Content uploaded by Catarina Teixeira
Author content
All content in this area was uploaded by Catarina Teixeira on Mar 28, 2019
Content may be subject to copyright.
Full Terms & Conditions of access and use can be found at
http://www.tandfonline.com/action/journalInformation?journalCode=tjma20
Journal of Management Analytics
ISSN: 2327-0012 (Print) 2327-0039 (Online) Journal homepage: http://www.tandfonline.com/loi/tjma20
Classification methodology for spare parts
management combining maintenance and
logistics perspectives
Catarina Teixeira, Isabel Lopes & Manuel Figueiredo
To cite this article: Catarina Teixeira, Isabel Lopes & Manuel Figueiredo (2018): Classification
methodology for spare parts management combining maintenance and logistics perspectives,
Journal of Management Analytics, DOI: 10.1080/23270012.2018.1436989
To link to this article: https://doi.org/10.1080/23270012.2018.1436989
Published online: 19 Feb 2018.
Submit your article to this journal
Article views: 4
View related articles
View Crossmark data
Classification methodology for spare parts management combining
maintenance and logistics perspectives
Catarina Teixeira*, Isabel Lopes and Manuel Figueiredo
ALGORITMI Research Centre, Department of Production and Systems, University of Minho,
Guimarães, Portugal
(Received 30 September 2017; revised 12 January 2018; accepted 1 February 2018)
Spare parts management is a function of maintenance management that aims to
support maintenance activities, giving real-time information on the available
quantities of each spare part and adopting the inventory policies that ensure
their availability when required, minimizing costs. The classification of spare
parts is crucial to control the vast number of parts that have different
characteristics and specificities. Spare parts management involves mainly two
areas, maintenance and logistics. Therefore, the integration of both input
information is recommended to make decisions. This paper presents a multi-
criteria classification methodology combining maintenance and logistics
perspectives that intends to differentiate and group spare parts to, subsequently,
define the most appropriate stock management policy for each group. The
methodology was developed based on a case study carried out in a multi-
national manufacturing company and is intended to be included in its
computerized maintenance management system to support decision-making.
Keywords: criticality; maintenance management; multi-criteria classification; spare
parts management
1. Introduction
Up until 1940, maintenance activities were not planned, they were only performed
when a failure occurred (Murthy, Atrens, & Eccleston, 2002). Between 1950 and
1960, the first scientific approach to maintenance management emerged, moving
from the corrective maintenance paradigm to the preventive maintenance paradigm,
reducing unplanned downtime in production (Dekker, 1996).
With maintenance planning, maintenance costs are more easily controlled and
reduced. Maintenance costs include not only the cost of labor and spare parts
(Chen, Ren, Bil, & Sun, 2015) but also the costs of equipment downtime due to break-
downs. An efficient and effective spare parts management is essential for maintenance
management since it influences the downtime of equipment. Therefore, management
of spare parts of manufacturing equipment affects the performance of maintenance
management and, consequently, the productivity of organizations. In organizations
with high dependence on production equipment, high equipment availability is
required. Therefore, spare parts are an important resource to ensure availability
(Roda, Macchi, Fumagalli, & Viveros, 2014). Spare parts inventory management
© 2018 Antai College of Economics and Management, Shanghai Jiao Tong University
*Corresponding author. Email: B7236@algoritmi.uminho.pt
Journal of Management Analytics, 2018
https://doi.org/10.1080/23270012.2018.1436989
has the functionality of providing support to maintenance service, such as to ensure
operability of the installed systems. However, spare parts management is a critical
issue as maintaining high inventories of spare parts ties up capital and often results
in relevant costs consuming a significant part of capital investments. Spare parts are
used in a large number of maintenance interventions and associated inventory costs
may be of two types: costs of holding stock and costs of the non-existence of a
spare part associated with production stoppages.
Spare parts inventories diverge from inventories of manufactured products and
materials in many aspects (Kennedy, Wayne Patterson, & Fredendall, 2002). Spare
parts are characterized by a large cost magnitude and by intermittent and highly
erratic demand and inventories are determined by the demand, instigated by preven-
tive and corrective maintenance interventions.
The availability of spare parts should be directly related to maintenance in order to
reduce failure downtime and costs. Inventory and maintenance management must be
seen as parts interconnected for optimizing company’s operations (Van Horenbeek,
Buré, Cattrysse, Pintelon, & Vansteenwegen, 2013). Computerized maintenance man-
agement system (CMMS) is now a central component of many companies’mainten-
ance departments, and they offer support on a variety of activities related to
maintenance. Concerning spare parts, they can track the movement of spare parts
and their requisition when necessary (Labib, 2004) and facilitate the integration of
logistics and maintenance perspectives (Cavalieri, Garetti, Macchi, & Pinto, 2008).
The integrated logistics and maintenance decisions will allow a more efficient mainten-
ance programming and execution.
According to Molenaers, Baets, Pintelon, and Waeyenbergh (2012), the classifi-
cation of spare parts is a relevant research area due to the financial resources and
service requirements involved. In literature, although there are already several
models that aim at the management of spare parts, frameworks that gather all the
aspects related to this subject are lacking. Through literature review, opportunities
were found to develop a spare parts management methodology that covers various
aspects of spare parts management.
This paper presents a multi-criteria classification to be included in the spare parts
management methodology that also will include demand forecasting and definition of
the inventory policy. The objective of the spare parts management methodology is to
group the spare parts (using the classification) and assign to each group the more
appropriate stock management policy according to the spare parts specificities.
Regarding spare parts classification methodologies, techniques that just consider
one criterion and also techniques that consider several criteria can be found in the lit-
erature. The large number of criteria used in some techniques makes them complex
and difficult to apply to the reality of industrial organizations. The presented method-
ology uses criteria that are easily obtained and generally gathered by organizations.
The criteria involved in the classification should consider the maintenance and logis-
tic perspective since both functions are involved in decision-making concerning spare
parts stock levels. In this way, the methodology includes the evaluation of the criticality
of the spare parts and the most relevant and easily measurable logistics criteria.
The paper is an extended version of the research presented by Teixeira, Lopes, and
Figueiredo (2017). This research work about spare parts management was performed
in the scope of a project that aimed to upgrade the CMMS of a company. This upgrade
consists of software architecture redesign, adding a set of advanced management
2C. Teixeira et al.
maintenance methodologies and functionalities (Lopes et al., 2016). Hence, the classi-
fication methodology for spare parts will be integrated in the developing CMMS.
The paper is organized as follows. In Section 2 a literature review is presented
about spare parts management, classification techniques, and criteria selection.
Section 3 presents the multi-criteria classification methodology. In Section 4, an
example is used to validate the methodology. Finally, in Section 5, the main con-
clusions and further work are presented.
2. Literature review
2.1. Spare parts management
In NP EN 13306 (2007) spare part is defined as an “item intended to replace a corre-
sponding item in order to restore the original required function of the item”. Accord-
ing to Gopalakrishnan and Banerji (2015) some of the specificities of spare parts are:
spare parts have a high tendency for obsolescence; stock out cost is greater than the
spare part price; spare parts are critical from an operational point view; number of
suppliers is small; there is a lack of information system.
Kennedy et al. (2002) refer that the stock management of spare parts has peculiars
characteristics, such as:
.The decision of whether to repair or replace has profound implications on main-
tenance inventory levels;
.The information about reliability is generally not available to the degree needed
for the prediction of failure times;
.When certain spare parts no longer take place in the system (because they are no
longer used for maintenance) it creates an obsolescence problem.
.The costs of breaking a spare parts inventory are difficult to quantify because it
includes costs associated the production losses.
Cavalieri et al. (2008) state that a large number of organizations have problems
related to maintenance management due to the lack of adequate inventory manage-
ment policies in accordance with maintenance needs.
According to Lynch, Adendorff, Yadavalli, and Adetunji (2013) to determine which
parts are critical to the maintenance process, the following features must be considered:
.Process and control of criticality: The criticality of the process is related to the
cost of inactivity, that is when a part fails and a replacement part does not
exist in stock. The control of criticality quantifies the reaction time, by taking
into account factors such as the availability of spare parts, the probability of
failure, the repair times, among many others.
.Specificity: Spare parts need categorization in order to distinguish generic and
exclusive parts. Generic parts usually have no supply problems, as there are
several suppliers available. On the other hand, the exclusive parts tend to have
a single supplier and an irregular lead time.
.Demand patterns: Spare parts usually have a low demand volume. This type of
demand pattern results in high storage costs, especially storing critical items at
high prices.
.Product value: Unlike finished products, spare parts do not create value, their
function is to prevent or reduce the downtime of production equipment.
Journal of Management Analytics 3
Stock management of spare parts plays an increasingly central role in modern
operations management. The tradeoff is clear: on the one hand, a large number of
spare parts represents a large amount of capital tied up, on the other hand, its inexis-
tence causes costs by stopping production (Aronis, Magou, Dekker, & Tagaras, 2004;
Zhu, Dekker, van Jaarsveld, Renjie, & Koning, 2017).
Huiskonen (2001) asserts that spare parts logistics differ from other materials since
the demand for spare parts is characterized by sporadic behavior and a potential varia-
bility when it occurs. The consumption rate is not stationary; therefore, the statistical
properties of demand are not independent of time (Cavalieri et al., 2008). According to
Syntetos, Babai, and Altay (2012), the demand for parts replacement is related to the
failure or replacement and as such the relevant patterns are different from those associ-
ated with ‘typical’stock keeping units (SKUs). Typically, the demand has a large var-
iance in frequency and quantity and is of intermittent nature which is characterized by
several periods of zero demand. Consequently, this type of demand is hard to predict
(Zorgdrager, Verhagen, & Curran, 2014). In order to categorize demand, the model
proposed by Ghobbar and Friend (2002) uses two variables, the coefficient of variabil-
ity and the intervals between consumptions.
According to Cavalieri et al. (2008), spare parts management should be inserted in
CMMS. The authors suggest the integration of logistics and maintenance perspectives.
Therefore, the authors proposed a decision-making framework composed of five
sequential steps (Figure 1).
Figure 1. Steps for spare parts management (Cavalieri et al., 2008).
4C. Teixeira et al.
The framework applications aim to sequence spare parts management. In each
step, it is necessary to define the appropriate methodologies and techniques to each
company. The framework application may eventually lead to fulfilling the gap of
the low level of usage of tools to manage spare parts in the industry based on a
factual and quantitative assessment (Cavalieri et al., 2008).
Spare parts classification is a relevant step to guide the whole management process.
Many advantages can be achieved by proper classification. The demand forecasting
process may be driven by data collected for different classes allowing performance
improvement measures to focus on the more critical classes (Roda et al., 2014).
With the classification, a suitable management of spare parts can be obtained. It
supports the choice of demand forecast and inventory control methods and establishes
different performance targets in service levels and turnover for each category (Huisko-
nen, 2001).
The classification of spare parts, and therefore groups’constitution, follows the
Group Technology (GT) concept. According to Shtubt (1989), GT is a manufacturing
concept based on the assumption that by decomposing a manufacturing system into
subsystems, overall performance improvement can be achieved. This concept has
been successfully employed in cellular manufacturing in which, parts with similar pro-
cessing requirements are identified and grouped into part families, and then machines
with different processing capacities are placed within a cell (Shahin & Janatyan, 2010).
GT principles have been applied in other areas beyond production, including in main-
tenance. In the approach developed by Talukder and Knapp (2002), GT concept was
applied to the problem of grouping equipment into blocks for the application of pre-
ventive maintenance. A heuristic for optimally grouping equipment into multiple
blocks within a system for block preventive replacement was developed. Almomani,
Abdelhadi, Seifoddini, and Xiaohang (2012) propose a platform to conduct planning
of preventive maintenance actions by using clustering, based on the GT concept, to
create preventive maintenance virtual cells of machines.
According to Huiskonen (2001) and Molenaers et al. (2012), there are two types of
criteria to classify spare parts. Those that evaluate the criticality of spare part for the
process and those that evaluate the criticality of control. Process criticality is con-
cerned with the consequences of failure or malfunctions for the plant (for example,
consequences related to loss of lives, environmental contamination or production
loss). Regarding control criticality, a spare part is considered critical if the possibility
to ensure immediate availability of the part is difficult to control. To assess the critical-
ity, spare parts classes should be defined based on quantitative and qualitative criteria
(Molenaers et al., 2012).
2.2. Classification techniques
The classification of spare parts is crucial due to a large number of parts and their
variety. As such, the categorization of spare parts is a way of controlling their diversity
and specificity (Molenaers et al., 2012). A classification of spare parts is generally
based on administrative efficiency considerations (such as inventory costs, usage
rates, etc.) derived from historical data of the company.
According to Dekker, Kleijn, and de Rooij (1998), the classification of spare parts
serve to highlight the most critical spare parts that must be kept in stock to ensure
production.
Journal of Management Analytics 5
Lenard and Roy (1995) identify that the segmentation of materials is indicated by
the following reasons:
.It is possible to evaluate the results of each group and to see if they fit the objec-
tives of management.
.Since the final decision on the policy to be adopted must remain in the hands of
management, the segmentation of the items allows the analysis of the effects of
each decision to be fast and intuitive;
.Segmentation of materials allows the same restrictions to be applied to materials
that have similar parameters, allowing the choice of parameters (service rate, the
frequency of Stock review) appropriate to each group and allows adjusting the
stock management policy which best suits each group of materials.
Two types of methods can be applied to classify spare parts: quantitative methods
and qualitative methods (Cavalieri et al., 2008).
In industry, the traditional classification method is ABC analysis, which is widely
used to determine service requirements of spare parts (Molenaers et al., 2012). The
classification helps companies to simplify stock management. The objective of ABC
analysis is to classify the inventory items or SKUs into three classes, namely: A
(very important items); B (moderately important items) and C (relatively unimportant
items) (Hatefi, Torabi, & Bagheri, 2014; Prakash & Chin, 2017). ABC analysis is easy
to use and supports the inventory management of materials that are fairly hom-
ogenous in nature (Flores, 1987). The criteria used to classify are product annual
demand and average unit price (Ramanathan, 2006). According to Braglia, Grassi,
and Montanari (2004) ABC classification is the most well-known and used classifi-
cation scheme to manage the spare parts inventory management problems.
Huiskonen (2001) mentions that as the variety of control characteristics of items
increases the one-dimensional ABC classification does not include all the control
requirements of different types of items. In the literature, it has been generally recog-
nized that a “classical”ABC analysis may not be able to provide a good classification
in practice (Altay Guvenir & Erel, 1998).
Another quantitative method is FSN, which classifies items into three categories:
fast-moving, F, slow-moving, S and non-moving, N. The method is based on the
analysis of the demand patterns and leads to a different kind of classification, that
is focused on the moving rates of spare parts (Bosnjakovic, 2010; Cavalieri et al.,
2008). This classification is useful when it is desirable to put in evidence that the obso-
lescent spare parts are non-moving after many years (Cavalieri et al., 2008).
The qualitative methods normally used for spare parts classification are based on
rough judgment or in scoring methods (Cavalieri et al., 2008). The VED (Vital, Essen-
tial, Desirable) classification is a qualitative method (Mukhopadhyay, Pathak, &
Guddu, 2003). The VED classification system is based on the maintenance expert’s
knowledge. Spare parts can be classified as vital, essential or desirable. Although its
apparent simplicity, the structuring can be a difficult task because its implementation
can suffer from subjective judgments of users (Cavalieri et al., 2008). Gajpal, Ganesh,
and Rajendran (1994) suggested the application of VED classification with an Ana-
lytic Hierarchy Process (AHP) procedure to limit the problem of subjective judgments.
The other qualitative method that is normally reported in the literature for spare
parts classification is AHP. AHP has been considered as a leading and one of the
6C. Teixeira et al.
most popular multi-criteria decision-making techniques. AHP attracts the attention of
researchers due to the fact that normally the input data are easy to obtain (Trianta-
phyllou & Mann, 1995). Thus, this methodology is presented in the literature as a poss-
ible option to create performance rankings. It is used in a wide range of fields,
especially in operations management, to solve complex decision problems by the
prioritization of alternatives (Gass & Rapcsák, 2004; Subramanian & Ramanathan,
2012; Wang, Ji, & Chaudhry, 2014). This technique can be used when the consider-
ation of qualitative and quantitative factors are required and it helps to define the criti-
cal factors through the definition of a hierarchical structure similar to a family tree
(Bevilacqua & Braglia, 2000).
In AHP, the relevant data are obtained from the use of a set of pairwise
comparisons. The application of AHP helps to reduce the complex decisions to a
series of simple comparisons and consequently, it helps to synthesize results
showing the best decision and the clear reason for the choice (Bevilacqua & Braglia,
2000). AHP uses a multi-level hierarchical structure of objectives, criteria, subcriteria,
and alternatives. These comparisons are used to define the weight of each criterion
and the relative performance measures of the alternatives for each criterion. This
method also verifies the comparisons consistency and provides a mechanism to
improve it in the cases where the comparisons are not consistent (Triantaphyllou &
Mann, 1995; Viriyasitavat, 2016; Xu, Da, & Chen, 2003).
AHP implementation can be structured in three steps (Bevilacqua & Braglia, 2000):
(1) Define decision criteria in the form a hierarchy of objectives, this means struc-
tured on different levels;
(2) Weight the criteria, subcriteria, and alternatives as a function of their impor-
tance for the corresponding element of the higher level;
(3) After a judgment matrix has been developed, a priority vector to weight the
elements of the matrix is calculated.
According to Roda, Macchi, Fumagalli, and Viveros (2012), for the classification of
spare parts to be carried out in a well-structured way, it cannot be based only on a
single criterion nor on qualitative judgments, then in the literature emerging research
reveals the need for a multi-criteria perspective to classify spare parts.
The classification methodology proposed by Braglia et al. (2004), called Multi-
Attribute Spare Tree Analysis (MASTA), uses several criteria based on the criticality
of the item. The method presents two sequential steps. The first step proposes the
identification of four classes of spare parts using a logical tree. In the second step,
the appropriate stock management strategies are defined for each of the four
defined classes. The AHP method is used to support the decision on each node of
the logical tree. For each factor used to make the classification (criticality of spare
parts for the factory, spare parts supply characteristics, inventory problems and
usage rate) a set of criteria that are measured considering three levels (critical, impor-
tant and desirable) are considered (Braglia et al., 2004).
In the methodology presented by Braglia et al. (2004) a classification is performed
considering a wide range of criteria. The chosen classification technique, AHP, uses a
comparison of criteria that presents as an end result, an orderly ranking of spare parts
based on the weights assigned to each criterion.
Journal of Management Analytics 7
Using only one criterion to classify spare parts is not appropriate considering all
their characteristics and specificities. In this way, multi-criteria classification should
be used. However, the use of too many criteria makes the model complex and difficult
to apply to the reality of industrial organizations since a large set of information needs
to be available and easily updated.
2.3. Criteria selection
The first stage of multi-criteria classification methods is the definition of significant
criteria.
According to Roda et al. (2014) maintenance managers consider the most impor-
tant spare parts those which their unavailability would result in severe consequences
for the production. On the other hand, inventory management and logistics managers
consider other parameters as more important, such as holding costs and demand pat-
terns that are important for the choice of a stock management policy. From a financial
point of view, the value of spare parts and the investment questions are the most sig-
nificant to define the importance of a spare part.
The most frequently used criteria are lead time, price, probability of failure and
number of potential suppliers (Bosnjakovic, 2010; Botter & Fortuin, 2000; Braglia
et al., 2004; Cakir & Canbolat, 2008; Molenaers et al., 2012; Stoll, Kopf, Schneider,
& Lanza, 2015), as indicated in Table 1.
The approach developed by Botter and Fortuin (2000) presents a case study in
which a pragmatic but structured approach is followed. The goal is to present a
Table 1. Criteria selection in case studies.
Criteria
Botter
and
Fortuin
(2000)
Braglia
et al.
(2004)
Cakir and
Canbolat
(2008)
Bosnjakovic
(2010)
Molenaers
et al. (2012)
Stoll
et al.
(2015)
Lead time X X X X X
Price X X X
Probability of
failure
XXX
Number of
potential
suppliers
XXX
Annual
demand
XX
Availability of
equipment
XX
Inventory
problem
XX
Availability of
technical
specifications
X
Installation
time
X
Others X X X X X X
8C. Teixeira et al.
solution to the control of service parts for the repair of professional electronic systems
on customer sites.
The method MASTA developed by Braglia et al. (2004) presents a model for
inventory management of spare parts. The model takes into account a set of attributes
for the classification of the parts in the paper industry. The model uses several criteria,
grouped into four types (spare part plant criticality, supply characteristics, inventory
problems and usage rate) each criterion has three levels: critical, important and
desirable.
In the study of Cakir and Canbolat (2008) a multi-criteria inventory classification
applied in a small electrical company is proposed. The classification combines the
potency of recent information technologies such as Java Servlets, MySql database
and the modeling principles of the fuzzy AHP methodology.
In the study of Bosnjakovic (2010) a methodology for spare parts inventory control
applying multi-criteria inventory model is proposed. It is based on ranking and clas-
sifying the spare parts in groups according to similar attributes. The used criteria for
classification are value-usage, criticality of spare parts (measured using four criteria:
plant production, supply, safety and inventory) and frequency of demand.
In the case study proposed by Molenaers et al. (2012) a multi-criteria classification
method based on spare parts criticality is presented. The contribution of their research
work was actual implementation of a classification method in an industrial
environment.
The approach developed by Stoll et al. (2015) was intended to evaluate spare parts
based on real inventory in cooperation with an industrial company. The goal was to
solve the problem of stockage of spare parts.
In the literature, several criteria have been used for the classification of spare parts.
But the wider the number of criteria considered to classify them, the greater the possi-
bility of the lack of recorded data for an adequate classification.
3. Research approach and methodology
In this section, the development of the multi-criteria classification methodology is pre-
sented. Initially, reference is made to the management of spare parts in the organiz-
ation in order to understand the main difficulties and problems that exist in the
management of spare parts. Subsequently, the main steps of the development of a
multi-criteria classification methodology are presented.
The spare parts management is carried out by a purchasing department (which
is not related to the maintenance department), that manages the spare parts stocks
to ensure the plant’s needs. The quantities of parts to order and the safety stock
value is defined by the information provided by the suppliers of the machines,
the experience of the maintenance department and the consumption history of
spare parts. These values are thus obtained taking into account criteria such as
the criticality of the machines (defined subjectively by the maintenance department)
or the unit cost.
The classification of the spare parts is carried out using two methods, the ABC
analysis based on a parameter, the consumption, and through the classification
FSN that analyzes the frequency of movements of the parts. At present, 18% of the
items in stock are classified as non-moving (FSN Classification). This classification
highlights spare parts that have not been moved for many years.
Journal of Management Analytics 9
The classifications based on ABC and FSN serves to identify the most important
spare parts and with greater inventory rotation. The information obtained from the
classification serves to put more attention on the parts, controlling the value in the
stock and the quantities to order. It is also used to analyze the performance of the
maintenance department regarding the consumption of spare parts.
Nevertheless, a single classifying criterion cannot generally represent the criticality
of an item. The classification only considers information related to the management of
inventories, namely it is not possible to know the information related to maintenance
management, the consequences that the inexistence of the spare part cause on
production. For instance, the lack of a slow-moving item can have a major impact
on productivity. Therefore, in the context of spare parts logistics, an approach that
includes criticality for the process as a fundamental criterion should be developed.
Spare parts management is not currently supported by the CMMS of the company.
The inventory management of spare parts is done by the enterprise resource planning
system using a “supermarket”of material that is used for maintenance actions.
The multi-criteria classification of spare parts was developed in order to assign an
adequate inventory policy to each spare part, combining a set of criteria.
The methodology for spare parts classification is divided into two steps (Figure 2),
the division is related with the fact that the purchasing department has insufficient
information about spare parts importance to maintenance and its impact on pro-
duction. The first step consists of defining criticality of spare parts for the process.
The criticality of spare parts is defined identifying the importance and need of spare
parts for production. In this case, it is intended to evaluate the consequences that
the lack of the spare part can bring to production. The main goal is to assign to
Figure 2. Inputs and outputs of the classification methodology.
10 C. Teixeira et al.
each spare part a level of criticality using three categories: Vital, Essential and Desir-
able. These terms have already been used in previous works. In this case, the meaning
of each category is the following:
.Vital: Part failure have a great impact on production processes;
.Essential: Part failure have a middle impact on production processes;
.Desirable: Part failure poses no risk to the production processes.
Therefore, the result of this step is to assign the spare parts to one of three levels of
criticality. This will be used in a second classification that aims to create groups of
spare parts sharing the same stock management policy.
Before defining the rules for criticality assignment, the most appropriate criteria
were selected. Two criteria, namely Function and Impact on Production, were defined.
The choice of the criteria was based on the analysis of the literature. This allowed to
know the importance of measuring the criticality of the spare parts, that is, to know the
impact that the failure of a certain spare part has on the productive process. The case
study showed that it was possible to obtain measurable information for each criterion.
The Function criterion is divided into three levels and the criterion Impact on Pro-
duction in four levels. Tabl e 2 presents the description of the criteria and respective levels.
In the second step criteria related to inventory management are added. With this
step, the methodology considers issues not only related to maintenance and pro-
duction but also issues related to inventory management.
The information related to the price and lead time criteria is obtained from the
department responsible for the purchase and management of spare parts. In this
way, it is intended to aggregate the information of this department to the maintenance
department in the same methodology of classification of spare parts.
Lead time is a relevant aspect to consider in spare parts classification. Logistically,
there are delays between the order of spares and their arrival. This situation is even
Table 2. List e description of criteria.
Criteria Description
Function The function performed by the spare part in the production process
1. Auxiliary function The spare part function consists on supporting the equipment
operation, it does not interfere directly on production (e.g. control,
comfort, structural integrity, economy, prevent misuse).
2. Safety function The spare parts function is to preserve operator safety, it may not
directly interfere on production
3. Indispensable
function
The spare part is involved in a primary function of the equipment
Production Impact Impact of the spare part failure on the production process
0. No Impact The spare part failure has no impact on production.
1. Quality losses The spare part failure causes defective products.
2. Productivity
reduction
The spare part failure causes a reduction in production rate.
3. Sudden stop The spare part failure causes an immediate stop of the machine,
causing the total equipment shutdown.
Journal of Management Analytics 11
more crucial when spare parts are vital since spare parts are not always available at the
supplier (Godoy, Pascual, & Knights, 2013).
Part cost (unit or inventory cost) is the most popular criteria (Bacchetti, Plebani,
Saccani, & Syntetos, 2010). In this case, the unit price is used for the classification.
4. Industrial case study
Spare parts management is not currently included in the CMMS of the company.
Management of spare parts is performed by a department that ensures all the
plant’s needs. For a more efficient and effective use of CMMS, it is intended that
CMMS be able to provide information relevant to the inventory management of
spare parts.
In this section, an application example of the classification methodology is pre-
sented. The development of the classification methodology was based on the infor-
mation and data made available by the organization. In this case, there are 16,094
spare parts registered in the organization’sinformationsystem.Forthestudyofthe
Price and Lead Time criteria, the information regarding the 16,094 spare parts was used.
The first step of the classification process, the identification of spare parts critical-
ity, is validated using a set of spare parts. In the second step of the classification, the
criteria and methodology are presented.
4.1. Criticality of spare parts
After defining the criteria and the respective levels, a method should be used to obtain
the three levels of criticality. In this case, a matrix of combinations that aims to associ-
ate the levels of the two criteria (function and impact on production) was used. It was
verified that both criteria have the same importance, therefore, the levels of the Func-
tion criterion were ordered from 1 to 3, and the levels of the Impact on Production
criterion were ordered from 0 to 3. In both cases, the smaller number represents the
less relevant level. The matrix considers the 12 possible combinations (Figure 3).
The combination matrix has been validated, with the help of the company, by analyz-
ing several different parts. This study reveals that among the 12 possible combinations,
3 never occur. For this reason, the combination matrix may have combinations that do
not occur depending on the organization where it is applied.
The result of this first step is the assignment of three levels of criticality to spare
parts: Vital, Essential and Desirable. The distribution of the 12 combinations for
the three categories was performed taking into account what is considered vital, essen-
tial and desirable for the organization.
Table 3 presents a sample of the analyzed spare parts, each one corresponding to
the nine existing combinations, as well as the corresponding level of criticality.
Figure 3. The combinations matrix.
12 C. Teixeira et al.
4.2. Multi-criteria classification
After defining the criticality of the spare parts, it is necessary to analyze their prices
and lead times. Therefore, a study was carried out taking into account all the spare
parts to define the most appropriate intervals.
The definition of the levels related to the lead time was performed using an analysis
of the lead time values observed in the organization. Tab le 4 shows the absolute and
relative frequencies for the lead time values. Through this table and the experience of
the maintenance team, the three levels were defined. The low level represents 28.71%
Table 3. Spare parts classification example.
Description Function Production Impact Sum Classification
Cable M12 3 2 5 Essential
Grippe finger –milling cutters 3 3 6 Vital
Vacuum cleaner bag Ringler 1 0 1 Desirable
Emergency button –ASI 2 3 5 Vital
ET-30 Emergency (stop) 2 0 2 Desirable
Interlock switch 2 2 4 Essential
Needles ICT 3 1 4 Essential
Support scanner 1 2 3 Essential
Glass selective 1 1 2 Desirable
Table 4. Lead time levels.
Lead
time
(Days)
Absolute
frequency
Accumulated absolute
frequency
Accumulated relative
frequency (%) Levels
0 1 1 0.01 Low
1 53 54 0.34
2 2375 2429 15.09
3 59 2488 15.46
5 2201 4689 29.14
8 114 4803 29.84 Medium
10 369 5172 32.14
14 2 5174 32.15
15 7520 12,694 78.87
20 52 12,746 79.20 High
21 11 12,757 79.27
25 3 12,760 79.28
30 2938 15,698 97.54
35 1 15,699 97.55
40 16 15,715 97.65
50 2 15,717 97.66
60 57 15,774 98.01
75 1 15,775 98.02
90 319 16,094 100.00
Journal of Management Analytics 13
of the lead time values, the mean level represents 49.6% and the high level represents
21.69% of the values.
The levels of the price criterion were defined according to what the organization
considers to be a high, medium or low price.
Table 5 shows that a large percentage of spare parts is contained at the low level
(87%) so the price criterion is intended to be crucial only when the value of the
spare part is medium or high.
To analyze the distribution of spare parts according to the unit price, a histogram
of unit prices was built and is presented in Figure 4. The histogram shows that prices
are mostly in the first class, i.e. 77.70% of prices are in the range [0; 84]. The remaining
22.3% of the spare parts prices are distributed in the remaining classes. The last class
has a range of values included in [1955; 35,677]. The minimum price registered for
spare parts is €0.01 which is the unit price of Spotlight Bearing Ball, Bit Holder,
among others. The maximum price registered is 35,676.07€which is the price of the
Head unit –High-Speed.
Next, ranges for the possible quantitative and qualitative results of each criterion
are defined in Table 6. The levels assigned to price and lead time criteria are adjustable
to the organization where this methodology may be applied.
After the definition of criteria and respective levels, it is necessary to select the
method for their comparison. Thus, the use of a decision tree is proposed. Figure 5
Table 5. Price levels.
Low Medium High
Levels ≤300€>300€and ≤1500€>1500€
Percentage 87 10 3
Figure 4. Distribution of spare parts unit prices.
14 C. Teixeira et al.
shows an example. In this case, the first criterion to be taken into account is criticality,
the second is the lead time and the third is the price of the spare part.
Therefore, each spare part is assigned to one of four different groups designated by
A, B, C and D. For each group, the stock management policy most appropriate to the
characteristics of the spare parts will be assigned.
Table 7 shows an example of spare parts classification through a small sample. The
table shows the information about the three criteria (Criticality, Lead time and Price)
used in the multi-criteria classification for a small sample and in the column “inventory
policy”is the group that would be assigned to the spare part. This result is achieved
using the decision tree mentioned above.
Table 6. Criteria levels.
Criteria /Levels Criticality Lead time Price
High Vital >3 weeks >1500 €
Medium Essential >5 days and ≤3 weeks >300 €and ≤1500€
Low Desirable ≤5days ≤300 €
Figure 5. Decision tree for the classification example.
Journal of Management Analytics 15
Table 7. Spare parts multi-criteria classification example.
Material Description Criticality
Criticality
level
Lead
time
Lead time
level Price
Price
level
Annual
consumption
2016
Inventory
policy
8600 862,658 Gripper finger –milling
cutters
Vital High 15 Medium 32.53 €Low 13 Group C
8600 862,886 Light bulb 24 V/2.6 W Desirable Low 15 Medium 2.00 €Low 0 Group C
8600 859,133 Ultrasonic sensor
microsonic
Vital High 15 Medium 152.10 €Low 1 Group C
8600 857,167 Interlock switch –
AB1450
Essencial Medium 15 Medium 80.45 €Low 0 Group D
8600 859,400 Needles ICT Essencial Medium 2 Low 0.46 €Low 26,987 Group A
8600 856,903 PRINTHEAD 300 DPI,
W-6308;20-2195-01
Essencial Medium 2 Low 694.00 €Medium 13 Group B
8600 863,419 Pump 9.7 mm; 231,976 Vital High 5 Low 4,330.68 €High 3 Group B
16 C. Teixeira et al.
Finally, it is important to define the most appropriate stock management policy for
each group of spare parts. In this case as an example, four stock management policies
may be proposed, as explained in Table 8.
In defining the parameters associated with the stock management policy, two
aspects should be taken into account: the number of machines for which the spare
parts are used and the demand for spare parts. To determine spare parts demand,
reliability studies should be performed. In the case of parts that are periodically
replaced (preventive interventions), it will be necessary to know the probability of
failure for the item during the preventive maintenance interval.
5. Conclusions
Manufacturing organizations depend on the availability of their equipment to
produce. Spare parts are important to maintain the efficiency and the good function-
ing of the equipment, avoiding losses of production.
The management of spare parts represents a complex problem for organizations
due to the large number of items involved, the amount of information to be considered
and the difficulties in collecting this data.
High inventory levels are very expensive due to both capital immobilization and
storage space. Thus, many studies are conducted in an attempt to optimize the
number of spares to be stored. For this, the factors affecting the decisions concerning
acquisition and storage must be known.
A spare part plays a key role in maintenance management and therefore can have a
strong impact on production. However, the criteria and methodologies used for their
classification and management should be different from those commonly used for the
management of finished goods inventories.
This paper presents a multi-criteria classification methodology for spare parts
management which combines maintenance and logistics perspectives and uses infor-
mation that is generally available in most organizations.
The first step intends to assign a level of criticality to each spare part. For this, two
criteria were used: function and production impact. The second step aims to classify
each spare part using two more criteria: lead time and price.
This multi-criteria classification methodology was validated through a case study
using a sample of spare parts made available by an automotive company.
Table 8. Inventory policies.
Inventory policy Description
No stock Unavailability of a spare part is a conscious decision
One spare part in
stock
This management policy implies ordering just when the spare part is
taken from stock.
Multi spare part
inventory
.Model 1
.Model 2
It implies stocking more than one piece of a particular item. Inventory
reorder level, safety stock and the size of replenishment orders have to
be calculated. More than a model can be settled down.
Journal of Management Analytics 17
In the future, it is necessary to assign the most appropriate stock management pol-
icies to each group of spare parts that resulted from the classification and choose the
adequate values for the parameters associated with each policy.
The development of this multi-criteria sorting tool will help the organization to
make better decisions concerning management of spare parts based on the quantitat-
ive and objective information.
Disclosure statement
No potential conflict of interest was reported by the authors.
Funding
This work was supported by European Structural and Investment Funds in the FEDER com-
ponent, through the Operational Competitiveness and Internationalization Programme
(COMPETE 2020) [Project n° 002814; Funding Reference: POCI-01-0247-FEDER-002814].
ORCID
Isabel Lopes http://orcid.org/0000-0002-8958-449X
References
Altay Guvenir, H., & Erel, E. (1998). Multicriteria inventory classification using a genetic algor-
ithm. European Journal of Operational Research,105(1), 29–37. doi:10.1016/S0377-2217
(97)00039-8
Almomani, M., Abdelhadi, A., Seifoddini, H., & Xiaohang, Y. (2012). Preventive maintenance
planning using group technology. Journal of Quality in Maintenance Engineering,18(4), 472–
480. doi:10.1108/13552511211281624
Aronis, K.-P., Magou, I., Dekker, R., & Tagaras, G. (2004). Inventory control of spare parts
using a Bayesian approach: A case study. European Journal of Operational Research,154
(3), 730–739. doi:10.1016/S0377-2217(02)00837-8
Bacchetti, A., Plebani, R., Saccani, N., & Syntetos, A. (2010). Spare parts classification and
inventory management: A case study. Salford Business School Working Papers Series,408,
1–36.
Bevilacqua, M., & Braglia, M. (2000). The analytic hierarchy process applied to maintenance
strategy selection. Reliability Engineering & System Safety,70(1), 71–83. doi:10.1016/
S0951-8320(00)00047-8
Bosnjakovic, M. (2010). Multicriteria inventory model for spare parts. Technical Gazette,17(4),
499–504.
Botter, R., & Fortuin, L. (2000). Stocking strategy for service parts –a case study. International
Journal of Operations & Production Management,20(6), 656–674. doi:10.1108/
01443570010321612
Braglia, M., Grassi, A., & Montanari, R. (2004). Multi-attribute classification method for spare
parts inventory management. Journal of Quality in Maintenance Engineering,10(1), 55–65.
doi:10.1108/13552510410526875
Cakir, O., & Canbolat, M. S. (2008). A web-based decision support system for multi-criteria
inventory classification using fuzzy AHP methodology. Expert Systems with Applications,
35(3), 1367–1378. doi:10.1016/j.eswa.2007.08.041
Cavalieri, S., Garetti, M., Macchi, M., & Pinto, R. (2008). A decision-making framework for
managing maintenance spare parts. Production Planning & Control,19(4), 379–396. doi:10.
1080/09537280802034471
18 C. Teixeira et al.
Chen, X., Ren, H., Bil, C., & Sun, Y. (2015). Integration of structural health monitoring with
scheduled maintenance of aircraft composite structures. International Journal of Agile
Systems and Management,8(3–4), 264–283.
Dekker, R. (1996). Applications of maintenance optimization models: A review and analysis.
Reliability Engineering & System Safety,51(3), 229–240. doi:10.1016/0951-8320(95)00076-3
Dekker, R., Kleijn, M. J., & de Rooij, P. J. (1998). A spare parts stocking policy based on equip-
ment criticality. International Journal of Production Economics,56–57,69–77. doi:10.1016/
S0925-5273(97)00050-9
Flores, B. (1987). Implementing multiple criteria ABC analysis. International Journal of
Operations & Production Management,7(1–2), 79–85. doi:10.1016/0272-6963(87)90008-8
Gajpal, P. P., Ganesh, L. S., & Rajendran, C. (1994). Criticality analysis of spare parts using the
analytic hierarchy process. International Journal of Production Economics,35(1–3), 293–297.
doi:10.1016/0925-5273(94)90095-7
Gass, S. I., & Rapcsák, T. (2004). Singular value decomposition in AHP. European Journal of
Operational Research,154(3), 573–584. doi:10.1016/S0377-2217(02)00755-5
Ghobbar, A. A., & Friend, C. H. (2002). Sources of intermittent demand for aircraft spare parts
within airline operations. Journal of Air Transport Management,8(4), 221–231. doi:10.1016/
S0969-6997(01)00054-0
Godoy, D. R., Pascual, R., & Knights, P. (2013). Critical spare parts ordering decisions using
conditional reliability and stochastic lead time. Reliability Engineering and System Safety,
119, 199–206. doi:10.1016/j.ress.2013.05.026
Gopalakrishnan, P., & Banerji, A. K. (2015). Maintenance and spare parts management.
New Delhi: PHI Learning.
Hatefi, S., Torabi, S., & Bagheri, P. (2014). Multi-criteria ABC inventory classification with
mixed quantitative and qualitative criteria. International Journal of Production Research,
52(3), 776–786. doi:10.1080/00207543.2013.838328
Huiskonen, J. (2001). Maintenance spare parts logistics: Special characteristics and strategic
choices. International Journal of Production Economics,71(1–3), 125–133. doi:10.1016/
S0925-5273(00)00112-2
Kennedy, W. J., Wayne Patterson, J., & Fredendall, L. D. (2002). An overview of recent literature
on spare parts inventories. International Journal of Production Economics,76(2), 201–215.
doi:10.1016/S0925-5273(01)00174-8
Labib, A. W. (2004). A decision analysis model for maintenance policy selection using a CMMS.
Journal of Quality in Maintenance Engineering,10(3), 191–202. doi:10.1108/
13552510410553244
Lenard, J. D., & Roy, B. (1995). Multi-item inventory control: A multicriteria view. European
Journal of Operational Research,87(3), 685–692. doi:10.1016/0377-2217(95)00239-1
Lopes, I., Senra, P., Vilarinho, S., Sá, V., Teixeira, C., Lopes, J., …Figueiredo, M. (2016).
Requirements specification of a computerized maintenance management system –A case
study. Procedia CIRP,52, 268–273. doi:10.1016/j.procir.2016.07.047
Lynch, P., Adendorff, K., Yadavalli, V. S. S., & Adetunji, O. (2013). Optimal spares and preven-
tive maintenance frequencies for constrained industrial systems. Computers and Industrial
Engineering,65(3), 378–387. doi:10.1016/j.cie.2013.03.005
Molenaers, A., Baets, H., Pintelon, L., & Waeyenbergh, G. (2012). Criticality classification of
spare parts: A case study. International Journal of Production Economics,140(2), 570–578.
doi:10.1016/j.ijpe.2011.08.013
Mukhopadhyay, S. K., Pathak, K., & Guddu, K. (2003). Development of decision support
system for stock control at area level in mines. IE (I) Journal-MN,84,11–16.
Murthy, D. N. P., Atrens, A., & Eccleston, J. A. (2002). Strategic maintenance management.
Journal of Quality in Maintenance Engineering,8(4), 287–305. doi:10.1108/
13552510810877674
Prakash, J., & Chin, J. F. (2017). Effects of inventory classifications on CONWIP system: A case
study. Journal of Management Analytics,4(3), 296–320. https://doi.org/10.1080/23270012.
2017.1344938
NP EN 13306. (2007). Terminologia da Manutenção. Tempo.
Journal of Management Analytics 19
Ramanathan, R. (2006). ABC inventory classification with multiple-criteria using weighted
linear optimization. Computers & Operations Research,33(3), 695–700. doi:10.1016/j.cor.
2004.07.014
Roda, I., Macchi, M., Fumagalli, L., & Viveros, P. (2012). On the classification of spare parts
with a multi-criteria perspective. IFAC proceedings volumes (IFAC-PapersOnline), (section
2), 19–24.
Roda, I., Macchi, M., Fumagalli, L., & Viveros, P. (2014). A review of multi-criteria classifi-
cation of spare parts : From literature analysis to industrial evidences. Journal of
Manufacturing Technology Management,25(4), 528–549. doi:10.1108/JMTM-12-2013-0185
Shahin, A., & Janatyan, N. (2010). Group technology (GT) and lean production: A conceptual
model for enhancing productivity. International Business Research,3(4), 105–118. Retrieved
from http://search.ebscohost.com/login.aspx?direct=true&db=buh&AN=55426085&site=
ehost-live&scope=site
Shtubt, A. (1989). Modelling group technology cell formation as a generalized assignment
problem. International Journal of Production Research,27(5), 775–782. doi:10.1080/
00207548908942586
Stoll, J., Kopf, R., Schneider, J., & Lanza, G. (2015). Criticality analysis of spare parts manage-
ment: A multi-criteria classification regarding a cross-plant central warehouse strategy.
Production Engineering,9(2), 225–235. doi:10.1007/s11740-015-0602-2
Subramanian, N., & Ramanathan, R. (2012). A review of applications of analytic hierarchy
process in operations management. International Journal of Production Economics,138(2),
215–241. doi:10.1016/j.ijpe.2012.03.036
Syntetos, A. A., Babai, M. Z., & Altay, N. (2012). On the demand distributions of spare parts.
International Journal of Production Research,50(8), 2101–2117. doi:10.1080/00207543.2011.
562561
Talukder, S., & Knapp, G. M. (2002). Equipment assignment to multiple overhaul blocks in
series systems. Journal of Quality in Maintenance Engineering,8(4), 319–330. doi:10.1108/
13552510210448522
Teixeira, C., Lopes, I., & Figueiredo, M. (2017). Multi-criteria classification for spare parts
management: A case study. Procedia Manufacturing,11, 1560–1567. doi:10.1016/j.promfg.
2017.07.295
Triantaphyllou, E., & Mann, S. H. (1995). Using the analytic hierarchy process for decision
making in engineering applications : Some challenges. International Journal of Industrial
Engineering: Theory, Applications and Practice,2(1), 35–44.
Van Horenbeek, A., Buré, J., Cattrysse, D., Pintelon, L., & Vansteenwegen, P. (2013). Joint
maintenance and inventory optimization systems: A review. International Journal of
Production Economics,143(2), 499–508. doi:10.1016/j.ijpe.2012.04.001
Viriyasitavat, W. (2016). Multi-criteria selection for services selection in service workflow.
Journal of Industrial Information Integration,1,20–25. https://doi.org/10.1016/j.jii.2016.03.
003
Wang, Y., Ji, W., & Chaudhry, S. S. (2014). A hybrid approach for the evaluation of supermarket
food safety. Journal of Management Analytics,1(2), 156–167. https://doi.org/10.1080/
23270012.2014.969790
Xu, S., Xu, L. Da, & Chen, X. (2003). Determining optimum edible films for kiwifruits using an
analytical hierarchy process. Computers & Operations Research,30(6), 877–886. https://doi.
org/10.1016/S0305-0548(02)00040-0
Zhu, S., Dekker, R., van Jaarsveld, W., Renjie, R. W., & Koning, A. J. (2017). An improved
method for forecasting spare parts demand using extreme value theory. European Journal
of Operational Research,261(1), 169–181. doi:10.1016/j.ejor.2017.01.053
Zorgdrager, M., Verhagen, W. J. C., & Curran, R. (2014). An evaluation of forecasting methods
for aircraft non-routine maintenance material demand. International Journal of Agile
Systems and Management,7(3–4), 383–402. doi:10.1504/IJASM.2014.065354
20 C. Teixeira et al.
