Performance-Based Replacement Time Analysis of Open Pit Excavators: A West African Case Study

Performance-Based Replacement Time Analysis of Open Pit Excavators: A West African Case Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Performance-Based Replacement Time Analysis of Open Pit Excavators: A West African Case Study Drissa Mohamed Malo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9169266/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Open-pit excavators are subject to degradation due to harsh working conditions. Determining their optimal replacement time remains a challenge for decision-makers. The majority of current models utilize mathematical predictions to minimize the total cost of ownership. A decision-making model based on the analyses of both historical and real-time performance and technical condition is required for timely and efficient replacements. This study employs the overall mining equipment effectiveness method, which monitors technical indicators such as productivity, utilization rate, mechanical availability rate, and technical availability rate. Operational data collected over 24 months from three open-pit excavators (with operating hours over 33,000 hours) were used to perform the case study. The findings showed that the overall mining equipment effectiveness approach is an effective tool that offers valuable information for well-informed equipment replacement decisions. Based on the results, there was no need for an immediate replacement in this case study. However, a review of the current maintenance practices was recommended to the mine. Physical sciences/Engineering Physical sciences/Mathematics and computing Overall Equipment Effectiveness Mining Availability Productivity Utilization Key Performance Indicators Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Mining contributes significantly to the global economy by providing raw materials and jobs (Martens and Rattmann 2001 ; Ranosz 2014 ). Excavation is an essential step in surface mining (Lukashuk et al. 2021 ; Altiti et al. 2021 ). Hydraulic excavators play a key role in open pits, where they are used to perform excavation tasks (Bezkorovainyy et al.). They offer greater manoeuvrability and versatility than the rope shovels (Litvin and Litvin 2020 ). However, they are subject to harsh conditions, which deteriorate their physical state (Nasonov and Lykov 2018 ; Ivanov et al. 2019 ; Velikanov et al. 2020 ; Zhang et al. 2021 ; Liu et al. 2024 ). One of the challenges faced by mining and construction companies is making data-driven, rational equipment replacement decisions (Jaafari and Mateffy 1990 ; Alarcón et al. 2012 ; Al-Chalabi et al. 2014 , 2015a , b ; Zvipore et al. 2015 ; Santelices et al. 2017 ; Al-Chalabi 2022 ; Pourrahimian et al. 2024 ; Castañón et al. 2024 ). Several authors (Malo et al. 2025 ; Lohmann 1986 ; Jaafari and Mateffy 1990 ; Ohnishi 1997 ; Fan et al. 2011 , 2013 , 2014 ; Al-Chalabi et al. 2014 ; Zvipore et al. 2015 ; Sahu et al. 2016 ; Al-Chalabi 2022 ) have developed mathematical prediction models to support production equipment replacement decisions. Most of them focus on the economic factors without taking into account the real-time technical assessment of the machinery. For improved replacement decision making, it is critical to evaluate the performance of key mining equipment such as excavators using an adequate set of key performance indicators (KPIs) (Gutiérrez-Diez et al. 2024 ; Castañón et al. 2024 ). Dynamic programming is one of the most popular techniques used in equipment replacement models (Zvipore et al. 2015 ; Sadeghpour et al. 2019 ; Altalabi et al. 2020 ; Bozhenyuk et al. 2024 ). It is an optimization technique used to determine the optimal equipment replacement time by minimizing cost and maximizing performance over time. Other widespread concepts employed in equipment replacement models are life cycle costing and total cost of ownership (Al-Chalabi et al. 2015a ; Al-Chalabi 2022 ). These analyses aim to optimize costs; however, they overlook the real-time technical and physical assessment of the equipment. Gutiérrez-Diez et al. (Gutiérrez-Diez et al. 2024 ) developed the overall mining equipment effectiveness (OMEE) tool, which is a novel approach based on the traditional overall equipment effectiveness (OEE). Their goal was to evaluate the effectiveness of an open-pit drill rig by monitoring factors such as availability rate, utilization rate, and productivity. Later, Castañón et al.(Castañón et al. 2024 ) used the OMEE methodology to determine the replacement of a drill rig. Their study revealed that the OMEE methodology is a reliable tool for drill rig replacement decisions. To our knowledge, research to date has not yet determined the applicability of the OMEE approach to other mining equipment types such as hydraulic excavators. The main purpose of this study is to provide an excavator replacement decision support approach by re-adapting the OMEE approach with KPIs that are specific to the hydraulic excavators. Real-time performance indicators are monitored to improve the replacement model’s accuracy. The replacement time of an actual fleet of three excavators is analyzed over 24 months to determine if replacements are required. 2. Methodology OEE is an important tool for assessing the performance of mining equipment (Paraszczak 2005 ; Samatemba et al. 2020 ; Toraman 2024 ). Gutiérrez-Diez et al. (Gutiérrez-Diez et al. 2024 ) developed a novel method known as the OMEE, based on the OEE approach, to study the effectiveness of drill rigs. They substituted OEE parameters such as, performance, availability, and quality with new ones (technical and mechanical availability, utilization rate, and performance index) that are more suitable for the mining environment. Our study employs a similar approach to monitor the performance of three surface mine excavators (A, B, and C) from a West African gold mine and identify when a replacement is required due to a decline in machine performance. The OMEE is a flexible tool designed to carry out activities such as monitoring the equipment performance, identifying weaknesses, determining how long the equipment can remain in operation, planning maintenance activities, and simulating mining activities (Castañón et al. 2024 ). This method brings a new approach to determining equipment replacement by considering four key variables: technical availability, mechanical availability rate, utilization rate, and productivity index, as indicated in Fig. 1 . Mining equipment undergoes continuous wear over its lifetime. Thus, maintenance strategies contribute greatly to maintaining its structural integrity and reliability for enhanced mining operations. A key challenge is determining the optimal replacement time of production assets such as excavators. The OMEE can help tackle this issue by tracking the equipment’s performance and economic and technical aspects as a whole. It assesses these factors in real time and indicates when the replacement is required (Castañón et al. 2024 ). 2.1 Case study The bench height in the mine where the case study was carried out ranges from 04 to 16 meters. The gold extraction process consists of the drilling, blasting, loading, hauling, and processing steps (Bustillo Revuelta 2018 ; Bud et al. 2023 ). All excavators used at the mine are 140-tonne Caterpillar 6015Bs. The data used in this study were collected over 24 months. All three excavators (A, B, and C) have operated simultaneously in an open pit gold mine in West Africa, where the rock mass mainly consists of sandstones. They are powered by C27, 12-cylinder diesel engines whose output is approximately 746 hp. Their bucket size is approximately 8.1 m 3 . They possess advanced hydraulic systems such as automatic flow proportioning technology, which enables hydraulic energy loss reduction. The excavators’ service meter units (SMU) at the end of the study are presented in Table 1 . Table 1 Excavator’s SMU at the end of the study Equipment SMU (hours) Excavator A 39,547 Excavator B 33,615 Excavator C 46,717 The study started by reviewing the time classification framework used by the mine and selecting KPIs that are suitable to assess the effectiveness of excavators, as shown in Fig. 2 . Secondly, data collection was performed using the fleet management system database and the daily information provided by the operators and ore spotters. Then, the analyses of the OMEE components are performed on the fleet and individual machines to evaluate the productivity, technical state, and reliability of the machines. Last but not least, the conclusions of these analyses were used to identify the weak assets or components to determine their optimal replacement time. 2.2. Time classification framework at the collaborating mine Identifying and reducing non-productive time is necessary to improve the equipment’s efficiency and performance(Assi and Haiawi 2021 ). The mine employs the standardized time usage model used in open pit mining (Lukacs 2020 ) as depicted in Fig. 3 . In the study, this time usage model was refined (as shown in Table 2 ) to facilitate the calculation of relevant KPIs in the OMEE method. This configuration assumes that all operational time must be accounted for. The time categories show how time is used. Table 2 Time usage model components (Lukacs 2020 ) Term Definition Formula Calendar Time (CT) The total time that is available in the Gregorian calendar. Scheduled Time (ST) The time during which the equipment is expected to operate or production is planned as per the business plan or forecast. ST = CT - UT Unscheduled Time (UT) The time during which the equipment is not expected to operate or production is not planned as per the business plan or forecast. Downtime (DT) The equipment is required as per the business plan or forecast, but it is unable to fulfill its function. DT = UD + SD + DS Unscheduled Downtime (UD) The equipment is required as per the business plan or forecast, but it is unable to fulfill its function due to unplanned maintenance activities. Scheduled Downtime (SD) The equipment is required as per the business plan or forecast, but it is unable to fulfill its function due to planned maintenance activities. Daily inspection time (DS) The equipment is required as per the business plan or forecast, but it is unable to fulfill its function due to daily maintenance inspections. Available Time (AT) The equipment is required as per the business plan or forecast, and it can fulfill its function. AT = ST - DT Standby (SB) The equipment is required as per the business plan or forecast; it can fulfill its function, but it is not operating. SB = SBE + SBO Operating Standby (SBO) The equipment is required as per the business plan or forecast and can fulfill its function, but is not operating due to operational management-related factors. External Standby (SBE) The equipment is required as per the business plan or forecast; it can fulfill its function, but is not operating due to external factors unrelated to the operational management. Operating Time (OT) The equipment is available, and it is under a system or human control. OT = AT - SB Operating Delay (OD) The equipment is being operated but is not performing any work due to environmental delays. Working Time (WT) The equipment is operating and performing work. WT = OT - OD Non-Productive Time (NP) The equipment operates and performs activities that do not directly correlate with production, but cannot be avoided to ensure operational efficiency and safety. Productive Time (PT) The equipment is operating and performing work that contributes directly to the production. PT = WT -NP 2.3. Key performance indicators Mining equipment tends to lose effectiveness and performance as it ages (Samatemba et al. 2020 ). KPIs are critical tools that are used to evaluate the historical and real-time performance of mining equipment operations and maintenance (Lin et al. 2024 ). These KPIs are generally compared with the business plan targets to identify the areas of improvement (Castañón et al. 2024 ). This study's main KPI selection criteria are their applicability to the excavators, easy measurability, and monitorability. The selected indicators give insight into the physical condition of the machinery and technical factors that influence the asset replacement decision (as shown in Table 3 ). Signs such as reduced output, repetitive malfunctions, and rising energy consumption rate indicate that the machinery is nearing the end of its operational life. This highlights the importance of KPI evaluation when making equipment replacement decisions. Table 3 KPIs selected for excavators Category KPI Temporal Scheduled time (ST) Operating time (OT) Unscheduled Downtime (UD) Scheduled Downtime (SD) Available Time (AT) Daily Inspection and Service Time (DS) Production Wet Tonnes Dry Tonnes Consumption Fuel 2.4 OMEE method The methodology employed in this study shows how indicators such as productivity, availability, reliability, maintenance, and operational management are connected to mining equipment replacement. The OMEE stems from the traditional OEE method (Gutiérrez-Diez et al. 2024 ). It is a supportive technique that helps compare how well similar types of mining equipment perform under similar conditions by observing KPIs such as utilization rate, technical availability, mechanical availability, and productivity index, as shown in Eq. ( 1 ). It facilitates the identification of significant variations in the mining equipment’s overall performance over time. $$\:OMEE=UR\times\:At\:\times\:Am\:\times\:\:Pi$$ 1 Where \(\:UR\) , \(\:At\) , \(\:Am\) , \(\:Pi\) represent the utilization rate, technical availability, mechanical availability, and productivity index, respectively. 2.4.1 Utilization rate It represents the proportion of time that the mining machinery is performing productive work. It can be defined as the ratio of the operating time to the available time, as shown in Eq. ( 2 ): $$\:UR=\:\frac{OT}{AT}\:\times\:100$$ 2 2.4.2 Productivity index Mining production is affected by several factors such as operational issues, human factors, environmental conditions, and geological conditions (Mishra et al. 2017 ). Therefore, tracking how well individual excavators perform in real time compared to the fleet’s productivity over time is important. Lama et al. (Lama et al. 2021 ) used a mining production index method to improve the efficiency of mining equipment. The productivity index used in this study is calculated by using Eq. ( 3 ): $$\:Pi=\:\frac{{P}_{m}}{{P}_{f}}\:\times\:100$$ 3 \(\:{P}_{m}\) represents the individual productivity of the machine. \(\:{P}_{f}\) represents the productivity of the excavator. 2.4.3 Availability rate The availability rate is the proportion of time that an asset is ready to perform its intended function. To facilitate the analysis, it was divided into: the technical availability rate (Eq. ( 4 )): it reflects downtime due to scheduled activities such as planned maintenance and planned component replacement. the mechanical availability rate (Eq. ( 5 )): it focuses on downtime caused by breakdowns and unplanned maintenance tasks (Gutiérrez-Diez et al. 2024 ; Castañón et al. 2024 ). 2.4.3.1 Technical availability rate $$\:At=\:\frac{ST-SD}{ST}\:\times\:100$$ 4 Where; \(\:ST\) represents the Scheduled time. \(\:SD\) is the scheduled downtime. 2.4.3.2 Mechanical availability rate The mechanical availability rate is calculated using the equation below: $$\:Am=\:\frac{ST-UD}{ST}\:\times\:100$$ 5 Where \(\:UD\) is the unscheduled downtime. 2.5 Moisture content The moisture content significantly affects the material handling and thus the excavator’s performance (Pekol 2019 ). The equivalent dry tons data were calculated using the measured moisture factors (Eq. ( 6 )). $$\:dry\:tons=wet\:tons\times\:(1-moisture\:factor)$$ 6 3. Results The economic replacement time analysis of the studied excavators focused on four key variables of the OMEE method: the utilization rate, the productivity index, and the technical and mechanical availability rates. 3.1 Database reports The individual excavator data was collected over 24 months. Relevant information was collected and organized to ease access to data and enable various analyses. Data collected on the mine's tons of material moved (TMM) are presented in Appendix 1 per month and in Appendix 2 per equipment. The fleet’s temporal KPIs are presented in Appendix 3, and Appendix 4 provides the same data for individual machines. Appendices 5 and 6 contain data on the TMM per hour and the fuel burn rate for the fleet and individual machines, respectively. 3.2 Utilization rate Mining operations experience events and conditions that may lower the operating time of excavators (Nasonov and Lykov 2018 ; Ivanov et al. 2020 ). Therefore, it is important to analyze the downtime and the reasons behind it. Table 4 and Fig. 4 illustrate the utilization rate of the fleet and individual excavators. It should be noted that Excavators A and B did not operate from months 12 to 14 and from months 12 to 13, respectively, due to equipment technical condition and management-unrelated external events. Excavator C operated during all months of the study and only experienced a drop in the utilization rate to 38% in month 8 due to equipment management-related decisions. Table 4 Utilization rate of the fleet and individual excavators Month Fleet Excavator A Excavator B Excavator C 1 75% 90% 67% 78% 2 84% 84% 81% 86% 3 81% 86% 77% 79% 4 78% 84% 76% 75% 5 78% 82% 76% 75% 6 82% 63% 89% 88% 7 79% 81% 76% 80% 8 49% 62% 47% 38% 9 86% 84% 86% 89% 10 90% 89% 90% 92% 11 54% 38% 37% 91% 12 26% 0% 0% 86% 13 24% 0% 0% 85% 14 37% 0% 37% 86% 15 58% 26% 80% 72% 16 87% 84% 89% 86% 17 75% 78% 73% 74% 18 76% 75% 77% 75% 19 80% 77% 81% 84% 20 82% 80% 82% 85% 21 81% 82% 82% 77% 22 83% 83% 82% 84% 23 80% 77% 80% 84% 24 82% 86% 86% 74% 3.3 Productivity index The production rate of mining equipment such as excavators is affected by several factors, such as the operator's skills, environmental and technical conditions of the equipment (Nasonov and Lykov 2018 ; Ivanov et al. 2020 ). The productivity index helps identify significant changes in the production rate of a specific rig by comparing it with the fleet’s productivity rate over a certain period. The three excavators required 29,706.57 operating hours to generate a TMM of 19,140,491 tons over the 24 months. Their average productivity in this period was 644.32 tons per hour. Table 5 Excavator fleet’s production rate and productivity index Month Production rate (tons/h) Productivity index 1 327 0.51 2 544 0.84 3 527 0.82 4 536 0.83 5 624 0.97 6 690 1.07 7 703 1.09 8 704 1.09 9 745 1.16 10 781 1.21 11 747 1.16 12 677 1.05 13 728 1.13 14 695 1.08 15 728 1.13 16 726 1.13 17 672 1.04 18 635 0.99 19 636 0.99 20 627 0.97 21 630 0.98 22 595 0.92 23 586 0.91 24 654 1.02 Data on the production rate and productivity index of the digging fleet are presented in Table 5 and Fig. 5 . Individual machines’ productivity indexes are presented in Fig. 6 . Productivity and productivity index could not be calculated in months with no operating hours for two excavators (A and B). 3.4 Availability rate Optimal maintenance strategies are critical for efficient mining asset management (Shahin et al. 2012 ; Pourjavad et al. 2013 ). To ensure the reliable, efficient, and safe operation of the equipment, it is essential to implement adequate maintenance strategies. The study separates the availability into two parts: technical and mechanical availability rates (Gutiérrez-Diez et al. 2024 ). The technical availability considers scheduled downtime. Mechanical availability addresses unscheduled downtime and helps identify the effectiveness of the maintenance practices. Table 6 and Fig. 7 illustrate the technical and mechanical availability rates of the individual excavators as well as the fleet. Table 6 Technical and mechanical availability rates Month Fleet Excavator A Excavator B Excavator C At Am At Am At Am At Am 1 87% 76% 100% 31% 99% 98% 62% 98% 2 90% 93% 95% 93% 98% 95% 77% 93% 3 98% 95% 98% 97% 99% 95% 97% 92% 4 96% 88% 96% 95% 96% 86% 94% 84% 5 84% 95% 95% 93% 62% 93% 96% 99% 6 99% 79% 99% 59% 99% 86% 98% 91% 7 95% 88% 98% 90% 91% 91% 95% 84% 8 98% 97% 98% 98% 95% 95% 100% 99% 9 97% 91% 97% 97% 96% 91% 97% 86% 10 98% 80% 96% 93% 97% 97% 100% 49% 11 98% 96% 99% 99% 99% 100% 97% 90% 12 98% 98% 100% 100% 100% 100% 95% 93% 13 97% 97% 100% 100% 100% 100% 91% 91% 14 99% 93% 100% 100% 98% 99% 97% 81% 15 93% 94% 100% 95% 97% 98% 81% 90% 16 97% 87% 99% 81% 97% 95% 96% 85% 17 97% 90% 96% 99% 95% 84% 99% 87% 18 98% 73% 97% 95% 97% 75% 99% 49% 19 96% 86% 97% 98% 99% 81% 93% 79% 20 94% 91% 93% 93% 93% 90% 95% 90% 21 99% 76% 98% 98% 98% 97% 100% 33% 22 93% 89% 98% 92% 90% 87% 92% 88% 23 96% 89% 99% 94% 99% 89% 90% 85% 24 97% 77% 98% 59% 98% 92% 97% 78% 3.5 OMEE index The OMEE index correlates all aforementioned factors to provide a holistic model that provides insight for rational decision making. Figure 8 represents the OMEE index of the fleet and individual machines. 4. Discussion Like the existing body of literature (Zhukovskiy and Koteleva 2017 ; Gutiérrez-Diez et al. 2024 ; Castañón et al. 2024 ) our study has shown that flexibility and adaptability are fundamental requirements in the mining environment. The OMEE approach helps assess the assets’ performance and identify weaknesses in the process(Gibbons and Burgess 2010 ). Unlike authors who based their replacement time model on mathematical predictions (Al-Chalabi et al. 2014 , 2015a , b ; Reina et al. 2016 ; Sahu et al. 2016 ; Enyindah and Amadi 2019 ; Al-Chalabi 2022 ), we employed a tool based on the analysis of historical and real-time data recorded from the equipment and operations. KPIs that are suitable for excavator performance analysis were selected (Holt and Edwards 2015 ). The developed model can assist decision makers with a rational approach to determine the equipment’s optimal replacement time by monitoring its past and current performances, as indicated by the findings of some studies (Shahin et al. 2012 ; Castañón et al. 2024 ). As suggested by the existing literature (Samatemba et al. 2020 ; Gutiérrez-Diez et al. 2024 ; Castañón et al. 2024 ) the study reveals the interconnection of maintenance and production indicators, such as productivity, utilization rate, and availability rate, with the equipment replacement time. We found that these indicators are key variables to consider for accurate equipment replacement time determination, as stated by Castañón et al. ’s (Castañón et al. 2024 ). Their evolution provides insights into the technical and physical aspects of the machine (Gutiérrez-Diez et al. 2024 ; Castañón et al. 2024 ). A correlated decrease in these KPIs may imply the proximity of the replacement time. To our knowledge, this is one of the first attempts to develop a tool that helps determine the economic replacement time of open pit excavators by analysing the influence of equipment condition, maintenance, and management strategies. 4.1 Productivity Evolution Data representativeness and validity are confirmed by the excavator fleet’s TMM and temporal KPIs (19,140,493 dry tons) collected over a scheduled time of 52,560 as illustrated in Table 4 – 7 . Table 4 and Table 6 present the monthly fleet collective data, whereas Table 5 and Table 7 provide information related to individual machines. Enyindah and Amadi (Enyindah and Amadi 2019 ) stated that the optimal replacement time of mining and construction hydraulic excavators is 8 years. The average excavator operating time at the mine is 4,951 hours per year. Enyindah and Amadi’s 8-year optimal replacement time (Enyindah and Amadi 2019 ) can be converted into 39,609 hours. Excavator C’s SMU (67,171 hours) was above 39,609 hours, Excavator A’s (39,547 hours) was about to reach 39,609 hours, while Excavator B was approximately 6000 hours below the benchmark at the end of the data collection period. Figures 5 and 6 do not reveal any sign of productivity decline when compared to the early months of data collection, when none of the excavators’ SMU had reached 39,609 hours. 4.2 Utilization Trends Table 4 and Fig. 4 illustrate the excavator utilization trend. Excavators A and B experienced a sharp drop in utilization to 0% (from months 11 to 14 for Excavator A, and from months 11 to 13 for Excavator B). These drops were due to external factors that are not related to the equipment performance or management. The fleet’s utilization is greatly affected by these excavators as it mirrors their trend. In contrast, Excavator C exhibits a good utilization rate, fluctuating between 92 and 72% except in month 8, where the utilization rate was 38%. Although the current trend does not suggest any equipment replacement requirement, future extended and repetitive drops in the utilization rate due to equipment performance-related factors could trigger a replacement recommendation. 4.3 Availability Rate Analysis Al-Chalabi (Al-Chalabi 2022 ) and Reina et al. (Reina et al. 2016 ) revealed that mining equipment experiences progressive deterioration due to the challenging operating conditions. The appropriate maintenance strategies have a positive influence on the equipment availability, helping to achieve better performance (Rasmekomen and Parlikad 2016 ; Olde Keizer et al. 2016 ; Alaswad and Xiang 2017 ; Gölbaşı and Demirel 2017 ; Balaraju et al. 2019 ; de Jonge and Scarf 2020 ). The fleet and individual machine technical and mechanical availability rates are presented in Table 6 and Fig. 7 . The trend analysis reveals Am is regularly below At, unlike Castañón et al.’s (Castañón et al. 2024 ) work, where Am outperforms At. This suggests that most of the downtime incurred by the machine is from breakdowns. The increasing gap between At and Am over time indicates the inefficiency of preventive maintenance programs, as less time is spent on preventive maintenance activities, leading to growing corrective maintenance downtime. The fuel burn rate (shown in Appendix 5), which can be a key engine and equipment deterioration indicator, did not show any notable increase. It can be concluded that the progressive drop in the availability rates is mainly driven by an inadequate maintenance strategy rather than equipment age. The recommendation is to review the mine’s current maintenance strategy. 4.4 OMEE Index Interpretation The OMEE index (as shown in Fig. 8 ) fluctuates between 0.88 and 0.25. The drops in the index in month 8 and between months 12 and 14 are driven by the low excavator utilization in this period. The remaining drops stem from the combined effects of availability rates and productivity. Despite the sharp drop from months 12 to 14, the OMEE has recovered and stabilized. The post-recovery OMEE values (0.60–0.62) are higher than those at the beginning of the study (0.25–0.55) when the machine SMUs were still low and no replacement was recommended by traditional approaches. Comparing the fleet’s OMEE index with those of individual machines revealed that fleet indicators are strongly affected by underperforming units. The sharp drops in the fleet OMEE index (months 12 to 14) are driven by the non-utilization of excavator A and C. The trend analysis does not reveal any decline in the OMEE when compared to the early values. It can be concluded that no equipment replacement recommendation for the excavators can be made at this stage. Future declines in the OMEE index, observed during future trend analyses, could trigger equipment replacement recommendations. 4.5 Evaluation and Replacement Time Determination Criteria A decision matrix was designed to support the replacement decision-making using the 4 components of OMEE? As shown in Table 7 . Each indicator has a threshold and relevant interpretations in the matrix. Recommendations, such as maintenance strategy review and equipment replacement, are triggered if thresholds are consistently crossed (more than 3 consecutive months). For instance, a utilization rate below the threshold of 40% caused by equipment condition triggers a replacement recommendation. However, no replacement is recommended if the low utilization is driven by external or management-related factors. Defective ground-engaging tools or operator inefficiencies, or operating conditions could cause the productivity index to get below 80%. Technical and mechanical availabilities help determine whether downtime is related to weak preventive maintenance policies or aging components. The OMEE index combines all factors into one and reflects the overall machine effectiveness. A value below 0.5, when not due to external causes, triggers a replacement recommendation. The proposed OMEE method is a dynamic model that allows assessing equipment both collectively and individually to identify areas of improvement and make rational data-driven equipment replacement decisions. Table 7 Equipment Replacement Decision Matrix OMEE Component Threshold Interpretation (if the threshold is exceeded for more than 3 consecutive months) Recommended Action Utilization rate < 40% (external reasons) Low usage not related to machine condition No action < 40% (management-related decisions) Low usage due to operational choices Review production requirements < 40% (machine performance) Low usage due to poor performance Consider for replacement Productivity index < 0.8 Poor performance Investigate bucket or cutting tools, operator skills, operating conditions Technical availability < 85% Weak preventive maintenance Review and improve maintenance planning Mechanical availability < 80% with Technical availability ≥ 85% and stable fuel burn Breakdowns due to poor preventive maintenance Review maintenance strategy < 80% with Technical availability rate ≥ 85% and increasing fuel burn (more than 5 liter / hour) Breakdowns due to aging components Consider for replacement OMEE index < 0.5 and utilization rate not < 40% due to management or external factors underperformance Consider for replacement On one hand, Excavator A and B’s utilization rates dropped to 0% due to external factors (non-technical and non-management-related) for 3 and 2 months, respectively. Their productivity index remained stable and acceptable during operating periods. Their technical availability rate was generally high, but mechanical availability was often below 80% and combined with technical availability < 85%. The fuel consumption remained consistent, indicating poor maintenance practices, rather than component aging. So, no replacement recommendation was triggered for these machines. On the other hand, Excavator C operated continuously over the 24 months of the study. Its utilization rate dropped only once below 40% (month 8) due to a management decision, not poor machine performance. Its productivity index was slightly better than the fleet average. The machine's high technical availability rates (≥ 85%) and declining mechanical availability triggered maintenance practices review. Despite high operating hours (46,717) well above the benchmarks, Excavator C’s performance remains strong and no replacement is required for this machine. The analysis of the OMEE index and its parameters shows that excavators with high operating hours (2 excavators’ SMUs were above 39,609 hours or 8 years of operation at the collaborating mine) can still exhibit strong performance if adequate preventive maintenance policies are adopted. This result goes in the same direction as Castañón et al.’s (Castañón et al. 2024 ) findings. It was recommended that the mine review its current maintenance strategy to migrate from a reactive repair strategy to a reliability-centered one (Prasetyo and Mercado Rosita 2020 ; Geisbush and Ariaratnam 2022 ). To increase the model's reliability, new studies with a larger sample size (more than three excavators) and a longer data collection time could be carried out. The model could be applied to other key mining equipment types, such as trucks, loaders, graders, and dozers. A hybrid model could also be designed to provide both the technical insights (offered by the proposed OMEE model) and the economic assessment obtained from a mathematical prediction model such as the LCCA. The proposed model is one of the first practical real-time performance monitoring tools that support excavator replacement decision-making. Conclusion The present work aimed to propose a performance analysis-based model to determine if the optimal replacement time of excavators has been achieved. A 24-month database collected between the middle and late life stage) was used to select KPIs that are suitable for open pit excavators. The study has revealed that some excavators with operating hours above the recommended replacement time, provided by mathematical prediction models (Enyindah and Amadi 2019 ), did not exhibit any decline in their overall performance and did not require any replacement. The study has also identified that the mine’s current strategy is inadequate for optimal equipment performance. The results of this study indicate that the OMEE is a strong and reliable tool that can help make excavator and mining equipment replacement decisions by tracking their historical and real-time performance. A key strength of the model developed in the study is its ability to pinpoint significant performance deviations, which allows for taking corrective actions. Another strength of the study is the holistic equipment performance assessment capability that it provides, which has proven to be a decisive factor to be taken into account to achieve production targets. It is a novel performance parameter monitoring-based model that supports excavator and key mining equipment replacement decisions. Declarations Acknowledgments The collaborating company has requested anonymity. The authors gratefully acknowledge the valuable input and infrastructural support provided. Declaration of competing interest The corresponding author states that there is no conflict of interest. Funding No funding was received to assist with the preparation of this manuscript. Data availability The data are presented within the article Author contributions : A single author wrote this manuscript who performed the study conception and design, data collection, and analysis. References Alarcón, L. F., Rodríguez, A. & Mourgues, C. Impact of Machine-Failure Costs on Equipment Replacement Policies: Tunneling Company Case Study. J. Constr. Eng. Manag. 138 , 767–774. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000480 (2012). Alaswad, S. & Xiang, Y. A review on condition-based maintenance optimization models for stochastically deteriorating system. Reliab. Eng. Syst. Saf. 157 , 54–63. https://doi.org/10.1016/j.ress.2016.08.009 (2017). Al-Chalabi, H. Development of an economic replacement time model for mining equipment: a case study. Life Cycle Reliab. Saf. 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Springer International Publishing, Cham, pp 787–794 (2021). Malo, D. M., Soulama, S. & Naon, B. Optimizing the economic replacement time for open-pit mining haul trucks: an advanced case study. Life Cycle Reliab. Saf. Eng. https://doi.org/10.1007/s41872-025-00354-1 (2025). Martens, P. & Rattmann, L. Mining and society: No mining, No future (In: XVII International Mining Congress and Exhibition of Turkey, 2001). Mishra, P. C., Mishra, U. S., Mall, M. & Mohanty, M. K. Attributes affecting production and productivity in opencast mines: a review. Int. J. Adv. Oper. Manage. 8 , 225–245. https://doi.org/10.1504/IJAOM.2016.081306 (2017). Nasonov, M. Y. & Lykov, Y. V. Effects of working conditions on excavator performance - longevity of metal structures. IOP Conf. Ser: Earth Environ. Sci. 194 , 042017. https://doi.org/10.1088/1755-1315/194/4/042017 (2018). Ohnishi, M. & MINIMAL-REPAIR AND REPLACEMENT PROBLEM UNDER AVERAGE COST CRITERION OPTIMAL OPTIMALITY OF (t, T)-POLICY. Journal of the Operations Research Society of Japan 40:373–389. (1997). https://doi.org/10.15807/jorsj.40.373 Olde Keizer, M. C. A., Teunter, R. H. & Veldman, J. Clustering condition-based maintenance for systems with redundancy and economic dependencies. Eur. J. Oper. Res. 251 , 531–540. https://doi.org/10.1016/j.ejor.2015.11.008 (2016). Paraszczak, J. Understanding and assessment of mining equipment effectiveness. Min. Technol. 114 , 147–151. https://doi.org/10.1179/037178405X53971 (2005). Pekol, A. Evaluation and Risk Analysis of Open-Pit Mining Operations. Berg Huettenmaenn. Monatsh . 164 , 232–236. https://doi.org/10.1007/s00501-019-0854-9 (2019). Pourjavad, E., Shirouyehzad, H. & Shahin, A. Selecting maintenance strategy in mining industry by analytic network process and TOPSIS. Int. J. Ind. Syst. Eng. 15 , 171–192. https://doi.org/10.1504/IJISE.2013.056095 (2013). Pourrahimian, E., Ead, R., El Menshawy, M. & AbouRizk, S. A Decision Support System for Equipment Replacement. In: Desjardins S, Poitras GJ, Nik-Bakht M (eds) Proceedings of the Canadian Society for Civil Engineering Annual Conference 2023, Volume 3. Springer Nature Switzerland, Cham, pp 175–188 (2024). Prasetyo, Y. T. & Mercado Rosita, K. K. Equipment Reliability Optimization Using Predictive Reliability Centered Maintenance: A Case-Study Illustration and Comprehensive Literature Review. In: 2020 7th International Conference on Frontiers of Industrial Engineering (ICFIE). pp 93–97 (2020). Ranosz, R. Mining and its role in the global economy. Gospodarka Surowcami Mineralnymi/Mineral Resour. Manage. 30 , 5–20 (2014). Rasmekomen, N. & Parlikad, A. K. Condition-based maintenance of multi-component systems with degradation state-rate interactions. Reliab. Eng. Syst. Saf. 148 , 1–10. https://doi.org/10.1016/j.ress.2015.11.010 (2016). Reina, A. et al. Maintenance Decision Support for Manufacturing Systems Based on the Minimization of the Life Cycle Cost. Procedia CIRP . 57 , 674–679. https://doi.org/10.1016/j.procir.2016.11.117 (2016). Sadeghpour, H., Tavakoli, A., Kazemi, M. & Pooya, A. A novel approximate dynamic programming approach for constrained equipment replacement problems: A case study. Adv. produc Eng. manag . 14 , 355–366. https://doi.org/10.14743/apem2019.3.333 (2019). Sahu, A. K., Narang, H. K., Sahu, A. K. & Sahu, N. K. Machine economic life estimation based on depreciation-replacement model (Cogent Engineering, 2016). Samatemba, B., Zhang, L. & Besa, B. Evaluating and optimizing the effectiveness of mining equipment; the case of Chibuluma South underground mine. J. Clean. Prod. 252 , 119697. https://doi.org/10.1016/j.jclepro.2019.119697 (2020). Santelices, G. et al. Integrating mining loading and hauling equipment selection and replacement decisions using stochastic linear programming (International Journal of Mining, 2017). Shahin, A., Shirouyehzad, H. & Pourjavad, E. Optimum maintenance strategy: a case study in the mining industry. Int. J. Serv. Oper. Manage. 12 , 368–386. https://doi.org/10.1504/IJSOM.2012.047626 (2012). Toraman, S. Evaluation of overall equipment effectiveness (OEE) for mining equipment (shovel-truck): A case study Manisa-Soma (Turkey) open pit mine. JSR-A 45–55. https://doi.org/10.59313/jsr-a.1433960 (2024). Velikanov, V. S., Dyorina, N. V., Panfilova, O. R. & Zalavina, T. Y. Determination of the possible causes factors of the functionality impairment of the career excavator. IOP Conf. Ser: Mater. Sci. Eng. 709 , 022032. https://doi.org/10.1088/1757-899X/709/2/022032 (2020). Zhang, L. et al. An autonomous excavator system for material loading tasks. Sci. Rob. 6 , eabc3164. https://doi.org/10.1126/scirobotics.abc3164 (2021). Zhukovskiy, Y. & Koteleva, N. Automated system for definition of life-cycle resources of electromechanical equipment. IOP Conf. Ser: Mater. Sci. Eng. 177 , 012014. https://doi.org/10.1088/1757-899X/177/1/012014 (2017). Zvipore, D. C., Nyamugure, P., Maposa, D. & Lesaoana, M. Application of the Equipment Replacement Dynamic Programming Model in Conveyor Belt Replacement: Case Study of a Gold Mining Company. Mediterranean J. Social Sci. 6 , 605 (2015). Additional Declarations No competing interests reported. Supplementary Files Appendix.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9169266","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":612105541,"identity":"c9f45f07-44f7-4136-925f-e5646713afda","order_by":0,"name":"Drissa Mohamed 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12:57:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":68043,"visible":true,"origin":"","legend":"\u003cp\u003eThe OMEE’s key components (Gutiérrez-Diez et al. 2024)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9169266/v1/eca6dd594afe0f87fa526749.png"},{"id":105566946,"identity":"eadd3758-1f5a-47d6-8d85-5bce519fcb9d","added_by":"auto","created_at":"2026-03-27 12:57:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":199865,"visible":true,"origin":"","legend":"\u003cp\u003eThe OMEE methodology\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9169266/v1/dc3fef36f98d08b82bbb082b.png"},{"id":105500017,"identity":"61de296c-b7fb-4dd7-a9f4-9213b741da95","added_by":"auto","created_at":"2026-03-26 17:18:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":100526,"visible":true,"origin":"","legend":"\u003cp\u003eThe time usage model used at the mine (Lukacs 2020)\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9169266/v1/ade660af874a0d6e50b18e95.png"},{"id":105500020,"identity":"b3cd0958-c34b-4e08-8a6e-92fbeb41ed6c","added_by":"auto","created_at":"2026-03-26 17:18:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":501460,"visible":true,"origin":"","legend":"\u003cp\u003eUtilization rate of the fleet and individual excavators\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9169266/v1/f3b40d9fe9e6af0ebc808bd6.png"},{"id":105500026,"identity":"d21c979e-1534-4904-98b3-88434c7ddb62","added_by":"auto","created_at":"2026-03-26 17:18:38","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":157153,"visible":true,"origin":"","legend":"\u003cp\u003eFleet’s production rate and productivity index\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9169266/v1/30c7a2c90dba1a065d1fd3c8.png"},{"id":105500024,"identity":"5ff6bd5d-314b-4a6e-bc25-6695c51055df","added_by":"auto","created_at":"2026-03-26 17:18:37","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":221198,"visible":true,"origin":"","legend":"\u003cp\u003eIndividual excavators’ productivity index.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-9169266/v1/480d92fe881810a9f2ec1311.png"},{"id":105500022,"identity":"2569771a-7524-42ed-aa81-2cb398568204","added_by":"auto","created_at":"2026-03-26 17:18:37","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":639327,"visible":true,"origin":"","legend":"\u003cp\u003eTechnical and mechanical availability rates\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-9169266/v1/a52247a79503f8a5299fcccc.png"},{"id":105500019,"identity":"721d6910-4912-4d2e-add3-30323a0f39f6","added_by":"auto","created_at":"2026-03-26 17:18:37","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":238051,"visible":true,"origin":"","legend":"\u003cp\u003eFleet and individual OMEE index\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-9169266/v1/f1dc50dbd8fb1f34e3f1f8d7.png"},{"id":106093504,"identity":"1013b2f8-e3b4-41ac-8ef1-f2f53f710803","added_by":"auto","created_at":"2026-04-03 11:37:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3491546,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9169266/v1/51612404-6df7-477a-af3e-8ee26c312812.pdf"},{"id":105567910,"identity":"65c0068f-800a-4f41-af90-c565996903f0","added_by":"auto","created_at":"2026-03-27 13:05:57","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":26369,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-9169266/v1/42a7d8647609f86640387792.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Performance-Based Replacement Time Analysis of Open Pit Excavators: A West African Case Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMining contributes significantly to the global economy by providing raw materials and jobs (Martens and Rattmann \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Ranosz \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Excavation is an essential step in surface mining (Lukashuk et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Altiti et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Hydraulic excavators play a key role in open pits, where they are used to perform excavation tasks (Bezkorovainyy et al.). They offer greater manoeuvrability and versatility than the rope shovels (Litvin and Litvin \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, they are subject to harsh conditions, which deteriorate their physical state (Nasonov and Lykov \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ivanov et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Velikanov et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). One of the challenges faced by mining and construction companies is making data-driven, rational equipment replacement decisions (Jaafari and Mateffy \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Alarc\u0026oacute;n et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Al-Chalabi et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015a\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003eb\u003c/span\u003e; Zvipore et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Santelices et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Al-Chalabi \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pourrahimian et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Casta\u0026ntilde;\u0026oacute;n et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral authors (Malo et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Lohmann \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Jaafari and Mateffy \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Ohnishi \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Fan et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Al-Chalabi et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Zvipore et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Sahu et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Al-Chalabi \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) have developed mathematical prediction models to support production equipment replacement decisions. Most of them focus on the economic factors without taking into account the real-time technical assessment of the machinery. For improved replacement decision making, it is critical to evaluate the performance of key mining equipment such as excavators using an adequate set of key performance indicators (KPIs) (Guti\u0026eacute;rrez-Diez et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Casta\u0026ntilde;\u0026oacute;n et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDynamic programming is one of the most popular techniques used in equipment replacement models (Zvipore et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Sadeghpour et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Altalabi et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Bozhenyuk et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It is an optimization technique used to determine the optimal equipment replacement time by minimizing cost and maximizing performance over time. Other widespread concepts employed in equipment replacement models are life cycle costing and total cost of ownership (Al-Chalabi et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015a\u003c/span\u003e; Al-Chalabi \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These analyses aim to optimize costs; however, they overlook the real-time technical and physical assessment of the equipment. Guti\u0026eacute;rrez-Diez et al. (Guti\u0026eacute;rrez-Diez et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) developed the overall mining equipment effectiveness (OMEE) tool, which is a novel approach based on the traditional overall equipment effectiveness (OEE). Their goal was to evaluate the effectiveness of an open-pit drill rig by monitoring factors such as availability rate, utilization rate, and productivity. Later, Casta\u0026ntilde;\u0026oacute;n et al.(Casta\u0026ntilde;\u0026oacute;n et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) used the OMEE methodology to determine the replacement of a drill rig. Their study revealed that the OMEE methodology is a reliable tool for drill rig replacement decisions. To our knowledge, research to date has not yet determined the applicability of the OMEE approach to other mining equipment types such as hydraulic excavators.\u003c/p\u003e \u003cp\u003eThe main purpose of this study is to provide an excavator replacement decision support approach by re-adapting the OMEE approach with KPIs that are specific to the hydraulic excavators. Real-time performance indicators are monitored to improve the replacement model\u0026rsquo;s accuracy. The replacement time of an actual fleet of three excavators is analyzed over 24 months to determine if replacements are required.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cp\u003eOEE is an important tool for assessing the performance of mining equipment (Paraszczak \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Samatemba et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Toraman \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Guti\u0026eacute;rrez-Diez et al. (Guti\u0026eacute;rrez-Diez et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) developed a novel method known as the OMEE, based on the OEE approach, to study the effectiveness of drill rigs. They substituted OEE parameters such as, performance, availability, and quality with new ones (technical and mechanical availability, utilization rate, and performance index) that are more suitable for the mining environment. Our study employs a similar approach to monitor the performance of three surface mine excavators (A, B, and C) from a West African gold mine and identify when a replacement is required due to a decline in machine performance. The OMEE is a flexible tool designed to carry out activities such as monitoring the equipment performance, identifying weaknesses, determining how long the equipment can remain in operation, planning maintenance activities, and simulating mining activities (Casta\u0026ntilde;\u0026oacute;n et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This method brings a new approach to determining equipment replacement by considering four key variables: technical availability, mechanical availability rate, utilization rate, and productivity index, as indicated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMining equipment undergoes continuous wear over its lifetime. Thus, maintenance strategies contribute greatly to maintaining its structural integrity and reliability for enhanced mining operations. A key challenge is determining the optimal replacement time of production assets such as excavators. The OMEE can help tackle this issue by tracking the equipment\u0026rsquo;s performance and economic and technical aspects as a whole. It assesses these factors in real time and indicates when the replacement is required (Casta\u0026ntilde;\u0026oacute;n et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Case study\u003c/h2\u003e \u003cp\u003eThe bench height in the mine where the case study was carried out ranges from 04 to 16 meters. The gold extraction process consists of the drilling, blasting, loading, hauling, and processing steps (Bustillo Revuelta \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Bud et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). All excavators used at the mine are 140-tonne Caterpillar 6015Bs. The data used in this study were collected over 24 months. All three excavators (A, B, and C) have operated simultaneously in an open pit gold mine in West Africa, where the rock mass mainly consists of sandstones. They are powered by C27, 12-cylinder diesel engines whose output is approximately 746 hp. Their bucket size is approximately 8.1 m\u003csup\u003e3\u003c/sup\u003e. They possess advanced hydraulic systems such as automatic flow proportioning technology, which enables hydraulic energy loss reduction. The excavators\u0026rsquo; service meter units (SMU) at the end of the study are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExcavator\u0026rsquo;s SMU at the end of the study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEquipment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSMU (hours)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcavator A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39,547\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcavator B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33,615\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcavator C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46,717\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe study started by reviewing the time classification framework used by the mine and selecting KPIs that are suitable to assess the effectiveness of excavators, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Secondly, data collection was performed using the fleet management system database and the daily information provided by the operators and ore spotters. Then, the analyses of the OMEE components are performed on the fleet and individual machines to evaluate the productivity, technical state, and reliability of the machines. Last but not least, the conclusions of these analyses were used to identify the weak assets or components to determine their optimal replacement time.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Time classification framework at the collaborating mine\u003c/h2\u003e \u003cp\u003eIdentifying and reducing non-productive time is necessary to improve the equipment\u0026rsquo;s efficiency and performance(Assi and Haiawi \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The mine employs the standardized time usage model used in open pit mining (Lukacs \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In the study, this time usage model was refined (as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) to facilitate the calculation of relevant KPIs in the OMEE method. This configuration assumes that all operational time must be accounted for. The time categories show how time is used.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTime usage model components (Lukacs \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDefinition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFormula\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCalendar Time (CT)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe total time that is available in the Gregorian calendar.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eScheduled Time (ST)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe time during which the equipment is expected to operate or production is planned as per the business plan or forecast.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eST\u0026thinsp;=\u0026thinsp;CT - UT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUnscheduled Time (UT)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe time during which the equipment is not expected to operate or production is not planned as per the business plan or forecast.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDowntime (DT)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe equipment is required as per the business plan or forecast, but it is unable to fulfill its function.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDT\u0026thinsp;=\u0026thinsp;UD\u0026thinsp;+\u0026thinsp;SD\u0026thinsp;+\u0026thinsp;DS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUnscheduled Downtime (UD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe equipment is required as per the business plan or forecast, but it is unable to fulfill its function due to unplanned maintenance activities.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eScheduled Downtime (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe equipment is required as per the business plan or forecast, but it is unable to fulfill its function due to planned maintenance activities.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDaily inspection time (DS)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe equipment is required as per the business plan or forecast, but it is unable to fulfill its function due to daily maintenance inspections.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAvailable Time (AT)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe equipment is required as per the business plan or forecast, and it can fulfill its function.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAT\u0026thinsp;=\u0026thinsp;ST - DT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStandby (SB)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe equipment is required as per the business plan or forecast; it can fulfill its function, but it is not operating.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSB\u0026thinsp;=\u0026thinsp;SBE\u0026thinsp;+\u0026thinsp;SBO\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOperating Standby (SBO)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe equipment is required as per the business plan or forecast and can fulfill its function, but is not operating due to operational management-related factors.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExternal Standby (SBE)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe equipment is required as per the business plan or forecast; it can fulfill its function, but is not operating due to external factors unrelated to the operational management.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOperating Time (OT)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe equipment is available, and it is under a system or human control.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOT\u0026thinsp;=\u0026thinsp;AT - SB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOperating Delay (OD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe equipment is being operated but is not performing any work due to environmental delays.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWorking Time (WT)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe equipment is operating and performing work.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWT\u0026thinsp;=\u0026thinsp;OT - OD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon-Productive Time (NP)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe equipment operates and performs activities that do not directly correlate with production, but cannot be avoided to ensure operational efficiency and safety.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProductive Time (PT)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe equipment is operating and performing work that contributes directly to the production.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePT\u0026thinsp;=\u0026thinsp;WT -NP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Key performance indicators\u003c/h2\u003e \u003cp\u003eMining equipment tends to lose effectiveness and performance as it ages (Samatemba et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). KPIs are critical tools that are used to evaluate the historical and real-time performance of mining equipment operations and maintenance (Lin et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These KPIs are generally compared with the business plan targets to identify the areas of improvement (Casta\u0026ntilde;\u0026oacute;n et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This study's main KPI selection criteria are their applicability to the excavators, easy measurability, and monitorability. The selected indicators give insight into the physical condition of the machinery and technical factors that influence the asset replacement decision (as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Signs such as reduced output, repetitive malfunctions, and rising energy consumption rate indicate that the machinery is nearing the end of its operational life. This highlights the importance of KPI evaluation when making equipment replacement decisions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKPIs selected for excavators\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKPI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eTemporal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScheduled time (ST)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOperating time (OT)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnscheduled Downtime (UD)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScheduled Downtime (SD)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAvailable Time (AT)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDaily Inspection and Service Time (DS)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eProduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWet Tonnes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDry Tonnes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFuel\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 OMEE method\u003c/h2\u003e \u003cp\u003eThe methodology employed in this study shows how indicators such as productivity, availability, reliability, maintenance, and operational management are connected to mining equipment replacement. The OMEE stems from the traditional OEE method (Guti\u0026eacute;rrez-Diez et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It is a supportive technique that helps compare how well similar types of mining equipment perform under similar conditions by observing KPIs such as utilization rate, technical availability, mechanical availability, and productivity index, as shown in Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). It facilitates the identification of significant variations in the mining equipment\u0026rsquo;s overall performance over time.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:OMEE=UR\\times\\:At\\:\\times\\:Am\\:\\times\\:\\:Pi$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:UR\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:At\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Am\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Pi\\)\u003c/span\u003e\u003c/span\u003e represent the utilization rate, technical availability, mechanical availability, and productivity index, respectively.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 Utilization rate\u003c/h2\u003e \u003cp\u003eIt represents the proportion of time that the mining machinery is performing productive work. It can be defined as the ratio of the operating time to the available time, as shown in Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e):\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:UR=\\:\\frac{OT}{AT}\\:\\times\\:100$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2 Productivity index\u003c/h2\u003e \u003cp\u003eMining production is affected by several factors such as operational issues, human factors, environmental conditions, and geological conditions (Mishra et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Therefore, tracking how well individual excavators perform in real time compared to the fleet\u0026rsquo;s productivity over time is important. Lama et al. (Lama et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) used a mining production index method to improve the efficiency of mining equipment. The productivity index used in this study is calculated by using Eq.\u0026nbsp;(\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e):\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:Pi=\\:\\frac{{P}_{m}}{{P}_{f}}\\:\\times\\:100$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{P}_{m}\\)\u003c/span\u003e \u003c/span\u003e represents the individual productivity of the machine.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{P}_{f}\\)\u003c/span\u003e \u003c/span\u003e represents the productivity of the excavator.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.4.3 Availability rate\u003c/h2\u003e \u003cp\u003eThe availability rate is the proportion of time that an asset is ready to perform its intended function. To facilitate the analysis, it was divided into:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003ethe technical availability rate (Eq.\u0026nbsp;(\u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)): it reflects downtime due to scheduled activities such as planned maintenance and planned component replacement.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ethe mechanical availability rate (Eq.\u0026nbsp;(\u003cspan refid=\"Equ5\" class=\"InternalRef\"\u003e5\u003c/span\u003e)): it focuses on downtime caused by breakdowns and unplanned maintenance tasks (Guti\u0026eacute;rrez-Diez et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Casta\u0026ntilde;\u0026oacute;n et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section4\"\u003e \u003ch2\u003e2.4.3.1 Technical availability rate\u003c/h2\u003e \u003cp\u003e \u003cdiv id=\"Equ4\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:At=\\:\\frac{ST-SD}{ST}\\:\\times\\:100$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhere;\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:ST\\)\u003c/span\u003e \u003c/span\u003e represents the Scheduled time.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:SD\\)\u003c/span\u003e \u003c/span\u003e is the scheduled downtime.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section4\"\u003e \u003ch2\u003e2.4.3.2 Mechanical availability rate\u003c/h2\u003e \u003cp\u003eThe mechanical availability rate is calculated using the equation below:\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\:Am=\\:\\frac{ST-UD}{ST}\\:\\times\\:100$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:UD\\)\u003c/span\u003e\u003c/span\u003e is the unscheduled downtime.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Moisture content\u003c/h2\u003e \u003cp\u003eThe moisture content significantly affects the material handling and thus the excavator\u0026rsquo;s performance (Pekol \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The equivalent dry tons data were calculated using the measured moisture factors (Eq.\u0026nbsp;(\u003cspan refid=\"Equ6\" class=\"InternalRef\"\u003e6\u003c/span\u003e)).\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$$\\:dry\\:tons=wet\\:tons\\times\\:(1-moisture\\:factor)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe economic replacement time analysis of the studied excavators focused on four key variables of the OMEE method: the utilization rate, the productivity index, and the technical and mechanical availability rates.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Database reports\u003c/h2\u003e \u003cp\u003eThe individual excavator data was collected over 24 months. Relevant information was collected and organized to ease access to data and enable various analyses. Data collected on the mine's tons of material moved (TMM) are presented in Appendix 1 per month and in Appendix 2 per equipment. The fleet\u0026rsquo;s temporal KPIs are presented in Appendix 3, and Appendix 4 provides the same data for individual machines. Appendices 5 and 6 contain data on the TMM per hour and the fuel burn rate for the fleet and individual machines, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Utilization rate\u003c/h2\u003e \u003cp\u003eMining operations experience events and conditions that may lower the operating time of excavators (Nasonov and Lykov \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ivanov et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, it is important to analyze the downtime and the reasons behind it. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrate the utilization rate of the fleet and individual excavators. It should be noted that Excavators A and B did not operate from months 12 to 14 and from months 12 to 13, respectively, due to equipment technical condition and management-unrelated external events. Excavator C operated during all months of the study and only experienced a drop in the utilization rate to 38% in month 8 due to equipment management-related decisions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUtilization rate of the fleet and individual excavators\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFleet\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExcavator A\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExcavator B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExcavator C\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Productivity index\u003c/h2\u003e \u003cp\u003eThe production rate of mining equipment such as excavators is affected by several factors, such as the operator's skills, environmental and technical conditions of the equipment (Nasonov and Lykov \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ivanov et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The productivity index helps identify significant changes in the production rate of a specific rig by comparing it with the fleet\u0026rsquo;s productivity rate over a certain period. The three excavators required 29,706.57 operating hours to generate a TMM of 19,140,491 tons over the 24 months. Their average productivity in this period was 644.32 tons per hour.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExcavator fleet\u0026rsquo;s production rate and productivity index\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProduction rate (tons/h)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProductivity index\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eData on the production rate and productivity index of the digging fleet are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Individual machines\u0026rsquo; productivity indexes are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Productivity and productivity index could not be calculated in months with no operating hours for two excavators (A and B).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Availability rate\u003c/h2\u003e \u003cp\u003eOptimal maintenance strategies are critical for efficient mining asset management (Shahin et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Pourjavad et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). To ensure the reliable, efficient, and safe operation of the equipment, it is essential to implement adequate maintenance strategies. The study separates the availability into two parts: technical and mechanical availability rates (Guti\u0026eacute;rrez-Diez et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The technical availability considers scheduled downtime. Mechanical availability addresses unscheduled downtime and helps identify the effectiveness of the maintenance practices. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e illustrate the technical and mechanical availability rates of the individual excavators as well as the fleet.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTechnical and mechanical availability rates\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMonth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eFleet\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eExcavator A\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eExcavator B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eExcavator C\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAt\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAt\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAt\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAt\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAm\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e62%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e98%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e93%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e77%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e93%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e97%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e92%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96%\u003c/p\u003e \u003c/td\u003e 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align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e97%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e96%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e85%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e84%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e87%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e97%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e49%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e81%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e93%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e79%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e93%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e93%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e90%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e97%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e33%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e87%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e92%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e88%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e89%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e85%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e92%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e97%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e78%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.5 OMEE index\u003c/h2\u003e \u003cp\u003eThe OMEE index correlates all aforementioned factors to provide a holistic model that provides insight for rational decision making. Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e represents the OMEE index of the fleet and individual machines.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eLike the existing body of literature (Zhukovskiy and Koteleva \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Guti\u0026eacute;rrez-Diez et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Casta\u0026ntilde;\u0026oacute;n et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) our study has shown that flexibility and adaptability are fundamental requirements in the mining environment. The OMEE approach helps assess the assets\u0026rsquo; performance and identify weaknesses in the process(Gibbons and Burgess \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Unlike authors who based their replacement time model on mathematical predictions (Al-Chalabi et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015a\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003eb\u003c/span\u003e; Reina et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Sahu et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Enyindah and Amadi \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Al-Chalabi \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), we employed a tool based on the analysis of historical and real-time data recorded from the equipment and operations. KPIs that are suitable for excavator performance analysis were selected (Holt and Edwards \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The developed model can assist decision makers with a rational approach to determine the equipment\u0026rsquo;s optimal replacement time by monitoring its past and current performances, as indicated by the findings of some studies (Shahin et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Casta\u0026ntilde;\u0026oacute;n et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs suggested by the existing literature (Samatemba et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Guti\u0026eacute;rrez-Diez et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Casta\u0026ntilde;\u0026oacute;n et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) the study reveals the interconnection of maintenance and production indicators, such as productivity, utilization rate, and availability rate, with the equipment replacement time. We found that these indicators are key variables to consider for accurate equipment replacement time determination, as stated by Casta\u0026ntilde;\u0026oacute;n et al. \u0026rsquo;s (Casta\u0026ntilde;\u0026oacute;n et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Their evolution provides insights into the technical and physical aspects of the machine (Guti\u0026eacute;rrez-Diez et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Casta\u0026ntilde;\u0026oacute;n et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A correlated decrease in these KPIs may imply the proximity of the replacement time. To our knowledge, this is one of the first attempts to develop a tool that helps determine the economic replacement time of open pit excavators by analysing the influence of equipment condition, maintenance, and management strategies.\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Productivity Evolution\u003c/h2\u003e \u003cp\u003eData representativeness and validity are confirmed by the excavator fleet\u0026rsquo;s TMM and temporal KPIs (19,140,493 dry tons) collected over a scheduled time of 52,560 as illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e present the monthly fleet collective data, whereas Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e provide information related to individual machines. Enyindah and Amadi (Enyindah and Amadi \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) stated that the optimal replacement time of mining and construction hydraulic excavators is 8 years. The average excavator operating time at the mine is 4,951 hours per year. Enyindah and Amadi\u0026rsquo;s 8-year optimal replacement time (Enyindah and Amadi \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) can be converted into 39,609 hours. Excavator C\u0026rsquo;s SMU (67,171 hours) was above 39,609 hours, Excavator A\u0026rsquo;s (39,547 hours) was about to reach 39,609 hours, while Excavator B was approximately 6000 hours below the benchmark at the end of the data collection period. Figures\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e do not reveal any sign of productivity decline when compared to the early months of data collection, when none of the excavators\u0026rsquo; SMU had reached 39,609 hours.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Utilization Trends\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrate the excavator utilization trend. Excavators A and B experienced a sharp drop in utilization to 0% (from months 11 to 14 for Excavator A, and from months 11 to 13 for Excavator B). These drops were due to external factors that are not related to the equipment performance or management. The fleet\u0026rsquo;s utilization is greatly affected by these excavators as it mirrors their trend. In contrast, Excavator C exhibits a good utilization rate, fluctuating between 92 and 72% except in month 8, where the utilization rate was 38%. Although the current trend does not suggest any equipment replacement requirement, future extended and repetitive drops in the utilization rate due to equipment performance-related factors could trigger a replacement recommendation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Availability Rate Analysis\u003c/h2\u003e \u003cp\u003eAl-Chalabi (Al-Chalabi \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Reina et al. (Reina et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) revealed that mining equipment experiences progressive deterioration due to the challenging operating conditions. The appropriate maintenance strategies have a positive influence on the equipment availability, helping to achieve better performance (Rasmekomen and Parlikad \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Olde Keizer et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Alaswad and Xiang \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; G\u0026ouml;lbaşı and Demirel \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Balaraju et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; de Jonge and Scarf \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The fleet and individual machine technical and mechanical availability rates are presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The trend analysis reveals Am is regularly below At, unlike Casta\u0026ntilde;\u0026oacute;n et al.\u0026rsquo;s (Casta\u0026ntilde;\u0026oacute;n et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) work, where Am outperforms At. This suggests that most of the downtime incurred by the machine is from breakdowns. The increasing gap between At and Am over time indicates the inefficiency of preventive maintenance programs, as less time is spent on preventive maintenance activities, leading to growing corrective maintenance downtime. The fuel burn rate (shown in Appendix 5), which can be a key engine and equipment deterioration indicator, did not show any notable increase. It can be concluded that the progressive drop in the availability rates is mainly driven by an inadequate maintenance strategy rather than equipment age. The recommendation is to review the mine\u0026rsquo;s current maintenance strategy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.4 OMEE Index Interpretation\u003c/h2\u003e \u003cp\u003eThe OMEE index (as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) fluctuates between 0.88 and 0.25. The drops in the index in month 8 and between months 12 and 14 are driven by the low excavator utilization in this period. The remaining drops stem from the combined effects of availability rates and productivity. Despite the sharp drop from months 12 to 14, the OMEE has recovered and stabilized. The post-recovery OMEE values (0.60\u0026ndash;0.62) are higher than those at the beginning of the study (0.25\u0026ndash;0.55) when the machine SMUs were still low and no replacement was recommended by traditional approaches. Comparing the fleet\u0026rsquo;s OMEE index with those of individual machines revealed that fleet indicators are strongly affected by underperforming units. The sharp drops in the fleet OMEE index (months 12 to 14) are driven by the non-utilization of excavator A and C.\u003c/p\u003e \u003cp\u003eThe trend analysis does not reveal any decline in the OMEE when compared to the early values. It can be concluded that no equipment replacement recommendation for the excavators can be made at this stage. Future declines in the OMEE index, observed during future trend analyses, could trigger equipment replacement recommendations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Evaluation and Replacement Time Determination Criteria\u003c/h2\u003e \u003cp\u003eA decision matrix was designed to support the replacement decision-making using the 4 components of OMEE? As shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Each indicator has a threshold and relevant interpretations in the matrix. Recommendations, such as maintenance strategy review and equipment replacement, are triggered if thresholds are consistently crossed (more than 3 consecutive months). For instance, a utilization rate below the threshold of 40% caused by equipment condition triggers a replacement recommendation. However, no replacement is recommended if the low utilization is driven by external or management-related factors. Defective ground-engaging tools or operator inefficiencies, or operating conditions could cause the productivity index to get below 80%. Technical and mechanical availabilities help determine whether downtime is related to weak preventive maintenance policies or aging components. The OMEE index combines all factors into one and reflects the overall machine effectiveness. A value below 0.5, when not due to external causes, triggers a replacement recommendation.\u003c/p\u003e \u003cp\u003eThe proposed OMEE method is a dynamic model that allows assessing equipment both collectively and individually to identify areas of improvement and make rational data-driven equipment replacement decisions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEquipment Replacement Decision Matrix\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOMEE Component\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThreshold\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInterpretation (if the threshold is exceeded for more than 3 consecutive months)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRecommended Action\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eUtilization rate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;40% (external reasons)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow usage not related to machine condition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo action\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;40% (management-related decisions)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow usage due to operational choices\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReview production requirements\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;40% (machine performance)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow usage due to poor performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConsider for replacement\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProductivity index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePoor performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInvestigate bucket or cutting tools, operator skills, operating conditions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTechnical availability\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;85%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeak preventive maintenance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReview and improve maintenance planning\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eMechanical availability\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;80% with Technical availability\u0026thinsp;\u0026ge;\u0026thinsp;85% and stable fuel burn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBreakdowns due to poor preventive maintenance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReview maintenance strategy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;80% with Technical availability rate\u0026thinsp;\u0026ge;\u0026thinsp;85% and increasing fuel burn (more than 5 liter / hour)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBreakdowns due to aging components\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConsider for replacement\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOMEE index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.5 and utilization rate not \u0026lt;\u0026thinsp;40% due to management or external factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eunderperformance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConsider for replacement\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOn one hand, Excavator A and B\u0026rsquo;s utilization rates dropped to 0% due to external factors (non-technical and non-management-related) for 3 and 2 months, respectively. Their productivity index remained stable and acceptable during operating periods. Their technical availability rate was generally high, but mechanical availability was often below 80% and combined with technical availability\u0026thinsp;\u0026lt;\u0026thinsp;85%. The fuel consumption remained consistent, indicating poor maintenance practices, rather than component aging. So, no replacement recommendation was triggered for these machines.\u003c/p\u003e \u003cp\u003eOn the other hand, Excavator C operated continuously over the 24 months of the study. Its utilization rate dropped only once below 40% (month 8) due to a management decision, not poor machine performance. Its productivity index was slightly better than the fleet average. The machine's high technical availability rates (\u0026ge;\u0026thinsp;85%) and declining mechanical availability triggered maintenance practices review. Despite high operating hours (46,717) well above the benchmarks, Excavator C\u0026rsquo;s performance remains strong and no replacement is required for this machine.\u003c/p\u003e \u003cp\u003eThe analysis of the OMEE index and its parameters shows that excavators with high operating hours (2 excavators\u0026rsquo; SMUs were above 39,609 hours or 8 years of operation at the collaborating mine) can still exhibit strong performance if adequate preventive maintenance policies are adopted. This result goes in the same direction as Casta\u0026ntilde;\u0026oacute;n et al.\u0026rsquo;s (Casta\u0026ntilde;\u0026oacute;n et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) findings. It was recommended that the mine review its current maintenance strategy to migrate from a reactive repair strategy to a reliability-centered one (Prasetyo and Mercado Rosita \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Geisbush and Ariaratnam \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo increase the model's reliability, new studies with a larger sample size (more than three excavators) and a longer data collection time could be carried out. The model could be applied to other key mining equipment types, such as trucks, loaders, graders, and dozers. A hybrid model could also be designed to provide both the technical insights (offered by the proposed OMEE model) and the economic assessment obtained from a mathematical prediction model such as the LCCA. The proposed model is one of the first practical real-time performance monitoring tools that support excavator replacement decision-making.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present work aimed to propose a performance analysis-based model to determine if the optimal replacement time of excavators has been achieved. A 24-month database collected between the middle and late life stage) was used to select KPIs that are suitable for open pit excavators. The study has revealed that some excavators with operating hours above the recommended replacement time, provided by mathematical prediction models (Enyindah and Amadi \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), did not exhibit any decline in their overall performance and did not require any replacement. The study has also identified that the mine\u0026rsquo;s current strategy is inadequate for optimal equipment performance. The results of this study indicate that the OMEE is a strong and reliable tool that can help make excavator and mining equipment replacement decisions by tracking their historical and real-time performance. A key strength of the model developed in the study is its ability to pinpoint significant performance deviations, which allows for taking corrective actions. Another strength of the study is the holistic equipment performance assessment capability that it provides, which has proven to be a decisive factor to be taken into account to achieve production targets. It is a novel performance parameter monitoring-based model that supports excavator and key mining equipment replacement decisions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eThe collaborating company has requested anonymity. The authors gratefully acknowledge the valuable input and infrastructural support provided.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDeclaration of competing interest\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe corresponding author states that there is no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received to assist with the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData availability\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe data are presented within the article\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthor contributions\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e:\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA single author wrote this manuscript who performed the study conception and design, data collection, and analysis.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlarc\u0026oacute;n, L. 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Social Sci.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e, 605 (2015).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Overall Equipment Effectiveness, Mining, Availability, Productivity, Utilization, Key Performance Indicators","lastPublishedDoi":"10.21203/rs.3.rs-9169266/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9169266/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOpen-pit excavators are subject to degradation due to harsh working conditions. Determining their optimal replacement time remains a challenge for decision-makers. The majority of current models utilize mathematical predictions to minimize the total cost of ownership. A decision-making model based on the analyses of both historical and real-time performance and technical condition is required for timely and efficient replacements. This study employs the overall mining equipment effectiveness method, which monitors technical indicators such as productivity, utilization rate, mechanical availability rate, and technical availability rate. Operational data collected over 24 months from three open-pit excavators (with operating hours over 33,000 hours) were used to perform the case study. The findings showed that the overall mining equipment effectiveness approach is an effective tool that offers valuable information for well-informed equipment replacement decisions. Based on the results, there was no need for an immediate replacement in this case study. However, a review of the current maintenance practices was recommended to the mine.\u003c/p\u003e","manuscriptTitle":"Performance-Based Replacement Time Analysis of Open Pit Excavators: A West African Case Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-26 17:18:29","doi":"10.21203/rs.3.rs-9169266/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ad3ac593-b93d-4941-977c-662374dce005","owner":[],"postedDate":"March 26th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":65130222,"name":"Physical sciences/Engineering"},{"id":65130223,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-04-02T05:40:48+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-26 17:18:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9169266","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9169266","identity":"rs-9169266","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}