A ANALYSIS OF THE POSSIBILITY OF REDUCING
SPARE PARTS IN THE MAINTENANCE CONTEXT 4.0
Luiz Gonzaga da
Costa Neto Gonzaga
Unimep, Brazil
E-mail: gonzaga@gonzagaengenharia.com.br
Alexandre Tadeu
Simon
PPGEP/UNIMEP,
Brazil
E-mail:
alexandre.simon@unimep.br
Fernando Celso
de Campos
PPGEP/UNIMEP,
Brazil
E-mail: Fernando.campos@unimep.br
Submission: 1/11/2021
Revision: 3/8/2021
Accept: 3/31/2021
ABSTRACT
Equipment
inspection arose from the need to maintain industrial facilities in
satisfactory physical condition, providing the minimum level of safety and
reliability in operation and maintenance. Machines and equipment cannot stop
operating without planning and managing the supply chain, maintaining large
quantities of spare parts stocks. The objective of this work is to analyze the
possibility of predictive and / or prescriptive monitoring to contribute to the
preventive maintenance of the real condition of the equipment, that is, only in
real need, avoiding preventive maintenance for pre-established intervals,
consequently reducing the stock of spare parts. To this end, a Systematic
Literature Review (RSL) was carried out using the keywords Maintenance 4.0 and
Supply Chain Management and Industrial Logistics, identifying the main authors
and most relevant journals.
Keywords: Maintenance 4.0, supply chain
management, Industrial logistics
1.
INTRODUCTION
The concept of Industry 4.0
aggregates the main technological innovations from various segments and applies
them in the manufacturing and service processes. These are technologies that
have allowed the emergence of new business models, products and services, and
have fostered significant improvements in existing models.
For Schwab (2016), new technologies
are transforming the way organizations perceive and manage their assets,
receiving improvements from digital resources that increase the values of both
products and services. The challenge with these new technologies is to
guarantee the operational reliability of automated equipment, enabling the
operational condition in real time, identifying and acting on the potential
point of failure, while notifying the situation, through the internet of things
(IoT) for a mobile device under the responsibility of Maintenance Planning and
Control (PCM).
A análise fornecida por sensores off-line, on-line ou contínuo, colocados nos equipamentos permitem constante monitoramento preditivo e/ou prescritivo, buscando o ponto potencial de falha, usando os dados comparativos sobre o desempenho, que podem notificar quando uma parte do equipamento está fora de seus parâmetros normais de operação.
The analysis provided by offline, online or
continuous sensors placed on the equipment allows constant predictive and / or
prescriptive monitoring, looking for the potential point of failure, using
comparative performance data, which can notify when a piece of equipment is
outside its normal operating parameters.
After the notification, preventive
maintenance of the actual condition of the equipment will be programmed, making
it possible to forecast the part, only those or the one that was identified in
the monitoring, providing a reduction of a good part of the spare parts stock,
in this way purchases will be made only according to with the need
The objective of this work is to
analyze the possibility of predictive and / or prescriptive monitoring to
contribute to the preventive maintenance of the real condition of the equipment,
that is, only in real need, avoiding preventive maintenance for pre-established
intervals, consequently reducing the stock of spare parts.
For this purpose, a Systematic
Literature Review (SLR) was carried out using the keywords Maintenance 4.0 and
Supply Chain Management and Industrial Logistics, identifying the main authors
and most relevant journals.
The article is structured as
follows: section 2 presents the Theoretical Foundation related to Industry 4.0,
Supply Chain Management and Industrial Logistics and the correlation between
these concepts, Section 3 shows the Research Method pointed to the study, the
Section 4 presents the Results, Section 5 presents the Analysis and Discussion
of the selected articles, Section 6 the Findings and Trends and Section 7
provides the main conclusions on the Theme
2.
THEORETICAL FOUNDATION
This section presents a review of
the concepts of Industry 4.0 and Supply Chain Management and Industrial
Logistics and their links
2.1.
Industry 4.0
With increasing advances in
manufacturing processes and technology, the term "Industry 4.0" is
becoming an increasingly important topic. This concept appeared first in an
article published in November 2011 by the German government that resulted from
a relative initiative to the high-tech strategy for 2020.
Schwab (2016) comments that in
Germany, there are discussions about Industry 4.0, a 2011 term at the Hannover
fair to describe how this will revolutionize the organization of global value
chains. By enabling smart factories, the fourth industrial revolution creates a
world where physical and virtual manufacturing systems cooperate globally and
flexibly, allowing for complete product customization and the creation of new
operational models.
Odważny et al. (2019) comment
that within this period, several models and ideas were developed, in particular
smart factory, Internet of Things (IoT), Internet of Services (IoS), cloud
computing and cyber-physical systems (CPS), complemented by Robotics, Big Data,
Cloud Manufacturing and Augmented Reality (Pereira & Romero, 2017).
2.2.
Supply Chain
Management
For Mastos et al. (2020) the fourth
industrial revolution and the digitalization of supply chains led companies to
realize that the adoption of Industry 4.0 / IoT solutions creates opportunities
for management. In the past decade, significant growth in Sustainable Supply
Chain Management (SSCM) and Industry 4.0 research has been observed. The
definition of SSCM given by Seuring and Muller (2008) is: “Management of
materials, information and capital flows, as well as cooperation between
companies in the supply chain, making goals of all three dimensions of
sustainable development (Environmental, Social and Economic), which are derived
from the requirements of customers and stakeholders ”.
For more sustainable production
processes and information sharing at SSCM, simulation and forecasting
techniques are used to address the needs of supply chains such as: flexibility,
increased productivity, less waste, optimization of a plant's internal and
external resources, through big data analysis, ІοΤ and learning techniques.
2.3.
Industrial
Logistics
Tang and Veelenturf (2019) comment
that the logistics function deals with the detailed coordination of a complex
operation involving human resources, materials, equipment, information and
finance and, in many cases, this coordination involves the movements of
materials, human resources and / or equipment, or exchanges of information
between human resources and / or devices, and financial transactions between
entities.
The preventive maintenance of the
equipment's real condition, will enable the department to buy, acquire
materials / parts only when necessary for use, and not stock of spare parts.
2.4.
Industry 4.0,
Supply Chain Management and Industrial Logistics
Industry 4.0, Supply Chain
Management and Industrial Logistics are interconnected through connectivity in
the context of Industry 4.0 and manufacturing companies identify opportunities
to develop their competitiveness in their operations and organizational
efficiencies.
For Benesova et al. (2020) changes
in Industry 4.0 will have an impact on the organization on the company's
architecture and on the production environment and on the Supply Chain. Glawar
et al. (2016) comment that without correct machine data it is difficult to plan
a good maintenance activity. Therefore, with real data it will be possible to
improve Maintenance Planning and Control (PCM), allowing purchases to be made
only in real need, avoiding spare parts stocks.
3.
RESEARCH METHOD
The research method adopted to
achieve the objective of this work was based on an RSL that is a scientific
process, allowing the evaluation of the literature, through the identification
of the selection and the evaluation of the existing studies (Tranfield, Denyer
& Esmart, 2003)
3.1.
SLR Protocol
This SLR was carried out through the
Software Start - State of the Through Systematic Review, version 3.4 Beta -
LAPES - Laboratory of Research in Software Engineering - UFSCAR.
The research protocol is shown in
Table 1:
Table 1: Research Protocol
|
Strategy |
Protocol |
|
Research question |
Será possível reduzir peças sobressalentes
no contexto da indústria 4.0 |
|
Data base |
Scopus, Web of
Science, Science Direct |
|
Key words |
“Maintenance 4.0” And “supply chain management” “Maintenance
4.0” And “Industrial logistics” |
|
Period |
2015 a Setembro/2020 |
|
Language |
Inglês, Português, Espanhol |
|
Exclusion
criteria |
•
Titles unrelated to the research objective. •
Duplicate
articles. •
Do not contain search strings in the title, abstract
and keywords. •
Articles published outside the period. •
Books,
Sites and Theses |
|
Inclusion criteria |
•
Articles that contain the title, abstract and
keywords in the search strings. •
Articles published in the period |
Source: Authors
3.2.
Initial Research
and Refinements
The initial research resulted in a
total of 37 works, in the period from 2015 to September / 2020, as shown in
Figure 1 by database.
Figure 1:
Initial result by database
Source: Start
Three initial results filtering
processes were carried out to select the articles to be evaluated:
· First filter: Exclusion of titles
not related to the research objective, duplicate articles, articles that do not
contain in the title, abstract and keywords the search strings, articles
published for the period, books, websites and theses, resulting in 19 articles
rejected and 6 duplicate articles, distributed by year of publication, shown in
Table 1.
· Second filter: Inclusion of articles
that contain the search strings in the title, abstract and keywords, articles
published from 2015 to September / 2020, with English, Portuguese, Spanish
languages, resulting in 12 articles distributed per year of publication, as
shown in Table 1.
Table 1: Result of exclusion
|
Articles |
2015 |
2016 |
2017 |
2018 |
2019 |
2020 |
Total |
|
Accepted |
0 |
0 |
0 |
3 |
1 |
8 |
12 |
|
Rejected |
0 |
0 |
3 |
5 |
3 |
8 |
19 |
|
Duplicates |
0 |
0 |
0 |
0 |
1 |
5 |
6 |
|
Total |
0 |
0 |
3 |
8 |
5 |
21 |
37 |
Source: Authors
· Third filter: It consisted of
reading the abstracts and conclusions of these 12 articles, in order to select
only those articles that address the research objective, resulting in 9
articles for analysis, shown in Table 2.
4.
RESULTS
Analyzes of the data of the 9
selected articles will be presented, by year of publication, by periodicals,
and by citations.
Table 2 shows the total number of
articles selected and published per year, and the first articles observed were
in the year 2018, in addition, the year 2020 presented the largest number of
publications on this topic. From the year 2018, the first published in the MM
Science Journal, by Pelantova, Cecak (2018) of the Faculty of Mechanical
Engineering, KSA - Czech Republic, whose focus of the article is the list of
spare parts materials and the origin of non- conformities of a production,
these facts being seen as meaningful and contextualized.
Table 2: Articles selected and published by year
|
Articles |
2015 |
2016 |
2017 |
2018 |
2019 |
2020 |
Total |
|
Accepted |
0 |
0 |
0 |
1 |
1 |
7 |
9 |
|
Rejected |
0 |
0 |
0 |
2 |
0 |
1 |
3 |
|
Total |
0 |
0 |
0 |
3 |
1 |
8 |
12 |
Source: Authors
Table 3 shows the journal that most
published on the topic: Sustainability with 2 of the 9 selected articles.
Table 3: Journals with numbers of
publications
|
Journals
|
Numbers
of publications |
|
Acta Logística |
01 |
|
Eksploatacja I
Niezawodnosc |
01 |
|
IEE Acces |
01 |
|
International Journal of Mathematical Enginnering and Management Scices
|
01 |
|
International Journal of Production Economic |
01 |
|
MM Science Journal
|
01 |
|
Sustainability |
02 |
|
Tehnicki Glasnik |
01 |
|
Total
|
09 |
Source: Authors
The analysis of citations shows the
number of times the work has been cited over time, with the articles with the
highest number of citations being presented in Table 4, identifying a total of
13 authors in the four most cited articles (Top Four).
The article with the highest number
of citations is that of Bokrantz et al. (2020) with 13 citations. This article
dealt with the relevance of how modernized maintenance operations, often called
“smart maintenance”, affect the performance of factories, followed by
HoffaDabrowska and Grzybowska (2020) with 06 citations, addressing in the
article that aspects of SSCM have become increasingly popular in recent years,
not only in economic, but also environmental and social aspects.
Table 4: Most cited articles (Top
Four)
|
Author / Year |
Articles |
Numbers of publications |
|
|
Bokrantz,
J. Skoogh, A. Berlin, C. Wuest, T. Stahre, J. (2020) |
Smart
Maintenance: an empirically grounded conceptualization |
13 |
|
|
Hoffa-Dabrowska,
P.; Grzybowska, K. (2020) |
Simulation modeling of the
sustainable supply chain |
06 |
|
|
Granillo-Macias, R., Simon-Marmolejo,
I., Gonzalez-Hernandez, I. J. Zuno-Silva, J. (2020) |
Traceability
in industry 4.0: A case study in the metal-mechanical sector |
06 |
|
|
Pelantova, V., Cecak, P. (2018) |
New aspects of maintenance management and the
material of spare parts |
05 |
|
Source: Authors
5.
ANALYSIS AND DISCUSSION
The selection of Small and Medium
Enterprises (SMEs) is encouraged by the fact that SMEs with growth speed in the
global economy, lack knowledge about Industry 4.0. They are characterized by
limited resources (skilled workers, infrastructure and budget), and for
European commission they are companies that employ less than 250 workers and
have a turnover of up to $ 50 million / year, while in Germany they are defined
as companies that it has less than 500 employees, however, flexibility in
decision making is relatively easier and faster compared to large companies,
due to the short bureaucratic path and few people (Harmoko, 2020).
The author also comments that
benefits of Industry 4.0, was initiated by a group of academic entrepreneurs
and collective German strategy of companies that equip their business
processes, especially the production process with Digitization, IoT, CPS and
intelligent factory, presenting the synchronization in real time of business
and production flows and their application has three main objectives: i) to
reduce time to market; ii) increase flexibility; and iii) increase efficiency.
With the fulfillment of these goals, the benefits of Industry 4.0 can be felt
by the company and the national economy. In Germany, the adoption of Industry
4.0 increased the company's efficiency by 25% and contributed about 1% per year
to the Gross Domestic Product (GDP) in 10 years, in addition to creating
390,000 jobs volume_up.
Valamede and Akkari (2020) comment
that the benefits of industry 4.0 relating to interconnectivity and data
analysis, human-machine association and employee training, support and enable
the planning and activities of the maintenance operation in a digital
environment. Smart factories will provide a fully integrated manufacturing environment,
where data can be transmitted in real time, making it possible to indicate the
real conditions of each equipment, and for Proto (2020) the emergence of
factories in Industry 4.0, fostered the diffusion of IoT technology and great
data analysis tools in the industrial sector, called logistics 4.0, profoundly
increasing the needs for transparency in the supply chain and integrity control
in good sales and delivery (selling the right product, at the right cost and
delivering it them at the right time and place).
According to Ales et al. (2019) the
challenge of Industry 4.0 with the issue of Industrial Internet of Things
(IIoT) is highly accentuated, including the issue of autonomous management and
communication of individual machines and equipment within a higher production
and complex units.
According to Bokrantz et al. (2020)
the world is changing rapidly, indicating surprising technological developments
and one of the most impactful technologies driving this change is Artificial
intelligence (AI) and Machine learning (ML) with more accessible technologies,
such as cloud for storage and computing power, stimulating companies to adopt
new organizational forms such as networks, ecosystems, platforms and
collaborative communities.
An important aspect of manufacturing
companies that requires design efforts is the plant maintenance function, due
to exploring the improvement of technological capacity such as ML, with the
increasing automation and introduction of digital technologies in production systems,
with expected benefits of drastic reduction machine downtime and increased
productivity. It is possible to advance in the understanding of what makes
certain practices effective, however the number of concepts grows, so does the
concern with problems due to the lack of clarity of the concept, manifesting in
the proliferation of concepts with different names. "E-maintenance",
"Prognostics and health management", "Predictive
maintenance", "Maintenance 4.0", Intelligent maintenance ".
Still Bokrantz et al. (2020) comment
in practice, "intelligent maintenance" is the term used by
professionals in local companies within the Swedish manufacturing industry,
which is also observed in other countries. According to the author, the lack of
consensus regarding a definition of E-maintenance, motivated the work of Iung
et al. (2009) and Aboelmaged (2015) who has reviewed 15 definitions of
electronic sample maintenance in publications over an 11-year period and
concludes that “there is little room for clarification and confusion in the
literature as to what constitutes an E-maintenance definition”, with some
explanations for this, inconsistency as a country with European and American
location and technological changes from Information and communication
technology (ICT) to AI.
For Hoffa-Dabrowska and Crzybowska
(2020) aspects of SSCM have become increasingly popular in recent years and
entrepreneurs pay more attention to the aspect of sustainable development in
their activities, especially in exhaust gas emissions. Supply Chain Management
(SCM) has developed into an important conceptual approach within the management
and administration of companies.
For Pelantova and Cecak (2018) the
guarantee of continuity and quality of production leads some organizations to
think about the status of the maintenance of their equipment and also depend on
the spare parts. Maintenance is influenced by many factors at the moment, and
must present new guidelines for maintenance management, where the list of spare
parts materials and the origin of non-conformities in a production plays an
important role, seeking to reduce costs in organizations
With the emergence of production
systems characterized by Industry 4.0 technologies, the problems of asset
traceability have become more relevant at different levels of the supply chain.
The management of intelligent assets promoted by Industry 4.0 is considered as
a process that, in addition to collecting information, allows to track and
guarantee the security of assets (Granillo-Macias, 2020).
Di Nardo et al. (2020) comment that
the logistic workflow is oriented towards Industry 4.0, with technologies
capable of identifying suppliers' performance indicators and implementing them
in the supply chain process. Currently, a customer is drafting a contract in
which cost, quality and availability control are included and the supply of
material takes approximately 5 to 45 days through the full service approach,
ensuring high levels of availability and reliability in an intelligent
environment. Industry 4.0, and also comment that the components of machinery
and equipment age progressively, being the main cause of wear in the form of
material degradation, with a tendency to change the parameter during the useful
life of an item.
With the monitoring of machines it
is possible to foresee the need for a maintenance intervention, making the
predictive monitoring strategy viable, and the maintenance activities will be
planned based on the real operating conditions. With this dynamic of the
digital model in maintenance, or Maintenance 4.0, it is possible to identify
the status of components and parts in real time of the machines, through
analysis of the sensor data, showing the signs of failures.
Predictive maintenance has a double
objective, according to Proto (2020): i) to reduce the frequency of maintenance
activity to the lowest possible state, leading to enormous cost savings in
keeping resources under normal working conditions; ii) avoid catastrophic
situations (product failures and failures, service interruptions) detecting
anomalies a priori of historical data and maintenance activity must be carried
out in time to prevent the occurrence of failure.
6.
FINDINGS AND TRENDS
According to Harmoko (2020) for
small and medium-sized companies, the lack of knowledge about Industry 4.0 is
characterized by limited resources (skilled workers, infrastructure and
budget), making it difficult to significantly improve their processes. The
benefits of industry 4.0 relating interconnectivity, data analysis, human-machine
association and employee training, Valamede and Akkari (2020) comment that they
support and enable the planning and activities of the maintenance operation in
a digital environment.
For Proto (2020) the emergence of
factories in Industry 4.0, fostered the diffusion of IoT technology and great
data analysis tools in the industrial sector, called logistics 4.0, profoundly
increasing the needs for transparency in the supply chain and integrity control
good sales and delivery (selling the right product, at the right cost and
delivering it to the right place at the right time).
Bokrantz et al. (2020) comment that
the number of maintenance concepts grows, worrying about the lack of clarity of
the concept, manifesting in the proliferation of concepts with different names
For Pelantova and Cecak (2018), the
guarantee of continuity and quality of production leads some organizations to
think about the maintenance status of their equipment that depends on spare
parts, because spare parts in a long supply chain mean an expensive price and
longer delivery time.
According to Di Nardo et al. (2020)
the logistical workflow is oriented towards Industry 4.0, with technologies
capable of identifying supplier performance indicators and implementing them in
the supply chain process, and for Granillo-Macias (2020) the problems of
traceability of assets become more relevant at different levels of the supply
chain.
And yet Di Nardo et al. (2020)
highlight that with machine monitoring it is possible to predict the need for a
maintenance intervention, making the predictive monitoring strategy viable, and
the maintenance activities will be planned based on the real operating
conditions
Predictive maintenance has a double
objective, according to Proto (2020): i) to reduce the frequency of maintenance
activity to the lowest possible state, leading to enormous cost savings in
keeping resources under normal working conditions: ii) avoid catastrophic
situations (product failures and failures, service interruptions) detecting anomalies
a priori of historical data and maintenance activity must be carried out in
time to prevent the occurrence of failure.
7.
CONCLUSÃO
The objective of this work was to
analyze the possibility of predictive and / or prescriptive monitoring to
contribute to the preventive maintenance of the real condition of the
equipment, that is, only in real need, avoiding preventive maintenance for
pre-established intervals, consequently reducing the stock of parts spare
parts, identifying a total of 9 articles analyzed
The systematic review contributed to
verify the current situation of the literature in relation to the maintenance
approach in the Industry 4.0 environment, in addition to pointing out the most
relevant works for analysis and understanding of the subject.
The analyzed studies showed that
Maintenance 4.0, through predictive and / or prescriptive monitoring with
sensors placed on machines and equipment, contributes to the identification of
the potential point of failure, providing preventive maintenance activity of
the real condition of the equipment, that is, carry out only in real need,
avoiding a stop for preventive maintenance for a pre-established time, allowing
a reduction of a good part of the stock of spare parts, and purchases will be
made according to the need.
With the application of technology,
the preventive maintenance of real condition, that is, in real time of the
measurement sensors, contributes to the improvement of the economic performance
of the manufacturing processes and provides process stability. Connected
technologies can help Maintenance Management and Supply Chain Management, with
the challenge of having the right part, in the right place and at the right
time.
The review carried out has the
following limitations: few studies were selected with the theme analyzed, the
selection and filtering criteria were based on the keywords, which may have led
to the non-inclusion of some articles relevant to the study, due to not
combining the keywords.
As suggestions for future research:
use the combination of keywords, as well as better definition of the concepts
of Maintenance 4.0, proposing a performance indicator for this concept.
Despite the limitations presented,
the article contributes to the literature, with the scope of action that the
theme allows.
ACKNOWLEDGMENTS
"This study was financed in
part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -
Brasil (CAPES) - Finance Code 001" (Portaria nº 206, 04/09/2018).
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