New Method to Study the Effectiveness of Mining Equipment: A Case Study of Surface Drilling Rigs

Abstract

The sustainable development of mining processes requires a deep knowledge of the effectiveness of mining equipment and is quite complex to analyze due to the intrinsic characteristics of the mining industry. In this regard, its measurement and control can lead to appropriate management, improving the mining processes’ efficiency, increasing safety levels, and reducing environmental impacts. This paper developed a new methodology to study the effectiveness of equipment in mining processes, researching the impacts of process management, process control, operating conditions, operational mining schedule, and maintenance programs on the effectiveness of drilling rig fleets using two actual coal mines located in the northwest of Spain and data collected over 10 years. Thus, a new method, called Overall Mining Equipment Effectiveness (OMEE), was developed, verifying its appropriateness and flexibility to analyze the effectiveness of mining equipment by examining the availability rate, utilization rate, and productivity index.

1. Introduction

The mining industry is an important sector for providing raw materials to the economy, and it is also crucial for the development of many countries. Thus, great efforts are made to analyze and optimize mining operations from different angles [1]. In this regard, the future of mining depends on developing sustainable models in which availability, reliability, safety, sustainability, quality, and production work together in a holistic approach [2]. Equipment selection and optimization are two of the most important challenges in mining processes, requiring an adequate assessment of the relevant inputs during the life cycle of the mining equipment to improve its effectiveness [3]. Siregar et al. [4] proposed an interesting study that simplified the analysis of the effectiveness of the mining process by considering availability, performance, and quality levels.

The important challenges facing the mining sector require that mining operations become more dynamic and agile [5]. Thus, the analysis of real-time performance can be useful for understanding the process behavior. Bhadani et al. [6] pointed out that appropriate key performance indicators (KPIs) could be a perfect way to analyze mining processes. Due to the complexity of mining processes, it is difficult to tackle the problem of KPI selection and find the appropriate ones to study a process [7]. The KPIs defined should focus on identifying mining activities to assess equipment performance and subsequently improve mining operations [8]. Each piece of mining equipment has specified diagnostic parameters, and it is vital to take into consideration these operational parameters to analyze performance properly [9,10]. In this regard, the constant development of digitalization technologies allows the capture of accurate KPIs, allowing the achievement of sustainable management of mining processes [11]. In this way, cross-checking against entire fleets of machines, which could improve the efficiency of mining machines, can be developed [12]. These studies could be useful for mining companies to optimize the equipment fleet and their optimal location [13].

Currently, the development of sustainable business models plays a crucial role in society. In this context, the challenges that the mining industry has to face in the near future are linked to the sustainable development goals (SDGs) defined by the United Nations [14,15], with the majority of the mining companies aligned with them [16]. Close cooperation between mining companies and governments is required to achieve the 17 SDGs [17,18,19], with new policies emerging in this direction [20]. The achievement of the goals from a holistic perspective in a socially responsible, economically profitable, and environmentally clean manner can provide enormous opportunities for mining regions [21].

Thus, new methods are needed for the assessment and optimal selection of equipment [22], considering the specificities of mining processes [10]. Some authors have evaluated mining equipment performance using methods such as life cycle cost (LCC) for analyzing the replacement time [23] or defining maintenance decisions [24]. However, there is an important lack of research in the evaluation of machines’ effectiveness in surface and underground mining [25]. The methodology used to evaluate the effectiveness should allow the analysis of sensitive inputs and the development of statistics to examine the machine’s performance under similar conditions [26]. Thus, the overall equipment effectiveness (OEE) of the mining process could be an important tool for the analysis [27,28]. The OEE is an expression used to evaluate effectiveness by combining availability, performance, and quality [29]. This methodology considers several inputs that allow the assessment of performance and productivity [30], identifying weak areas in the production process and displaying certain machines that may not be operating at an optimum efficiency level [4].

Mining equipment degrades throughout its operating life. A vital question that arises in mining companies is when to replace certain equipment [31]. This decision is not easy to make, and managers should study the appropriate information about the equipment’s performance to make replacement decisions. Most replacement models study the life cycle cost (LCC) [32], balancing the acquisition cost of new equipment against operation costs and maintenance costs [23] to try to decide on the optimal replacement time (ORT) [24,33,34]. Hence, mining companies need to define models to find a relationship between productivity, effectiveness, maintenance, and the replacement of equipment [1]. The decision to replace equipment should combine equipment effectiveness, technological evolution, maintenance optimization, and spare parts inventory [35].

The increasing usage of statistics concepts in databases captured from equipment input features could allow the analysis of the effects on the mining equipment of the maintenance strategy through a more objective approach [36]. Analyzing maintenance costs through the life cycle of the equipment could avoid inefficient processes [37] and subsequently optimize the mining operations. Maintenance is crucial to ensuring the efficient use of mining equipment and is an important tool to overcome failure and prevent future problems [35]. Appropriate maintenance management informs users about what is expected to happen regarding the breakdown of the main components throughout an equipment fleet [38]. A preventive and predictive culture could anticipate failures while improving availability and reliability [39], minimizing downtimes and associated costs [40].

The maintenance process plays a key role in the sustainability of the operations [41], able to reach 40–50% of the total operating cost, which shows the importance of monitoring the operability and maintainability of the equipment fleet [42]. An accurate maintenance policy not only has a direct impact on productivity but also has a great impact on SDGs [43], reliability, and safety levels [44]. While some studies [45] have analyzed how to reduce downtime in mining equipment using an adequate statistical model, there is still a lack of studies evaluating the effectiveness of machines in the mining industry, requiring easy-to-use models to apply in real cases [46]. This study aimed to develop such a model, called Overall Mining Equipment Effectiveness (OMEE), which brings together several complex factors, such as availability, utilization, and productivity, that are difficult to monitor to analyze the real-time performance of the mining equipment. OMEE allows the analysis of the impacts of the maintenance program, management strategies, operating conditions, process control, and operational mining schedule on the effectiveness of mining equipment fleets.

2. Methods

Mining processes comprise a vast array of complex factors to evaluate, which include management strategies, equipment characteristics, maintenance programs, mine design, mine planning, operating conditions and operator’s skills, among other elements. The dynamic model developed in this study aims to accomplish the following main objectives: (i) equipment state monitoring; (ii) weak area detection; (iii) maintenance plan development; (iv) residual mining equipment’s life estimation; and (v) future mining operations simulation. The model, named OMEE, has been built to be flexible and adaptative according to mining operations. Thus, it introduces a novel methodology to evaluate the effectiveness of mining equipment through the analysis of the four factors shown in Figure 1.

The OMEE method has been applied to analyze the surface drill rig fleet performance in two case studies located in the northwest of Spain for 10 years. Specifically, these studies pertain to open-pit coal mines characterized by benches ranging from 8 to 18 m high, depending on slope stability and economic factors. The extraction process consists of removing an overburden located in the upper part and then the coal seams. The process consists of the following phases: drilling, blasting; loading, and hauling.

The equipment is composed of Tamrock Pantera 1100 (Sandvick AB, Sandviken, Sweden) for the drilling phase, Hitachi EX5500 excavators (Hitachi Construction Machinery Co., Ltd., Hitachinaka-shi, Japan) for loading and Caterpillar 789C trucks (Caterpillar Inc., Irving, TX, USA) for hauling. Additional pieces of equipment, such as Caterpillar D10 (Caterpillar Inc., Irving, TX, USA) and Komatsu D 475 (Komatsu Ltd., Tokyo, Japan) dozers, are used in auxiliary processes of the mine. Figure 2 gives an overview of one of the case studies analyzed.

Initially, adequate KPIs are defined to evaluate the specific parameters of the surface drill rig in the process, followed by the development of a specific maintenance program for the drilling rig equipment. Subsequently, data collection from the mining equipment has been conducted daily for the period of ten years using onboard instruments and the mining operator’s input. The database has been designed to obtain the relevant information about mining equipment performance, maintenance programs and spare parts inventory, following a systematic four-step process: (1) definition of the monitoring parameters, (2) data storage, (3) data interpretation, and (4) data analytics, cross-checking analysis, knowledge extraction and decision-making. Data collection has been combined using a data mining approach, which provides data and statistics to analyze the real-time performance behavior of the drilling rigs. Moreover, daily worksheets have been devised to collect pertinent information about mining equipment performance, maintenance activities and spare parts inventory. Including drilling parameters, machine patterns, spatial patterns and any other specific factors. Finally, the OMEE method is employed to cross-check against the entire drilling rig fleet, assessing the influence of the maintenance program, management strategy and operating conditions on the effectiveness of the drilling rig equipment, as well as identify potential weak areas in the process.

2.1. Equipment Characteristics

A total of fifteen surface drill rigs, all of which are of the same brand and model, Tamrock Pantera 1100, have been studied. Each rig is equipped with a HL-1000 hydraulic top hammer rock drill, with the power pack consisting of a Caterpillar diesel engine and a gearbox that divides the power for hydraulic pumps and a flushing air compressor. This drill rig has been designed for drilling in large open-pit mining (Figure 2).

2.2. Key Performance Indicators

A mining operation comprises different mining processes. Each of these processes can be developed with multiple objectives, which can be analyzed through appropriate performance indicators. KPIs can be used to measure real-time behaviors and play a crucial role in providing quick and precise information linked to each process condition, despite the fact that it can be difficult to define them properly, as previously mentioned.

In this regard, the KPIs of the study have been selected considering that they must be based on their quantifiability and relevance in reflecting the mining process, as well as affordable to monitor routinely.

Table 1. KPIs selected for the surface drilling rig.

Measurements Basis	KPI
Temporal	Total time
	Operating time
	Breakdown time
	Preventive maintenance time
	Predictive maintenance time
	Reconditioning maintenance time
	Relocation time
	Idle time
	Other time
	Engine time
	Hydraulic hammer time
Production	Drilling meter
	Blasting volume
Consumption	Fuel

details the KPIs adapted to surface drilling rigs, covering the measurement bases of temporal, production and consumption.

2.3. Maintenance Program

Equipment availability and reliability are crucial to develop a sustainable mining operation and, therefore, it is necessary to monitor the equipment performance. Mining machines are rarely utilized to full capacity and the effectiveness depends on elements such as the operator’s skills, operating conditions, management strategy, management experience, maintenance policy and others. Given heavy working conditions, mining equipment suffers constant degradation. As a result, mining companies have recognized that an appropriate maintenance policy can improve efficiency and reliability and avoid unexpected issues [47]. In this study, an appropriate maintenance strategy has been developed to minimize the degradation of the surface drilling rig, avoid unexpected equipment breakdowns, keep machines in good condition and achieve accurate production. Moreover, a planning maintenance program has been established to minimize losing periods, while a spare parts inventory has been developed by finding a relationship with real-time performance (

Table 2. Maintenance strategy defined for the surface drilling rig.

Maintenance Type	Activity
Preventive maintenance	Oil changes and analyses
	Filters changes
	Lubrication
	Periodical visual overhauls
	Periodical mechanical overhauls
	Periodical cleaning program
	Daily greasing program
	Daily overhauls
Predictive maintenance	Replace components
	Schedule maintenance management
	Reliability data mining
Corrective maintenance	Replace spare parts
	Repair breakdowns
	Solve failure issues
	Spare parts inventory
Reconditioning maintenance	Keep mining equipment in good conditions
	Repair components
	Spare parts inventory
	Reliability
	Safety
	Periodical reconditioning program
Engineering maintenance	Appropriate equipment operating conditions
	Reiterative failures
	Technical modifications
	Cover current legislation

).

2.4. OMEE Method

The newly proposed method seeks to optimize the mining equipment usage by assessing availability, utilization and productivity based on evaluating the most sensitive inputs in real time. This method provides comprehensive insights into the whole life cycle of the mining equipment and, therefore, allows us to know specific areas where efforts need to be focused to improve the mining equipment’s effectiveness.

The OMEE method is founded on the OEE method, considering the specificities of the mining processes. For this reason, previous factors such as availability, performance and quality used in the OEE method have been substituted for other factors more specific and adequate for analyzing mining processes: mechanical availability rate, technical availability rate, utilization rate, and productivity index (Equation (1)). The OMEE method facilitates the comparison of the effectiveness of a relatively homogeneous mining fleet, performing under similar conditions. It can determine how the performance of mining equipment changes over time and it could be an important tool to define when to replace mining equipment.

OMEE = At × Am × U × P

(1)

where At is the technical availability rate, Am is the mechanical availability rate, U represents the utilization rate, and P is the productivity index.

2.4.1. Availability Rate

Availability is defined as the ratio of the total performance hours during which mining equipment appropriately performs its functions. This study divides availability into two parts: (i) technical availability rate, Equations (2)–(4), and (ii) mechanical availability rate, Equation (5). This allows us to analyze the breakdown time to determine if the maintenance policy works accurately. Equation (2) defines the technical availability rate

$$\mathrm{A}\mathrm{t}=\frac{\mathrm{H}\mathrm{t}-\mathrm{H}\mathrm{m}}{\mathrm{H}\mathrm{t}}\times 100$$

(2)

where Ht is total performance hours, and Hm means maintenance hours.

The total performance hours are the scheduled period of time when the drilling rig performed its functions in the open-pit mines, and it is defined as follows.

Ht = Ns × Hs

(3)

where Ns is the number of shifts, and Hs, means hours per shift.

Maintenance hours are the period of time when the maintenance program is applied in the drilling rig.

Hm = Hpv + Hpd + Hrc

(4)

where Hpv is preventive maintenance hours, Hpd means predictive maintenance hours, and Hrc represents reconditioning maintenance hours. Equation (5) displays the mechanical availability rate.

$$\mathrm{A}\mathrm{m}=\frac{\mathrm{H}\mathrm{t}-\mathrm{H}\mathrm{b}}{\mathrm{H}\mathrm{t}}\times 100$$

(5)

where Hb represents breakdown hours.

2.4.2. Utilization Rate

The utilization rate, Equation (6), can be defined as the ratio of the available time when the drilling rig is used to perform its functions during the available time.

$$U=\frac{Ha-Hl}{Ha}\times 100$$

(6)

where Ha is available hours, and Hl represents losses hours.

Equation (7) shows how the available hours are the period of time when the mining equipment can be used to perform its specific functions.

$$\mathrm{H}\mathrm{a}=\mathrm{H}\mathrm{t}-\mathrm{H}\mathrm{m}-\mathrm{H}\mathrm{b}$$

(7)

Equation (8) defines the loss of time, which is this period of time when the mining equipment can be used to perform its specific functions but, for some reason, it is not making these functions.

$$Hl=Hmv+Hi+Hot$$

(8)

where Hmv is moving hours from one workplace to another inside the open-pit mine, Hi represents idle hours, and Hot means other hours.

Other hours, denoted as Hot, represent the duration when the mining equipment is not unable to perform its functions due to: (i) bad weather conditions, (ii) strikes, (iii) absenteeism, and (iv) transport of the mining equipment from an open-pit mine to another open-pit mine. Conversely, idle hours, labeled as Hi, represent the period of time when the mining equipment is not performing its specific functions due to inappropriate short-term planning.

2.4.3. Productivity Index

Mining processes must be managed optimally to achieve maximum mining equipment productivity. Several factors significantly influence production efficiency, such as the operator´s skill or attitude, but mining equipment can still underperform due to mechanical and technical issues or inappropriate operating management. In mining processes, the operating conditions are vital to achieve production targets and, for this reason, it is important to estimate how the drilling rig is performing in real time. In this regard, OMEE introduces the productivity index to determine drilling rig production (Equation (9)).

$$\mathrm{P}=\frac{\mathrm{P}\mathrm{e}}{\mathrm{P}\mathrm{f}}\times 100$$

(9)

where Pe is the production of a drilling rig, and Pf represents the production of a drilling rig fleet.

The production of individual drilling rigs and the total fleet is defined by the number of meters drilled and the number of operating hours, m/Ho, defining these last factors using Equation (10).

$$Ho=Ht-Hm-Hb-Hl$$

(10)

where Ho represents operating hours.

3. Results

The performance behavior of the drilling rig has been assessed using specific parameters. Their effectiveness has been studied using the OMEE method through the analysis of the four factors detailed in Section 2. Furthermore, other important data have been determined to understand perfectly the mining process such as maintenance process data, warehouse control data and economic control.

It is important to note that it has been possible to evaluate the performance of open-pit mine 1 spans a period of ten years. On the other hand, open-pit mine 2 started its mining operation in year 4 of this research, lacking information from years 1, 2, and 3, as can be seen in some of the following tables and figures. Henceforth, these are called Mine 1 and Mine 2.

3.1. Database Reports

The database has been designed to gather relevant information at different levels, based on the type of analysis required. This setup enables retrieving the data about every machine, machine brand or machine model, as well as for each operating workplace and mine. The material blasted from the two open-pit coal mines is presented in

Table 3. Volume blasted in the two case studies.

Year	Mine 1 (m3)	Mine 2 (m3)
1	8,726,000
2	9,570,000
3	9,588,000
4	9,895,000	2,283,000
5	10,388,000	3,035,000
6	8,245,000	4,314,000
7	6,372,000	4,379,000
8	8,589,000	6,269,000
9	10,334,000	6,663,000
10	13,307,000	6,824,000

. The performance in Mine 1 has been collected in

Table 4. Mine 1. Drilling rig performance, temporal KPIs (h).

Year	Ht	Ho	Hb	Hm	Hi	Hmv	Hot
1	14,661.0	11,444.5	570.5	1197.5	799.0	649.5	0.0
2	15,644.0	12,689.0	639.5	1514.0	465.5	319.0	17.0
3	19,121.0	14,411.0	475.5	3086.0	125.0	950.0	73.5
4	20,771.0	14,743.5	884.5	3231.0	427.0	1362.0	123.0
5	18,679.5	13,760.5	781.5	2669.5	892.5	575.5	0.0
6	18,573.0	13,346.5	745.0	2940.5	1042.5	498.5	0.0
7	18,722.0	9993.5	765.5	2764.5	4741.0	457.5	0.0
8	18,558.5	13,109.0	1044.5	2707.0	1189.0	503.0	6.0
9	22,457.0	16,083.0	1111.0	3541.5	1044.5	673.0	4.0
10	26,597.5	19,338.0	1252.0	4340.5	790.5	866.5	10.0

, temporal performance, and

Table 5. Mine 1. Drilling rig performance.

Year	Production (m)	Fuel (L)	m/Ho	L/m
1	437,552.0	351,323.0	38.23	0.80
2	487,677.0	402,765.0	38.43	0.83
3	561,192.0	419,260.0	38.94	0.75
4	531,515.0	470,832.0	36.05	0.89
5	512,875.0	473,423.0	37.27	0.92
6	510,783.0	446,725.0	38.27	0.87
7	364,763.0	372,264.0	36.50	1.02
8	493,998.0	496,871.0	37.68	1.01
9	587,560.0	559,002.0	36.53	0.95
10	707,969.0	723,389.0	36.61	1.02

, drilling meters and fuel consumption. On the other hand,

Table 6. Mine 2. Drilling rig performance, temporal KPIs (h).

Year	Ht	Ho	Hb	Hm	Hi	Hmv	Hot
4	5470.0	3367.0	260.5	871.0	507.5	459.0	5.0
5	8125.0	4555.0	434.0	1367.0	1330.0	444.0	0.0
6	12,479.0	6531.0	404.5	2089.0	2992.5	462.0	0.0
7	13,395.0	6870.0	407.0	2019.5	3771.5	327.0	0.0
8	20,367.0	9923.0	789.0	2003.0	7095.0	557.0	0.0
9	19,071.0	9927.5	196.0	1805.0	6280.5	552.0	310.0
10	20,259.0	9737.0	96.0	1803.0	7772.0	676.0	175.0

and

Table 7. Mine 2. Drilling rig performance.

Year	Production (m)	Fuel (L)	m/Ho	L/m
4	114,309.0	133,550.0	33.95	1.17
5	154,811.0	187,961.0	33.99	1.21
6	220,922.0	241,913.0	33.83	1.10
7	220,186.0	235,648.0	32.05	1.07
8	296,665.0	303,908.0	29.90	1.02
9	280,981.0	309,533.0	28.30	1.10
10	286,633.0	282,746.0	29.44	0.99

display the KPIs for Mine 2.

3.2. Availability Rate

The influence of scheduled maintenance programs on mining equipment effectiveness and availability has been widely proven. Appropriate maintenance policies should be developed to improve safe conditions, reliability, availability and effectiveness, avoiding unexpected failures and breakdowns. For this reason, the study divides the analysis of the availability rate into two elements, technical and mechanical availability rate. This approach allows us to assess the availability rate in a complementary and interrelating way, examining the breakdown hours’ evolution over time and the influence of scheduled maintenance programs on breakdown hours. It allows for potential modification to maintenance policies in case of an increase in breakdown hours.

As previously outlined, the technical and mechanical availability rates have been calculated using the method OMEE.

Table 8. Technical availability rate, At, and mechanical availability rate, Am.

At (%)			Am (%)
Year	Mine 1	Mine 2	Mine 1	Mine 2
1	91.8		96.1
2	90.3		95.9
3	83.9		97.5
4	84.4	84.1	95.7	95.2
5	85.7	83.2	95.8	94.7
6	84.2	83.3	96.0	96.8
7	85.2	84.9	95.9	97.0
8	85.4	90.2	94.4	96.1
9	84.2	90.5	95.1	99.0
10	83.7	91.1	95.3	99.5

shows the results of the technical and mechanical availability rates for both drilling rig fleets used in the two different open-pit mines. Additionally, Figure 3 and Figure 4 show how the technical and mechanical availability rates evolved over 10 years. These graphs present a comparison between the technical and mechanical availability rates of the two drilling rig fleets performing in both mines.

As can be seen in Figure 3, the scheduled maintenance strategy accounts for approximately 15% of the total hours. Thus, this strategy improves mechanical availability by roughly 95%, as shown in Figure 4, which implies that breakdowns are around 5% of the total performance hours. Therefore, the evolution of mechanical availability remains at a similar range, without a remarkable decrease, and it can be stated that the scheduled maintenance strategy is adequate.

3.3. Utilization Rate

Mining equipment may not be operating during the optimum period due to inappropriate operating conditions, inaccurate management strategy or bad short-term planning. It is crucial to analyze the downtime and the associated reasons.

Table 9. Utilization rate (%).

U (%)
Year	Mine 1	Mine 2
1	88.8
2	94.1
3	92.6
4	88.5	77.6
5	90.4	71.9
6	89.6	65.4
7	65.8	62.6
8	88.5	56.5
9	90.3	58.2
10	92.1	53.0

shows the utilization rate results for the two drilling rig fleets, while Figure 5 represents the continuous evolution of the utilization rate over 10 years, showing a comparison between the utilization rate performance of the two equipment fleets in both mines. While the utilization rate in Mine 1 hovers around 90% over time, Mine 2 displays values below 80% (see Figure 5). This fact could imply inadequate management strategy and the short-term planning developed in Mine 2.

3.4. Productivity Index

The productivity index is an important parameter since it can be used to determine the production rate, and potential deviations, of the drilling rig. This index depends on the rock properties and the technical and operational features of the drilling rig. Several empirical methods have been developed to predict drilling performance in different rocks. In this study, the rock mass drilled is composed of sandstone occasionally interspersed with slate.

Table 10. Technical properties of the rock mass.

Rock Type	DRI	Net Penetration Rate (m/min)	Hole Diameter (mm)	Drilling Capacity (m/Ho)
Slate	50–70	0.95–1.35	115	40–50
Sandstone	35–55	0.75–1.10	115	32–42

shows the technical properties of the rock mass drillability in both mines.

The equipment degradation throughout their life cycle is analyzed using historical trends of their production rate. The data collected show a production of 15,026,804 drilling meters for 275,163,000 m³ blasting. To accomplish these production figures, the drilling rigs have been working 404,854 operating hours and their production rate is 37.12 m/Ho.

Table 11. Production rate (m/Ho). Productivity index.

Production Rate (m/Ho)			Productivity Index
Year	Mine 1	Mine 2	Mine 1	Mine 2
1	38.23		1.03
2	38.43		1.04
3	38.94		1.05
4	36.05	33.95	0.97	0.91
5	37.27	33.99	1.00	0.92
6	38.27	33.83	1.03	0.91
7	36.50	32.05	0.98	0.86
8	37.68	29.90	1.02	0.81
9	36.53	28.30	0.98	0.76
10	36.61	29.44	0.99	0.79

presents the results of the productivity index and production rate, observing that short-term planning in Mine 1 is more efficient than in Mine 2.

The equipment degradation throughout their life cycle could be analyzed using historical trends of their production rate. Figure 6 and Figure 7 show a comparison between both, the production rate and the productivity index of the drilling rig fleets performing in both mines. Upon analyzing Figure 6 and Figure 7, it is observed that the production rate and the productivity index in Mine 1 consistently remain higher than those in Mine 2. Therefore, it can be stated that the management strategy and the short-term planning in Mine 1 are more efficient than in Mine 2, which means that the operating conditions and the effectiveness of the drilling rigs are deemed more adequate in Mine 1.

3.5. OMEE Index

The proposed OMEE index allows the assessment of mining equipment performance by evaluating multiple streams of data by revealing correlations and uncovering hidden useful information in real time. This index allows us to identify the working conditions of the mining equipment.

Table 12. OMEE index.

	OMEE
Year	Mine 1	Mine 2
1	0.81
2	0.84
3	0.79
4	0.69	0.57
5	0.75	0.52
6	0.75	0.48
7	0.53	0.44
8	0.72	0.39
9	0.71	0.40
10	0.72	0.38

represents the results of the OMEE index calculated from the performance of the two fleets working in the case studies. Additionally, Figure 8 displays the evolution of the OMEE index and provides a comparison between the OMEE index and the performance of both fleets over the course of ten years.

4. Discussion

The development of the OMEE method to study the effectiveness of drilling rig is based on four steps: (i) acting, collecting information and data, (ii) observing, analyzing and reporting data, (iii) evaluating, evaluating and reviewing the process, and (iv) planning, identifying issues and suggesting solutions. This approach aligns with the methodology proposed in previous research [48]. The list of selected KPIs should assist in measuring the drilling rig performance behavior [49] and, subsequently, develop an appropriate method to analyze the mining process, identify poor performance and estimate improvement potential. To this end, the selected KPIs adhere to known recommendations [50], i.e., they are (1) correlated with the strategic aims, (2) significant and effective in representing and explaining the process, and (3) reliable, comprehensive, consistent and comparable. In this regard, the KPIs implemented allow us to determine the real-time performance of the equipment with a high degree of conformity.

The availability rate emerges as a critical parameter for analyzing the effectiveness of the maintenance policy. Results show that the mechanical availability rate, in both mines, ranges from 94.4% to 99.5% of the total hours, which means that the breakdown hours are from 5.6% to 0.5% of the total hours. On the other hand, the technical availability rate is from 83.2% to 91.8% of the total hours. These results determine that the maintenance strategy developed is reliable and works optimally, in accordance with some previous research that emphasizes how an accurate maintenance program can improve availability [42,44,45]. While other authors mention that the drilling rig equipment suffers due to its hard-working conditions, accelerating degradation [33], this study has determined that an appropriate scheduled maintenance strategy, accounting for approximately 15% of the total hours, plays a vital role in minimizing the breakdowns hours, around 5% of the total hours, minimizing the continuous degradation of the drilling rig equipment throughout its operating life. This underscores the importance of a preventive and predictive culture to anticipate failures and improve availability [38,39].

The utilization rate stands as a crucial parameter to determine how the management strategy is working. Results determine that the utilization rate is different in both mines. Mine 1 has a utilization rate with a minimum value of 88.5% and a maximum value of 94.1% of the available hours, except for year 7 when the availability rate was 65.8% due to market conditions, power station did not purchase coal. On the other hand, the utilization rate in Mine 2 ranges between 77.6% and 53% of the available hours, with a trend of continuously descending throughout its operating life, which implies important deviations in comparison with Mine 1. After analyzing the results, it has been found that the management strategy developed in Mine 2 was inadequate. The idle hours were continuously increasing throughout its operating life, which means that the management strategy, the operating management, and the short-term planning were inaccurate. As noted by Samatemba et al. [51], equipment selection is one of the most challenging elements in the mining industry, especially in matching the correct number of mining equipment required to perform. In this case, the short-term management strategy of Mine 2 was inappropriate, with an incorrect number of drilling rig equipment to develop their functions, which led to a negative impact on the mining operation’s effectiveness. On the other hand, results obtained from Mine 1 can be considered appropriate from the management strategy, the operating management, and the short-term planning point of view.

Most of the rock drilled and blasted is sandstone, obtaining a drilling capacity between 32 and 42 m/Ho with the top hammer HL1000. The average production rate calculated is 37.12 m/Ho, which means that the drilling rig equipment is usually performing properly. The production rate in Mine 1 ranges between 38.94 m/Ho and 36.05 m/Ho, achieving always optimal values over time, while it is between 33.99 m/Ho and 28.30 m/Ho in Mine 2, reaching low performance levels compared to the overall average value, 37.12 m/Ho, which is also reducing over time. The productivity index enables analysis of the operating conditions, the management strategy, and the operator´s skill in both open-pit mines. Mine 1 reaches values between 0.97 and 1.05, fluctuating around 1 over the years, which implies that the equipment performance is adequate and the management strategy and the operating conditions are appropriate. On the other hand, the productivity index ranges from 0.76 and 0.91 in Mine 2, together with a low production rate, which shows that the management strategy and the operating conditions in Mine 2 are not accurate. Operator skill could be considered appropriate as some operators have been working in both mines using different drilling rig equipment without suffering relevant deviations in the production rate and the productivity index. Concerning the production rate, it remained stable over the years in both case studies, indicating the maintenance strategy is appropriate.

Thus, this study shows how the OMEE index is a crucial parameter in determining the effectiveness of the drilling rig equipment. It allows us to estimate the performance accuracy of the equipment through the analysis of the availability rate, utilization rate, and productivity index. Furthermore, it enables comparative analysis between current and historical performances of the mining equipment or similar mining equipment fleets, located in different mines, working under similar conditions. Results obtained show that the OMEE index is 0.75 in Mine 1, which means that the drilling rig equipment performance is accurate and the management strategy, the management maintenance, and the operation conditions are appropriate. Figure 8 shows that the OMEE index trends slightly downward over time in Mine 1 due to its continued degradation. There is an important relationship between maintenance, productivity, and replacement in mining equipment [1,3] and, for this reason, the OMEE index could be a potential tool to analyze this relationship. On the other hand, the OMEE index obtained from Mine 2 runs from 0.57 to 0.38, showing a downward trend over time, indicating inaccurate equipment performance due to inappropriate utilization rate and low productivity index.

The case studies analyzed are an example of the potential that this method can offer to the mining industry in achieving sustainable goals, with a user-friendly approach. The method presented can be used to analyze individual and collective performance of mining equipment, identify the key influencing factors in the mining process [52], pinpoint the weak areas of the process, and estimate the potential improvements [53]. Mining equipment is subjected to continuous degradation throughout its operation life, and most of the replacement models study the life cycle cost (LCC) [24,30]. This research shows that the OMEE method could be an important tool for replacement decisions, analyzing the trend over time of the mechanical and technical availability rates and the production index. The flexibility of the approach extends its applicability to other types of mining and similar sectors such as civil works. It is also found that an appropriate preventive and predictive culture anticipates failures while improving availability and reliability, in accordance with previous research [39,44]. Further research could be conducted in the KPIs’ selection, analyzing potential additional factors to include for other types of equipment. Following the recommendations of Lukacs et al. [8], KPIs could be modified in future studies to standardize data and facilitate interpretations for similar mining operational activities. Technological advancements and progressive digitalization might be used onboard the mining equipment to facilitate input data collection, automatic control, and intelligent management.

5. Conclusions

The OMEE model developed in this study has been verified as a productive and effective methodology to assess mining equipment performance and develop dynamic mining processes. It is a versatile system that can be adapted to varying operating conditions and types of mining equipment. The OMEE has been applied to two actual open-pit mines, with data gathered over 10 years from drilling rigs, finding out crucial information about the functioning of the equipment thanks to the approach proposed.

The study of both mines shows, firstly, how effectiveness deviations can be identified. Secondly, it estimates how effectiveness deviations influence the mining process and, lastly, where these deviations occur. The proposed system enhances understanding of the equipment in terms of mining equipment state monitoring, weak area detection, maintenance plan development, residual equipment life estimation, and future mining operations simulation. The OMEE can be a valuable approach to measure mining equipment effectiveness and cross-check between fleets during their life cycle when working under similar conditions.