Abstract
The bi-dimensional irregular strip packing is a difficult problem in the cutting and packing field. Its main feature, and central source of complexity, is the irregularity of the shape of the items. Consequently, mathematical solvers are only able to obtain optimal solutions for small instances and heuristics are often employed in the literature. In such algorithms, it is not possible to guarantee that the optimum solution is found. In such cases, a restricted version of the problem can be adopted in order to improve the performance. One possible restriction is the adoption of pairwise clustering, i.e., elimination of items by joining two pieces. In this work, an automatic pairwise clustering method is proposed for the dotted board model, which limits the placement of items to equally distributed discrete points. The clustered problems are then used as input to an irregular strip packing solver. The results obtained in this paper can be used as an initial guideline for the use of clustering in a discrete grid, which was beneficial in some of the tested cases.

Abstract
Optimization problems that are motivated by real-world settings are often complex to solve. Bridging the gap between theory and practice in this field starts by understanding the causes of complexity of each problem and measuring its impact in order to make better decisions on approaches and methods. The Job-Shop Scheduling Problem (JSSP) is a well-known complex combinatorial problem with several industrial applications. This problem is used to analyse what makes some instances difficult to solve for a commonly used solution approach - Mathematical Integer Programming (MIP) - and to compare the power of an alternative approach: Constraint Programming (CP). The causes of complexity are analysed and compared for both approaches and a measure of MIP complexity is proposed, based on the concept of load per machine. Also, the impact of problem-specific global constraints in CP modelling is analysed, making proof of the industrial practical interest of commercially available CP models for the JSSP.

Abstract
The emergence of complex supply chains is one of the most important consequences of globalization. The management of these supply chains requires increased efforts by organizations that, on one hand, are increasingly pressured by customers in terms of service levels, on the other hand, must manage their suppliers from various locations and with different local requirements. In this context, an appropriate management of information flows is needed to create the adequate visibility level for managing supply chain risk. This chapter presents an overview on the concepts of risk management, visibility and information management in supply chains. This study proposes a conceptual framework for the selection of risk mitigation strategies in the supply chain and characterizes the external and internal information flows decision makers need to implement two categories of risk mitigation strategies: redundancy and flexibility. © 2018, IGI Global.

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.