The new generation of industry, i.e. Industry 4.0, pertains to the processing of immense amounts of data, resulting, among other things, from the large-scale use of microcontrollers to control machines, an increase in the scale of automation, the use of the Internet of Things technology — e.g. in sensors installed at different stages of the production process, the implementation of the digital twin concept, and many other technologies designed to collect data (e.g. GPS or RFID). These data are collected in the enterprise’s variety of resources and databases. These data can be a valuable source of information and knowledge if the right approach to advanced data analysis is adopted, which depends, among other things, on the enterprise’s existing IT infrastructure. This paper sets out to present conclusions formulated on the basis of research consisting in the analysis of multinational manufacturing companies’ existing IT infrastructures. Three basic model solutions of IT architecture occurring in multi-site enterprises were identified, which made it possible to identify the main problems stemming from the IT architecture in place and concerning the analysis of data for the needs of company management. Additionally, this paper discusses the challenges faced by multi-site manufacturing companies. One such activity is the modification and expansion of the company’s IT infrastructure, including the implementation of Big Data and Master Data Management (MDM) solutions. The contribution provided by this paper consists in the analysis of the IT infrastructure in large, multi-site enterprises, which enabled the identification of problems and challenges related to advanced data analysis in this type of companies.
Currently, advanced data analytics in large multi-site industrial enterprises is a strategic element in making management decisions. Integrated supply chain management (SCM), machinery park management, or data analysis from industrial devices (including using Industrial Internet of Things -IIoT) requires the organization of appropriate analytical platform architecture, the selection of the analytical tools for Big Data, the implementation of advanced algorithms based on machine learning and the development of management dashboards for ongoing tracking the KPI's of assets. This article presents the issues related to the acquisition, analysis, and management of large amounts of data from various enterprise departments. These data come from multiple systems, and they are indifferent data recording standards. They are essential because they form the basis of advanced data analysis in supply chain management in multi-site enterprises. This article discusses the proposal of an analytical platform for SCM and the development of analytical processing for SCM in the multi-site industrial enterprise.
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