This study evaluates shovel and dump truck performance using three key indicators: average utilization (61–65%), availability (77–78%), and OEE (41–44%). Actual performance often lags behind ideal global benchmarks due to site-specific operational conditions. The results offer practical reference values for these KPIs, enabling mining operations to benchmark their fleet performance against industry data. While universal standards are challenging to establish, these findings support more informed equipment management decisions. Ultimately, they help optimize fleet utilization, enhance operational efficiency, and promote sustainable mining practices by providing a foundation for continuous improvement and comparative analysis across diverse mining environments.

In resource-rich but data-constrained mining regions, deterministic estimates of equipment productivity often mask critical operational risks, leading to flawed strategic decisions on fleet investment, maintenance, and national resource forecasting. This paper bridges a key gap in the literature by introducing a probabilistic Overall Equipment Effectiveness (OEE) framework that quantifies uncertainty in the shovel–truck fleet performance at one of the world's largest copper mines. Using Monte Carlo simulation calibrated with extensive field data—including photogrammetry-based cycle production and dispatch logs—we model joint variability in availability, utilization, and performance efficiency. Results reveal wide OEE distributions: 8–52% (mean: 25%) for shovels and 27–53% (mean: 38%) for dump trucks, where low utilization, driven by suboptimal dispatch and operational coordination, is the dominant constraint. Critically, we demonstrate that probabilistic OEE is essential for robust, risk-aware planning in aging fleets. The framework offers a low-cost, transferable tool for evidence-based resource policy and operational optimization in developing economies.

Effective maintenance strategies are critical for ensuring operational reliability, minimizing downtime, and optimizing resource utilization in fleet-based industrial operations. Among these, mining truck fleets represent a particularly high-risk, high-cost context where equipment failures can lead to substantial productivity losses and safety hazards. Despite the operational importance, existing literature lacks a structured framework to guide maintenance strategy selection that considers the practical constraints of data availability, diagnostic capability, and operational variability. To address this gap, this study proposes an evaluation framework that supports the selection and implementation of appropriate maintenance strategies. The framework is developed through a critical literature analysis, which is synthesized using a Frame of References approach. Unlike generic taxonomies, this model classifies maintenance strategies based on decision logic, response timing, data dependency, required infrastructure, and alignment with organizational capabilities. Building upon this structure, a two-level decision-support framework is introduced. The first decision tree assists practitioners in determining the appropriate class of maintenance strategy—corrective, planned, proactive, or predictive—based on operational constraints and system criticality. The second tree refines this selection by mapping available technological resources and data maturity to suitable analytical methods (e.g., rule-based, statistical, or AI-driven). While the framework is demonstrated in the context of mining truck operations, its modular design makes it applicable to other asset-intensive sectors, including logistics, construction, and heavy manufacturing. By bridging analytical insights with real-world constraints, this study offers a practical tool for organizations seeking to develop scalable, reliable, and context-sensitive maintenance strategies. 

Utilization of the shovel fleet as a capital-intensive and operationally important asset in open-pit mines is a key indicator for mine production analysis. This paper investigates shovel utilization in surface mining using a novel smart platform integrated with the shovel operating joystick. It utilizes a unique algorithm to identify and differentiate operational and non-operational time based on comparing real-time data and average loading cycle time. This data is then employed to calculate overall uptime and identify downtime periods. A field study was carried out on six electric cable shovels consisting of P&H 2100 and TZ WK-12, at Sarcheshmeh Copper Mine. The analysis revealed that the average utilization of the whole fleet is equal to 33%, ranging from 16 to 48%, which is dramatically lower than the mine expectations. The statistical analysis showed that in 10–13% of the operating time, the utilization is higher than 75%, which is a moderately acceptable level. Finally, according to the outcomes of the field study and the developed smart platform, it could be concluded that improvements in dispatching system accuracy, revising the grade blending strategies, increasing processing plant flexibility and improved operator training could enhance shovel fleet utilization and whole mine productivity.

Work-related injuries, particularly musculoskeletal disorders (MSDs), incur significant costs for companies in terms of sick leave and reduced productivity. Maintaining correct ergonomic posture is crucial to prevent these injuries and mitigate the impact of psychosocial factors. Digital technology plays a vital role in creating efficient and flexible work environments that cater to individual needs. Rather than relying solely on specialists, workers can utilize digital applications to prevent workload and strain injuries. This study investigates the effectiveness of a digital AI-based intervention program aimed at preventing work-related injuries and improving the physical work environment by addressing musculoskeletal disorders caused by incorrect postures. Through interviews with tool users in an industry setting, a web-based prototype application was tested to enhance workplace safety and improve physical health. The application employs digital AI tools to provide real-time feedback to workers. The interviews specifically assess how users evaluate and effectively utilize the tool to enhance working postures and the overall work environment. The study seeks to evaluate the efficacy of the digital AI-based intervention program and gather insights on users’ perceptions and utilization of the application. This research has the potential to contribute to a safer and healthier workplace by harnessing the power of technology. The study seeks to evaluate the efficacy of the digital AI-based intervention program and gather insights on users’ perceptions and utilization of the application.

Despite the high cost and low efficiency, compressed air is mostly used in underground mining for ore extraction, hoisting, and mineral processing operations. Failures of compressed air systems not only threaten the health and safety of workers but also contribute to inefficient control of airflow and stopped all equipment that operates by compressed air. In such uncertain conditions, mine managers are faced with the big challenge to supply enough compressed air, and therefore, the reliability evaluation of these systems is essential. This paper aims to analyze the reliability of the compressed air system using the Markov modeling approach as a case study, Qaleh-Zari Copper Mine, Iran. To achieve this, the state space diagram was constructed considering all relevant states for all compressors in the main compressor house of the mine. The failure and repair rate of all main and reserve compressors were calculated for all possible transitions between states to obtain the probability of being of the system in each of the states. Moreover, the probability of failure at any time period was considered to study the reliability behavior. The results of this study show that there is 31.5% probability that the compressed air supplying system is in operating condition with two main and one standby compressors. The system probability that two main compressors are remain in the operation without failure for one months is 92.32%. Furthermore, the lifetime of the system is estimated 33 months when at least one main compressor is active.

همزمان با گسترش تکنولوژی و تجهیزات از سوی صنایع معدنی به‌منظور تأمین اهداف تولید و افزایش رقابت‌پذیری در بازار، موضوع مدیریت دارایی‌های فیزیکی ازجمله ناوگان ماشین‌آلات حمل‌ونقل از اهمیت به خصوصی برخوردار گردیده است. قابلیت اطمینان یکی از شاخص‌های اصلی درزمینهٔ مدیریت دارایی‌های فیزیکی محسوب می‌شود و عمدتاً تابعی از زمان خرابی و عوامل ریسک متعددی مانند شرایط محیطی و عملیاتی نیز است. یکی از روش‌های پرکاربرد برای بررسی رابطه میان این عوامل و متغیر زمان، مدل رگرسیونی نرخ مخاطرات متناسب است که با توجه به چگونگی فرم تابع خطر پایه در آن، ضرایب رگرسیونی به دو روش پارامتری و نیمه پارامتری قابل برآورد خواهد بود. در این مقاله، داده‌های خرابی یک دستگاه دامپتراک در معدن مس سونگون با استفاده از مدل‌های نیمه پارامتری و پارامتری با تابع خطر پایه وایبل تحلیل و نتایج به‌دست‌آمده مقایسه شده‌اند. اگرچه که نتایج حاصل از دو روش تقریباً مشابه بود اما بر اساس معیار سنجش آکاییک، مدل پارامتری وایبل، از کارایی بیشتری برای توصیف قابلیت اطمینان و مخاطره ماشین موردمطالعه برخوردار است. بر اساس نتایج به‎دست‌آمده ، فاکتورهای ریسکی همچون شرایط جاده (P-value=0.08 ,[i] HR= 0.78)، مهارت اپراتور (P-value<0.001 , HR= 0.92)، فاصله حمل (P-value=0.02 , HR= 1.17) و دمای محیط (P-value=0.05 , HR= 1.02) به‌عنوان عوامل اثرگذار بر میزان مخاطره دامپتراک شناسایی شدند. این مدل در تعیین بازه‌های بازرسی تعمیرات پیشگیرانه به کار گرفته شد.

With recent technological developments in the mining industry, the issue of physical asset management has become more important. Asset management in the mining industry has a key role to achieve production goals and increase competitiveness in the market. Reliability is one of the key indicators of asset management. Reliability is a function of the failures time and various risk factors such as environmental and operational conditions. One of the most widely used methods for investigating the relationship between the risk factors and reliability is the proportional hazard regression model. The regression coefficients can be estimated using two approaches, i.e. parametric and semi-parametric. In this paper, the failure data of a dump truck in the Sungun copper mine is analyzed using both semi-parametric and parametric approaches. The results show that the obtained estimates of both approaches were close to each other, according to Akaike Information Criterion (AIC), the Weibull parametric model is more efficient than the semi-parametric model at describing the reliability and hazard rate. In addition, it is found that road condition; operator skill, ambient temperature, and haulage distance have a significant effect on the hazard rate of the dump truck. This analysis is applied to maintenance management to keep the reliability of the dump truck at an acceptable level.

Reliability of mining equipment is influenced by different operational and environmental risk factors. However, in practice, including all relevant factors in reliability analysis is not possible. Ignoring the risk factors leads to unobserved heterogeneity and biased estimation of reliability characteristics. This research proposes a semi-parametric mixed-effects model to assess the simultaneous effects of observed and unobserved risk factors on reliability. Furthermore, the model can be used for investigating inter-cluster dependency and variability. To illustrate the model, a comprehensive case study is presented using data from three mines in Iran. The results show that the model can address the effect of unobserved risk factors. Moreover, it is found that the hazard of dump trucks is significantly associated with operator skill, season and the elevation difference between dumping and loading points.

The large tonnage mines use railway transportation as a system for mineral transportation. The rolling stock health condition study and improvement not only reduce the lifecycle cost of the assets but also ensures safe, reliable, punctual, and efficient transportation. The remaining useful life estimation is an efficient approach to condition-based maintenance, prognostics, and health management. This paper aims to predict the remaining life of a mining rolling stocks in the presence of operational environmental effects. In this study, the operation and failure data were obtained from Malmbana, LKAB, Sweden, a Swedish Railway Company, considering the effective operational factors. The failure behaviour of the railcar was evaluated and then the proportional hazard model was used to estimate the conditional reliability functions and accordingly the remaining useful life at the various initial survival time. Finally, the reliability-based time interval is applied to plan the maintenance operations. Results of this study show that the operator’s skill level and the maintenance quality had a significant influence on the reliability performance. By considering the proposed PM plan according to the desired reliability level, the remaining lifetime of the rolling stock will be improved by 78.10%, on average.