![Research Strategy. Source: adapted from [14,15].](https://www.researchgate.net/publication/337409217/figure/fig1/AS:833897856249856@1575828150556/Research-Strategy-Source-adapted-from-14-15_Q320.jpg)
![Development process of review protocol. Source: Adapted from [15].](https://www.researchgate.net/publication/337409217/figure/fig2/AS:833897856249857@1575828150648/Development-process-of-review-protocol-Source-Adapted-from-15_Q320.jpg)
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Vol.62: no.spe: e19190002, 2019
http://dx.doi.org/10.1590/1678-4324-smart-2019190002
ISSN 1678-4324 Online Edition
Brazilian Archives of Biology and Technology. Vol.62: no.spe: e19190002, 2019 www.scielo.br/babt
Review – Smart Energy
Energy Efficiency in the Food Industry: A
Systematic Literature Review
Angela Morandini Pradella1*
https://orcid.org/0000-0002-4624-5885
Eduardo de Freitas Rocha Loures1,2
https://orcid.org/0000-0002-1963-6186
Sergio E. Gouvea da Costa1,3
https://orcid.org/0000-0002-9882-5857
Edson Pinheiro de Lima1,3
https://orcid.org/0000-0001-9331-1569
1Pontifical Catholic University of Paraná (PUCPR), Industrial and Systems Engineering Graduate
Program, Curitiba, Paraná, Brazil; 2 Universidade Tecnológica Federal do Paraná (UTFPR), DAELT,
Curitiba, Paraná, Brazil; 3 Universidade Tecnológica Federal do Paraná (UTFPR), DAELT, Pato
Branco, Paraná, Brazil.
Received: 2018.11.05; Accepted: 2019.07.26.
*Correspondence: angelapradella@gmail.com; Tel.: +55-49-99981-3722 (A.M.P)
Abstract: Governments and private companies have increased efforts to identify effective
actions for improving energy efficiency in manufacturing processes. The objective of this
work is to improve the decision-making process by increasing the quality of information
related to energy indicators in the food industry. This research involves developing a
systematic literature review (SLR) to identify energy efficiency indicators in the food industry,
which serve as inputs for a sectoral evaluation based on multicriteria techniques. The SLR
identified six criteria evaluated by food industry experts, which form the proposed basis for
evaluating the performance of related sectors. These criteria are: benchmarks, key
performance indicators, framework, monitoring, ISO 50001, and information communication
technologies (ICTs) in sectoral evaluations. The criteria were evaluated by experts using the
Analytic Hierarchy Process (AHP), which prioritizes the most important food industry issues
using an evaluation scale. Weights were attributed to each issue and positioned according
to the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE)
HIGHLIGHTS
Indicators relating to four groups were identified in food industry and
manufacturing literature.
Results suggest greater concern of the food industry with the technologies of
Industry 4.0.
• A differentiated approach in the food industry with method multi-criteria AHP and
PROMETHEE.
• Energy indicators are poorly understood by the food industry, and therefore have
not been implemented.
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to evaluate each sector by the identified criteria. The evaluated criteria are applicable to the
three sectors surveyed, with emphasis on the beverage sector. Among the evaluated criteria,
ICTs were highlighted by Industry 4.0 as a concern for the food sector.
Keywords: energy efficiency; manufacture; food industries; analytic hierarchy process
(AHP); preference ranking organization method for enrichment evaluations (PROMETHEE).
INTRODUCTION
The effects of climate change have put pressure on businesses to address green
practices as well as financial performance to stay competitive with global requirements. As a
primary consumer of natural resources with extensive carbon emissions, the manufacturing
industry drives the need for sustainable manufacturing processes [1,2]. Energy is an
increasingly critical factor for reducing carbon emissions and implementing cleaner
production. Enhanced energy performance and its management may provide companies
with a competitive advantage and an important strategic asset, enabling them to react with
flexibility in change and development scenarios [3–5], and to maximize the use of their
energy sources and assets, thereby reducing the cost of energy as well as that of its
consumption [6].
Energy efficiency is a key phrase in modern industry that has become an essential
factor for competitiveness, sustainability, and environmental performance [7]. Energy
efficiency is defined as the output ratio for a given production device or system production
for a facility operating under standard conditions or the volume of energy consumed by this
production device, system, or facility to deliver its output [8].
Another way of measuring energy efficiency is using indicators to show the relationships
between energy consumption and products. Indicators must consider the influences of
economic and technical aspects [9]. In [10], performance indicators serve as metrics to
determine whether systems are operating as designed and help to define progress in a
given direction. This enables better tracking and control of energy consumption, which is
extremely important to increase energy efficiency in production.
Analysis of energy indicators can also link consumption-related factors such as: energy
efficiency, environmental policies, changes in energy prices, changes in international trade
of energy-intensive intermediary or final products, and structural impacts stemming from
economic cycles to develop tools for improving energy efficiency [11].
As discussed in [12], food is a basic human need. The food industry, therefore, is of
major importance. Given the food industry’s high energy consumption, food processing
industries must adapt measures and implement actions to promote efficient energy use.
According to [13], another indicator for assessing energy performance and efficiency in food
production facilities is benchmarking, a method that consists of comparing Specific Energy
Consumption (SEC) among facilities with similar characteristics [13].
This article aims to analyze energy efficiency mastery through a systematic literature
review (SLR) of the methods and related indicators used in the manufacturing and food
industries for three sectors (beverages, meats, and grains). Indicators were ranked by the
level of importance allocated to each, with the objective of understanding the extent to which
indicators identified by literature have been practically adopted by industry. The following
assessment method was used: Analytic Hierarchy Process (AHP) and Preference Ranking
Organization Method for Enrichment Evaluations (PROMETHEE).
Energy Efficiency in Food Industry: A Systematic Literature Review 3
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MATERIAL AND METHODS
The systematic review protocol was based on the stages proposed by [14,15], as shown
in Figure 1.
Figure 1: Research Strategy. Source: adapted from [14,15].
The first stage, called Planning the review, consists of two stages, Identification of need
for a literature review and Development of a literature review protocol. The second stage,
Collect and select, features two stages called Identification of documents and Selection of
relevant documents. The third stage, Analyze, is comprised of Categorization of documents
and Data extraction. The last stage, Results, which includes Document findings, involves
reviewing all collected documents to extract pertinent information.
The literature review, described in Stage I, Step 1, Planning the review, is necessary
because of the difficulty of tracking the advances and achievements related to energy
efficiency initiatives that are currently adopted by the food industry.
Stage I, Step 2 addresses the development of the review protocol described in Figure 2,
and details all stages associated with research document selection. As discussed in [14], the
systematic review starts with identification of research keywords and terms, which were
constructed based on study scope, literature, and discussions in the field.
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Figure 2: Development process of review protocol. Source: Adapted from [15].
The searches were based on the words energy management and indicator or (KPI) with
“AND” and “OR” as Boolean operators between keywords and search fields. Strings defined
for the search were: Title: (“energy management” AND “indicator” OR “KPI”) OR Abstract:
(“energy management” AND “indicator” OR “KPI”) OR Keywords: (“energy management”
AND “indicator” OR “KPI”). These boundaries did not guarantee documents focused on the
research topic, which led to establishment of exclusion criteria to exclude papers unrelated
to energy issues in the context of production or the food industry.
English was chosen as the language because of the high number of English language
publications, which provided a higher number of relevant documents in the survey. A
twenty-one year time window (1996 to present) was chosen. This timeframe is directly linked
to an increase of publications on energy topics that began in 1996. Figure 3 provides a
detailed view of the protocol through which search criteria and requirements were defined.
Three online databases were used to search for published articles: Scopus, Web of
Science, and Science Direct. Database searches are conducted in similar formats, but must
be customized for search engine particularities.
Collect and select
Execution of the search protocol, described in Figure 2, followed Stage II collection and
selection, as per Figure 1. Documents were identified using a preliminary document survey
to select relevant documents in each databases.
Analyze
Stage III analyze is the next step, outlined in Figure 1. Searching all three databases
resulted in 448 documents, which were categorized as per Step 1. Documents were
processed using an Excel spreadsheet to remove duplicate materials as well as those that
were inaccessible in the database. This data extraction step (Step 2) yielded 328 papers.
Next, titles and abstracts were read to select and discard documents meeting the exclusion
criteria defined in the research protocol. A total of 49 articles dealing with energy
management or efficiency in the manufacturing or food industries were thus selected for
complete reading for execution of Step 3 document findings. Table 1 displays the protocol
used for the database survey.
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Table 1: Review Protocol. Source: Author.
Item
Description
Keywords
"energy management" AND "indicator" OR "KPI" OR "energy
efficiency" AND "indicator" OR "KPI"
Boolean
Operators
AND between keywords; OR between database search fields; between
keywords
Search fields
title; abstract; keywords in first search string; title in second search
string
Exclusion
criteria
Articles not dealing with energy issues in the context of production
Language
English
Publication
type
Article
Period
1996 to present
Table 2 shows the strings adopted within each of the database to return the most effective
papers for this research.
Table 2: Modification of the search string by database. Source: Author.
Database
Search String
Science Direct
TITLE ("energy management" AND "indicator" OR "KPI") OR
ABSTRACT (("energy management" AND "indicator" OR "KPI")
-KEYWORDS ("energy management" AND "indicator" OR "KPI")
TITLE ("energy efficiency" AND "indicator" OR "KPI")
Web of Science
TS=((("energy management") AND ("indicator") OR ("KPI")))) AND
Language: (English) AND Types of documents: (Article)
TI=((("energy efficiency") AND ("indicator") OR "KPI")))) AND
Language: (English) AND Types of documents: (Article)
Scopus
TITLE ("energy management" AND "indicator" OR "KPI") OR ABS
("energy management" AND "indicator" OR "KPI") OR KEY
("energy management" AND "indicator" OR "KPI" AND (LIMIT-TO)
(LANGUAGE, "English")) AND (LIMIT-TO (SRCTYPE, "j") TITLE
"energy efficiency" AND "indicator" OR "KPI") AND DOCTYPE (ar)
AND PUBYEAR>1995
In the Analyze phase, the selected articles were categorized by publication (Table
3). The applied filters identified the most relevant papers for developing better
understanding of energy efficiency indicators across the manufacturing and food
industries.
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Table 3: Journals with the highest numbers of publications used in this survey. Source: Author.
Publication
N° of Papers
Energy
50
Journal of Cleaner Production
41
Energy Policy
27
Applied Energy
25
Energy Conversion and Management
23
Energy and Buildings
19
Energy Procedia
14
Renewable and Sustainable Energy
Reviews
11
Renewable Energy
10
Energy Efficiency
7
Energy Economics
5
Multicriteria sectoral analysis
The AHP method was applied in Stage IV, Step 1 for multicriteria sectoral analysis,
which involved expert assessment as well as modelling and analysis. Food industry
assessment criteria was prioritized and weighted for input to the second
method—PROMETHEE. In Step 2, PROMETHEE was used for diagnostic modelling
and assessment of each sector according to the six criteria.
Results
Stage 5 results involved findings from the literature as well as specialist inputs from
each of the three food industry sectors. Standouts from the literature indicated a criteria
that were both assessed in literature and practically applied by specialists in industry.
RESULTS AND DISCUSSION
Sector Analysis
The literature review informed mapping of energy efficiency indicators used in both
general manufacturing and in the food industry and identified gaps between the sectors.
From the results of the literature analysis, six criteria were identified for the research design
of this study, which were evaluated by specialists from each sector.
Different aspects of energy efficiency integration in production management
(measurement, control, improvement, and enablers) were analyzed using literature focused
on general industry and the food industry specifically. Politicians and companies have
discussed energy efficiency for decades; however, the results highlighted in this section
indicate numerous differences among sectors in terms of advances and adoption of
indicators.
As per [6], Table 4 summarizes elements identified in literature related to
manufacturing. Elements relating to the food industry included measurement, control,
improvements, and facilitators for production processes.
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Manufacturing-related KPIs are widely available as a form of measurement, but these
do not apply to plant-specific food industry processes because of the inherent complexity of
monitoring variables. KPIs are linked to energy content and productivity; productivity is
represented by P/E, where P represents the quantity produced and E represents the
quantity of energy in kW. In this sense, the literature indicates a major gap; KPIs are rarely
used in the food industry. Using KPIs may significantly improve energy efficiency in plants
and reduce the energy consumption of the production process.
Benchmarking is widely adopted in manufacturing to track energy consumption and is
used for comparisons among sectors or countries. Benchmarking in the food industry takes
the form of overall plant assessments, but its adoption is of low significance in the food
sector. The lack of benchmarking in the food sector applies to both plants and production
equipment; therefore, it is difficult to compare plants producing the same types of products.
Table 4: Approaches in manufacturing and food industry and the gaps identified in literature. Source:
adapted from [6].
Measurement
KPI
Manufacturing
Food Industry
Gap identified in Food
Industry in literature
There are a number
of energy efficiency
KPIs at the sector
and country level.
There are no specific
plant energy
efficiency KPIs.
KPIs are little used in
food industry, due to the
difficulty in deploying
them in production
processes.
Benchmarks
Implemented for
comparison within
sectors or
countries, more
common in energy
intensive sectors.
Benchmark used in
plant for assessment
in general but little
used in the sector.
Shortage of benchmarks
for machines and plants
and comparisons with
plants producing the
same type of product.
Control and Improvement
Monitori
ng
Energy saving;
Energy Audits;
Machine time.
Energy saving;
Energy Audit.
Need for food industry
specific controls.
Frameworks
Environmental
indicators;
Economic
evaluation;
Sustainability
indicators.
No framework
available in the area.
Need for area-specific
frameworks for decision
support.
ISO
Energy
management
standards (ISO
50001)
Energy management
standards (ISO
50001)
Low adoption of ISO
50001 energy efficiency
program in the sector.
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Enabler
Disaggregat
ed indicators
Number of
disaggregated
indicators: IDA,
DEA, SEC.
Available for food
products: LDMI,
Pinch, LCA, PCA,
SEC.
Need for better
understanding in
deployment of
disaggregated indicators
ICT
Significant potential
for energy
efficiency MES,
ERP systems can
be enhanced.
Use of MES, ERP
but not targeted
energy efficiency.
Consensus on potential
of ICT (Information and
Communication
Technologies) as enabler
for energy efficient
manufacturing.
There are many methods and tools for monitoring and control, such as energy audits
and energy savings in both the manufacturing and food industries. However, these studies
have no controls (tools, techniques, indicators, and metrics) that can be replicated. These
were only adopted for case studies, to assess specific processes. Thus, the results are not
relevant for other plants in the food sector. To improve energy efficiency, the food industry
needs conceptual structures and tools for assessment, support, and decision-making.
There are several decision-making support systems (e.g., multi-criteria decision
support) that can be adopted and enhanced to optimize efficiency. Additionally, there are
references available related to assessment of energy efficiency metrics (e.g., indicators in
sustainability and economic assessment). For companies benefiting from decision-making
support tools, plant level production management must be adapted to address energy
efficiency issues. Plant assessment frameworks are not available for the food industry to
guide the cost-benefit assessment of investing in the energy efficiency and decision-making
support tools that have been confirmed by the literature and by general industry.
The energy management ISO is available for manufacturing and food production plants,
but most production planning and control systems do not integrate energy efficiency and
relevant performance standards. The broadest assessments of these standards meet the
needs of energy-intensive manufacturing facilities by addressing production energy
management (including aspects like adaptation, benefits, and cost of standards). Food
industry facilities that are not energy-intensive, however, do not benefit from adopting ISO
standards for energy efficiency. The ISO standards represent an important enabling factor,
as comparability and competitiveness in production energy management can be improved.
Information and Communication Technologies (ICT) can help manage and reduce
energy consumption in manufacturing processes. They may help to control production
processes and assess potential energy-saving investments. Company ICT infrastructure
may consist of different systems such as the Enterprise Resource Planning Systems (ERP),
Manufacturing Execution Systems (MES), or Supply Chain Management Systems (SCM).
The IC proposal is addressed in Industry 4.0 and the food industry will need to develop and
improve its processes through an inter-equipment communication channel.
The gaps identified in the literature (Fig. 6) concerning the food industry were
corroborated by specialist evaluations for each sector. Degrees of importance were
assigned to the six criteria in the AHP method and each sector was ranked by the criteria
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using the PROMETHEE method.
Notably, the perception of Brazilian specialists is different from that expressed in the
literature because of different nomenclature and indicators that apply only to Brazilian food
production processes. These differences made the task of comparing measurable indicators
more complex because of the different terminology used by Brazilian companies. Despite
this limitation, the assessment was conducted using the indicators with common definitions
in literature and industry: benchmarking, KPI, framework, monitoring, ISO 50001, and ICT.
For the assessment, specialists were selected from the beverage, meat, and grain
sectors, which are representative of the Brazilian food production industry. The next section
uses the AHP method to evaluate prioritization vectors attributed to each of the identified
criteria.
Sector Analysis using the Analytic Hierarchy Process (AHP)
The AHP method is used to generate priorities by comparing discrete and continuous
pairs [16]. The method is based on three principles of analytical thinking: (a) construction of
hierarchies, (b) establishment of priorities, and (c) logical consistency testing [17]. For the
food industry, the problem was structured hierarchically by objective, criteria, and
sub-criteria. To define the importance of two elements at the same hierarchical level,
comparative matrixes are built and preference scores are attributed to each element
according to a scale developed by [18].
Values were assigned in AHP using the Super Decisions software tool, a tool that
provides a user-friendly interface for assessing inputs and compatibility with PROMETHEE,
which was applied in the second part of sector evaluation. Figure 3 shows the AHP model
structure adopted by the proposed evaluation. First, six sub-criteria were identified: BC
(Benchmarking), KPI (Key Performance Indicator), FR (Framework), MON (Monitoring), ISO
50001, and ICT (Information and Communication Technologies). Next, weights for each
sub-criterion were entered into PROMETHEE.
Figure 3: AHP structure. Source: Author.
The results from AHP are graphically depicted in a radial format (Chart 1). As the chart
shows, ICTs are the most important indicator in the food industry. ICTs are used to qualify
industry competencies and concerns in Industry 4.0, resulting in a high prioritization factor
for ICTs in the food industry.
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Chart 1: Analysis of results by AHP. Source: Author.
The results indicate that benchmarking assessment is also a high prioritization vector in
the food industry. Benchmarking can be used to compare machinery, plants, or sector
efficiencies. Monitoring was also identified as an industry concern with a medium
prioritization vector related to tracking production processes and detecting failure
occurrences.
For KPIs and ISO, the resulting vectors show lower concerns, confirming the assessors’
perceptions. The framework shows a weight near three, which likely indicates a lack of
knowledge of its deployment in the industry. After performing diagnostics with AHP, weight
information was used for assessment by the PROMETHEE method, as shown in Figure 4.
Figure 4: Objective for applying two methods: AHP and PROMETHEE. Source: Author.
Sector Assessment using the PROMETHEE method
PROMETHEE is based on two phases: building over-classification relationships, which
add information among alternatives and criteria, and exploring these relationships to support
decision-making [19]. The weights adopted from the AHP method (prioritization vectors)
were transferred to the PROMETHEE method based on the fact that the higher the weight,
the more important the criteria.
PROMETHEE’s preference structure is based on pairwise comparisons between two
alternatives of a given criteria. The six evaluated criteria evaluated are highlighted in Item 1
and were assessed on a weight scale of 1 to 9 points in AHP. Item 2 has an ascending scale
with fields (min/max). Field “weight” contains weights from AHP. Item 3 provides statistics
based on parameter definitions and will be used to describe the method’s results. The final
Item 4 indicates the three evaluated sectors (beverages, meats and grains) and positions
the results of each sector with respect to the evaluated criteria. Figure 5 shows the results
obtained with PROMETHEE.
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Figure 5: PROMETHEE Results. Source: Author.
Figure 6 shows the ranking of the three assessed sectors: beverages, meat, and grain,
by the six criteria. This assessment positioned each sector by the criteria using the weights
attributed to each. Generally, the beverages sector was the most compliant with the
assessed criteria, resulting in a positive flow. The other two sectors displayed negative
flows, but with similar values, indicating that the importance ascribed to the criteria was low.
Figure 6: PROMETHEE Rankings. Source: Author.
For each alternative, two indices are calculated from the preference indices: the positive
flow (Φ+) represents the extent to which a given alternative is better than the others, while
the negative flow (Φ-), expresses the extent to which the alternative is exceeded by others,
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with the resulting “Phi” for each criteria. Figure 7 shows that the beverages sector has a Phi
+, corroborating the AHP results that indicate this sector has the highest scores for the
assessed criteria.
Figure 7: PROMETHEE Flow Table. Source: Author.
The PROMETHEE method enables sensitivity analysis to be performed by simulating
different scenarios. Figure 8 shows the results of this simulation. Increased use of ICT can
be projected in the coming years due to Industry 4.0 adoption by companies, suggesting that
the beverages sector, followed by the meat sector, will be better supported by these
technologies. This would enhance understanding and adoption of the other indicators
evaluated in this study.
Figure 8: Visual PROMETHEE—ICT scenario. Source: Author.
Another simulated scenario evaluates the impact of monitoring across the three sectors.
The increase led to slight growth in the beverages sector and marked growth for the grains
sector, while the meat industry decreased due to multiple controls based on the high volume
of product export, as shown in Figure 9.
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Figure 9: Visual PROMETHEE—Monitoring scenario. Source: Author.
The results obtained with both methods, AHP and PROMETHEE, allowed each of the
six criteria to be organized by weight to define indicators for the three food industry sectors,
according to the literature.
CONCLUSION
The objective of this paper was to present a systematic literature review for the field of
energy efficiency measurement and indicators in the food industry, as well as sector
assessment by specialists from the beverages, meats and grains sectors. The main
contribution of this survey is identifying energy indicators used in the food industry as well as
the six criteria assessed in each sector. These criteria were considered by the literature as
the most important for understanding industry evolution of energy efficiency. The
assessment was performed using the AHP and PROMETHEE methods, which facilitated
results ranking for each sector. The assessments emphasized that the topic of energy
efficiency is still unclear for companies and that the understanding of the indicators that may
be adopted for the measurements remains low.
There were a few notable characteristics identified by this survey. Although in both
manufacturing and the food industry, process measurement, control, and enhancement in
solutions are available, advances in these improvements are far from the implementation
stage, because of the factors highlighted in the discussion section. In many cases, solutions
are inadequate for energy management in production at the company, plant, and process
levels. There is a major gap between available solutions available and effective
implementation by companies.
To reduce the gap between theory and practice, research should focus on improving the
perception and understanding of the indicators identified in the literature and their
applications in the food industry. It is necessary to determine when, where, and how plant
level energy efficiency KPIs should be measured and displayed. The use of standardized
KPIs may help the food industry to better implement and adopt benchmarking.
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Assessing each of the three sectors with the AHP and PROMETHEE methods reveals
each sector’s performance for each of the identified criteria. The results identified a gap in
understanding and applicability of available indicators, with a low level of concern for the
importance of Industry 4.0, which may promote energy and process efficiency.
The work is constrained by the number of food industry sectors evaluated; interviews
could be increased to yield results with more applicability. Proposing energy management
systems that utilize KPIs helps to increase future research opportunities that can be
replicated across different sectors of the food industry. Future research will improve the
understanding of indicators, their applicability, and the level of importance attributed to
energy efficiency in the food sector. Because the field is broad, there is a range of
opportunities to develop unique tools such as ICTs or frameworks. This article contributes by
surveying indicators to be used by the food industry as well as by providing sector
assessment by specialists. The results achieved through the evaluations suggest that
adopting process indicators is a key concern.
Acknowledgments: The present work was carried out with the support of the Coordination of
Improvement of Higher Education Personnel–Brazil (CAPES)–Financing Code 001.
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