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Procedia Manufacturing 38 (2019) 1582–1591
2351-9789 © 2019 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the Flexible Automation and Intelligent Manufacturing 2019 (FAIM 2019)
10.1016/j.promfg.2020.01.127
10.1016/j.promfg.2020.01.127 2351-9789
© 2019 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientic committee of the Flexible Automation and Intelligent Manufacturing 2019 (FAIM 2019)
Available online at www.sciencedirect.com
ScienceDirect
Procedia Manufacturing 00 (2019) 000–000
www.elsevier.com/locate/procedia
2351-9789 © 2019 The Authors, Published by Elsevier B.V.
Peer review under the responsibility of the scientific committee of the Flexible Automation and Intelligent Manufacturing 2019
29th International Conference on Flexible Automation and Intelligent Manufacturing
(FAIM2019), June 24-28, 2019, Limerick, Ireland.
Continuous improvement in maintenance: a case study in the
automotive industry involving Lean tools
G. F. L. Pinto, F. J. G. Silva*, R. D. S. G. Campilho, R. B. Casais, A. J. Fernandes, A.
Baptista
ISEP – School of Engineering, Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 431, 4200-072 Porto, Portugal
Abstract
Maintenance function assumes a key role in today’s industry. The automotive industry is not an exception and there are strict
rules to comply with. Indeed, the IATF 16949:2016 imposes the implementation of key performance indicator as a mean to
control the overall manufacturing performance. This work presents a case study carried out in a multinational company related
with the production of parts for the automotive industry where it was necessary to implement key performance indicators to
comply with the IATF 16949: 2016 standard and a model was also created for the management of spare parts linked to the
maintenance of existing equipment. The introduction of these changes forced the application of some Lean tools, with a view to
improving procedures and information flows. The work was completed successfully, and key performance indicators were
implemented, whose support data, which is now collected and calculated automatically on a routine basis, and the spare-parts
management was validated with a view to optimization of warehouse space and at a conveniently low inventory level in this type
of parts, without endangering critical equipment in production. The SMED methodology was applied, which allowed the setup
time to be reduced by 11%, and the Lean 5S tool was used to organize the mould exchange activities. An OEE of more than 90%
has been achieved.
© 2019 The Authors, Published by Elsevier B.V.
Peer review under the responsibility of the scientific committee of the Flexible Automation and Intelligent Manufacturing 2019
Keywords: Automotiveindustry; Maintenance;; MTBF; MTTR; OEE; Lean; Continuous improvement; Performance Indicators; SMED.
* Corresponding author. Tel.: +351 228340500; fax: +351 228321159.
E-mail address: fgs@isep.ipp.pt
Available online at www.sciencedirect.com
ScienceDirect
Procedia Manufacturing 00 (2019) 000–000
www.elsevier.com/locate/procedia
2351-9789 © 2019 The Authors, Published by Elsevier B.V.
Peer review under the responsibility of the scientific committee of the Flexible Automation and Intelligent Manufacturing 2019
29th International Conference on Flexible Automation and Intelligent Manufacturing
(FAIM2019), June 24-28, 2019, Limerick, Ireland.
Continuous improvement in maintenance: a case study in the
automotive industry involving Lean tools
G. F. L. Pinto, F. J. G. Silva*, R. D. S. G. Campilho, R. B. Casais, A. J. Fernandes, A.
Baptista
ISEP – School of Engineering, Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 431, 4200-072 Porto, Portugal
Abstract
Maintenance function assumes a key role in today’s industry. The automotive industry is not an exception and there are strict
rules to comply with. Indeed, the IATF 16949:2016 imposes the implementation of key performance indicator as a mean to
control the overall manufacturing performance. This work presents a case study carried out in a multinational company related
with the production of parts for the automotive industry where it was necessary to implement key performance indicators to
comply with the IATF 16949: 2016 standard and a model was also created for the management of spare parts linked to the
maintenance of existing equipment. The introduction of these changes forced the application of some Lean tools, with a view to
improving procedures and information flows. The work was completed successfully, and key performance indicators were
implemented, whose support data, which is now collected and calculated automatically on a routine basis, and the spare-parts
management was validated with a view to optimization of warehouse space and at a conveniently low inventory level in this type
of parts, without endangering critical equipment in production. The SMED methodology was applied, which allowed the setup
time to be reduced by 11%, and the Lean 5S tool was used to organize the mould exchange activities. An OEE of more than 90%
has been achieved.
© 2019 The Authors, Published by Elsevier B.V.
Peer review under the responsibility of the scientific committee of the Flexible Automation and Intelligent Manufacturing 2019
Keywords: Automotiveindustry; Maintenance;; MTBF; MTTR; OEE; Lean; Continuous improvement; Performance Indicators; SMED.
* Corresponding author. Tel.: +351 228340500; fax: +351 228321159.
E-mail address: fgs@isep.ipp.pt
2 G. F. L. Pinto et al./ Procedia Manufacturing 00 (2019) 000–000
1. Introduction
Competitiveness in the automotive industry is very strong. Companies have to be competitive and have very
effective management systems. Costs in the companies’ productive process should be reduced to achieve higher
effectiveness. There are several factors influencing the production process, such as machine failures causing
unwanted stoppages, unskilled human resources making longer production times and accidents at work, inefficient
layouts causing a long time in the exchange of semi-products between machines, among others. All these problems
must be controlled to make production efficient, meeting the customer's expectation about product quality, at a
reduced production cost [1,2]. To avoid or minimize production outages, Maintenance and Quality departments
must have well-developed process control. The maintenance department needs to keep up with the complexity of the
current industrial process is important. Industrial competition and the development of new industrial products and
processes will require the maintenance department to continue its work in order to reduce costs, increase efficiency
and improve safety without compromising the quality of the final product or process [3]. The performance of
industrial maintenance continues to play a key role. However, its strategy should be based on a good logistics
system and in solid production planning, by establishing the best practices in all processes [4]. In the automotive
industry, there are strict rules to comply with, and the standard International Automotive Task Force (IATF) 16949:
2016 [5] imposes the implementation of the key performance indicator as a mean to control the quality of
manufactured products, ensuring a high level of performance for the equipment used in the manufacture of the
products.
The main objective of this work was to implement routines that would automatically obtain indicators that are
indispensable for compliance with the IATF standard 16949: 2016 [5], such as Mean Time Before Failure (MTBF),
Mean Time to Repair (MTTR) and Overall Equipment Effectiveness (OEE). For this, it was necessary to improve
some procedures using Lean and tools such as PDCA cycle, 5S, SMED and Pareto diagram. The management tool
PDCA Cycle is a tool that means Plan, Do, Check, and Act. Its aims at continuous improvement in processes
making them faster and more accurate. In a general way, 5S (sort, set in order, shine, standardize, and sustain) helps
to eliminate waste that results from a poorly organized work area. The SMED tool allows to reduce of an efficient
and faster way the waste for the machine setup, in the process of manufacture. The Pareto diagram is a quality tool
that allows to detect the most frequent defect type or the most common sources of defects in lean manufacturing.
After automating the calculations of the previous indicators, a new goal was established to reduce downtime and
improve overall product quality. This work is divided into four chapters. In the first one, an introduction is made to
the work where the main goals and steps to achieve them are described. In the second, a literature review is done
regarding the methodologies and tools used. In the third chapter, the practical work is presented. Finally, the
conclusions and proposals for future work in this area are presented.
2. Literature Review
Maintenance is a critical issue to achieve excellent industrial performance. According to Smith et al., [6]
maintenance is focused on “preserving the functional capacities of equipment and systems in operation” and aims,
according to Moubray, [7] “to ensure that physical items continue to do what the users want them to do”. Kobbacy
et al. [8], state that maintenance can generally be considered as a "set of activities necessary to maintain physical
assets in the desired operational condition, or to restore them to this condition". Production is becoming increasingly
demanding, and the introduction of Lean Manufacturing has reflected the impact on product quality and process
productivity, which leads to production costs reduction and customer satisfaction [9]. In this context, maintenance is
extremely relevant. In the last decades, industrial maintenance has evolved a lot, becoming a strategic sector for
companies. Global competitiveness has increased exponentially and maintenance has to be seen not as a loss, but as
an asset to business management. The main objectives are to increase production, using the minimum resources,
focusing on the assets of each company. The facilities need to transform themselves technologically so that the
process is more robust and better controlled [8]. Maintenance is very important in several factors, for example, the
availability of assets and facilities, optimization of reliability, costs and safety, among others. Regarding Kardec et
al. [10], the main idea is to move from corrective to preventive maintenance.
G.F.L. Pinto et al. / Procedia Manufacturing 38 (2019) 1582–1591 1583
Available online at www.sciencedirect.com
ScienceDirect
Procedia Manufacturing 00 (2019) 000–000
www.elsevier.com/locate/procedia
2351-9789 © 2019 The Authors, Published by Elsevier B.V.
Peer review under the responsibility of the scientific committee of the Flexible Automation and Intelligent Manufacturing 2019
29th International Conference on Flexible Automation and Intelligent Manufacturing
(FAIM2019), June 24-28, 2019, Limerick, Ireland.
Continuous improvement in maintenance: a case study in the
automotive industry involving Lean tools
G. F. L. Pinto, F. J. G. Silva*, R. D. S. G. Campilho, R. B. Casais, A. J. Fernandes, A.
Baptista
ISEP – School of Engineering, Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 431, 4200-072 Porto, Portugal
Abstract
Maintenance function assumes a key role in today’s industry. The automotive industry is not an exception and there are strict
rules to comply with. Indeed, the IATF 16949:2016 imposes the implementation of key performance indicator as a mean to
control the overall manufacturing performance. This work presents a case study carried out in a multinational company related
with the production of parts for the automotive industry where it was necessary to implement key performance indicators to
comply with the IATF 16949: 2016 standard and a model was also created for the management of spare parts linked to the
maintenance of existing equipment. The introduction of these changes forced the application of some Lean tools, with a view to
improving procedures and information flows. The work was completed successfully, and key performance indicators were
implemented, whose support data, which is now collected and calculated automatically on a routine basis, and the spare-parts
management was validated with a view to optimization of warehouse space and at a conveniently low inventory level in this type
of parts, without endangering critical equipment in production. The SMED methodology was applied, which allowed the setup
time to be reduced by 11%, and the Lean 5S tool was used to organize the mould exchange activities. An OEE of more than 90%
has been achieved.
© 2019 The Authors, Published by Elsevier B.V.
Peer review under the responsibility of the scientific committee of the Flexible Automation and Intelligent Manufacturing 2019
Keywords: Automotiveindustry; Maintenance;; MTBF; MTTR; OEE; Lean; Continuous improvement; Performance Indicators; SMED.
* Corresponding author. Tel.: +351 228340500; fax: +351 228321159.
E-mail address: fgs@isep.ipp.pt
Available online at www.sciencedirect.com
ScienceDirect
Procedia Manufacturing 00 (2019) 000–000
www.elsevier.com/locate/procedia
2351-9789 © 2019 The Authors, Published by Elsevier B.V.
Peer review under the responsibility of the scientific committee of the Flexible Automation and Intelligent Manufacturing 2019
29th International Conference on Flexible Automation and Intelligent Manufacturing
(FAIM2019), June 24-28, 2019, Limerick, Ireland.
Continuous improvement in maintenance: a case study in the
automotive industry involving Lean tools
G. F. L. Pinto, F. J. G. Silva*, R. D. S. G. Campilho, R. B. Casais, A. J. Fernandes, A.
Baptista
ISEP – School of Engineering, Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 431, 4200-072 Porto, Portugal
Abstract
Maintenance function assumes a key role in today’s industry. The automotive industry is not an exception and there are strict
rules to comply with. Indeed, the IATF 16949:2016 imposes the implementation of key performance indicator as a mean to
control the overall manufacturing performance. This work presents a case study carried out in a multinational company related
with the production of parts for the automotive industry where it was necessary to implement key performance indicators to
comply with the IATF 16949: 2016 standard and a model was also created for the management of spare parts linked to the
maintenance of existing equipment. The introduction of these changes forced the application of some Lean tools, with a view to
improving procedures and information flows. The work was completed successfully, and key performance indicators were
implemented, whose support data, which is now collected and calculated automatically on a routine basis, and the spare-parts
management was validated with a view to optimization of warehouse space and at a conveniently low inventory level in this type
of parts, without endangering critical equipment in production. The SMED methodology was applied, which allowed the setup
time to be reduced by 11%, and the Lean 5S tool was used to organize the mould exchange activities. An OEE of more than 90%
has been achieved.
© 2019 The Authors, Published by Elsevier B.V.
Peer review under the responsibility of the scientific committee of the Flexible Automation and Intelligent Manufacturing 2019
Keywords: Automotiveindustry; Maintenance;; MTBF; MTTR; OEE; Lean; Continuous improvement; Performance Indicators; SMED.
* Corresponding author. Tel.: +351 228340500; fax: +351 228321159.
E-mail address: fgs@isep.ipp.pt
2 G. F. L. Pinto et al./ Procedia Manufacturing 00 (2019) 000–000
1. Introduction
Competitiveness in the automotive industry is very strong. Companies have to be competitive and have very
effective management systems. Costs in the companies’ productive process should be reduced to achieve higher
effectiveness. There are several factors influencing the production process, such as machine failures causing
unwanted stoppages, unskilled human resources making longer production times and accidents at work, inefficient
layouts causing a long time in the exchange of semi-products between machines, among others. All these problems
must be controlled to make production efficient, meeting the customer's expectation about product quality, at a
reduced production cost [1,2]. To avoid or minimize production outages, Maintenance and Quality departments
must have well-developed process control. The maintenance department needs to keep up with the complexity of the
current industrial process is important. Industrial competition and the development of new industrial products and
processes will require the maintenance department to continue its work in order to reduce costs, increase efficiency
and improve safety without compromising the quality of the final product or process [3]. The performance of
industrial maintenance continues to play a key role. However, its strategy should be based on a good logistics
system and in solid production planning, by establishing the best practices in all processes [4]. In the automotive
industry, there are strict rules to comply with, and the standard International Automotive Task Force (IATF) 16949:
2016 [5] imposes the implementation of the key performance indicator as a mean to control the quality of
manufactured products, ensuring a high level of performance for the equipment used in the manufacture of the
products.
The main objective of this work was to implement routines that would automatically obtain indicators that are
indispensable for compliance with the IATF standard 16949: 2016 [5], such as Mean Time Before Failure (MTBF),
Mean Time to Repair (MTTR) and Overall Equipment Effectiveness (OEE). For this, it was necessary to improve
some procedures using Lean and tools such as PDCA cycle, 5S, SMED and Pareto diagram. The management tool
PDCA Cycle is a tool that means Plan, Do, Check, and Act. Its aims at continuous improvement in processes
making them faster and more accurate. In a general way, 5S (sort, set in order, shine, standardize, and sustain) helps
to eliminate waste that results from a poorly organized work area. The SMED tool allows to reduce of an efficient
and faster way the waste for the machine setup, in the process of manufacture. The Pareto diagram is a quality tool
that allows to detect the most frequent defect type or the most common sources of defects in lean manufacturing.
After automating the calculations of the previous indicators, a new goal was established to reduce downtime and
improve overall product quality. This work is divided into four chapters. In the first one, an introduction is made to
the work where the main goals and steps to achieve them are described. In the second, a literature review is done
regarding the methodologies and tools used. In the third chapter, the practical work is presented. Finally, the
conclusions and proposals for future work in this area are presented.
2. Literature Review
Maintenance is a critical issue to achieve excellent industrial performance. According to Smith et al., [6]
maintenance is focused on “preserving the functional capacities of equipment and systems in operation” and aims,
according to Moubray, [7] “to ensure that physical items continue to do what the users want them to do”. Kobbacy
et al. [8], state that maintenance can generally be considered as a "set of activities necessary to maintain physical
assets in the desired operational condition, or to restore them to this condition". Production is becoming increasingly
demanding, and the introduction of Lean Manufacturing has reflected the impact on product quality and process
productivity, which leads to production costs reduction and customer satisfaction [9]. In this context, maintenance is
extremely relevant. In the last decades, industrial maintenance has evolved a lot, becoming a strategic sector for
companies. Global competitiveness has increased exponentially and maintenance has to be seen not as a loss, but as
an asset to business management. The main objectives are to increase production, using the minimum resources,
focusing on the assets of each company. The facilities need to transform themselves technologically so that the
process is more robust and better controlled [8]. Maintenance is very important in several factors, for example, the
availability of assets and facilities, optimization of reliability, costs and safety, among others. Regarding Kardec et
al. [10], the main idea is to move from corrective to preventive maintenance.
1584 G.F.L. Pinto et al. / Procedia Manufacturing 38 (2019) 1582–1591
Author name / Procedia Manufacturing 00 (2019) 000–000 3
Fig. 1. Types of Maintenance.
Some problems in maintenance routines were already clearly identified, such as lack of proactive maintenance,
recurring problems, poorly planned maintenance activities, lack of tracking and vision in the maintenance program,
predictive maintenance practices, lack of people’s commitment in the medium and long term, non-application of
80/20 rule and inefficiency in the application of new processes and equipment [11]. Fig.1 describes the types of
maintenance used in the companies.
Warehouses of spare parts are very important in maintenance tasks. Gulati et al. [11] believe that the best practice
for replenishing all the necessary material is good inventory planning. Stocks should be managed regarding cost,
delivery time and failure frequency. Usually, after the acquisition of an asset, the supplier provides an FMEA
analysis of the equipment, fundamentally translating into the main parts subject to degradation and their preventive
maintenance. The computerized maintenance systems include a database with all the assets’ inventory, in which
each asset has a set of specific details, guaranteeing the good maintenance of them [11].
Improvements in process performance have grown more and more, and so, there is a need to measure and
develop the key performance indicators (KPIs). Performance indicators can be structured into three groups:
economic, technical and organizational. In the area of maintenance, the technical indicators are usually the most
requested, namely: failure rate (λ), MTBF and MTTR [1]. MTBF represents the reliability of the company's assets
and represents the mean time between failures. Its calculation formula is deducted by the total operating time and
number of system failures. Its function (Eq. 1) is essential to inform the behaviour of the asset, ensuring the good
functionality of the asset [1,11]. The MTBF can be calculated by the ratio of "Total Operating Time" to "Total
Faults", or by the inverse of the failure rate (), Eq. 2. The time between failures can be considered the time between
the first fault occurred and the second fault. The higher the MTBF, the greater the equipment reliability is. MTTR is
exactly the time it takes to restore a device, bringing it back to good functionality. MTTR is calculated by the ratio
of "Total Repair Time" to "Total Failures" (Eq. 3). The total repair time includes the time of diagnosis, time to
gather the necessary resources and tools, repair, test the equipment and deliver it in the best operating conditions.
=
⁄ [min]
(1)
= 1/
[min]
(2)
= / [min]
(3)
Otherwise, OEE is an indicator that allows the assessment of the overall performance. This indicator can act on a
set of equipment, but also on an individual basis. The main function is to indicate the behaviour that the equipment
or set of equipment is presenting on a permanent basis. Performance, availability and quality are three parameters
used to calculate OEE [12]. Sousa et al. [13] stratified the calculation of OEE by the product between
"Performance", "Availability" and "Quality", as shown in Eq. 4. Nakajima [14] defined the ideal value for the OEE
metric at 85% or higher. The three parameters should present minimum values of 90% regarding availability, 95%
to performance and 99% to quality. The way each of these parameters is usually calculated can be seen in [13].
Moreira et al. [1] through an adequate selection of solvents and other specific products used in the printing industry,
achieved a 2 to 4% increase in the OEE of a printing company. Sousa et al. [13], using the OEE indicator, was able
to identify that the biggest cause of the low efficiency of a sector of a cork stopper company was the successive
micro-stops to which the equipment was subject. Antoniolli et al. [18] achieved a gain of 16% in the OEE of a
company producing automotive air conditioning systems through the application of Standard Work and other
4 G. F. L. Pinto et al./ Procedia Manufacturing 00 (2019) 000–000
techniques of optimization of manufacturing processes. Guariente et al. [2], through the application of concepts of
autonomous maintenance, increased by 8% the OEE of this same company of air conditioning systems for motor
vehicles.
ൌൈൈሾΨሿ
(4)
Lean can be understood as a set of tools that assist in the identification and constant elimination of waste,
improving as well the quality and productivity, and reducing the costs and production time [15 -17]. Lean can be
interpreted as continuous improvement of the process through a continuous cycle of improvements: less material,
less investment, reduced inventory, increased space and minimized human resources [18]. Ohno [19] and Womack
[20] typified the eight types of waste that can normally be thrown into the company when starting continuous
improvement processes. According to Shingo et al. [21], Lean philosophy has as focus the five following
fundamental Lean principles: value specification, value flow mapping, productive flow system, pulled production
and lean manufacturing. The quest for perfection must be in the mind of the whole organizational structure. Rosa et
al. [22] improved the productivity of a production line of control cables for motor vehicles by improving line
balancing, upgrading of equipment, elimination of supply problems to the line, study and improvement of operator
movement and increase the reliability of production line. Neves et al. [16], through the application of some Lean
tools such as the PDCA, 5S and 5W2H cycle, managed to save 10% of the time usually spent by operators in a
textile trimmings industry.
3. Problem statement and Methodology
This work was developed based on a company dedicated to the production of rubber seals for the automotive
industry. This company had not established practices for collecting, processing and controlling data on maintenance
activities, as stipulated in IATF 16949: 2016 [5]. According to the standard, it is necessary to monitor OEE, MTTR,
MTBF and preventive maintenance compliance metrics. The MTBF was calculated by the company but was not
linked to the calculation of the OEE indicator and was kept as a register but not giving rise to any continuous
improvement process nor was it properly communicated to all stakeholders in order to generate improvement
processes. All operators had free access to the spare-parts warehouse but were not adequately trained to understand
the importance of proper management of this type of components. This situation led to ruptures in the stock of
critical parts, which generated equally critical stops in the productive sector. It was thus established that there would
have to be a very close control of the spare parts warehouse, with restricted access to the employee who was
responsible for the management and supply of those spare parts to the maintenance teams. Although the company
had a good organization, some sectors presented some functioning deficiencies, which were based on shortcomings
in the employees' skills, lack of planning in the acquisition of components for the molds used in the seals
manufacturing process, lack of planning in the exchange of molds to be used in production and poor labeling and
organization in the location of molds into the warehouse. Detection of these organizational shortcomings led to the
drawing up of a list of needs in terms of vocational training, with a view to eliminating shortcomings that were
essentially based on the lack of skills to perform the functions to which the employees were assigned. After a first
approximation to the actual situation in the shop floor, it was possible to elaborate Table 1 where some of the
problems are described and their consequences.
Table 1 – Identified problems in the studied sector.
Proble m
Consequence
Type of associated waste
Lack of data control
Low traceability of equipment performance
Waste of time; Information
Lack of access control to spare-parts
Stock racking due to bad registration and parts cont rol
Waiting time; Inventory
SMED technique Application
High setup time
Waiting time; Transport
The absence of organization in the
studied sector
Lack of autonomy and organization in the exchange of
reference and materials
Waiting time; Inadequate
movement
People commitment
Unmotivated collaborators
Waste of time
G.F.L. Pinto et al. / Procedia Manufacturing 38 (2019) 1582–1591 1585
Author name / Procedia Manufacturing 00 (2019) 000–000 3
Fig. 1. Types of Maintenance.
Some problems in maintenance routines were already clearly identified, such as lack of proactive maintenance,
recurring problems, poorly planned maintenance activities, lack of tracking and vision in the maintenance program,
predictive maintenance practices, lack of people’s commitment in the medium and long term, non-application of
80/20 rule and inefficiency in the application of new processes and equipment [11]. Fig.1 describes the types of
maintenance used in the companies.
Warehouses of spare parts are very important in maintenance tasks. Gulati et al. [11] believe that the best practice
for replenishing all the necessary material is good inventory planning. Stocks should be managed regarding cost,
delivery time and failure frequency. Usually, after the acquisition of an asset, the supplier provides an FMEA
analysis of the equipment, fundamentally translating into the main parts subject to degradation and their preventive
maintenance. The computerized maintenance systems include a database with all the assets’ inventory, in which
each asset has a set of specific details, guaranteeing the good maintenance of them [11].
Improvements in process performance have grown more and more, and so, there is a need to measure and
develop the key performance indicators (KPIs). Performance indicators can be structured into three groups:
economic, technical and organizational. In the area of maintenance, the technical indicators are usually the most
requested, namely: failure rate (λ), MTBF and MTTR [1]. MTBF represents the reliability of the company's assets
and represents the mean time between failures. Its calculation formula is deducted by the total operating time and
number of system failures. Its function (Eq. 1) is essential to inform the behaviour of the asset, ensuring the good
functionality of the asset [1,11]. The MTBF can be calculated by the ratio of "Total Operating Time" to "Total
Faults", or by the inverse of the failure rate (), Eq. 2. The time between failures can be considered the time between
the first fault occurred and the second fault. The higher the MTBF, the greater the equipment reliability is. MTTR is
exactly the time it takes to restore a device, bringing it back to good functionality. MTTR is calculated by the ratio
of "Total Repair Time" to "Total Failures" (Eq. 3). The total repair time includes the time of diagnosis, time to
gather the necessary resources and tools, repair, test the equipment and deliver it in the best operating conditions.
=
⁄ [min]
(1)
= 1/
[min]
(2)
= / [min]
(3)
Otherwise, OEE is an indicator that allows the assessment of the overall performance. This indicator can act on a
set of equipment, but also on an individual basis. The main function is to indicate the behaviour that the equipment
or set of equipment is presenting on a permanent basis. Performance, availability and quality are three parameters
used to calculate OEE [12]. Sousa et al. [13] stratified the calculation of OEE by the product between
"Performance", "Availability" and "Quality", as shown in Eq. 4. Nakajima [14] defined the ideal value for the OEE
metric at 85% or higher. The three parameters should present minimum values of 90% regarding availability, 95%
to performance and 99% to quality. The way each of these parameters is usually calculated can be seen in [13].
Moreira et al. [1] through an adequate selection of solvents and other specific products used in the printing industry,
achieved a 2 to 4% increase in the OEE of a printing company. Sousa et al. [13], using the OEE indicator, was able
to identify that the biggest cause of the low efficiency of a sector of a cork stopper company was the successive
micro-stops to which the equipment was subject. Antoniolli et al. [18] achieved a gain of 16% in the OEE of a
company producing automotive air conditioning systems through the application of Standard Work and other
4 G. F. L. Pinto et al./ Procedia Manufacturing 00 (2019) 000–000
techniques of optimization of manufacturing processes. Guariente et al. [2], through the application of concepts of
autonomous maintenance, increased by 8% the OEE of this same company of air conditioning systems for motor
vehicles.
ൌൈൈሾΨሿ
(4)
Lean can be understood as a set of tools that assist in the identification and constant elimination of waste,
improving as well the quality and productivity, and reducing the costs and production time [15 -17]. Lean can be
interpreted as continuous improvement of the process through a continuous cycle of improvements: less material,
less investment, reduced inventory, increased space and minimized human resources [18]. Ohno [19] and Womack
[20] typified the eight types of waste that can normally be thrown into the company when starting continuous
improvement processes. According to Shingo et al. [21], Lean philosophy has as focus the five following
fundamental Lean principles: value specification, value flow mapping, productive flow system, pulled production
and lean manufacturing. The quest for perfection must be in the mind of the whole organizational structure. Rosa et
al. [22] improved the productivity of a production line of control cables for motor vehicles by improving line
balancing, upgrading of equipment, elimination of supply problems to the line, study and improvement of operator
movement and increase the reliability of production line. Neves et al. [16], through the application of some Lean
tools such as the PDCA, 5S and 5W2H cycle, managed to save 10% of the time usually spent by operators in a
textile trimmings industry.
3. Problem statement and Methodology
This work was developed based on a company dedicated to the production of rubber seals for the automotive
industry. This company had not established practices for collecting, processing and controlling data on maintenance
activities, as stipulated in IATF 16949: 2016 [5]. According to the standard, it is necessary to monitor OEE, MTTR,
MTBF and preventive maintenance compliance metrics. The MTBF was calculated by the company but was not
linked to the calculation of the OEE indicator and was kept as a register but not giving rise to any continuous
improvement process nor was it properly communicated to all stakeholders in order to generate improvement
processes. All operators had free access to the spare-parts warehouse but were not adequately trained to understand
the importance of proper management of this type of components. This situation led to ruptures in the stock of
critical parts, which generated equally critical stops in the productive sector. It was thus established that there would
have to be a very close control of the spare parts warehouse, with restricted access to the employee who was
responsible for the management and supply of those spare parts to the maintenance teams. Although the company
had a good organization, some sectors presented some functioning deficiencies, which were based on shortcomings
in the employees' skills, lack of planning in the acquisition of components for the molds used in the seals
manufacturing process, lack of planning in the exchange of molds to be used in production and poor labeling and
organization in the location of molds into the warehouse. Detection of these organizational shortcomings led to the
drawing up of a list of needs in terms of vocational training, with a view to eliminating shortcomings that were
essentially based on the lack of skills to perform the functions to which the employees were assigned. After a first
approximation to the actual situation in the shop floor, it was possible to elaborate Table 1 where some of the
problems are described and their consequences.
Table 1 – Identified problems in the studied sector.
Proble m
Consequence
Type of associated waste
Lack of data control
Low traceability of equipment performance
Waste of time; Information
Lack of access control to spare-parts
Stock racking due to bad registration and parts cont rol
Waiting time; Inventory
SMED technique Application
High setup time
Waiting time; Transport
The absence of organization in the
studied sector
Lack of autonomy and organization in the exchange of
reference and materials
Waiting time; Inadequate
movement
People commitment
Unmotivated collaborators
Waste of time
1586 G.F.L. Pinto et al. / Procedia Manufacturing 38 (2019) 1582–1591
Author name / Procedia Manufacturing 00 (2019) 000–000 5
Based on the detected problems, improvement actions were enumerated and a corresponding order of priority
was established grounded on their easiness of implementation, global importance and impact on results (Table 2).
Table 2 – Proposals for improvement and selection of the priority level for implementation.
Suggestion
Implementation easiness
Importance
Result
Implementation order
Creation of documentation for data control
4
5
20
1º
Involvement of people
3
5
15
2º
SMED technique Application
3
4
12
3º
5S tool Application
3
4
12
4º
Monitoring access to spare parts
3
3
9
5º
(1-Easy, 5-Difficult) and its importance (1-Not Important, 5-Very Important).
From Table 2, it was concluded that the first improvement action to be implemented is the "creation of
documentation for data control", in order to enable traceability in the process. Thus, new forms used for the
collection of the information were standardized, allowing the information to be passed in a simpler and more
perceptible way. Moreover, a form-filling procedure was also created to guide current employees, as well as new
ones to be hired. After the first completed action, the SMED technique is applied in parallel to the first task, which
will aim at reducing the setup time in the studied sector and a better organization, using the 5S tool. A new
procedure was created to collect data on failure events as well as the effective operating time regarding each
workstation/equipment. According to Equation 1 and Equation 2 above, in order to calculate the MTBF to the
equipment, there is a need to record the data. The creation of standardized and automated spreadsheets has the
purpose of reducing the time of introduction, as well as the calculation of them and also allows a better perception of
the results for the various sectors. The Magnetization Inspection Machine (MI) sector was selected as an example to
be described in this study. Data are now collected weekly and per shift. A meeting is held at the beginning of each
week to analyze data from the previous week to gather suggestions for improvement and action plans to correct
problems and implement concrete improvement actions. Based on the MTBF, information about the time of micro-
stops due to equipment and moulds failures was extracted, which is shown by equipment and per week. The data are
then shown in the form of a graph, which now contains the time spent in micro -stops (in minutes), combining in a
Pareto’s diagram the information on the top 10 equipment responsible for the greatest s topping times.
A closer look at the numbers evidenced by the calculation of the MTBF and the study of the reasons behind these
values allowed to realize that much of the non-productive time of the equipment was due to adjustments to be made
whenever it was necessary to change from product to be manufactured. This evidenced the need to apply SMED
methodology in order to minimize these setup times and increase the equipment production time.
Although the production department already had a procedure for recording the time of scheduled and
unscheduled stops, the MTTR is not calculated or monitored as required by the automotive standard, IATF 16949:
2016. In order to perform the MTTR calculation, there is a need to control the number of stops and the stop times for
maintenance execution. Once these two factors are gathered, and according to Equation 4, the mean time to repair of
the equipment is calculated. Thus, the procedure was revised and, as in the case of the MTBF, the collection of data
was standardized and calculations have been adjusted to meet the requirements of the above-referred standard.
On the basis of the above data, it was possible to calculate the availability of the equipment, that is, one of the
essential factors for the calculation of the OEE. Given that data on productivity and quality already existed, this was
the only factor missing in the calculation of this indicator.
4. Results and Discussion
As can be seen in the chart of Fig. 2, the equipment that contributed most intensively to the overall equipment
downtime was the Up/Down Washer Claws, with a 604-minute stop in a week, followed closely by another seven
equipment, which contribute to 95% of the stopping time, based on the top 10 of equipment with longer stopping
time in the week considered. An investigation into the causes behind the micro-stoppages registered and which
require the intervention of the maintenance team, are operations of fine-tuning, repair of small faults and help in
solving small problems. This chart shows clearly that there is room for a strong improvement since the numbers
6 G. F. L. Pinto et al./ Procedia Manufacturing 00 (2019) 000–000
indicated represent about 8% of the working time, considering that they work 5 days a week and each day has 3
shifts of 8 hours.
Fig. 2 – Pareto’s diagram for the top 10 stops of the week considered in this work.
Once the equipment's downtimes had been properly parameterized, it became possible to advance to the OEE
calculation, since all other parameters (productivity and quality) were duly recorded and calculated. Table 3 shows
the source of the data and the Department responsible for its delivery.
Table 3 – Parameters for OEE calculation.
The MTTR of the equipment is five minutes for all sectors. There are several reasons for the large fluctuation of
values, such as lack of manpower, lack of personnel management in relation to corrective maintenance and lack of
commitment on the part of the operators/lack of autonomous maintenance. The shortage of labour, causes a lack of
time and attention of the operators to execute autonomous adjustments. The need to carry out more detailed training
actions related to daily problems in equipment was identified, and this problem is transversal to all sectors of the
company. After calculating the values for the MTBF and MTTR, the values referring to the OEE were then
calculated. The OEE calculations were performed by each OEE parameter and by company individual sectors.
These values were also compared to the reference values indicated by Nakajima [14], as can be seen in Table 4. This
comparison allowed to analyze in which of the three slopes there was a greater margin for improvement, helping to
concentrate the focus on the parameter that is more away from that reference. As can be observed, Availability is the
parameter of the OEE that is further away from the objective outlined by Nakajima [14].
Data
Department
Performance
Production planning control
Production Planning
✓ Recording hours planned for production vs. actual hours of production
Availability
Maintenance time control
Maintenance
✓ Business days of work
✓ Maintenance times
✓ Equipment setup
✓ Extra hours
✓ MTBF and MTTR
Quality
Control of the quality rate
Quality
✓ Recording of the production of pieces by equipment;
✓ Registration of non -compliant parts by equipment.
G.F.L. Pinto et al. / Procedia Manufacturing 38 (2019) 1582–1591 1587
Author name / Procedia Manufacturing 00 (2019) 000–000 5
Based on the detected problems, improvement actions were enumerated and a corresponding order of priority
was established grounded on their easiness of implementation, global importance and impact on results (Table 2).
Table 2 – Proposals for improvement and selection of the priority level for implementation.
Suggestion
Implementation easiness
Importance
Result
Implementation order
Creation of documentation for data control
4
5
20
1º
Involvement of people
3
5
15
2º
SMED technique Application
3
4
12
3º
5S tool Application
3
4
12
4º
Monitoring access to spare parts
3
3
9
5º
(1-Easy, 5-Difficult) and its importance (1-Not Important, 5-Very Important).
From Table 2, it was concluded that the first improvement action to be implemented is the "creation of
documentation for data control", in order to enable traceability in the process. Thus, new forms used for the
collection of the information were standardized, allowing the information to be passed in a simpler and more
perceptible way. Moreover, a form-filling procedure was also created to guide current employees, as well as new
ones to be hired. After the first completed action, the SMED technique is applied in parallel to the first task, which
will aim at reducing the setup time in the studied sector and a better organization, using the 5S tool. A new
procedure was created to collect data on failure events as well as the effective operating time regarding each
workstation/equipment. According to Equation 1 and Equation 2 above, in order to calculate the MTBF to the
equipment, there is a need to record the data. The creation of standardized and automated spreadsheets has the
purpose of reducing the time of introduction, as well as the calculation of them and also allows a better perception of
the results for the various sectors. The Magnetization Inspection Machine (MI) sector was selected as an example to
be described in this study. Data are now collected weekly and per shift. A meeting is held at the beginning of each
week to analyze data from the previous week to gather suggestions for improvement and action plans to correct
problems and implement concrete improvement actions. Based on the MTBF, information about the time of micro-
stops due to equipment and moulds failures was extracted, which is shown by equipment and per week. The data are
then shown in the form of a graph, which now contains the time spent in micro -stops (in minutes), combining in a
Pareto’s diagram the information on the top 10 equipment responsible for the greatest s topping times.
A closer look at the numbers evidenced by the calculation of the MTBF and the study of the reasons behind these
values allowed to realize that much of the non-productive time of the equipment was due to adjustments to be made
whenever it was necessary to change from product to be manufactured. This evidenced the need to apply SMED
methodology in order to minimize these setup times and increase the equipment production time.
Although the production department already had a procedure for recording the time of scheduled and
unscheduled stops, the MTTR is not calculated or monitored as required by the automotive standard, IATF 16949:
2016. In order to perform the MTTR calculation, there is a need to control the number of stops and the stop times for
maintenance execution. Once these two factors are gathered, and according to Equation 4, the mean time to repair of
the equipment is calculated. Thus, the procedure was revised and, as in the case of the MTBF, the collection of data
was standardized and calculations have been adjusted to meet the requirements of the above-referred standard.
On the basis of the above data, it was possible to calculate the availability of the equipment, that is, one of the
essential factors for the calculation of the OEE. Given that data on productivity and quality already existed, this was
the only factor missing in the calculation of this indicator.
4. Results and Discussion
As can be seen in the chart of Fig. 2, the equipment that contributed most intensively to the overall equipment
downtime was the Up/Down Washer Claws, with a 604-minute stop in a week, followed closely by another seven
equipment, which contribute to 95% of the stopping time, based on the top 10 of equipment with longer stopping
time in the week considered. An investigation into the causes behind the micro-stoppages registered and which
require the intervention of the maintenance team, are operations of fine-tuning, repair of small faults and help in
solving small problems. This chart shows clearly that there is room for a strong improvement since the numbers
6 G. F. L. Pinto et al./ Procedia Manufacturing 00 (2019) 000–000
indicated represent about 8% of the working time, considering that they work 5 days a week and each day has 3
shifts of 8 hours.
Fig. 2 – Pareto’s diagram for the top 10 stops of the week considered in this work.
Once the equipment's downtimes had been properly parameterized, it became possible to advance to the OEE
calculation, since all other parameters (productivity and quality) were duly recorded and calculated. Table 3 shows
the source of the data and the Department responsible for its delivery.
Table 3 – Parameters for OEE calculation.
The MTTR of the equipment is five minutes for all sectors. There are several reasons for the large fluctuation of
values, such as lack of manpower, lack of personnel management in relation to corrective maintenance and lack of
commitment on the part of the operators/lack of autonomous maintenance. The shortage of labour, causes a lack of
time and attention of the operators to execute autonomous adjustments. The need to carry out more detailed training
actions related to daily problems in equipment was identified, and this problem is transversal to all sectors of the
company. After calculating the values for the MTBF and MTTR, the values referring to the OEE were then
calculated. The OEE calculations were performed by each OEE parameter and by company individual sectors.
These values were also compared to the reference values indicated by Nakajima [14], as can be seen in Table 4. This
comparison allowed to analyze in which of the three slopes there was a greater margin for improvement, helping to
concentrate the focus on the parameter that is more away from that reference. As can be observed, Availability is the
parameter of the OEE that is further away from the objective outlined by Nakajima [14].
Data
Department
Performance
Production planning control
Production Planning
✓ Recording hours planned for production vs. actual hours of production
Availability
Maintenance time control
Maintenance
✓ Business days of work
✓ Maintenance times
✓ Equipment setup
✓ Extra hours
✓ MTBF and MTTR
Quality
Control of the quality rate
Quality
✓
Recording of the production of pieces by equipment;
✓ Registration of non -compliant parts by equipment.
1588 G.F.L. Pinto et al. / Procedia Manufacturing 38 (2019) 1582–1591
Author name / Procedia Manufacturing 00 (2019) 000–000 7
Table 4 – World reference values vs. Company internal values.
Parameters
World Reference
Company Reference
Performance
95%
98%
Availability
90%
85%
Quality
99.9%
99.3%
Fig. 3 shows in detail the evolution of the three parameters for the above-mentioned sector regarding the first
three months of 2018. This makes it simpler to compare and evaluate individual evolution. Thus, it is possible to
notice a positive evolution of Performance in these three months, but, on the other hand, there was a decrease in the
Availability, while the Quality maintained the values unchanged in that period. These figures reflect that concerns
should be centred essentially on Availability, a parameter that has deteriorated over time. The causes of this
degradation of values must still be understood.
Fig. 3 – Individual evolution of the studied sector in the first three months of 2018: Performance, Availability and Quality
Fig. 4 shows the evolution of the company's OEE, representing an overview of the company during the first
semester of 2018, making possible to have a global overview of the situation into the company. Overall data show
that March of 2018 was a month with extremely low Availability, which depleted the overall OEE indicator, but
thanks to the implementation of some measures, which will be explained later, a remarkable, but not consistent
recovery in the following three months was achieved.
Fig. 4 – Company’s OEE evolution in the first semester of 2018
In order to minimize downtime, increase availability and reduce the impact of setups on this Availability, a
SMED study was conducted on some processes. The SMED technique in the company is well developed and
controlled, however, it was possible to improve the time spent in placing the poles and components in the reference
exchange cart. A closer look at the setup process led to the conclusion that it took the workers too long to find tools
on the shelves, which were not conveniently organized and labelled. Thus, it was necessary to apply the 5S
8 G. F. L. Pinto et al./ Procedia Manufacturing 00 (2019) 000–000
methodology to organize these shelves, facilitating the search of the necessary tool for each manufactured product.
This 5S study allowed a reduction in the demand for the necessary tool from 13min10sec to 2min10sec, that is,
about 85%. Given that the internal and external tasks were already well-defined and optimized, the time saved
through the SMED study was summed up to the time gained through the 5S study. Thus, the total setup time
decreased from 1h38min5sec to 1h27min5sec, thus saving 11 minutes, which corresponds to a saving of about 11%.
This reduction can be considered relatively low considering the reductions of 50% in setup time achieved by
Martins et al. [23] in a process of production of cross-linked cables for the automotive industry, or 43% presented
by Sousa et al. [13] in the production of cork stoppers, or even a 58.3% reduction obtained by Rosa et al. [24] at
setup time in a production line of metal cables for the automotive industry. However, these more significant
reductions announced above had a greater margin of progression, due to the lack of previous work developed on
these processes in terms of SMED. It should be pointed out once again that the process studied here was already
well studied in terms of the balance between internal and external tasks, as regards the exchange of tools.
As previously mentioned, control of the stock of spare parts was also significantly improved by assigning
responsibilities for access to the warehouse of a single employee at each shift. This evolution allowed minimizing
the constraints on production and maintenance due to the inexistence of spare parts that allowed a quick resolution
of unexpected problems in the operation of the equipment. A new preventive maintenance policy, taking into
account the equipment history and correspondent fine-tuning of the operations to be performed in each intervention,
as well as the adjustment of the time between preventive maintenance operations, also allowed to reduce by 42% the
cases of unexpected failure of equipment operation, which contributed to a sustainable increase of the parameter
Availability that affects the OEE of the company.
Taking into account the organizational evolution above-mentioned, it was also detected that it was necessary to
block free access to the mould warehouse since the formation of the operators did not allow to ensure a correct
organization of the replacement of the moulds in the shelves. Thus, access to the mould warehouse was only carried
out by the production manager at each shift, which ensured that the organization of the moulds on the shelves
remained faithful to the previously stipulated. During the course of the work, and in the light of developments, it
was possible to conclude that employee training was not able to keep pace with the organizational evolution. A
training plan was then drawn up for the employees so that they acquired competencies in organizational terms and
realized the importance of complying with the stipulated procedures. The training plan was divided into different
stages, with a view to the partial involvement of the group of workers who needed this training. The standardization
of knowledge is important in order to achieve a more stable production process.
The results of the training were also reflected in the overall performance of the company by improving the
indicators that were now being measured and calculated, in accordance with the IATF standard 16949: 2016.
The performance indicator OEE was applied to all sectors. Nine sectors showed above-average values. Two
sectors that presented values below the average, although one of them is old and little used and the other presented
some breaks in spare parts supply. However, the values shown achieved are satisfactory. The remaining sectors are
close to average.
Overall, the company demonstrated overall equipment efficiency above the company's goal. Regarding the period
from January to June of 2018, during which the new solutions were implemented and tested, the OEE value was
90.22%, which is above the value understood as the global reference for this indicator: 85%. It should also be noted
that in the same period, the OEE never reached values below that required by the company's top management, which
is 83%. The OEE value of 90.22% obtained through this work is significantly higher than those obtained by Sousa et
al. [13] for each of the shifts analyzed in the production of cork stoppers, who found values between 55 and 76%.
The values obtained are also better than those obtained by Moreira et al. [1], which ranged from 72% to 75% in the
printing industry. It can thus be observed that, in general, the existing competitiveness in the automotive
components industry requires much more precise management of the production processes, leading to higher OEE.
This can even be proven through the work developed by Guariente et al. [2], also in an automotive components
industry, where the OEE obtained increased from 70% to 82% through improvements introduced in the maintenance
procedures and management system, these values being more in line with those obtained through this study
(90.22%). The values obtained in the present study also demonstrate that there was already a prior concern about the
company's overall performance, reflecting Performance and Quality levels in line with what is typical in the
manufacture of components for the automotive industry.
G.F.L. Pinto et al. / Procedia Manufacturing 38 (2019) 1582–1591 1589
Author name / Procedia Manufacturing 00 (2019) 000–000 7
Table 4 – World reference values vs. Company internal values.
Parameters
World Reference
Company Reference
Performance
95%
98%
Availability
90%
85%
Quality
99.9%
99.3%
Fig. 3 shows in detail the evolution of the three parameters for the above-mentioned sector regarding the first
three months of 2018. This makes it simpler to compare and evaluate individual evolution. Thus, it is possible to
notice a positive evolution of Performance in these three months, but, on the other hand, there was a decrease in the
Availability, while the Quality maintained the values unchanged in that period. These figures reflect that concerns
should be centred essentially on Availability, a parameter that has deteriorated over time. The causes of this
degradation of values must still be understood.
Fig. 3 – Individual evolution of the studied sector in the first three months of 2018: Performance, Availability and Quality
Fig. 4 shows the evolution of the company's OEE, representing an overview of the company during the first
semester of 2018, making possible to have a global overview of the situation into the company. Overall data show
that March of 2018 was a month with extremely low Availability, which depleted the overall OEE indicator, but
thanks to the implementation of some measures, which will be explained later, a remarkable, but not consistent
recovery in the following three months was achieved.
Fig. 4 – Company’s OEE evolution in the first semester of 2018
In order to minimize downtime, increase availability and reduce the impact of setups on this Availability, a
SMED study was conducted on some processes. The SMED technique in the company is well developed and
controlled, however, it was possible to improve the time spent in placing the poles and components in the reference
exchange cart. A closer look at the setup process led to the conclusion that it took the workers too long to find tools
on the shelves, which were not conveniently organized and labelled. Thus, it was necessary to apply the 5S
8 G. F. L. Pinto et al./ Procedia Manufacturing 00 (2019) 000–000
methodology to organize these shelves, facilitating the search of the necessary tool for each manufactured product.
This 5S study allowed a reduction in the demand for the necessary tool from 13min10sec to 2min10sec, that is,
about 85%. Given that the internal and external tasks were already well-defined and optimized, the time saved
through the SMED study was summed up to the time gained through the 5S study. Thus, the total setup time
decreased from 1h38min5sec to 1h27min5sec, thus saving 11 minutes, which corresponds to a saving of about 11%.
This reduction can be considered relatively low considering the reductions of 50% in setup time achieved by
Martins et al. [23] in a process of production of cross-linked cables for the automotive industry, or 43% presented
by Sousa et al. [13] in the production of cork stoppers, or even a 58.3% reduction obtained by Rosa et al. [24] at
setup time in a production line of metal cables for the automotive industry. However, these more significant
reductions announced above had a greater margin of progression, due to the lack of previous work developed on
these processes in terms of SMED. It should be pointed out once again that the process studied here was already
well studied in terms of the balance between internal and external tasks, as regards the exchange of tools.
As previously mentioned, control of the stock of spare parts was also significantly improved by assigning
responsibilities for access to the warehouse of a single employee at each shift. This evolution allowed minimizing
the constraints on production and maintenance due to the inexistence of spare parts that allowed a quick resolution
of unexpected problems in the operation of the equipment. A new preventive maintenance policy, taking into
account the equipment history and correspondent fine-tuning of the operations to be performed in each intervention,
as well as the adjustment of the time between preventive maintenance operations, also allowed to reduce by 42% the
cases of unexpected failure of equipment operation, which contributed to a sustainable increase of the parameter
Availability that affects the OEE of the company.
Taking into account the organizational evolution above-mentioned, it was also detected that it was necessary to
block free access to the mould warehouse since the formation of the operators did not allow to ensure a correct
organization of the replacement of the moulds in the shelves. Thus, access to the mould warehouse was only carried
out by the production manager at each shift, which ensured that the organization of the moulds on the shelves
remained faithful to the previously stipulated. During the course of the work, and in the light of developments, it
was possible to conclude that employee training was not able to keep pace with the organizational evolution. A
training plan was then drawn up for the employees so that they acquired competencies in organizational terms and
realized the importance of complying with the stipulated procedures. The training plan was divided into different
stages, with a view to the partial involvement of the group of workers who needed this training. The standardization
of knowledge is important in order to achieve a more stable production process.
The results of the training were also reflected in the overall performance of the company by improving the
indicators that were now being measured and calculated, in accordance with the IATF standard 16949: 2016.
The performance indicator OEE was applied to all sectors. Nine sectors showed above-average values. Two
sectors that presented values below the average, although one of them is old and little used and the other presented
some breaks in spare parts supply. However, the values shown achieved are satisfactory. The remaining sectors are
close to average.
Overall, the company demonstrated overall equipment efficiency above the company's goal. Regarding the period
from January to June of 2018, during which the new solutions were implemented and tested, the OEE value was
90.22%, which is above the value understood as the global reference for this indicator: 85%. It should also be noted
that in the same period, the OEE never reached values below that required by the company's top management, which
is 83%. The OEE value of 90.22% obtained through this work is significantly higher than those obtained by Sousa et
al. [13] for each of the shifts analyzed in the production of cork stoppers, who found values between 55 and 76%.
The values obtained are also better than those obtained by Moreira et al. [1], which ranged from 72% to 75% in the
printing industry. It can thus be observed that, in general, the existing competitiveness in the automotive
components industry requires much more precise management of the production processes, leading to higher OEE.
This can even be proven through the work developed by Guariente et al. [2], also in an automotive components
industry, where the OEE obtained increased from 70% to 82% through improvements introduced in the maintenance
procedures and management system, these values being more in line with those obtained through this study
(90.22%). The values obtained in the present study also demonstrate that there was already a prior concern about the
company's overall performance, reflecting Performance and Quality levels in line with what is typical in the
manufacture of components for the automotive industry.
1590 G.F.L. Pinto et al. / Procedia Manufacturing 38 (2019) 1582–1591
Author name / Procedia Manufacturing 00 (2019) 000–000 9
5. Conclusions
The performance indicators - MTBF, MTTR and OEE - were implemented, being these ones a requirement of the
IATF 16949: 2016 standard, allowing the monitoring of results and respective evolutions.
Through the creation and standardization of documents related to indicators, tracking and data processing, it was
possible to analyze the behaviour of each sector and identify the largest individual problems. Based on a weekly
periodic analysis, the causes of the problems, corrective actions and verification of the effectiveness of the actions
taken were discussed. Based on an implementation of continuous improvement actions, Lean improvement projects
were carried out together with results monitoring. In this case, the SMED and 5S methodologies were applied to
reduce the external setup time, since it was the one that was the biggest problem in the exchange of moulds in this
company. The moulds and components organization led to a reduction of external setup time by about 11%. It
should also be pointed out that the reduction of the time lost in the setups, as well as a better management in the
attendance of malfunctions and small problems in the production, allowed to improve the OEE to values such as
90.22%, which cannot be compared with previous values in the same because this indicator was not previously
calculated. The improvement achieved was mainly due to the increase in Availability, which resulted from the
factors described above: shorter set-up times and more careful maintenance actions. There is still room for
progression in order for all these values to be improved since there was a lack of training and motivation in the
workers, which led to the need to develop training measures. After that, surely the values to be reached by the
indicators that have now been routinely calculated will certainly be even more encouraging.
Regarding the improvement of the spare parts management, a new procedure was proposed and new rules of
access to the warehouse of these components have been established. It should be noted that obtaining more adequate
management of spare parts was one of the main objectives of this work.
Acknowledgements
The authors want to thank Uchiyama Portugal due to its willingness to receive Master’s Students from ISEP.
References
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[3] R. Manzini, A. Regattieri, H. Pham, E. Ferrari, Maintenance for Industrial Systems. Springer Verlag, London, 2010. ISBN: 978-1-84882-574-
1.
[4] M. Ben-Daya, S. O. Duffuaa, A. Raouf, J. Knezevic, D. Ait-Kadi, Handbook of maintenance management and engineering. Springer Verlag,
London, UK, 2009. ISBN: 978-1-84882-471-3.
[5] IATF 16949:2016 - Quality management system requirements for automotive production and relevant service parts organization, IATF –
International Automotive Task Force, 2016.
[6] A. M. Smith, G. R. Hinchcliffe, RCM--Gateway to World Class Maintenance, Butterworth-Heinemann, Oxford, UK, 2003. ISBN:
9780750674614.
[7] J. Moubray, Reliability-Centered Maintenance, Industrial Press Inc., South Norwalk, CT, U.S.A.,1997. ISBN: 978-0831131463.
[8] K. A. H. Kobbacy, D. N. P. Murthy, Complex System Maintenance Handbook. Springer-Verlag, London, UK, 2008. ISBN: 978-1-84800-
010-0.
[9] M. Oliveira, I. Lopes, C. Rodrigues, Use of Maintenance Indicators by Companies of the Industrial Hub Manaus. Procedia CIRP, 52 (2016)
157-160.
[10] A. Kardec, J. R. B. Lafraia, Gestão Estratégica e Confiabilidade (in Portuguese). Qualitymark, Rio de Janeiro, Brazil, 2002. ISBN: 978-
8573037326.
[11] R. Gulati and R. Smith, Maintenance and Reliability Best Practices (2nd Edition), Industrial Press Inc., South Norwalk, CT, U.S.A., 2012.
ISBN: 978-0831134341.
[12] L. Wilson, How to Implement Lean Manufacturing. McGraw-Hill Education, NY, U.S.A., 2010. ISBN: 978-0071835732.
[13] E. Sousa, F. J. G. Silva, L. P. Ferreira, M. T. Pereira, R. Gouveia, R. P. Silva, Applying SMED methodology in cork stoppers production,
Procedia Manufacturing 17 (2018) 611-622.
[14] S. Nakajima, Introduction to TPM: Total Productive Maintenance, Productivity Press, NY, U.S.A., 1988. ISBN: 13: 978-0915299232.
10 G. F. L. Pinto et al./ Procedia Manufacturing 00 (2019) 000–000
[15] C. Rosa, F. J. G. Silva, L. P. Ferreira, T. Pereira, R. M. Gouveia, Establishing standard methodologies to improve the production rate of
assembly-lines used for low added-value products, Procedia Manufacturing 17 (2018) 555-562.
[16] P. Neves, F. J. G. Silva, L. P. Ferreira, T. Pereira, A. Gouveia, C. Pimentel, Implementing lean tools in the manufacturing processes of
trimmings products, Procedia Manufacturing 17 (2018) 696 -704.
[17] D. Correia, F. J. G Silva, R. M. Gouveia, T. Pereira, L- P. Ferreira, Improving manual assembly lines devoted to complex electronic devices
by applying Lean tools, Procedia Manufacturing 17 (2018) 663-671.
[18] I. Antoniolli, P. Guariente, T. Pereira, L. P. Ferreira, F. J. G. Silva, Standardization and optimization of an automotive components
production line, Procedia Manufacturing 13 (2017) 1120–1127.
[19] T. Ohno, Toyota Production System: Beyond Large-Scale Production (1st Edition), Productivity Press, NY, U.S.A., 1988. ISBN: 978-
0915299140.
[20] J. P. Womack and D. T. Jones, Lean Thinking: Banish Waste And Create Wealth In Your Corporation (2nd Edition). Free Press, NY, U.S.A.,
2010. ISBN: 978-0743249270.
[21] S. Shingoand A. P. Dillon, A Study of the Toyota Production System: From an Industrial Engineering Viewpoint. Productivity Press, NY,
U.S.A., 1989. ISBN: 978-0915299171.
[22] C. Rosa, F. J. G. Silva, L. P. Ferreira, Improving the quality and productivity of steel wire-rope assembly lines for the automotive industry,
Procedia Manufacturing 11 (2017) 1035–1042.
[23] M. Martins, R. Godina, C. Pimentel, F. J. G. Silva, J. C. O. Matias, A practical study of the implementation of SMED to electron-beam
machining in automotive industry, Procedia Manufacturing 17 (2018) 647-654.
[24] C. Rosa, F. J. G. Silva, L. P. Ferreira, R. Campilho, SMED methodology: The reduction of setup times for Steel Wire-Rope assembly lines
in the automotive industry. Procedia Manufacturing 13 (2017) 1034–1042.
G.F.L. Pinto et al. / Procedia Manufacturing 38 (2019) 1582–1591 1591
Author name / Procedia Manufacturing 00 (2019) 000–000 9
5. Conclusions
The performance indicators - MTBF, MTTR and OEE - were implemented, being these ones a requirement of the
IATF 16949: 2016 standard, allowing the monitoring of results and respective evolutions.
Through the creation and standardization of documents related to indicators, tracking and data processing, it was
possible to analyze the behaviour of each sector and identify the largest individual problems. Based on a weekly
periodic analysis, the causes of the problems, corrective actions and verification of the effectiveness of the actions
taken were discussed. Based on an implementation of continuous improvement actions, Lean improvement projects
were carried out together with results monitoring. In this case, the SMED and 5S methodologies were applied to
reduce the external setup time, since it was the one that was the biggest problem in the exchange of moulds in this
company. The moulds and components organization led to a reduction of external setup time by about 11%. It
should also be pointed out that the reduction of the time lost in the setups, as well as a better management in the
attendance of malfunctions and small problems in the production, allowed to improve the OEE to values such as
90.22%, which cannot be compared with previous values in the same because this indicator was not previously
calculated. The improvement achieved was mainly due to the increase in Availability, which resulted from the
factors described above: shorter set-up times and more careful maintenance actions. There is still room for
progression in order for all these values to be improved since there was a lack of training and motivation in the
workers, which led to the need to develop training measures. After that, surely the values to be reached by the
indicators that have now been routinely calculated will certainly be even more encouraging.
Regarding the improvement of the spare parts management, a new procedure was proposed and new rules of
access to the warehouse of these components have been established. It should be noted that obtaining more adequate
management of spare parts was one of the main objectives of this work.
Acknowledgements
The authors want to thank Uchiyama Portugal due to its willingness to receive Master’s Students from ISEP.
References
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London, UK, 2009. ISBN: 978-1-84882-471-3.
[5] IATF 16949:2016 - Quality management system requirements for automotive production and relevant service parts organization, IATF –
International Automotive Task Force, 2016.
[6] A. M. Smith, G. R. Hinchcliffe, RCM--Gateway to World Class Maintenance, Butterworth-Heinemann, Oxford, UK, 2003. ISBN:
9780750674614.
[7] J. Moubray, Reliability-Centered Maintenance, Industrial Press Inc., South Norwalk, CT, U.S.A.,1997. ISBN: 978-0831131463.
[8] K. A. H. Kobbacy, D. N. P. Murthy, Complex System Maintenance Handbook. Springer-Verlag, London, UK, 2008. ISBN: 978-1-84800-
010-0.
[9] M. Oliveira, I. Lopes, C. Rodrigues, Use of Maintenance Indicators by Companies of the Industrial Hub Manaus. Procedia CIRP, 52 (2016)
157-160.
[10] A. Kardec, J. R. B. Lafraia, Gestão Estratégica e Confiabilidade (in Portuguese). Qualitymark, Rio de Janeiro, Brazil, 2002. ISBN: 978-
8573037326.
[11] R. Gulati and R. Smith, Maintenance and Reliability Best Practices (2nd Edition), Industrial Press Inc., South Norwalk, CT, U.S.A., 2012.
ISBN: 978-0831134341.
[12] L. Wilson, How to Implement Lean Manufacturing. McGraw-Hill Education, NY, U.S.A., 2010. ISBN: 978-0071835732.
[13] E. Sousa, F. J. G. Silva, L. P. Ferreira, M. T. Pereira, R. Gouveia, R. P. Silva, Applying SMED methodology in cork stoppers production,
Procedia Manufacturing 17 (2018) 611-622.
[14] S. Nakajima, Introduction to TPM: Total Productive Maintenance, Productivity Press, NY, U.S.A., 1988. ISBN: 13: 978-0915299232.
10 G. F. L. Pinto et al./ Procedia Manufacturing 00 (2019) 000–000
[15] C. Rosa, F. J. G. Silva, L. P. Ferreira, T. Pereira, R. M. Gouveia, Establishing standard methodologies to improve the production rate of
assembly-lines used for low added-value products, Procedia Manufacturing 17 (2018) 555-562.
[16] P. Neves, F. J. G. Silva, L. P. Ferreira, T. Pereira, A. Gouveia, C. Pimentel, Implementing lean tools in the manufacturing processes of
trimmings products, Procedia Manufacturing 17 (2018) 696 -704.
[17] D. Correia, F. J. G Silva, R. M. Gouveia, T. Pereira, L- P. Ferreira, Improving manual assembly lines devoted to complex electronic devices
by applying Lean tools, Procedia Manufacturing 17 (2018) 663-671.
[18] I. Antoniolli, P. Guariente, T. Pereira, L. P. Ferreira, F. J. G. Silva, Standardization and optimization of an automotive components
production line, Procedia Manufacturing 13 (2017) 1120–1127.
[19] T. Ohno, Toyota Production System: Beyond Large-Scale Production (1st Edition), Productivity Press, NY, U.S.A., 1988. ISBN: 978-
0915299140.
[20] J. P. Womack and D. T. Jones, Lean Thinking: Banish Waste And Create Wealth In Your Corporation (2nd Edition). Free Press, NY, U.S.A.,
2010. ISBN: 978-0743249270.
[21] S. Shingoand A. P. Dillon, A Study of the Toyota Production System: From an Industrial Engineering Viewpoint. Productivity Press, NY,
U.S.A., 1989. ISBN: 978-0915299171.
[22] C. Rosa, F. J. G. Silva, L. P. Ferreira, Improving the quality and productivity of steel wire-rope assembly lines for the automotive industry,
Procedia Manufacturing 11 (2017) 1035–1042.
[23] M. Martins, R. Godina, C. Pimentel, F. J. G. Silva, J. C. O. Matias, A practical study of the implementation of SMED to electron-beam
machining in automotive industry, Procedia Manufacturing 17 (2018) 647-654.
[24] C. Rosa, F. J. G. Silva, L. P. Ferreira, R. Campilho, SMED methodology: The reduction of setup times for Steel Wire-Rope assembly lines
in the automotive industry. Procedia Manufacturing 13 (2017) 1034–1042.
