Abstract
The decision to adopt new technologies is the most important stage in integrating a new technology into the ongoing processes of the organization and also to obtain benefits from its routine use. This paper proposes an integrated framework that combines Diffusion of Innovations (DOI) Theory, Technology-Organization-Environment (TOE) framework and Institutional Theory (INT) to characterize the critical factors influencing advanced technologies adoption in manufacturing context. This conceptual framework identifies three contextual environments - innovation, internal organizational and external environmental - that can influence the adoption decision along with some sub-contexts from the literature that may be considered. This framework can be used as starting point to explore in depth influential factors in advanced technologies in manufacturing contexts. Additionally, this framework can assist companies to develop adoption process plans as well as managerial practices that consider the role of these factors and thus lead to successful implementations.

Abstract
The digital economy is creating disruptions in traditional industries and markets. Industrial business associations (IBAs) may face serious challenges in a near future to meet the needs and requirements of their members, particularly in supporting their growing international trade activities and internationalization processes. Digital platforms are already transforming different types of businesses across all markets. An IBA may use a digital platform, not only to keep up with the current technological trends of markets, but also to improve the internationalization support provided to their associate small and medium enterprises (SMEs). Therefore, the aim of this chapter is to present the view of these potential digital platforms' managers, by presenting the results of an exploratory field research based on 24 interviews with IBAs from Portugal, France, and the UK. Another goal is to identify current digital platforms that are being used by IBAs and to critically evaluate their potential for supporting internationalization processes of SMEs. By using these findings, a set of requirements and features for digital platforms supporting SME internationalization in the context of IBAs are derived in this chapter. These results can be used by platform designers and by IBAs for designing and developing more effective digital platforms that can meet the specific internationalization needs of their users and managers.

Abstract
The integration of IoT infrastructures across production systems, together with the extensive digitalisation of industrial processes, are drastically impacting manufacturing value chains and the business models built on the top of them. By exploiting these capabilities companies are evolving the nature of their businesses shifting value proposition towards models relying on product servitization and share, instead of ownership. In this paper, we describe the semantic data-model developed to support a digital platform fostering the reintroduction in the loop and optimization of unused industrial capacity. Such data-model aims to establish the main propositions of the semantic representation that constitutes the essential nature of the ecosystem to depict their interactions, the flow of resources and exchange of production services. The inference reasoning on the semantic representation of the ecosystem allows to make emerge nontrivial and previously unknown opportunities. This will apply not only to the matching of demand and supply of manufacturing services, but to possible and unpredictable relations. For instance, a particular kind of waste being produced at an ecosystem node can be linked to the requirements for an input material needed in a new product being developed on the platform, or new technologies can be suggested to enhance processes under improvement. The overall architecture and individual ontologies are presented and their usefulness is motivated via the application to use cases.

Abstract
Organizations are nowadays more and more structured in projects in the so-called project-based organizations (PBO). The advantages of PBOs - operational and managerial focus and effectiveness - are counterweight by difficulties in information / knowledge sharing and overall coordination. Project management (PM) applications have been adopted by PBOs with success, mostly at the project level. More innovation-oriented PBOs are keen to experiment and adopt a range of different project supporting applications to optimize several aspects of project management, resulting in “ecosystems” of PM related applications. This paper addresses the problems arising from the implementation of a global project coordination and collaboration application in a research center whose ecosystem of PM applications is extensive. The main challenge has been managing change. An action-research approach was followed to successfully implement the new application while reflecting theoretically on the process and results. The main conclusion is that the requirements elicitation and negotiation are as important as the management of change regarding processes and individual practices. © 2018 The Authors. Published by Elsevier Ltd..

Abstract
Production scheduling in the presence of real-time events is of great importance for the successful implementation of real-world scheduling systems. Most manufacturing systems operate in dynamic environments vulnerable to various stochastic real-time events which continuously forces reconsideration and revision of pre-established schedules. In an uncertain environment, efficient ways to adapt current solutions to unexpected events, are preferable to solutions that soon become obsolete. This reality motivated us to develop a tool that attempts to start filling the gap between scheduling theory and practice. The developed prototype is connected to the MRP software and uses meta heuristics to generate a predictive schedule. Then, whenever disruptions happen, like arrival of new tasks or cancelation of others, the tool starts rescheduling through a dynamic-event module that combines dispatching rules that best fit the performance measures pre-classified by Kano’s model. The proposed tool was tested in an in-depth computational study with dynamic task releases and stochastic execution time. The results demonstrate the effectiveness of the model. © 2020, Springer Nature Switzerland AG.

Abstract
Neural networks have been applied as a machine learning tool in many different areas. Recently, they have gained increased attention with what is now called deep learning. Neural networks algorithms have several parameters that need to be tuned in order to maximize performance. The definition of these parameters can be a difficult, extensive and time consuming task, even for expert users. One approach that has been successfully used for algorithm and parameter selection is metalearning. Metalearning consists in using machine learning algorithm on (meta)data from machine learning experiments to map the characteristics of the data with the performance of the algorithms. In this paper we study how a metalearning approach can be used to obtain a good set of parameters to learn a neural network for a given new dataset. Our results indicate that with metalearning we can successfully learn classifiers from past learning tasks that are able to define appropriate parameters.