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The aim of this paper is to characterize adaptation processes in business relationships. The nature of adaptive behavior is described by outlining activities and events in these relationships. The role of perceived product importance and complexity in the character of the adaptations processes is sought.

A case study approach is adopted and two long-term relationships between buyers and sellers of capital equipment in the mining industry are investigated. Perspectives from both sides of the dyad (buyer and seller) were attained through in-depth interviews.

Findings show that supplier-based adaptations occur more frequently than customer-based adaptations. The market antecedents of concentration and resource dependency are identified as drivers of adaptive behavior. Furthermore, product importance and complexity are key drivers to adaptation processes and the development of long-term relationships. Supplier's brand name and the choice of a direct channel strategy are identified as indicators of long-term commitment to the market. Moreover, two-task related factors were extremely relevant as selection criteria for capital equipment: the functional suitability and the degree of standardization/customization of the equipment.

The findings are specific to the market environment and recommendations are given for the realm of the mining industry. Multi-case studies in multi-contexts should be conducted to enable generalization and potential theory-building.

A number of important managerial implications for buyers and sellers of capital equipment in the mining industry are given.

This paper contributes to knowledge by providing rich descriptions of adaptation processes. This real life evidence enables the identification of major drivers of adaptive behavior and, consequently, the development of long-term successful relationships.

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.

It is well known in the mining industry that the increase in failures and breakdowns is due mainly to a poor maintenance policy for the equipment, in addition to the difficult access that specialized personnel have to combat the breakdown, which translates into more machine downtime. For this reason, this study aims to propose a remote assistance model for diagnosing and repairing critical breakdowns in mining industry trucks using augmented reality techniques and data analytics with a quality approach that considerably reduces response times, thus optimizing human resources.

In this work, the six-phase CRIPS-DM methodology is used. Initially, the problem of fault diagnosis in trucks used in the extraction of material in the mining industry is addressed. The authors then propose a model under study that seeks a real-time connection between a service technician attending the truck at the mine site and a specialist located at a remote location, considering the data transmission requirements and the machine's characterization.

It is considered that the theoretical results obtained in the development of this study are satisfactory from the business point of view since, in the first instance, it fulfills specific objectives related to the telecare process. On the other hand, from the data mining point of view, the results manage to comply with the theoretical aspects of the establishment of failure prediction models through the application of the CRISP-DM methodology. All of the above opens the possibility of developing prediction models through machine learning and establishing the best model for the objective of failure prediction.

The original contribution of this work is the proposal of the design of a remote assistance model for diagnosing and repairing critical failures in the mining industry, considering augmented reality and data analytics. Furthermore, the integration of remote assistance, the characterization of the CAEX, their maintenance information and the failure prediction models allow the establishment of a quality-based model since the database with which the learning machine will work is constantly updated.

Consumption of fossil fuels due to their non-renewability has always been one of the fundamental problems among energy-related issues. Major dependence of mining equipment and activities on energy, fuel and adequate fuel allocation has become of great importance in fuel consumption of mines. Therefore, this study aims to propose a model for optimal fuel allocation for mining industry.

In this study, partial least squares structural equation modeling (PLS-SEM), as one of the well-known statistical methods, is used to model and analyze fuel consumption pattern in mine industry.

To show the applicability of the proposed model, the study investigates the model for a real mine in Iran. In this regard, real data of important factors affecting mine fuel consumption are collected. Results of statistical models construct a general formula to calculate the fuel consumption based on three main variables.

Policymaking is one of the important tasks in energy-related organizations. One of the main sectors that uses noticeable amount of fossil fuels is mining industry. Despite the government subsidy for mining in Iran, there is a significant price difference between the price of allocated fuel for mine and the price of the same fuel in the open market. Therefore, this study implements PLS-SEM approach to formulate the fuel consumption pattern under all possible fuel consumption indicators to enable policymakers to make reliable decision for future purposes.

While increased mechanization and automation make considerable contributions to mine productivity, unexpected equipment failures and imperfect planned or routine maintenance prohibit the maximum possible utilization of sophisticated mining equipment and require significant amount of extra capital investment. Traditional preventive/planned maintenance is usually scheduled at a fixed interval based on maintenance personnel's experience and it can result in decreasing reliability. This paper deals with reliability analysis and prediction for mining machinery. A software tool called GenRel is discussed with its theoretical background, applied algorithms and its current improvements. In GenRel, it is assumed that failures of mining equipment caused by an array of factors (e.g. age of equipment, operating environment) follow the biological evolution theory. GenRel then simulates the failure occurrences during a time period of interest based on Genetic Algorithms (GAs) combined with a number of statistical procedures. The paper also discusses a case study of two mine hoists. The purpose of this paper is to investigate whether or not GenRel can be applied for reliability analysis of mine hoists in real life.

Statistical testing methods are applied to examine the similarity between the predicted data set with the real-life data set in the same time period. The data employed in this case study is compiled from two mine hoists from the Sudbury area in Ontario, Canada. Potential applications of the reliability assessment results yielded from GenRel include reliability-centered maintenance planning and production simulation.

The case studies shown in this paper demonstrate successful applications of a GAs-based software, GenRel, to analyze and predict dynamic reliability characteristics of two hoist systems. Two separate case studies in Mine A and Mine B at a time interval of three months both present acceptable prediction results at a given level of confidence, 5 percent.

Potential applications of the reliability assessment results yielded from GenRel include reliability-centered maintenance planning and production simulation.

Compared to conventional mathematical models, GAs offer several key advantages. To the best of the authors’ knowledge, there has not been a wide application of GAs in hoist reliability assessment and prediction. In addition, the authors bring discrete distribution functions to the software tool (GenRel) for the first time and significantly improve computing efficiency. The results of the case studies demonstrate successful application of GenRel in assessing and predicting hoist reliability, and this may lead to better preventative maintenance management in the industry.

This paper describes the development of a hybrid knowledge base system and genetic algorithms to select the optimum excavating and haulage equipment in opencast mining. The knowledge base system selects the equipment in broad categories based on the geological, technical and environmental characteristics of the mine. To further identify the make, size and number of each piece of equipment that minimizes the total cost of the operation, the problem is solved using the genetic algorithms mechanism. Results of four case studies are presented to show the validation of the developed system.

The purpose of this paper is to present a practical model to determine the economic replacement time (ERT) of production machines. The objective is to minimise the total cost of capital equipment, where total cost includes acquisition, operating, maintenance costs and costs related to the machine’s downtime. The costs related to the machine’s downtime are represented by the costs of using a redundant machine.

In total, four years of cost data are collected. Data are analysed, practical optimisation model is developed and regression analysis is done to estimate the drilling rigs ERT. The artificial neural network (ANN) technique is used to identify the effect of factors influencing the ERT of the drilling rigs.

The results show that the redundant rig cost has the largest impact on ERT, followed by acquisition, maintenance and operating costs. The study also finds that increasing redundant costs per hour have a negative effect on ERT, while decreases in other costs have a positive effect. Regression analysis shows a linear relationship between the cost factors and ERT.

The proposed approach can be used by the decision maker in determining the ERT of production machines which used in mining industry.

The research proposed in this paper provides and develops an optimisation model for ERT of mining machines. This research also identifies and explains the factors that have the largest impact on the production machine’s ERT. This model for estimating the ERT has never been studied on mining drilling rigs.

Globally, rapid urbanisation characterised by increasing demand for housing and infrastructure needs has resulted in sand mining. In Ghana, sand mining can create or destroy the livelihoods of people in urban and rural areas. This paper examines the interaction between sand mining and land-based livelihood security in Awutu Senya District (ASD) and Awutu Senya East Municipality (ASEM).

Based on pragmatism philosophy, the study used a mixed methods approach to collect quantitative data and qualitative data from 431 household heads, ten core staff of the Assemblies, five traditional leaders, two tipper truck drivers' associations and ten farmer groups. Statistical Product and Service Solutions, version 21 and NVivo 12 facilitated quantitative data analysis and qualitative data analysis, respectively.

The study revealed that sand mining had different consequences on land-based livelihood security. Some block makers and truck drivers acknowledged positive effects of sand mining on their livelihoods while the majority of the household respondents and other key informants claimed that sand mining had negative effects on their livelihoods.

This paper focuses on two selected local government areas in Ghana. Therefore, the results may be generalised on the country with caution because local government areas have different characteristics. Further research is needed to contact the customers of sand in Accra.

This study provides new insight into the connections between sand mining and people's livelihood security in two local government areas. It also introduces a novel idea of collaboration among stakeholders to address negative effects associated with unsustainable sand mining.

This paper aims to propose an empirically grounded governance framework based on complex adaptive systems (CAS) principles to facilitate formation of well-connected regional supply chains that foster economic development, adaptability and resilience of mining regions.

This study is an exploratory case study of the South Australian (SA) mining industry that includes 38 semi-structured interviews with the key stakeholders and structural analysis of the regional supply network (RSN).

Findings demonstrate the applicability of the CAS framework as a structured approach to the governance of the mining industry regional supply chains. In particular, the findings exemplify the relationship between RSN governance, its structure and interconnectivity and their combined impact on the adaptability and resilience of mining regions.

The data set analysed in the current study is static. Longitudinal data would permit a deeper insight into the evolution of the RSN structure and connectivity. The validity of the proposed framework could be further strengthened by being applied to other industrial domains and geographical contexts.

The proposed framework offers a novel insight for regional policy-makers striving to create an environment that facilitates the formation of well-integrated regional supply chains in mining regions through more focussed policy and strategies.

The proposed framework is one of the first attempts to offer a holistic structured approach to governance of the regional supply chains based on CAS principles. With the current transformative changes in the global mining industry, policy-makers and supply chain practitioners have an urgent need to embrace CAS and network paradigms to remain competitive in the twenty-first century.

Longwall mining is a special mining method with high productivity and smooth operation and the drum shearer is known as the most important component in longwall mines due to its direct role in the coal cutting and production process. Therefore, its reliability is important in keeping the mine production at a desired level. Hence, reliability analysis is essential in identifying and removing existing problems of this machine in order to achieve a better production condition. This paper seeks to learn about the reliability of the shearer machine in order to locate critical subsystems. The improvement of the reliability of the critical subsystems, to enhance the optimum operation of the shearer machine, is the main objective of this research.

A basic methodology was used in this paper for the reliability modeling of the shearer machine. First, failure and performance data from a two‐year period at the Tabas Coal Mine‐Iran was classified and sorted. The tests for validating the assumption of independent and identical distribution (iid) of TBF data are done and the best modeling method for each subsystem was selected among the renewal process, homogeneous Poisson process and non‐homogeneous Poisson process. Finally, the reliability of subsystems and the machine were assessed.

The study revealed that six important subsystems of the shearer machine are; water system, haulage, electrical system, hydraulic system, cutting arms, and cable system. Pareto analysis shows that the 30 percent of failures and stoppages of the shearer were related to the water system and this system is the most critical subsystem of the machine. The failure rate analysis shows that the failure rates of the hydraulic, haulage and electrical systems were decreasing, meanwhile, the failure rates of the water system, cutting arms and cable system were increasing. The reliability of drum shearer reaches the zero value after 100 hours.

This paper, for the first time, defines a practical set of subsystems for the coal shearer based on field data and machine design.