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Because of its effectiveness, process mining is rapidly becoming ubiquitous. A recent IBM Institute for Business Value (IBV) survey found that 65 percent of organizations report actively using process mining to improve processes. And in partnership with software-as-a-service (SaaS) providers to add even greater insight into their processes, 69 percent compare their organization’s data with other SaaS customers. And as many as 77 percent of supply chain executives say they are at least at the implementation stage of process and task mining.

The IBM Institute for Business Value and APQC, in cooperation with Oxford Economics, surveyed 2,000 C-level executives in first half of 2022 from 13 countries in all major geographies and across 22 industries. The IBV and APQC implemented an in-depth analysis of how organizations use benchmarking and process mining tools, the benefits they gain from use of these tools and how they anticipate using them in the future.

Big data and digital technologies also creates new possibilities for measuring performance and revealing process improvement opportunities through process mining ? a relatively new discipline that applies data science to discover, validate and improve workflows in real time.

By utilizing data from IT systems to create a process model and then examining the end-to-end process, process mining enables root causes of variations from norms to be identified using specialized algorithms, and these insights enable management to see if processes are functioning as intended and identify new opportunities to optimize them.

More recently, the scope of process mining initiatives has widened to encompass more sophisticated mission-critical functions, notably human capital, cybersecurity and sales. Organizations that embrace process mining outperform others across key business measures, including profitability, innovation, agility, customer satisfaction and technological sophistication. 10;

client/server(C/S) systems have revolutionized the systems development approach. Among the drivers of the C/S systems is the lower price/performance ratio compared to the mainframe‐based transaction processing systems. Data mining is a process of identifying patterns in corporate transactional and operational databases (also called data warehouses). As most Fortune 500 companies are moving quickly towards the client server systems, it is increasingly becoming important that a data mining approaches should be adapted for C/S systems. In the current paper, we describe different data mining approaches that are used in the C/S systems.

Ores and minerals are extracted from the earth’s crust depending on the type of deposit. Iron ore mines come under massive deposit patterns and have their own mine development and life cycles. This study aims to depict the development and life cycle of large open-pit iron ore mines and the intertwined organizational design of the departments/sections operated within the industry.

Primary data were collected on the site by participant observation, in-depth interviews of the field staff and executives, and field notes. Secondary data were collected from the literature review to compare and cite similar or previous studies on each mining activity. Finally, interactions were conducted with academic experts and top field executives to validate the findings. An organizational ethnography methodology was employed to study and analyse four large-scale iron ore mines of India’s largest iron-producing state, Odisha, from January to April 2023.

Six stages were observed for development and life cycle, and the operations have been depicted in a schematic diagram for ease of understanding. The intertwined functioning of organizational set-up is also discovered.

The paper will benefit entrepreneurs, mining and geology students, new recruits, and professionals in allied services linked to large iron ore mines. It offers valuable insights for knowledge enhancement, operational manual preparation and further research endeavours.

Several studies have examined the impact of market fundamentals on house prices. However, the effect of economic sectors on housing prices is limited despite the existence of two-speed economies in some countries, such as South Africa. Therefore, this study aims to examine the impact of mining activities on house prices. This intends to understand the direction of house price spreads and their duration so policymakers can provide remediation to the housing market disturbance swiftly.

This study investigated the effect of mining activities on house prices in South Africa, using quarterly data from 2000Q1 to 2019Q1 and deploying an auto-regressive distributed lag model.

In the short run, we found that changes in mining activities, as measured by the contribution of this sector to gross domestic product, impact the housing price of mining towns directly after the first quarter and after the second quarter in the non-mining cities. Second, we found that inflationary pressure is instantaneous and impacts house prices in mining towns only in the short run but not in the long run, while increasing housing supply will help cushion house prices in both submarkets. This study extended the analysis by examining a possible spillover in house prices between mining and non-mining towns. This study found evidence of spillover in housing prices from mining towns to non-mining towns without any reciprocity. In the long run, a mortgage lending rate and housing supply are significant, while all the explanatory variables in the non-mining towns are insignificant.

These results reveal that enhanced mining activities will increase housing prices in mining towns after the first quarter, which is expected to spill over to non-mining towns in the next quarter. These findings will inform housing policymakers about stabilising the housing market in mining and non-mining towns. To the best of the authors’ knowledge, this study is the first to measure the contribution of mining to house price spillover.

It is recognised by valuers that the effects of coal mining on land in Great Britain can have a significant impact oon the value of a particular site when it is being considered for potential surface development purposes. In this respect a great many buildings are erected successfully each year within mining areas after the effects of mining have been taken into account. If, however, the adverse effects of mining are not recognised and during the site development stage a problem suddenly appears, it is obvious that this may lead to considerable increases in cost and/or delay to the proposed development for which the land was originally purchased. Obviously with sufficient prior appreciation and investigation of the mining position such situations could be avoided. It is with this in mind that the following paper aims to indicate in broad outline, but in such detail relevant to the valuer, some of the major influences of coal mining which require to be recognised and equated in monetary terms when assessing potential site values for development purposes in mining areas.

This paper aims to develop a computer simulation processing method to simulate the mining operation of self-advancing semi-continuous mining technology and optimize the shift step of belt conveyor by using simulation modeling framework based on intelligent objects (SIMIO). The method would effectively solve the challenge of field testing such large-scale equipment.

The four operational modes of self-advancing semi-continuous mining technology at single bench had been illustrated. The operational system of this technology was analyzed and broken down to single units. By analyzing the time constitution of one operation cycle, the theoretical optimization model of shift step can be established and the optimization criteria is the time utilization ratio being maximum. Once the simulation flow was determined, a three-dimensional (3D) computer simulation model of this mining technology was developed by adapting the SIMIO simulating software to the theoretical model. The models were run to investigate the outputs from different operational modes using geological and mining data from East open-pit mine.

The result of these simulations showed that the four-mining-width one-shift (FMWOS) is at maximum production capacity during all operation modes. If transfer equipment is necessary, then this mode can adapt, but system will become more complex. There are minor differences between two-mining-width one-shift and three-mining-width one-shift. If transfer equipment is not necessary, then the two-mining-width one-shift can adapt during actual production.

The simulation results show that the proposed method can achieve the optimal shift step of a belt conveyor and effectively reduce the time loss caused by the coordination of multiple pieces of equipment while simultaneously improving operational efficiency.

Numerous research questions in e-HRM research are directly related to the usage of diverse information systems by HR professionals, line managers, employees, and/or applicants. Since they are regularly based on Internet technologies, information systems in e-HRM automatically store detailed usage data in log files of web servers. Subsumed as “web mining,” such data are frequently used as inputs for innovative data analysis in e-commerce practice. Though also promising in empirical e-HRM research, web mining is neither discussed nor applied in this area at present. Our chapter therefore aims at a methodological evaluation of web mining as an e-HRM research approach. After introducing web mining as a possible approach in e-HRM research, we examine its applicability by discussing available data, feasible methods, coverable topics, and confirmable privacy. Subsequently, we classify the approach methodologically by examining major issues. Our evaluation reveals that “web mining” constitutes a promising additional research approach that enables research to answer numerous relevant questions related to the actual usage of information systems in e-HRM.

Purpose: The main aim of the study is to provide a tool for non-financial information in decision-making. We analysed the non-financial data in the annual reports in order to show the usage of this information in financial decision processes.

Need for the Study: Main financial reports such as balance sheets and income statements can be analysed by statistical methods. However, an expanded financial reporting framework needs new analysing methods due to unstructured and big data. The study offers a solution to the analysis problem that comes with non-financial reporting, which is an essential communication tool in corporate reporting.

Methodology: Text mining analysis of annual reports is conducted using software named R. To simplify the problem, we try to predict the companies’ corporate governance qualifications using text mining. K Nearest Neighbor, Naive Bayes and Decision Tree machine learning algorithms were used.

Findings: Our analysis illustrates that K Nearest Neighbor has classified the highest number of correct classifications by 85%, compared to 50% for the random walk. The empirical evidence suggests that text mining can be used by all stakeholders as a financial analysis method.

Practical Implications: Combining financial statement analyses with financial reporting analyses will decrease the information asymmetry between the company and stakeholders. So stakeholders can make more accurate decisions. Analysis of non-financial data with text mining will provide a decisive competitive advantage, especially for investors to make the right decisions. This method will lead to allocating scarce resources more effectively. Another contribution of the study is that stakeholders can predict the corporate governance qualification of the company from the annual reports even if it does not include in the Corporate Governance Index (CGI).