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.

Information technology has become a strong modeling approach to support the complexities involved in a process. One example of this technology is the multiagent system which, from a decentralized supply chain configuration perspective, supports the information sharing processes that any of its nodes will be able to carry out to support its process in a collaborative manner: for example, the planning and decision-making process. Therefore, this paper presents a novel collaborative planning model in supply chain networks that considers a multiagent system modeling approach to support the decision-making process from a decentralized perspective. The hypothesis presented herein involves collaborating in the information exchange process while the decision-making process will improve by increasing, for instance, the profit rate level until a certain collaboration level.

A new fuzzy mathematical programming model for supply chain planning under supply, process and demand uncertainty is proposed in this paper. A tactical supply chain planning problem has been formulated as a fuzzy mixed integer linear programming model where data are ill-known and modelled by triangular fuzzy numbers in the setting of possibility theory. The fuzzy model provides alternative decision plans to the decision maker (DM) for different grades of possibility. Finally, the proposed model is tested by using data from a real automobile supply chain.

This paper shows how enterprise engineering and integration (EEI) can help in the improvement of inter-enterprises business processes of established extended enterprise for supply chain, focusing in the modeling of collaborative decisional model. The study has been developed in the automotive sector, identifying the relationships between an automotive manufacturer (OEM, original equipment manufacturer) and its first-tier suppliers. To develop the study, we have considered the well-known EEI architectures such as CIMOSA and GRAI and particularly IE-GIP methodology (an extension of CIMOSA) and DAROMS methodology (an extension of GRAI).