This paper presents an integrated reference model for digital manufacturing platforms, based on cutting edge reference models for the Industrial Internet of Things (IIoT) systems. Digital manufacturing platforms use IIoT systems in combination with other added-value services to support manufacturing processes at different levels (e.g. design, engineering, operations planning, and execution). Digital manufacturing platforms form complex multi-sided ecosystems, involving different stakeholders ranging from supply chain collaborators to Information Technology (IT) providers. This research analyses prominent reference models for IIoT systems to align the definitions they contain and determine to what extent they are complementary and applicable to digital manufacturing platforms. Based on this analysis, the Industrial Internet Integrated Reference Model (I3RM) for digital manufacturing platforms is presented, together with general recommendations that can be applied to the architectural definition of any digital manufacturing platform.

Collaborative networks (CN) consist of autonomous and heterogeneous partners, and each defines its own objectives and formulates its own strategies, which are selected and activated to achieve these objectives. The heterogeneity that characterises network partners could lead to contradictions appearing among the strategies formulated in each CN enterprise.Consequently, the strategies formulated in one enterprise could negatively influence the achievement of the objectives defined in other enterprises of the same network. These contradictions lead to strategies misalignments, which worsens the network performance. In order to deal with these misalignments, a DSS is proposed to support the process of selecting the strategies among all those formulated, with the aim of achieving higher alignment levels.The proposed DSS considers the impacts that each strategy formulated in each enterprise has on the performance of the objectives defined by each network partner. This allows enterprises to select a set of aligned strategies. The selection of proper strategies to be activated in each enterprise strongly influences the CN's performance level, and higher levels of network adaptability, agility and competitiveness are achieved. The proposed DSS is validated under real conditions in a food industry network. The DSS is evaluated by emulating real collaborative conditions and is compared with the equivalent non-collaborative decision making perspective used for selecting strategies. The results demonstrate that the collaborative approach outperforms the performance level of the non-collaborative one and is more effective for handling the robustness and the long-term operation of the CN.Enterprises are continuously dealing with new decisions, and, when carrying out the decision-making process, they have to reach a decision after considering a set of potential options. The decision-making process increases in complexity when more than one actor is involved in the decision; this occurs in collaborative networks (CN). Camarinha-Matos and Afsarmanesh [1] define CN as a network that consists of a variety of autonomous, geographically distributed and heterogeneous entities that collaborate to better achieve common or compatible goals, and to jointly generate value. In CN, each enterprise defines its own objectives and formulates its own strategies; therefore, distinct interests are involved, which may lead to conflictive situations that derive from disagreements in the selection of strategies.The processes carried out by the enterprises that belong to a CN are characterised by being collaborative; and have been largely studied with the aim of dealing with the associated complexity in the CN context. Works that are worth mentioning and propose decision-making systems to address the associated complexity, which occurs in the CN context, include [2] [3][4]. The work carried out by [5] consolidates the wide variety of knowledge available in the collaborative domain, and proposes a comprehensive analysis of the most important collaborat...

The industrial Internet of Things (IIoT) is having a significant impact in the manufacturing industry, especially in the context of horizontal integration of operational systems in factories as part of information systems in supply chains. Manufacturing companies can use this technology to create data streams along the supply chain that monitor and control manufacturing and logistic processes, to in the end make these data streams interoperable with other software systems and to enable smart interactions among supply chain processes. However, the provision of these data streams may expose manufacturing operational systems to cyber-attacks. Therefore, cybersecurity is a critical aspect to design trustworthy gateways, which are system components that implement interoperability mechanisms between operational systems and information systems. Gateways must provide security mechanisms at different system layers to minimize threats. This paper presents the Device Drivers security architecture: trustworthy gateways between operational technology and information technology used in the virtual factory open operating system (vf-OS) platform, which is a multisided platform orientated to manufacturing and logistics companies to enable collaboration among supply chains in all sectors. The main contribution of this paper is the evaluation of fallback mechanisms to improve resilience. In situations when the system may be under attack, the proposed mechanisms provide means to quickly recover component availability, by applying alternative security measures to minimize the threat at the same time. Other significant contributions are: a description of the threat model for Device Drivers, a presentation of the security countermeasures implemented in the vf-OS system, the mapping of the vf-OS response objectives to the different characteristics of a trustworthy system: security, privacy, reliability, safety, and resilience and how the proposed countermeasures complement this response.

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Enterprise resilience is a key capacity to guarantee enterprises' long-term continuity. This paper proposes a quantitative approach to enhance enterprise resilience by selecting optimal preventive actions to be activated to cushion the impact of disruptive events and to improve preparedness capability, one of the pillars of the enterprise resilience capacity. The proposed algorithms combine the dynamic programming approach with attenuation formulas to model real improvements when a combined set of preventive actions is activated for the same disruptive event. A numerical example is presented that shows remarkable reductions in the expected annual cost due to potential disruptive events.Sustainability 2019, 11, 4327 2 of 13 manage this complexity. Prior and Hagmann [9] state that today, a significant challenge, lies in the accurate characterization and quantification of resilience. Dalziell and McManus [10] suggest that resilience was firstly proposed in the field of ecology by Holling [11], who stated that resilience is how a system behaves when it is in equilibrium, and it is stressed and moved from this stability. In other words, it is a system's ability to withstand a major disruption within acceptable degradation parameters, and to recover in an acceptable time and with composite costs and risks [12]. Doorn [13] performed an analysis that shows that different disciplines employ various definitions of resilience and conceptions of its relation to vulnerability. Erol et al. [14] define enterprise resilience (ER) as the capacity to lower the level of vulnerability to expected and unexpected disruptions, its ability to change itself and to adapt to its changing environment, and its ability to recover in the shortest possible time. Scholz et al. [15] state that resilience incorporates the capability of a system to cope with the adverse effects that a system has been exposed to and distinguish between specified (known events) and general resilience (unknown events). Hollnagel [16] defines resilience as a system's capacity to prevent, recognize, anticipate and react to risks before their adverse consequences take place. Levalle and Nof [17] focuses the attention on supply chains and defines resilience as the inherent ability of a supply network agent and/or the emergent capability of a supply network to (i) anticipate errors and conflicts, (ii) prevent them from creating disruptions to normal operation, and (iii) overcome disruptions with minimum quality of service loss, within sustainable use of resources. In light of this, Kamalahmadi and Parast [18] consider three phases for supply chain resilience, which are (i) anticipation; (ii) resistance; and (iii) recovery and responses. In the same line, Ponomarov and Holcomb [19] identify readiness, response, and recovery, as the main necessary capacities to build resilient organizations. Takin into account that resilience is a multidisciplinary and multifaceted concept, its enhancement requires efficient strategies to deal with and manage disruptive events.Most author...