The sustainable development of an organisation requires a holistic approach to the evaluation of an enterprise’s goals and activities. The essential means enabling an organisation to achieve goals are business processes. Properly managed, business processes are a source of revenue and become an implementation of business strategy. The critical elements in process management in an enterprise are process monitoring and control. It is therefore essential to identify the Key Performance Indicators (KPIs) that are relevant to the analysed processes. Process monitoring can be performed at various levels of management, as well as from different perspectives: operational, financial, security, or maintenance. Some of the indicators known from other fields (such as personnel management, finance, or lean manufacturing) can be used in mining. However, the operational mining processes require a definition of specific indicators, especially in the context of increasing the productivity of mining machines and the possibility of using sensor data from machines and devices. The article presents a list of efficiency indicators adjusted to the specifics and particular needs of the mining industry resulting from the Industry 4.0 concept, as well as sustainable business performance. Using the conducted research and analysis, a list of indicators has been developed concerning person groups, which may serve as a benchmark for mining industry entities. The presented proposal is a result of work conducted in the SmartHUB project, which aims to create an Industrial Internet of Things (IIoT) platform that will support process management in the mining industry.

This paper presents a literature review of maintenance strategies formulation. The purpose of this article is to provide an overview of different published concepts of maintenance strategies, distinguish the most common approaches to this issue and find a general tendency in strategies classification. Furthermore, the review is aimed to point out the importance of unscheduled downtime which might occur during equipment runtime in a production plant. The paper classifies the existing maintenance concepts and emphasizes key assumptions of the analysed strategies. The literature study and carried out analysis could be useful to find appropriate reliability assurance methods. In addition, defined maintenance approaches might help in decision making process in a company. The paper is a comprehensive overview of discussed strategies, which indicates the most frequent maintenance models in the analysed papers.

Industry 4.0 and the Internet of Things are now very common concepts as solutions that can revolutionize the industry. Constanttechnological progress increases the possibilities of using computer tools and solutions to support processes in industry and productionoptimization. The use of the Internet of Things is particularly important in complex processes in mining, enabling the extractionof valuable information from data. The integration of physical facilities in the enterprise enables the digitization of productionprocesses and the increase of efficiency and security.This article presents an overview of the selected internet of things platforms and analytical tools that can be used in industry, withparticular emphasis on the mining sector. It is pointed out, that the number of suppliers of IoT technologies and analytical toolsoffering advanced data analytics services for industry is significant and constantly evolving. The aim of the article is to evaluateselected IoT solutions based on the following criteria: offering predictive analytics, implemented artificial intelligence (AI) ormachine learning (ML) algorithms, a mining-oriented process approach, advanced data visualization, interoperability, real-timedata capture, remote device management and cloud-based technology. The review was prepared to provide knowledge about IoTvendors operating on the market, as well as to indicate the functionalities that are the most popular among solutions.

This paper investigates the application of process mining methodology on the processes of a mobile asset in mining operations as a means of identifying opportunities to improve the operational efficiency of such. Industry 4.0 concepts with related extensive digitalization of industrial processes enable the acquisition of a huge amount of data that can and should be used for improving processes and decision-making. Utilizing this data requires appropriate data processing and data analysis schemes. In the processing and analysis stage, most often, a broad spectrum of data mining algorithms is applied. These are data-oriented methods and they are incapable of mapping the cause-effect relationships between process activities. However, in this scope, the importance of process-oriented analytical methods is increasingly emphasized, namely process mining (PM). PM techniques are a relatively new approach, which enable the construction of process models and their analytics based on data from enterprise IT systems (data are provided in the form of so-called event logs). The specific working environment and a multitude of sensors relevant for the working process causes the complexity of mining processes, especially in underground operations. Hence, an individual approach for event log preparation and gathering contextual information to be utilized in process analysis and improvement is mandatory. This paper describes the first application of the concept of PM to investigate the normal working process of a roof bolter, operating in an underground mine. By applying PM, the irregularities of the operational scheme of this mobile asset have been identified. Some irregularities were categorized as inefficiencies that are caused by either failure of machinery or suboptimal utilization of the same. In both cases, the results achieved by applying PM to the activity log of the mobile asset are relevant for identifying the potential for improving the efficiency of the overall working process.