CLUSTERING THE TECHNICAL CAPABILITY IN THE BRAZILIAN
AERONAUTICAL MAINTENANCE INDUSTRY
Marcio Cardoso Machado
Professor of Production Department, Mechanical Engineering Division,
Aeronautical
Email:
cardoso@ita.br.
Ligia
Maria Soto Urbina
Professor of Production Department, Mechanical Engineering Division,
Aeronautical
Email:
ligia@ita.br.
Rodrigo
Arnaldo Scarpel
Professor of Production Department, Mechanical Engineering Division,
Aeronautical
Email: rodrigo@ita.br.
Acknowledgment
Coordenação de Aperfeiçoamento de Pessoal de Nível
Submission:
Accept:
Abstract
In
the sectors where equipment requiring services of maintenance are
technologically complex and advanced, such as in the air transportation,
knowing and managing the technical capability of the enterprises of the sector
can be a good way maximizing the efforts of training. This paper attempts to
present a process of technical capability clustering for the aeronautical
maintenance industry, in order to provide a usable overview of the sector
competences. The findings present a unique insight into the understanding of
competences clustering that may be used across different industries.
Keywords: aeronautical maintenance;
technical capability; clustering process.
Aircraft maintenance is
a highly regulated, safety critical, complex and competitive industry (BRUECKER, et al. 2012). and resulting activities
are an essential part of the continued airworthiness, aiming to provide, both,
in civilian and military areas, the total service condition for the aircraft at
the time that an operator request, with the expected quality and minimal cost
(KNOTTS, 1999). This service is extremely important to support the air
transportation in countries like Brazil, which are characterized by having a
large territory with strong civilian and military air traffic, linking the
various regions of the country. Regarding this subject, a review of relevant
academic literature showed that this subject has received little attention in
the academic community, opening up opportunities for research. In fact, it is
possible to note that several studies focus on issues relating to the conduct
of management and technical-economic activities, at sector or industry level.
Thus,
Phillips et al. (2010) studies review
current aircraft maintenance practices, while Machado et al. (2009) make a preliminary analysis of the managerial
capability of Brazilian maintenance companies for aeronautical equipment, using
as reference an European maintenance process model (EURSPACE, 2003). Also,
Durand (2008) studies relevant aspects of aircraft maintenance, related to the
expected changes in the American Air Force maintenance organizational
structure, as a result of implementing a resource planning system (Expeditionary
Combat Support System).
Some
of the papers found in the literature review explore the aircraft maintenance
from other perspectives that seek to improve the efficiency of the sector. As
an example, Rodrigues et al. (2010) study the costs perspective, while Papakostas
et al. (2010) focus their efforts in the selection of maintenance strategies.
Vilela et al. (2010) examines the relationship of accidents with aircraft
maintenance and operational safety recommendations. Other authors focus on
classical maintenance subjects like scheduling aircraft maintenance personnel (DE BRUECKER; VAN
DEN BERGH; BELIEN; DEMEULEMEESTER, 2012); estimate the probability of
failure for complex systems (JACOB; DUBOIS; CARDOSO; CEBERIO;
KREINOVICH, 2011), maintenance planning (SAMARANAYAKE, 2006;
SAMARANAYAKE, et al., 2007) and human risk factors in aircraft maintenance
technicians (CHANG; WANG, 2010).
In
this context, this paper aims to expand knowledge about the technical
capability aircraft maintenance industry in Brazil exploring the information
provided by Agência Nacional de Aviação Civil (ANAC), with the main goal of
presenting a process of technical capability clustering for the aeronautical
maintenance industry, in order to provide a usable overview of the sector
competences.
More
specifically, this paper begins with an exposition of the basic concepts of
maintenance activity in general and aviation in particular. The following is an
exploratory research carried out from secondary data in order to detect
technical patterns of the aeronautical maintenance in the Brazilian regulatory
context, which circumscribes and certifies the activities that companies have
the technical competence to perform. Finally, it was possible to present a
unique insight into the understanding of competences clustering.
To
achieve world class performance, companies are struggling efforts to improve
quality and productivity and reduce costs (MISHRA et al., 2006). For several
companies, some of these efforts should include an analysis of the maintenance
function activities. An effective maintenance is essential for many operations.
It is possible through that, to extend the product life cycle, improve the
equipment availability and keep them in good conditions. On the other hand,
maintenance neglect can lead to more frequent failures, equipment
underutilization and the consequent delay in production schedules. In
accordance with Niu, et al. (2010) and Muchiri, et al. (2011), maintenance, as
a strategic function to support business, plays an important role in supporting
the production function and its management. In fact, besides maintaining
equipment functioning, maintenance management also supports the good
performance or even implementation of production management techniques such as
lean manufacturing, just-in-time and six-sigma. The effectiveness in the
maintenance management depends on the appropriate deployment of resources such
as replacement parts, tools, equipment or workforce. This feature imposes a
strategic approach to the maintenance activities. To consider maintenance just
as a tactical element in companies is a limited view. In fact, such function
also has a strategic dimension with implications for the facilities project and
maintenance programs, upgrading knowledge and skills of the workforce and
deploying the work load and tools for the accomplishment of the various
maintenance activities. The maintenance management becomes, therefore, an
important element to be studied scientifically and that is what has happened.
For Sherwin (2000), maintenance systems should be tailored to the nature of
work that will be managed. In other words, in the steel industry, for example,
will be established procedures for maintenance management that will differ from
those one used in the aerospace industry.
The
aircraft maintenance can be divided into two activities that, despite being
fully associated, possess different characteristics. The first activity is
related to aircraft maintenance as single equipment, and the second activity
concerns components maintenance that will serve as inputs to the first one.
This distinction is necessary because the aircraft maintenance operations
follow rules that go beyond the technical expertise necessary to perform
maintenance activities.
Aircraft
maintenance can also be classified as preventive maintenance (hard time and
condition monitoring), corrective maintenance (corrective) or predictive
maintenance (on condition) (KNOTTS, 1999).
§
preventive maintenance – According to Soro, et al. (2010), preventive
maintenance is the practice of replacing components or subsystems before they
fail, usually with predetermined frequency (hard time) or due to inspection and
test (condition monitoring). The goal is to maintain continuous operation of
the system, in this case the aircraft;
§
corrective maintenance – In accordance with Moayed and Shell (2009) this is
one that occurs after the identification and diagnosis of a problem. During
this diagnostic maintenance technicians have to identify the failed parties to
implement their correct actions and repair;
§
predictive maintenance - it takes into account the continuous monitoring of
the operational limits of a given component or subsystem. If any tendency for
the occurrence of a component or subsystem functional failure appears, it
should be removed for maintenance. Some mechanisms for the implementation of
predictive maintenance are the PDM (Product Data Management) and PHM (Product
History Management).
Any
company that wants to be classified as an aircraft repair station, should
submit a request to the (ANAC) for a certification, specifying which aircraft,
engine, propeller, rotor, equipment or component, they will perform the
maintenance service. Based on Brazilian Civil Aviation Regulation RBHA 145 (
Certified
companies from the RBHA 145 will be classified according to their competence.
Thus, when applying for a certification, the company should specify to ANAC the
equipment for which it intends to offer the maintenance service, so that if
approved, it will be classify into one or more patterns and classes of
maintenance. This classification prevents that the repair station offers
services than they are not licensed to perform. Understanding how aircraft
maintenance enterprises combine their certifications, can be an important way
to provide a usable overview of the sector competences and an insight into the
understanding of competences of this industry (
The
Brazilian Repair Station Certificates issued by the ANAC, refers to aircraft
maintenance companies and they are based on patterns and classes as shown in Table
1.
Table 1 – Aircraft
Maintenance Companies Patters and Classes
Pattern |
Class |
Pattern
C – Maintenance, modifications and cells repair. |
(C1) - Composite
structure aircraft, with maximum approved takeoff weight up to (C2)- Metal structure
aircraft, with maximum approved takeoff weight up to 5670kg (aircraft) or (C3)- Composite
structure aircraft, with maximum approved takeoff weight over (C4)-
Metal structure aircraft, with maximum approved takeoff weight over 5670kg
(aircraft) or |
Pattern
D – Maintenance, modifications and aircraft engines repair. |
(D1) – Conventional engines with up to 400 H.P., per model. (D2) - Conventional engines with over 400 H.P., per model. (D3)
– Turbine engines, per model. |
Pattern
E – Maintenance, modifications, and aircraft propellers and rotors repair. |
(E1) - Wood propellers, metal or composite, fixed pitch, per model. (E2) – All other propellers, per model. (E3) – Helicopters
rotors, per model. |
Pattern
F – Maintenance and aircraft equipment repair. |
(F1) - Communications and navigation aircraft equipment, per model (F2) - Aircraft
instruments, per instrument type. (F3) - Mechanical
accessories, aircraft electrical and electronics, per accessory model. |
Pattern H – Specialized services. |
(H) - Single Class -
Specific activities for the maintenance implementation that aeronautical
authority upheld, per type service (e.g., nondestructive testing, floats,
emergency equipment, rotor shovels, screen coating). |
Source:
The
number of combined certifications (companies that possesses two certifications
simultaneously) regarding to different classes and patterns of certifications
are shown in Table 2. Diagonally it
is possible to note the absolute total of certifications for each class and
pattern and also the number of companies certified in more than one class and
pattern of certificate are shown combining lines and columns.
Therefore,
data in Table 2 demonstrate
that, from the total of 279 certificates issued to the C2 type, 115 were issued
for aircraft repair stations that also have certifications D1 type. In the same
way, from the total of 106 certifications issued for the F2 type, 100 were
issued for aircraft repair stations that also have certifications F3 type.
Table
2: Combined certifications matrix
However,
according to Fávaro et al. (2009), an important aspect to be considered in a
cluster analysis is the use of variables with different measures, which can
lead to a distortion of the group structure. This influence of variables
different magnitudes can be solved with variables standardization.
Thus,
the data presented in Table 2 were
standardized by the maximum amplitude method, which attributes to each variable
the maximum value of 1, and is calculated by dividing the value of each
variable by the maximum value of the class analyzed. shows those standardized
values of the variables obtained from Table 2.
Table
3: Standardized Combined Certifications Matrix.
In
Table 3, it is possible to verify the different similarity degrees between
different classes of certification. The closer the values are to 1, the higher
is the level of composed certifications. In order to a more accurate analysis
it was applied generating clusters method.
4.1.
Generating
Clusters Method
According
to Wedel and
There
are two different methods of generating no overlapping clusters commonly
distinguished: hierarchical methods and nonhierarchical. Hierarchical methods
do not identify a set of clusters directly. These methods identify hierarchical
relationships between objects by using some measure of similarity between them.
Some examples of hierarchical methods are the single-link, complete-link,
group-average, centroid clustering and Ward's method. Nonhierarchical methods
derive clusters from the sample directly from a data matrix, typically by
optimizing an objective function. The methods k-means and k-harmonic means are
examples of non-hierarchical methods in which a quadratic function is
minimized.
Regarding
fuzzy method, two different methods of generating clusters can be
distinguished: procedures based on fuzzy set theory and mixture procedures.
Mixture procedures assume segments are no overlapping, but due to the limited
information presented in data, subjects are assigned to segments with
uncertainty, reflected in probabilities of each cluster, while the fuzzy
procedures assume that consumers have partial membership in several segments (WEDEL;
KAMAKURA, 2000).
A
classification scheme per clustering method elaborated by Wedel and Kamakura
(2000) is presented in Figure 1.
In
this study it was chosen the hierarchical method of generating clusters, since,
according to Webb (2002), this is the most commonly used method to summarize
data, which is the goal of this study.
Figure 1: Classification of Clustering Methods Source:
Wedel and |
4.2.
Clustering
Hierarchical Methods
Clustering
hierarchical methods are widely applied in different knowledge areas.
Hierarchical classifications typically result in a dendrogram, a tree structure
that represents the hierarchical relations among all objects being clustered.
According to Wedel and
Hierarchical
cluster algorithms operate on the basis of the relative dissimilarity of the
objects being clustered. A variety of similarity, dissimilarity and distance
measures can be used in hierarchical cluster analysis. Those similarity
measures assess the strength of the relationship between the objects clustered
and are derived from the variables measured on the objects. According to Wedel
and
Agglomerative
hierarchical algorithms are the most commonly used hierarchical methods and
they work in the following way (MINGOTI; LIMA, 2006): in the first stage each
of the N objects to be clustered is considered as a unique cluster. The objects
are then, compared among themselves by using a measure of distance such as
Euclidean, for example. The two clusters with smaller distance are joined. The
same procedure is repeated over and over again until the desirable number of
clusters is achieved. Only two clusters can be joined in each stage and they
cannot be separated after they are joined. A linkage method is used to compare
the clusters in each stage and also to decide which of them should be combined.
Table
4 – Most commonly used dissimilarity measures
Measures |
Formula |
Euclidean Distance |
|
Correlation Coefficient |
|
City Block Distance |
|
Mahalanobis Distance |
|
Minkowski Distance |
|
Angular Distance |
|
|
|
Source:
Wedel and
The Table 5 presents
clusters dissimilarity definitions for some of the most commonly used methods.
According to Johnson and Wichern (2002) apud Mingoti and
Table
5 – Most common used cluster dissimilarity relation
Algorithm |
Recurrence relation of distance |
Single Linkage |
Shortest distance between two
cluster members |
Complete Linkage |
Greatest distance between two
cluster members |
Average Linkage |
Average distance between two
cluster members |
Centroid Linkage |
Distance between segment averages
of variables |
Ward |
Minimum increase in total sum of
squares |
Source: Johnson and Wichern (2002) apud Mingoti and
4.3.
Certifications
Cluster
In
this study, data were classified by using information from the standardized
matrix shown in Table 3. To perform the hierarchical analysis of clusters
generation, it was chosen Euclidean distance with subsequent application of the
Ward method, due to the quantitative analysis.
The Figure 2 presents the
aeronautical maintenance homologation certificates cluster dendrogram by
pattern.
Figure
2 – Certifications cluster dendrogram.
When
the clusters are observed, it can be verified that in the first cluster, from
left to right, the E3 pattern (Helicopter Rotors) is in an isolated branch,
situation justified by its specificity.
Still
in the first cluster, the “D3” patterns (turbine engines) and “F3” (aircraft
mechanical, electrical and electronic accessories) are in the same branch,
which is also justified as the turbine engines have a large quantity of
accessories that also need specific maintenance. The “D3-F3” pattern branches
are associated to the “C4-H” pattern branches (“C4” related to metallic
structure aircrafts, with maximum takeoff approved weight above
In
the second cluster, it can be observed that the “C3” pattern (combined structure
aircraft, with maximum takeoff approved weight above
In
the third cluster, it is possible to identify a certification cluster for
propellers maintenance certifications (“E1 and E2”), which is justifiable by
it.
In
the fourth cluster, are concentrated companies certifications that perform
maintenance in conventional engines, “D1 and D2” pattern, along with “C1 and
C2” patterns, associated to modifications and repairs of smaller aircrafts
cells, which consequently use, generally, conventional engines.
This
analysis, from the dendrogram, enabled to find out that the aeronautical
maintenance companies are trying to certify themselves into groups and
maintenance classes patterns that will possibly increase their services scope
for certain types of aircraft. Therefore, we can say that the training of the
workforce must also follow the same structure as identified in the dendrogram.
However, the high degree of specialization of technical training can make it
difficult for the same technician has different capabilities, generating costs
in hiring. These issues may be targeted for future research.
Aircraft
maintenance activities are an essential part of the continued airworthiness,
aiming to provide, both, in civilian and military areas, the total service
condition for the aircraft at the time that an operator request, with the
expected quality and minimal cost. This service is extremely important to
support the air transportation in countries like
It is
observed that, depending on the complexity and the advancement of the
technologies used in aircraft, technical training required for the aircraft
maintenance is too qualified, requiring that companies have on their staffs,
human resources qualified to maintenance activities they intend to accomplish.
Finding this qualified workforce cannot be so easy, since hiring skilled labor
is a problem that affects not only the aviation industry but also other sectors
those necessities a workforce with a similar level of technical skills.
This
paper has presented a process of technical training clustering in aircraft
maintenance industry that offers a useful overview of the skills sector. More
than that, the clustering process implies that the process of qualification of
the workforce can be achieved by combining the different skills identified in
each cluster.
The clustering process used in this work follows a basic methodology of
clustering, however the insights arising from the clustering process is unique
to the understanding of the characteristics of technical training in a
particular sector of the economy. From this process of clustering is possible
to question the way it gives the training of the workforce for the sector.
Finally,
it is possible to infer that the clustering process can be useful across
different sectors, given that there are other sectors that have similar
characteristics to the aviation industry by using equipment complex and
technologically advanced. As a recommendation, we suggest the application of
this clustering process in other industries.
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