The main objective of deliverable D2.4 is to focus on defining how to implement the capabilities and structure of the i4Q Framework by identifying the four key elements of the Usage viewpoint: the tasks, roles, activities and parties in i4Q, considering both human beings, and Software systems. In addition, we consider the relationship among viewpoints, to support the activities and usage of the overall system.

This deliverable contains a guideline to manage data quality in manufacturing. It establishes a conceptual basis by introducing several concepts, such as data and information, data life cycle, information needs, data and information quality, and production system levels. The guideline uses the Plan-Do-Study-Act (PDSA) cycle and focuses on the Plan and Do steps. It outlines an information flow analysis for producers to understand which data quality factors the organization must manage. Furthermore, it suggests three types of measures to manage data quality factors. Awareness measures aim to raise awareness of data quality issues and factors among employees. They require the least effort but are also not very reliable unless strictly controlled. Programmatic measures are functions in software that force users into behavior that ensures high data quality. Examples are input validations and auto-complete. These measures are much more reliable but may be costly to implement. Organizational measures cover complex cases where other measures are not feasible. They focus on larger-scale organizational activities (e.g., work instructions, training, and new roles) to promote behavior that minimizes data quality issues.

This deliverable presents a technical overview of the i4Q Trusted Networks with Wireless and Wired Industrial Interfaces solution (i4QTN), covering the technological description of their components and its mapping against the i4Q Reference Architecture. In addition, an analysis of the current state of solution up to M17 is performed, showing the relationship between the technical requirements, described in previous deliverables, and the current functionalities of the solution. After describing in detail, the developments performed, final sections of this deliverable explain the remaining implementation works.

The main objective of deliverable D2.5 is to focus on the functional components in i4Q solutions, their structure, interrelation, interfaces and interactions between them using the reference architecture as a framework. On the other hand, the deliverable reflects the relationships and interactions of the i4Q solutions with external elements of the environment to support the activities and usages of the overall system.

This deliverable is one of the two results of Task 3.1, “Manufacturing Data Quality Strategy”. It presents QualiExplore, a web-based software tool for visualizing information quality characteristics and factors. Producers need to know the latter to assess their relevance for the specific use case and identify measures to manage them. This i4Q tool is an example of a measure to raise awareness of data quality. D3.1 outlines these measures in its proposed activity framework. QualiExplore is a standalone application using a permissive open source license (Apache 2.0), so stakeholders can easily exploit it.

According to recent surveys, one of the top three corporate hazards is the fear of cyber-attacks. Cloud computing, privacy protection, mobility, and the internet of things are all driving forces in the realm of IT security in industrial settings. This document presents general description and technical application i4Q Security Guidelines (i4Q SG) which intends to highlight important features of ICS cybersecurity, including best practices and key aspects for defending against an increasing array of cyber-related threats

This document delivers the second release of the i4Q RA, representing the second output of T2.1. The first released of i4Q RA, delivered at M3, was not designed from scratch; as presented in D2.1 the preliminary version of the i4Q RA was mainly inspired by IIRA architectural model, based on the three-tiers approach. According to IIRA approach, the task of i4Q RA definition was performed in parallel with those of its viewpoints analysis (T2.3, T2.4, T2.5, T2.6). Another key input has been the definition of the digital models and ontologies (T2.2) to be used in the i4Q Framework. The operational needs characterising the realization, deployment and use of each solution represented a valid input and helped to better identify the RA, refining its modules, components and functionalities. The definition of the second version of the RA also took into account all the considerations formalized in the description of the use case scenarios and the requirements collection (T1.3 and T1.4); special attention was paid to the needs emerging from the pilots, which have been mapped in detailed blueprints to verify the alignment between the specific components to be used for industrial problems and the RA building blocks.

The Blockchain Traceability of Data (i4QBC) solution aims to enhance the level of trust that different solutions and components can place on data. It provides services of immobility and finality of data, serving as the source of truth, enabling trust in data by providing the possibility for full provenance and audit trail of data stored. This deliverable explains the design choices for selection of Hyperledger Orion as the i4Q Blockchain Infrastructure. Hyperledger Orion is a centralised, trusted blockchain database that provides tamperevidence, provenance, data lineage, authenticity, and nonrepudiation through data centric Application Programming Interfaces. This document presents Hyperledger Orion and includes detailed deployment instructions. The code is available at the project’s repository, so a blockchain database server is ready for use by the i4Q ecosystem.

To fight against a growing range of cyber-related risks, industrial enterprises need rapid and demonstrable improvements in their Operational Technology (OT) and Industrial Control Systems (ICS) cyber security. Because of their potential impact on system performance, utilities and other users of these systems may be cautious to embrace popular security technologies.