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Case Study: Model for Economic Lifetime of
Drilling Machines in the Swedish Mining Industry
Hussan Al-Chalabi, Jan Lundberg, Alireza Ahmadi & Adam Jonsson
To cite this article: Hussan Al-Chalabi, Jan Lundberg, Alireza Ahmadi & Adam Jonsson (2015)
Case Study: Model for Economic Lifetime of Drilling Machines in the Swedish Mining Industry,
The Engineering Economist, 60:2, 138-154, DOI: 10.1080/0013791X.2014.952466
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The Engineering Economist, 60:138–154, 2015
Published with license by Taylor & Francis Group, LLC
ISSN: 0013-791X print/1547-2701 online
DOI: 10.1080/0013791X.2014.952466
Case Study: Model for Economic Lifetime of Drilling
Machines in the Swedish Mining Industry
HUSSAN AL-CHALABI,1
JAN LUNDBERG,2
ALIREZA AHMADI,2
AND ADAM JONSSON3
1
Division of Operation, Maintenance and Acoustics, Lule˚a University
of Technology, Lule˚a, Sweden and Mechanical Engineering Department,
College of Engineering, University of Mosul, Mosul, Iraq
2
Division of Operation, Maintenance and Acoustics, Lule˚a University
of Technology, Lule˚a, Sweden
3
Division of Mathematic Science, Lule˚a University of Technology, Lule˚a,
Sweden
The purpose of this article is to develop a practical economic replacement decision
model to identify the economic lifetime of a mining drilling machine. A data-driven
optimization model was developed for operating and maintenance costs, purchase price,
and machine resale value. Equivalent present value of these costs by using discount rate
was considered. The proposed model shows that the absolute optimal replacement time
(ORT) of a drilling machine used in one underground mine in Sweden is 115 months.
Sensitivity and regression analysis show that the maintenance cost has the largest impact
on the ORT of this machine. The proposed decision-making model is applicable and
useful and can be implemented within the mining industry.
Introduction
Economic globalization increases competition among mining companies, pushing them to
achieve higher production rates by increasing automation and mechanization and using new
and more effective equipment. This forces companies to use more reliable capital equipment
with higher performance capabilities; naturally, these are more expensive. The equipment
used in underground mining industries is subject to degradation throughout its operating
life. This increases the operating and maintenance costs and reduces production rates,
causing a negative economic effect. In addition, the equipment used in underground mining
is subject to a harsh working environment, and this accelerates degradation. Given all of
these factors, key questions for the mining industry include the following. When should
the company replace the equipment to minimize cost? How can the maintenance manager
C Hussan Al-Chalabi, Jan Lundberg, Alireza Ahmadi, and Adam Jonsson
This is an Open Access article. Non-commercial re-use, distribution, and reproduction in any
medium, provided the original work is properly attributed, cited, and is not altered, transformed, or
built upon in any way, is permitted. The moral rights of the named author(s) have been asserted.
Address correspondence to Hussan Al-Chalabi, Division of Operation, Maintenance and Acous-
tics, Lule˚a University of Technology, Lule˚a 97187, Sweden. E-mail: hussan.hamodi@ltu.se
138
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Model for Economic Lifetime of Drilling Machines 139
convince finance and production managers to replace capital equipment at a specified time
in its life cycle? To answer these questions, life cycle cost analysis should be done in
advance of an equipment replacement decision.
The optimum replacement age of equipment is defined as the time at which the total cost
is at its minimum value (Jardine and Tsang 2006). In the mining industry, the costs associated
with owning equipment can be grouped into categories: initial purchase, installation, direct
downtime, maintenance and operating, financing, and cost recovery on disposal. The sum
of these costs represents the total cost required to own the mining equipment (Hall 2007).
Life cycle cost analysis helps decision makers justify equipment replacement on the basis
of the total costs over the equipment’s useful life. It allows the maintenance manager to
specify the optimal replacement time at the time of the equipment’s purchase.
Cost function models can be allocated to the various categories to allow easy estimation
of the total cost. Such models can be generally classified as detailed models, analogous
models, and parametric models. A detailed model uses estimates of material quantities
and prices, labor time, and rates to estimate the direct costs of equipment. Analogous
models identify similar equipment and adjust costs to account for differences between it
and the target equipment. Cost estimation with a parametric model is based on predicting
the equipment’s total cost by using regression analysis based on technical information
and historical cost (Asiedu 1998). Life cycle cost (LCC) analysis should not be seen as
a method for defining the total cost of the equipment but as a help in decision making;
thus, LCC analysis should be restricted to costs that can be controlled. In general, LCC
is determined by summing up all of the potential costs associated with equipment over its
lifetime. It is well known that the value of expenditure today costs more than the same value
of expenditure next year because of the “time value of money.” A discount rate is used
to take into consideration the time value of money. To compare costs incurred at different
times we must shift expenditure to a reference point in time. Thus, in this article, we are
interested in estimating the equivalent present value of earlier or future costs.
Literature Survey
Standard models for economic replacement time decision contain an estimation of the dis-
counted costs by minimizing the LCC of the equipment. The assumption of these models
is that equipment will be replaced at the end of its economic lifetime by a continuous se-
quence of identical equipment (Hartman and Tan 2014). Recently, a number of researchers
have studied the economic lifetime of capital equipment. Some consider the optimal life-
time of capital equipment using economic theories and vintage capital models, represented
mathematically by nonlinear Volterra integral equations with unknown limits of integration
(Boucekkine et al. 1997; Cooley et al. 1997; Hritonenko 2005; Hritonenko and Yatsenko
2003; Yatsenko 2005). Others use the theory of dynamic programming considering tech-
nological changes under finite and infinite horizons (Bellman 1955; Bethuyne 1998; Elton
and Gruber 1976; Hartman 2005; Hritonenko and Yatsenko 2008; Mardin and Arai 2012).
Yatsenko and Hritonenko (2005) studied the lifetime optimization of capital equipment
using integral models. The study designs a general investigation framework for optimal
control of the models. Hritonenko and Yatsenko (2007) studied optimal equipment replace-
ment without paradoxes. Using an integral model to calculate the economic lifetime of
equipment and considering technological changes (TCs), they showed that the economic
lifetime of equipment is shorter when the embodied TC is more intense. Hartman and
Murphy (2006) offered a dynamic programming approach to the finite-horizon equipment

140 H. Al-Chalabi et al.
replacement problem with stationary cost. Their model studies the relationship between the
infinite-horizon solution (continuous replacement of equipment at the end of its economic
lifetime) and the finite-horizon solution. Hritonenko and Yatsenko (2009) constructed a
computational algorithm to solve a nonlinear integral equation. The solution is important
for finding the optimal policy of equipment replacement under technological advances.
Kärri (2007) considered the optimal replacement time of an old machine, using an opti-
mization model that minimizes the machine cost. The model is built to handle capacity
expansion and replacement situations. Using real costs without inflation, Kärri (2007) mod-
eled the costs of the old machine with simple linear functions. He also used an optimization
model that maximizes profit. Scarf and Bouamra (1999) addressed the capital replacement
problem using a discounted cost criterion over a finite time horizon. They presented a robust
approach to solving the fleet replacement problem in which the fleet size is allowed to vary
at replacement. A survey of multiple and single asset solution techniques under a variety of
settings, including tax, variable utilization, various uncertainties, and technological change,
was addressed by Hartman and Tan (2014). They also illustrated a number of open problems
that are worthy of future research. Generally speaking, these studies focus on estimating
the economic lifetime of equipment, considering technological changes and using integral
models, theories of dynamic programming, vintage capital models, and algorithms to solve
nonlinear integral equations.
Despite the available information, it can be difficult for users to implement complex
models to calculate the optimal replacement time of equipment. Moreover, these models
sometimes require specific types of data that, as in our case study, are not available. These
can include data on production output, technological labor/output coefficient, revenue,
profit, etc. Thus, the aim of this study is to identify the replacement age of a mining drilling
machine from an economic point of view, using available data from a mining company,
specifically, the operating and maintenance costs, purchase price, and machine resale value.
In this study, equivalent present value of these costs was considered by using a discount rate.
Description of Drilling Machine
The drilling machines typically used in mines are manufactured by different companies
and have different technical characteristics; for example, capacity and power. An example
of a drilling machine and its components is presented in Figure 1.
The drilling machine is divided into several subsystems connected in series configu-
ration (see Atlas Copco Rock Drills AB 2010). If any subsystem fails, the operator will
stop the machine to fix it. Thus, all machine subsystems work simultaneously to achieve
the desired function.
Data Collection
The cost data used in this study were collected over 4 years in the Maximo computerized
maintenance management system (CMMS). The cost data contain corrective maintenance
costs, preventive maintenance costs, and repair time. The corrective and preventive main-
tenance costs contain spare parts and labor (repair person) costs. In CMMS, the cost data
are recorded based on calendar time. Because drilling is not a continuous process, the
operating cost is estimated by considering the utilization of the machines. It is important
here to mention that all cost data used in this study are real costs without inflation. Due to
the company regulations, all cost data are encoded and expressed as currency unit (cu) for
this study. Samples of cost data can be seen in Table 1.

Figure 1. Drilling machine. Source: Andreas Nordbrandt, Vice President Service Operations, Atlas
Copco Rock Drills AB (2010).
© Atlas Copco Rock Drills AB. Reproduced by permission of Atlas Copco Rock Drills AB.
Permission to reuse must be obtained from the rightsholder.
Methodology and Model Development
In this study the following assumptions are made for the optimization model:
• The cost of capital is given by the mining company involved in this study.
• Acquisition cost of the machine remains constant at each replacement.
• There is no installation cost for the machine.
• The optimization model is used for a ﬁnite time horizon.
• Production losses due to lead time during machine replacement are not considered.
• The machine will be used as a redundant machine after it reaches to its scrap value.
The taxes are included in the purchase price and operating and maintenance costs; for
that reason, taxes are not included as an independent parameter in the optimization model.
The study develops a practical optimization model based on the total cost. Associated
operating and maintenance costs, as well as purchase price and machine resale value, are
considered. The maintenance costs (MC) for each month of operation consist of corrective
maintenance (CM) and preventive maintenance (PM) costs:
MC = CM + PM. (1)
The corrective and preventive maintenance costs are given by
CM = SPc + LCc (2)
PM = SPp + LCp. (3)

Table 1
Sample of cost data
Work
description
Actual
working
time (h)
Actual
materi-
als cost
(cu)
Total
real cost
(cu)
Actual
labor
(cu)
Actual
service
cost (cu)
Actual
start
date
Inventory
descrip-
tion
Work
type
Extension
Extender
2 bolts
of
V-feeder
1 28.148 28.598 0.45 0 20xx-
03-15
13:23
Feeder PM
FU1 Atlas
L2C/2
5 9.836 14.018 0 4.182 20xx-
03-15
13:24
PM
Mount the
sensor
cables
6 0 2.7 2.7 0 20xx-
03-15
22:41
Steering
system
CM
Atlas
Copco
L2C
16 0 7.2 7.2 0 20xx-
03-16
13:17
Electrical
system
CM
Replacing
the hose
feeding
shift
0.5 0 0.225 0.225 0 20xx-
03-19
07:30
Hoses CM
Because drilling is not a continuous process in the collaborating mine, operating cost
(energy cost and steel rod cost) is calculated for each month based on the utilization of
the drilling machine. The company plans to use the machine for 120 months. Therefore,
extrapolation for the operating and maintenance cost data was done. Figures 2 and 3
illustrate the maintenance and operating costs determined by the data extrapolation.
In Figures 2 and 3, the dots represent the real historical data for maintenance and
operating costs. Curve fitting was done using Table Curve 2D (Alfasoft AB, Göteborg,
Sweden) software to show the behavior of these costs before and after the time when data
were collected. Note that the fitting would be better if more data were available for a time
period of more than 4 years. This software uses the least squares method to find a robust
(maximum likelihood) optimization for nonlinear fitting. It is worth mentioning that the
drilling machine in this case study has no multilevel preventive maintenance programme. In
addition, it was new at the start of utilization. This is the main reason why the maintenance
cost is quite low in earlier months. The history shows that when the maintenance costs
started growing, the user company began to keep track of cost data by using CMMS.
The Lorentzian cumulative equation of extrapolation for expected maintenance cost
obtained by the software is expressed as
Y =
a
π
arctan
x − b
c
+
π
2
, (4)

Figure 2. Maintenance cost.
where Y represents the expected maintenance cost, a = 217.42, b = 112.37, c = 13.63,
r2
(adj.) = 0.97, and X represents the time (1, 2, 3, 4, . . . , n months). Similarly, the
Lorentzian cumulative equation of extrapolation for expected operating cost is expressed
as
Y =
a
π
arctan
x − b
c
+
π
2
, (5)
Figure 3. Operating cost.

where Y represents the expected operating cost, a = 79.89, b = 109.2, c = 13.85, r2
(adj.)
= 0.91, and X represents the time (1, 2, 3, 4, . . . , n months).
As the figures show, the operating and maintenance costs increase over time. In fact,
the number of failures increases with time and/or the machine consumes more energy due
to machine degradation.
A declining balance depreciation model is used to estimate the resale value of
the machine after each month of operation. The machine’s resale value is its value if
the company wants to sell it at any time during its planned lifetime. The resale value of the
machine, denoted S(i), is assumed to be given by the following formula (Eschenbach 2010;
Luderer et al. 2010):
S (i) = BV1 × (1 − Dr)i
, (6)
where i represents time (month), i = 1, 2, 3, . . . , 120, and BV1 is the machine’s value at
the first day of operation. In addition,
BV1 = PP × a, (7)
where a represents the percentage that multiplied by the machine purchase price to represent
the machine value at the first day of use. During discussions with us, company experts agreed
that the machine’s purchase price decreases by 10% on the first day of use (i.e., a = 0.9).
In this study, the machine purchase price is 6,000 cu. Hence, the machine’s value on the
first day of use is 5,400 cu.
The depreciation rate that allows for full depreciation by the end of the planned lifetime
of the machine is modeled by the following formula (Luderer et al. 2010):
Dr = 1 −
SV
BV1
1
T
, (8)
where T represents the planned lifetime of the machine, 120 months in the case study. The
machine was assumed to reach scrap value after 10 years. The machine’s resale value is
given by
S (i) = (PP × a) × (1 − Dr)i
. (9)
The declining balance depreciation model is suitable in this case because it assumes
that more depreciation occurs at the beginning of the equipment’s planned lifetime and less
at the end. It also considers that the equipment is more productive when it is new and its
productivity declines continuously due to equipment degradation. Therefore, in the early
years of its planned lifetime, a machine will generate more revenue than in later years.
In accountancy, depreciation refers to two aspects of the same concept. The first is the
decrease in the equipment’s value. The second is the allocation of the cost of the equipment
to periods in which it is used. The scrap value is an estimate of the value of the equipment
at the time it is disposed of. In this case study, 50 cu is assumed to be the scrap value of
the machine at end of its planned lifetime, a figure given to us by experts at the company.
Figure 4 shows the drilling machine’s resale values using the declining balance depreciation
model.
It is clear from Figure 4 that the machine’s resale values decrease with time until it
reaches scrap value at the end of its planned lifetime.
The next step in the calculations is to calculate the total ownership cost over each
operating month. In this study, the economic lifetime of the drilling machine is defined as

Figure 4. Machine resale value.
the machine age that minimizes the machine total ownership cost. The total ownership cost
over period i is denoted by TOCi, i = 1, 2, 3, . . . , n, where n is the number of operating
months. By deﬁnition,
TOCi = PP +
RT
i=1
(MCi + OCi) − S (i) , (10)
where MCi and OCi is the maintenance and operating costs for the ith month.
The reason for using total ownership cost is that the machine’s PP, OC, and MC
represent costs, whereas the resale value represents income for the company when it is
willing to sell the machine.
The objective is to determine the optimal replacement time that minimizes the total
ownership cost over the machine’s planned horizon. We assume that the replacement
machines (i.e., the new machines) have the same performance and cost as the existing
machine (i.e., identical machines). The number of replacement cycles during the planned
horizon is modeled as
M =
Planned lifetime
Replacement time
=
T
RT
. (11)
Figure 5 illustrates the expected total ownership cost of the machine over the planned
horizon.
As Figure 5 shows, the total ownership cost increases with time for two reasons: ﬁrst,
operating and maintenance costs increase over time; second, the machine’s resale value
decreases over time until reaches its scrap value.
The optimal replacement time is the value of RT that minimizes the total ownership
cost value, as shown in Eq. (12). A discount rate of 10% was used to consider the time
value of money as mentioned by the collaborating mining company.
TOCvalue =
⎡
⎣
⎧
⎨
⎩
⎛
⎝PP +
⎡
⎣
RT
i=1
MCi + OCi
⎤
⎦ − S (i)
⎞
⎠ ×
1
(1 + r)
i
12
⎫
⎬
⎭
× M
⎤
⎦ . (12)

Figure 5. Expected total ownership cost.
Results and Discussion
Figure 6 shows the results when MATLAB (MathWorks, Natick, MA) software is used to
enable a variation of the parameter RT of Eq. (12) for a planned horizon of 120 months.
This is done to identify the optimal replacement time (ORT) of a drilling machine that
minimizes TOCvalue. The ﬁgure shows the TOCvalue versus a different replacement time RT.
To show the behavior of the optimization curve for a period more than the planned
horizon, we assume that the optimization is used for a new ﬁnite time horizon of 240
months; see Figure 7. The total ownership cost for each operating month of the new
planned horizon (i.e., i = 1, 2, 3, . . . , 240) is computed by using the total ownership cost
Figure 6. Total ownership cost versus replacement time of existing drilling machine.

Figure 7. Optimal replacement time of existing drilling machine.
function obtained from Figure 5. This function is the ﬁt of the calculated total ownership
cost over the machine’s old planned horizon (i.e., 120 months).
As is evident, the absolute lowest possible TOCvalue can be achieved by replacing the
machine with an identical new one every 115 months. However, it must be noted that RT
= 115 months generates the absolute minimum cost. As Figures 6 and 7 also show, within
that, there is a range (e.g., 110–122 months) when the minimum TOCvalue can still be
achieved in practice. In this study, we call it the optimum replacement range. Finding the
optimum replacement range is an important result of our study because it can help users in
their planning. A decision to replace equipment before or after this optimum replacement
range incurs greater cost for the company. The use of a lower replacement age (i.e., less
than 110 months) incurs higher costs due to the high investment cost. Meanwhile, if the
lifetime of the machine exceeds the upper limit of this range (i.e., more than 122 months),
losses will increase for two reasons:
1. The cost of operation and maintenance increases when the operating time increases
due to machine degradation.
2. The machine’s resale value will decrease each month of operation until it reaches
its scrap value at the end of its planned lifetime.
Sensitivity Analysis
We next perform a sensitivity analysis to identify the effect of purchase price and operating
and maintenance costs on the ORT of the drilling machine. However, because most of
the factors may be interrelated, we use a multisensitivity analysis to identify the effect of
multiple changes of cost factors.

Figure 8. Effect of increasing purchase price.
Single-Variable Sensitivity Analysis
A single-variable sensitivity analysis varies one factor and keeps the others constant. The
factors considered in our sensitivity analysis include machine purchase price, as well as
operating and maintenance costs. Figure 8 illustrates the effect of an increasing purchase
price on the ORT of the drilling machine.
Figure 8 shows that the ORT is an increasing step function of PP (based on the per-
centage of purchase price); the ORT remains constant for a specific range of PP increments
and then increases stepwise. As an example, if the purchase price increases from 1 to 4%,
the ORT is constant. This means that the ORT increases stepwise at specific PP percentage
increments; that is, 5, 12, 19, 26, 34, and 42%.
Figure 9 illustrates the effect of decreasing machine operating cost (based on the
percentage of operating cost) on the ORT. It is obvious that when the machine’s operating
cost decreases, the ORT will increase stepwise, although it remains constant within a
specific range of decreasing OC. This means that the ORT is not sensitive to a specific
range of operating cost reductions and will increase stepwise at a specific OC rate of
reduction; that is, 15 and 34%.
Figure 10 illustrates the effect of decreasing machine maintenance costs on the ORT
of the drilling machine. When the maintenance cost decreases, the ORT will increase as a
step function of MC reduction. In addition, note that the ORT increases at reduction steps
of MC—that is, 7, 15, 23, 30, 36, 42, and 48%—and remains constant within these steps.
Figures 8, 9, and 10 show that with increasing purchase price and decreasing operating
and maintenance costs, the ORT of a new model of this machine will increase stepwise at a
Figure 9. Effect of decreasing operating cost.

Figure 10. Effect of decreasing maintenance cost.
specific percentage of these factors. This may occur because there is a significant effect of
these factors on the total ownership cost at these specific percentages of increasing factor
of purchase price (IFPP), reduction factor of operating cost (RFOC), and reduction factor
of maintenance cost (RFMC).
Multivariable Sensitivity Analysis
To increase our understanding of the correlation of input and output variables in the op-
timization model, a multisensitivity analysis was performed considering three different
cases. MATLAB software was used to enable a variation of the three factors, IFPP, RFMC
and RFOC, to show their effects on the ORT of the drilling machine. In all three cases, the
purchase price increases while the operating and maintenance costs decrease. Case 1 repre-
sents the effect of decreasing machine maintenance costs while increasing purchase price
and decreasing operating costs at different percentages at the same time. Figure 11 shows
the correlation between decreasing machine maintenance cost and increasing purchase
price for a given 15% reduction in the cost of operation. As the figure shows, decreasing
maintenance cost while increasing purchase price has a positive effect on increasing the
machine’s optimal replacement time.
Case 2 studies the effect of increasing machine purchase price while simultaneously
decreasing the maintenance cost at a given percentage of operating cost reduction. Case 3
considers the effect of decreasing the machine’s operating cost while decreasing mainte-
nance cost at a given percentage of increasing purchase price.
Figure 11. Effect of RFMC and IFPP for a given 15% RFOC.

From the results of the three cases (Figure 11), it is clear that the ORT of a new
model of this machine will increase as a result of increasing its purchase price, decreasing
the maintenance cost, and decreasing the cost of operation at different percentages. The
explanation is that a new model of this machine is assumed to be more reliable than the
old ones. This will lead to a decreased failure rate in a new model of this machine, which,
in turn, reduces the maintenance cost. In addition, a new model of this machine is more
productive than an old one; thus, it will ﬁnish the same job in less time. This will decrease
the energy consumption of a new model of this machine, which leads to a reduction in the
operating cost of it.
Regression Analysis
Our regression analysis of the ORT results obtained from the previous three cases uses
Minitab (Minitlab Inc., State College, PA) software and the least squares method. The
ORT of a new model of drilling machine is modeled as a linear function of IFPP, RFOC,
and RFMC. IFPP is deﬁned as the percentage increment on the machine’s purchase price.
RFOC is the percentage reduction in the machine’s operating cost, and RFMC is the
percentage reduction in the machine’s maintenance cost. The regression analysis results in
the following mathematical model:
ORT = 114 + 0.133 × IFPP + 0.0682 × RFOC + 0.164 × RFMC, (13)
where IFPP = 5%, RFOC = 6%, and RFMC = 12%. The ORT resulting from the regression
model is calculated as follows:
ORT = 114 + 0.133 × 5 + 0.0682 × 6 + 0.164 × 12 = 117 (month).
The ORT obtained from the regression model is compatible with the values shown in
Figure 11. The other values of IFPP, RFOC, and RFMC can be calculated and checked as
well.
The high R2
adjusted value obtained from regression analysis, R2
(adj.) = 98.6%,
indicates that the ORT of a new model of this machine depends linearly on the IFPP, RFOC,
and RFMC. Following the results of the sensitivity and regression analyses, the rank of the
factors affecting the ORT of a new vintage model of a drilling machine is as follows:
1. The reduction in maintenance cost.
2. The increase in purchase price.
3. The reduction in operating cost.
Many studies have considered reliability, maintainability, and optimum replacement
decisions; readers are referred to, for example, Ahmadi and Kumar (2011), Wijaya et al.
(2012) and Dandotiya and Lundberg (2012) for further studies in the recent literature.
Graphical User Interface
During the study, we noticed that the user company is not always able to go through
the process introduced here. Therefore, to facilitate the decision-making process and to
enhance the company’s ability to make the right decision at the right time, we developed
a graphical user interface (GUI) to compute the ORT. The proposed GUI is designed to
enable checking of the effect of changing any of the factors; that is, IFPP, RFOC, or RFMC.
Figure 12 represents the GUI for case 1.

Figure 12. Graphical user interface.
The selected input factors appear on the left side of Figure 12; the program calculates
the ORT of the machine according to the selected input. The generated fields shown on
the right of the figure represent the ORT values, calculated after applying the proposed
optimization model. A plot representing the ORT trend appears in the figure’s central
column. From this, decision makers can determine the best time economically to buy a
new machine. They can choose one of three factors: purchase price, operating cost, or
maintenance cost. They can determine its effect on the ORT by observing the plot on the
interface. This method also provides decision makers with useful information if they are
negotiating with manufacturers over the purchase price of a new model of this machine.
Concluding Remarks
This article presents a comprehensive and practical approach that can be used to provide
the optimal replacement time of an underground mining drilling machine. The following
conclusions can be derived from this study:
1. Although many other models require reliability and failure data to identify the
optimum replacement age, the approach presented herein is based on financial data
on the purchase price, operating and maintenance costs, and the machine’s resale
value. This makes it very practical for industries.
2. According to the results obtained from the optimization curve, the absolute ORT of
the drilling machine at the case study’s mine is 115 months of operation. However,
the ORT has a practical range of 110 to 122 months, during which the total ownership
cost remains almost constant. This means that the company has the flexibility to
make replacements within the optimum replacement age range; that is, 12 months.
Therefore, there is no fixed date or age at which the TOCvalue is minimum. In
general, a range of months provides the minimum TOCvalue.
3. The results of the sensitivity analysis indicate that increasing the purchase price and
decreasing the operating and maintenance costs have a positive effect on increasing
the ORT.
4. The results of the regression analysis show that the ORT of the new machine depends
linearly on its IFPP, RFOC, and RFMC. These results confirm the computation and
the results of the sensitivity analysis.
5. The results of regression analysis show that the reduction in maintenance cost has
the largest impact on the ORT, followed by the increase of purchase price and

reduction of operating cost. Hence, the manufacturer must make a greater effort
to improve the reliability and maintainability of the drilling machine to reduce the
costs associated with maintenance and to increase the ORT. However, a detailed
RAMS analysis is required to identify the weakest points of the machine from
reliability, maintainability and supportability points of views.
6. Economists at the user company can easily use the GUI to estimate the ORT of a
new machine and see the behavior of its ORT at IFPP, RFOC, and RFMC. These
factors will provide a clear view of the ORT of the new machine. Knowing this will
help the user company determine when to buy a new machine and assist them in
any negotiations with the manufacturer over the purchase price.
Acknowledgments
The people at Boliden AB and Atlas Copco who helped in this research are gratefully
acknowledged. The authors also thank Behzad Ghodrati for his help. Our sincerest gratitude
is extended to the reviewers and the editor of this journal for the valuable comments that
we received from them, which helped to improve this article.
Funding
The authors thank Atlas Copco for their financial support.
Nomenclature
a Purchase price percentage at first day of operation (%)
BV1 Machine’s value at first day of operation (cu)
LCc Labor cost for corrective maintenance (cu)
LCp Labor cost for preventive maintenance (cu)
M Number of replacement cycles
S(i) Resale value (cu)
SPc Spare part cost for corrective maintenance (cu)
SPp Spear part cost for preventive maintenance (cu)
T Planned lifetime (month)
TOCvalue Total ownership cost multiplied by number of replacement cycles (cu)
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Biographical Sketches
Hussan Al-Chalabi received a B.Sc.Eng. degree in mechanical engineering from Mosul University,
Iraq, in 1994 and an M.Sc degree in mechanical engineering in thermal power from Mosul University,
Iraq, in 2008. Then he joined the Department of Mechanical Engineering at Mosul University as a
lecturer. In 2011, he joined the Division of Operation, Maintenance and Acoustics at Lule˚a University
of Technology as a doctoral student.
Jan Lundberg is Professor of Machine Elements at Lule˚a University of Technology and also
Professor in Operation & Maintenance with a focus on product development. During 1983–2000, his
research concerned engineering design in the field of machine elements in industrial environments.

During 2000–2006, his research concerned industrial design, ergonomics, and related problems as
cultural aspects of design and modern tools for effective industrial design in industrial environments.
Since 2006 his research is completely focused on maintenance issues including methods for measuring
failure sources, how to design out maintenance, and how to design for easy maintenance.
Alireza Ahmadi is an assistant professor in the Division of Operation and Maintenance Engineering,
Lule˚a University of Technology (LTU), Sweden. He received his Ph.D. degree in operation and
maintenance engineering in 2010. Alireza has more than 10 years of experience in civil aviation
maintenance as a licensed engineer and production planning manager. His research topic is related to
the application of RAMS and maintenance optimization.
Adam Jonsson is a senior lecturer in the Department of Engineering Sciences and Mathematics,
Lule˚a University of Technology, Sweden. He received his Ph.D. in statistics in 2008. Jonsson’s
research is in applied probability and social welfare economics.

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