## 1 Introduction  
In recent years, the concept of Industry 4.0 (4.0) has become increasingly popular in the business world, with this term referring to the fourth industrial revolution, a period of increasing digitalisation and automation that is increasingly revolutionising manufacturing industries. In essence, 14.0 is closely associated with the connection of digital systems to physical objects and equipment, giving rise to what are known as smart factories, thus making it possible to harness data and innovative technologies to significantly improve efficiency and operations in companies and organisations along supply chains [1]. With the rise of the internet of things (IoT), a significant amount of data has been generated, and the ability to make full use of large databases is vital for any organisation. Alongside the IoT, the concept of big data has emerged, due to the large amount of data that companies must deal with in the context of their activity [2]. Automation is playing an increasingly important role in modern manufacturing industries, providing an increasingly viable way for an industrial production company to maintain market competitiveness, and it should be noted that a key element in the implementation of automated systems in various areas of activity has always been the need for highly efficient product- and asset-tracking processes capable of increasing productivity for companies, including the physical tracking of goods within industrial facilities and the monitoring of their life cycles and the updating and management of their status [3]. In recent decades, because of strong technological development, as well as the need to meet consumer expectations, there has been an exponential increase in the quantity and complexity associated with vehicle electrical systems and wiring, thus influencing the need to respond to these product challenges with greater complexity and the integration of new technologies by many companies operating in the context of various sectors along the automotive supply chain. Companies involved in the design and production of test and control media and equipment for all these electrical systems and wiring harnesses for automobiles are included in this scenario [1]. Recent advances in IoT technologies such as radio-frequency identification technology already allow production management systems with real-time traceability, which are very visible and effective in terms of planning, execution, and control of the shop floor in smart factories. However, the amount of data to be collected via such technology has increased sharply, resulting in challenges in the realisation of analytical decision-making processes in the context of manufacturing industries [2]. In turn, determining methodologies for extracting valuable information from production data to help identify problems and bottlenecks in production through the application of I4.0 technologies will be a key element in decision-making for achieving optimal operations. However, it can happen that various pieces of data, within the entire set of data to be managed in an organisation's activity, are missing, incorrect, incomplete, or duplicated, depending on the type of system selected for traceability purposes [4]. Some of the most recent technologies that apply the IoT to the automatic identification and collection of production data and to aid logistics and organisational decisions on the shop floor, including the technology mentioned above, are revolutionising supply chain management and production processes, above all by complementing or entirely replacing less advanced traceability methods, such as barcode technology, as the main system for locating and controlling objects and goods in an industrial environment [5]. With asset management being a crucial aspect of any production operation and activity, without proper management of equipment and materials the production process under consideration can be undesirably interrupted, leading to delays and costly downtime [6].