed prototype was validated in an in-depth computational study through several instances of dynamic problems with stochastic characteristics. Moreover, a more robust analysis was done, which demonstrated that there is statistical evidence that the proposed prototype performance is better than single method of scheduling and proved the effectiveness of the prototype.

Purpose: The study purpose was to analyse the excellence and operational efficiency of Portuguese industrial companies through the measurement of lean practices implementation. Additionally, it intended to propose a new model to assess the lean production system.Design/methodology/approach: The research presents an in-depth lean literature review that served as a basis for the creation of a questionnaire. The survey was addressed to Portuguese industrial companies to obtain data about their lean implementation success. Collected data were analysed, resorting descriptive and exploratory statistics. A principal component analysis was applied to reduce the amount of data and define a new model that characterizes the lean practices adoption level. Some Chi-square tests were used to assess the independence of some variables.Findings: The results indicate that a significant percentage of organizations use lean practices within their activity. Concerning the lean implementation maturity, a plus side revealed by the study concerns the adoption of the teamwork, internal information shared principles, increase of process capability to produce conforming products, and reduction in set up times. On the other hand, responsibilities decentralization, more employees acting as team leaders, implementation of employees’ suggestions, and interconnection with suppliers, are some of the principles that need to be given greater attention by the Portuguese industrial organizations. The key contribution consists of a new model for lean determinants based on three dimensions: work method, productive process elements, and work efficiency.Originality/value: The present research provides a new Lean determinants model that allows understanding the importance of the operational efficiency provided by a mature lean production system, being the key to the competitiveness in the global business market.

In the last few years, the industrial, scientific, and technological fields have been subject to a revolutionary process of digitalization and automation called Industry 4.0. Its implementation has been successful mainly in the economic field of sustainability, while the environmental field has been gaining more attention from researchers recently. However, the social scope of Industry 4.0 is still somewhat neglected by researchers and organizations. This research aimed to study Industry 4.0 and sustainability themes using data science, by incorporating open data and open-source tools to achieve sustainable Industry 4.0. To that end, a quantitative analysis based on open data was developed using open-source software in order to study Industry 4.0 and sustainability trends. The main results show that manufacturing is a relevant value-added activity in the worldwide economy; that, foreseeing the importance of Industry 4.0, countries in America, Asia, Europe, and Oceania are incorporating technological principles of Industry 4.0 in their cities, creating so-called smart cities; and that the industries that invest most in technology are computers and electronics, pharmaceuticals, transport equipment, and IT (information technology) services. Furthermore, the G7 countries have a prevalent positive trend for the migration of technological and social skills toward sustainability, as it relates to the social pillar, and to Industry 4.0. Finally, on the global scale, a positive correlation between data openness and happiness was found.

Nowadays, it has been observed an increasing awareness and understanding on the subject of sustainable companies and business models, addressing multi-disciplinary approaches that cover not only economical problems, but also social and environmental challenges. Supply chains and especially collaborative networks managers are increasingly aware of these sustainability issues, continuously seeking to meet current human needs while preserving environmental safety. Only this way, focusing on its sustainable growing, it is possible to preserve companies' steadiness. In order to achieve this goal, sustainable networks must ensure that each partner is fully aligned and committed with economic, environmental and social axes that rule the network operational behaviour. Nevertheless, in order to achieve this level of maturity within such complex and turbulent environments, organisations need to improve the quality of their performance assessment approaches, integrating the different sustainability perspectives. To accomplish this, it is critical to establish specific indicators responsible to formalise and evaluate partners' behaviour, according to well-identified objectives, as well as fuse this information in a comprehensive and user-friendly way. This paper presents a new approach, based on a fuzzy logic-based algorithm, for sustainable network performance and risk assessment.

Consumer needs and expectations of specific target groups – such as elderly, obese, disabled or diabetic persons – are arising as challenging opportunities for European companies which are asked to supply innovative customised goods of high quality at affordable price. This is particularly true in the fashion as well as in the orthopaedic sector where there are many different competences to conjugate to offer dedicated products to the mentioned target groups. This paper aims at proposing a reference model to support companies in defining collaborative supply networks for customised production. In particular, this work describes the implementation of the developed model in a real case highlighting the changes implied at network level to address the need for fashionable and healthy products.