BLE-based mesh networks are based on a simple flooding algorithm with some mechanisms to reduce network saturation, called managed flooding. The operating parameters of the network establish its performance, but in an industrial environment the operating conditions are not permanent, so a system that can adjust to these changes is necessary. A global decision system is not valid since each part of the network may have different properties. An autonomous system that does not introduce an overhead of message exchange is necessary for its operation. This paper proposes an algorithm based on the information provided by a single control message exchange that allows each node to autonomously select its operating parameters to improve the quality of links with neighbouring nodes and thus improve the overall performance of the network.

The next evolutionary technological step in the industry presumes the automation of the elements found within a factory, which can be accomplished through the extensive introduction of automatons, computers and Internet of Things (IoT) components. All this seeks to streamline, improve, and increase production at the lowest possible cost and avoid any failure in the creation of the product, following a strategy called Zero Defect Manufacturing . Machine Learning Operations (MLOps) provide a ML-based solution to this challenge, promoting the automation of all product-relevant steps, from development to deployment. When integrating different machine learning models within manufacturing operations, it is necessary to understand what functionality is needed and what is expected. This article presents a maturity model that can help companies identify and map their current level of implementation of machine learning models.

Economic globalization and the increase in consumption by society have created a need for companies to optimize and improve production processes. Thanks to new technologies, it is possible to increase their effectiveness to achieve the required objectives. The degree of automation in factories is already high, so changing the production process does not generate a significant increase in efficiency. Consequently, it is required to insert new tools that allow a more significant increase of the factory resources. This is where the concept of “Industry 4.0” is born. The aim of this work is to stablish an action protocol to implement the status of a Manufacturing Execution System (MES) in a factory. A maturity model will be proposed to analyze the state of implementation of the Manufacturing Execution Systems of Industry 4.0 based on three of the three dimensions (technical, operational, and human). The levels of development in each of them are based on the Capability Maturity Model Integration ( CMMI).

This research project proposes the development of a digital twin (DT) that simulates the behavior of the zero-defect planning system of a supply chain. The research will focus on the incorporation of new zero-defect manufacturing (ZDM) technologies generated from the DT perspective. The production technologies to be proposed will be oriented toward the development of new models and optimization algorithms for the ZDM planning problem in the new digitalized supply network context. The modeling domain will involve up to the second-tier supplier in the supply chain at the tactical and operational decision levels.

As at any decision level in operations planning and control (OPC), operational decisions are influenced by the technological advances underpinning the Industry 4.0 (I4.0) paradigm. In this increasingly digitized environment, scheduling problems have to cope with stochastic demand, dynamic task allocation flow, routing flexibility, or task rescheduling. The ability to virtually replicate the scheduling process in an I4.0 environment enables its optimization, simulation, prediction, and automatic analysis in real time. These features are necessary in manufacturing environments with a zero-defect manufacturing strategy (ZDM) because these factors allow scheduling problems to be adapted to this strategy’s requirements. Therefore, a scheduling problem in a ZDM environment driven by a digital twin (DT) will favor better production system performance. With this approach, the present article provides an overview of the scientific literature for this combined set of concepts. It presents the academic and research implications of the present research, discusses its results and limitations, and indicates where future research into this theme is to be directed.

An increasingly common application for DLTs is their exploitation in enterprise systems operated by a consortium of organizations, who may assume different roles and whose interaction takes place over a blockchain network (in this scope, often permissioned), which holds a data state that they need to query frequently. In this regard, one of the main drawbacks of DLTs is their unsuitability for the efficient execution of complex queries on the data stored by the nodes comprising the network. Arising from this issue, many solutions propose to dump the ledger contents into databases, which, due to their own nature and purpose, are certainly optimized for the execution of such queries. However, most proposals on this area are not intended for querying the shared state held by the network, just the transaction history. Actually, through conducting a study on the state of the art, a lack of support for handling and querying complex structured data has been identified. DELTA provides a solution for the synchronization, with negligible overload, of the state of a DLT into a database, enabling (a) the operation with smart contracts whose data possess a complex structure and (b) the efficient execution of elaborate queries on these data. By using DELTA, query times decrease, at least, between one and five orders of magnitude, depending on the DLT, compared to queries directed to the ledger nodes.

Blockchain databases were designed to improve trust in centralized ecosystems, which dominate the market today, by introducing tamper-evidence features on top of a classical database. Compared to decentralized ledger technologies, blockchain databases are easier to use, and they can significantly reduce the operational and development costs. However, the existing blockchain databases do not equip multiple parties with tools to efficiently control common data written to the ledger. This paper describes Orion, a new open source blockchain database that introduces multi-sig and proof capabilities with extensive key-level access control, which enable parties to mutually control and validate a value written to the database. These unique capabilities, together with additional blockchain properties, provide users with features such as tamper-evidence, provenance, data lineage, authenticity, and non-repudiation, while using a standard data model and transactional APIs. We found our technology to be extremely useful in improving the integrity of a system and reducing mistakes, disputes, and fraud.

Production lines in manufacturing environments benefit from quality diagnosis methods based on learning techniques since their ability to adapt to the runtime conditions improves performance, and at the same time, difficult computational problems can be solved in real time. Predicting the divergence of a product’s physical parameters from an acceptable range of values in a manufacturing line is a process that can assist in delivering consistent and high-quality output. Costs are saved by avoiding bursts of defective products in the pipeline’s output. An innovative framework for the early detection of a product’s physical parameter divergence from a specified quality range is designed and evaluated in this study. This framework is based on learning automata to find the sequences of variables that have the highest impact on the automated sensor measurements that describe the environmental conditions in the production line. It is shown by elaborate evaluation that complexity is reduced and results close to optimal are feasible, rendering the framework suitable for deployment in practice.

This paper discusses the concept of trustworthy systems, the role of regulations in ensuring their security, and the challenges and opportunities that arise in regulating trustworthy systems. We examine case studies of situations where regulations have succeeded or failed to prevent harm to consumers or society, and provide recommendations for how regulators, industry, and consumers can work together to create and maintain trustworthy systems. By addressing these challenges effectively, regulators can help ensure that trustworthy systems are in place to protect consumers, promote fair competition, and prevent harm to society in the digital age.

Operations management researchers and practitioners have shown increasing interest in incorporating sustainability into supply chain (SC) design models. This means that sustainability must be considered in all aspects of the SC, including location, inventory and transportation (LIT) decisions. Hence the aim of this article is to propose an optimisation model that incorporates: (i) LIT decisions in an integrated manner; (ii) the three sustainability (3S) aspects, i.e. economic, environmental and social, into each named decisions; and (iii) a closed-loop supply chain (CLSC) structure. The proposed formulation is a multi-objective mixed integer non-linear programming (MO-MINLP) model whose objectives consider minimisation of economic and social costs (economic aspect) and carbon emissions (environmental aspect), and maximisation of the social impact of SC operations (social aspect). A transformation technique is applied to one of the objective functions, which results in an MO-MILP model solved by the lexicographic method. This article focuses on commodity industries where only one finished product is manufactured. Hence the 3S-LIT model is validated with a randomly generated dataset and against a recently published alternative model applied to the copper mining industry.

The next evolutionary technological step in the industry presumes the automation of the elements found within a factory, which can be accomplished through extensive introduction of automatons, computers and Internet of Things (IoT) components. All this seeks to streamline, improve, and increase production at the lowest possible cost and avoid any failure in the creation of the prod- uct, following a strategy called “Zero Defect Manufacturing”. Machine Learning Operations (MLOps) provide a ML-based solution to this challenge, promoting the automation of all product-relevant steps, from development to deployment. When integrating different machine learning models within manufacturing opera- tions, it is necessary to have a good understanding of what functionality is needed and what is expected. This article presents a maturity model that can help compa- nies identify and map their current level of implementation of machine learning models.

This article provides an analysis of existing mathematical models for supply chain planning (SCP) with an emphasis on lean manufacturing that consider aspects of flexibility, resilience and sustainability to manage demand in today's changing and disruptive industrial environments. The incorporation of Industry 4.0 technologies to obtain a more autonomous supply chain (SC) is also considered. For this purpose, several reference models that can be used as a basis for new mathematical developments to improve the performance of SCs are analyzed. Finally, the problem to be addressed is described and the possible inputs, outputs, objectives, constraints, modeling and solution approaches are identified.