The increasing use of wireless technologies in
Industry 4.0 is a reality and a current necessity. While the
foreseeable use of 5G networks will play an important role in this
field, the use of different communication networks in Industrial
Internet of Things (IIoT) networks is nowadays an active field of
research, since it is considered that the industry of the future will
be supported by a heterogeneous set of network technologies...

Recently more digital twins have been developed and used in the manufacturing industry. Some are based on detailed models of a particular machine/process oriented to monitoring, but others can represent a full manufacturing plant for production planning. These models involve a lot of parameters, and it is not easy to evaluate the effect of their change in the manufactured product or how they are correlated among them. This paper presents a prescriptive analysis tool that allows testing and ranking multiple scenarios using a previously generated digital twin. This is thus, a simulation, evaluation, and prescription tool. It uses a set of available models, allowing the selection of the parameters and defining the scenarios to be simulated, as well as the evaluation configuration. The tool performs exhaustive simulations making initially all possible combinations of the parameters selected and obtaining the simulation results for each of them. After this the evaluation is performed over some of the outputs of the model, leading to a ranking of all the simulations. The prescription obtained can be later used to configure a machine or to change some production parameters to optimize the system

This article aims to introduce the challenge (i.e., integration of new collaborative models and tools) posed by the
automation and collaboration of industrial processes in Industry 4.0 (I4.0) smart factories. Small- and mediumsized enterprises (SMEs) are particularly confronted with new technological and organisational changes, but a
conceptual framework for production planning and control (PPC) systems in the I4.0 context is lacking. The main
contributions of this article are to: (i) identify the functions making up traditional PPC and smart production
planning and control in I4.0 (SPPC 4.0); (ii) analyse the impact of I4.0 technologies on PPC systems; (iii) propose
a conceptual framework that provides the systematic structuring of how a PPC system operates in the I4.0
context, dubbed SPPC 4.0. Thus SPPC 4.0 is proposed by adopting the axes of the RAMI 4.0 reference architecture
model, which compiles and contains the main concepts of PPC systems and I4.0. It also provides the technical
description, organisation and understanding of each aspect, which can provide a guide for academic research andindustrial practitioners to transform PPC systems towards I4.0 implementations. Finally, theoretical implications
and research gaps are provided.

This article aims to introduce the challenge (i.e., integration of new collaborative models and tools) posed by the
automation and collaboration of industrial processes in Industry 4.0 (I4.0) smart factories. Small- and mediumsized enterprises (SMEs) are particularly confronted with new technological and organisational changes, but a
conceptual framework for production planning and control (PPC) systems in the I4.0 context is lacking. The main
contributions of this article are to: (i) identify the functions making up traditional PPC and smart production
planning and control in I4.0 (SPPC 4.0); (ii) analyse the impact of I4.0 technologies on PPC systems; (iii) propose
a conceptual framework that provides the systematic structuring of how a PPC system operates in the I4.0
context, dubbed SPPC 4.0. Thus SPPC 4.0 is proposed by adopting the axes of the RAMI 4.0 reference architecture
model, which compiles and contains the main concepts of PPC systems and I4.0. It also provides the technical
description, organisation and understanding of each aspect, which can provide a guide for academic research and
industrial practitioners to transform PPC systems towards I4.0 implementations. Finally, theoretical implications
and research gaps are provided.

The objective of this paper is to examine the use and applications of reinforcement learning (RL) techniques in the production planning and control (PPC) field addressing the following PPC areas: facility resource planning, capacity planning, purchase and supply management, production scheduling and inventory management. The main RL characteristics, such as method, context, states, actions, reward and highlights, were analysed. The considered number of agents, applications and RL software tools, specifically, programming language, platforms, application programming interfaces and RL frameworks, among others, were identified, and 181 articles were sreviewed. The results showed that RL was applied mainly to production scheduling problems, followed by purchase and supply management. The most revised RL algorithms were model-free and single-agent and were applied to simplified PPC environments. Nevertheless, their results seem to be promising compared to traditional mathematical programming and heuristics/metaheuristics solution methods, and even more so when they incorporate uncertainty or non-linear properties. Finally, RL value-based approaches are the most widely used, specifically Q-learning and its variants and for deep RL, deep Q-networks. In recent years however, the most widely used approach has been the actor-critic method, such as the advantage actor critic, proximal policy optimisation, deep deterministic policy gradient and trust region policy optimisation.

This paper addresses the i4Q project vision, including stakeholders’ requirements and expectations, aiming to present the digital technologies, as well as a multi-dimensional benchmarking instrument that supports the i4Q design and development. It also sets clear specifications that drive the creation of i4Q. It analyses the current systems of the demonstration scenarios, to establish the starting point (Key Performance Indicators’ (KPIs)) for the implementation of their industrial use cases and to understand how, data reliability and manufacturing quality, are impacted by i4Q. Finally, it focuses on the most suitable KPIs and identifies the most relevant regulation and trustworthy systems for data management in the i4Q Solutions.

With the target of Zero-Defect Manufacturing, the European Project Industrial Data Services for Quality Control in Smart Manufacturing (i4Q) aims to develop 22 software solutions based on Artificial Intelligence (AI) to optimize manufacturing processes, ensuring quality, effectiveness and interoperability among manufacturing companies. The solutions will be implemented in real manufacturing scenarios, where the outcomes of the project will be tested and evaluated. One of the solutions, i4Q Line Reconfiguration Toolkit, will be deployed in FACTOR, a manufacturing company dedicated to metal machining and precision turning. This paper covers: (i) the introduction of the pilot case and the project solutions; (ii) the goal that FACTOR aims to achieve by the implementation of the solutions; (iii) the main features of the Line Reconfiguration Toolkit solution and their alignment with the requirements exposed by FACTOR, and (iv) the expected results when applying i4Q Line Reconfiguration Toolkit in FACTOR.

Nowadays, manufacturing companies are eager to access insights from advanced analytics, without requiring them to have specialized IT workforce or data science advanced skills. Most of current solutions lack of easy-to-use advanced data preparation, production reporting and advanced analytics and prediction. Thanks to the increase in the use of sensors, actuators and instruments, European manufacturing lines collect a huge amount of data during the manufacturing process, which is very valuable for the improvement of quality in manufacturing, but analyzing huge amounts of data on a daily basis, requires heavy statistical and technology training and support, making them not accessible for SMEs. The European i4Q Project, aims at providing an IoT-based Reliable Industrial Data Services (RIDS), a complete suite consisting of 22 i4Q Solutions, able to manage the huge amount of industrial data coming from cheap cost-effective, smart, and small size interconnected factory devices for supporting manufacturing online monitoring and control. This paper will present a set of i4Q services, for data integration and fusion, data analytics and data distribution. Such services, will be responsible for the execution of AI workloads (including at the edge), enabling the dynamic deployment industrial scenarios based on a cloud/edge architecture. Monitoring at various levels is provided in i4Q through scalable tools and the collected data, is used for a variety of activities including resource monitoring and management, workload assignment, smart alerting, predictive failure and model (re)training.

Increased consumerism and the competitiveness of the global market have led to more stringent requirements in terms of product quality and manufacturing lines. The i4Q European project’s Rapid Manufacturing Line Qualification and Reconfiguration set of solutions aims to develop new and improved strategies and methods for process qualification, process reconfiguration and optimization using existing manufacturing data and intelligent algorithms. The set of solutions provides manufacturing lines’ diagnosis and prescription, process capacity forecasting, manufacturing line reconfiguration propositions, and data quality certifications and audit procedures. With this information, plant managers can make the required changes to the plant to improve manufacturing products’ quality, machines’ life cycle, plants’ productivity and so on.

The i4Q Project aims to provide a complete set of solutions consisting of IoT-based Reliable Industrial Data Services (RIDS), the so-called 22 i4Q Solutions, able to manage the huge amount of industrial data coming from cheap cost-effective, smart, and small size interconnected factory devices for supporting manufacturing online monitoring and control. This papers presents the reference architecture used to guide the development of IoT-based Reliable Industrial Data Services for Manufacturing Quality Control. Based on industry standards and known best practices, the reference architecture adopts a three-tiered architectural model to represent the main system architectural components, and provides four different architectural viewpoints to address business, usage, functional, and implementation concerns.

European manufacturing companies collect a large amount of data during their manufacturing processes thanks to the increasing use of sensors, actuators and instruments. This amount of data is very valuable for improving manufacturing quality, with a view to the Zero Defects objective. In order to take full advantage of the vast amount of accumulated data, quality must be targeted, and its Security, Trustiness and Traceability must be preserved. Data quality is influenced by several factors: including human errors, communication problems or inaccuracies, so any system used must be sufficiently reliable. This paper describes the technical approach to develop solutions to control manufacturing Data Security, Trustiness and Traceability with a zero-defect approach and provided by the i4Q project. The approach is exemplified with the industrial production line of injected plastic spare parts for the automotive sector.

Based on a scientific literature review in the conceptual domain defined by smart manufacturing scheduling
(SMS), this article identifies the benefits and limitations of the reviewed contributions, establishes and discusses a
set of criteria with which to collect and structure its main synergistic attributes, and devises a conceptual
framework that models SMS around three axes: a semantic ontology context, a hierarchical agent structure, and
the deep reinforcement learning (DRL) method. The main purpose of such a modelling research is to establish a
conceptual and structured relationship framework to improve the efficiency of the job shop scheduling process
using the approach defined by SMS. The presented model orients the job shop scheduling process towards greater
flexibility, through enhanced rescheduling capability, and towards autonomous operation, mainly supported by
the use of machine learning technology. To the best of our knowledge, there are no other similar conceptual
models in the literature that synergistically combine the potential of the specific set of Industry 4.0 principles and
technologies that model SMS. This research can provide guidance for practitioners and researchers’ efforts to
move toward the digital transformation of job shops.