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Teodora Rajković*, Lena Ðorđević Milutinović, Danica Lečić-Cvetković
University of Belgrade, Faculty of Organizational Sciences, Serbia
Management: Journal of Sustainable Business and Management Solutions in Emerging Economies 2020/25(3)
*Corresponding author: Rajković Teodora, e-mail: teodora.rajkovic@fon.bg.ac.rs
Research Question: This paper presents a simulation model aimed at improving total adequacy calculation for performance
indicators of the first phase of production management. Motivation: Defining a set of adequate performance indicators that
are specific and highly important for the observed planning phase has a significant impact on the effectiveness of the
production process management. The result of the developed model is the value of selected performance indicators
adequacy. Consequently, it can be concluded whether defined performance indicators are essential for the observed phase
or other performance indicators should be selected. The monitoring of defined performance indicators should provide
complete information about the observed phase and improvement of the planning phase management. Idea: The main idea
of this paper is to develop a user-friendly simulation model, in accordance with the basic principles of spreadsheet
engineering, for improving the selection of adequate performance indicators. This model is intended for use in Small and
Medium Enterprises (SMEs) for the improvement of production management. Data: The data used in the paper are grounded
on the scientific articles, reviews and empirical studies, related to the performance and performance indicators in production
management. The set of most adequate performance indicators based on a relevant literature review and used in the model,
represents performance indicators for the planning phase that are the most suitable for SMEs. Tools: The simulation model
is developed in a spreadsheet environment. The use of spreadsheet applications enables simple data entry, processing,
editing, analyzing and output reports compiling. Findings: Simulation results show the overall efficiency of the developed
model in the determination of adequate performance indicators. They influence overall adequacy of performance indicators
on the observed phase. Contribution: The main contribution of this paper lies in the model developed to maximize the
adequacy of the performance indicators, suitable for use in the production management of SMEs.
Keywords: Production management, Production planning, Adequate performance indicators, Simulation model, Spread-
sheet engineering
JEL Classification: M11, C63
Spreadsheet Model for Determining Adequate
Performance Indicators of Production
Management
DOI: 10.7595/management.fon.2020.0011
Abstract:
1. Introduction
With constant changes in market conditions in today's competitive business environment, every manufac-
turing company needs to define the performance and performance indicators, to measure them and to mon-
itor their values. Based on these values, the enterprise should monitor business results and correct control
actions. Determining a set of adequate performance indicators of production management represents a
complex and demanding activity. The selection of a set of adequate performance indicators for production
management phases depends on the quality and validity of the information they provide, where the total ad-
equacy of performance indicators should reach maximum value (Rajkovic, Makajic-Nikolic, Vujosevic, &
Lecic-Cvetkovic, 2019). The purpose of this paper is to develop a model for defining a set of adequate per-
formance indicators for one phase of production management. This model is based on the main principles
of spreadsheet engineering. The paper analyses adequate performance indicators of planning in Small and
Medium Enterprises (SMEs) in the manufacturing industry, as the first phase of production management. A
SME's business is usually characterized by insufficient funds for software solution development and its im-
plementation represents a striking characteristic of a SME’s business (Zach, Munkvold, & Olsen, 2014).
Consequently, the authors of the paper suggest a spreadsheet simulation model, whose implementation is
not expensive and which end-users can relatively easily apply. Spreadsheet models are widely available
and suitable for end-users, as well as for domain experts with a modest level of spreadsheet programming
skills. Spreadsheets represent a flexible and functional tool that supports simulation, decision-making
processes and the analysis of the environment from many different perspectives (Djordjevic, 2017). In ad-
dition, they represent a custom tool for problem-solving, which is easier to develop and implement than
other tools, while significantly low cost of these activities indicates other essential benefits (Teixeira & Ama-
ral, 2016).
The remainder of this paper is organized as follows. The second chapter briefly describes the implementa-
tion of spreadsheet engineering principles in the production management of SMEs; it explicates character-
istics of spreadsheets and spreadsheet control models and their implications in production management;
finally, it explains adequate performance indicators and total adequacy of performance indicators. The third
chapter expounds the model created for the calculation of adequacy of selected performance indicators for
one phase of production management and presents simulation results. The fourth chapter outlines final re-
marks and directions for future research.
2. Performance Indicators and Spreadsheet Engineering in Production Management
Production management can be defined as the management of the transformation process of inputs into out-
puts (Gupta & Starr, 2014). The main purpose of the production process is the production of a certain quan-
tity of products with defined all required manufacturing steps, including standard, minimal costs, defined
timeline, and projected quality (Halevi, 2001; Rajkovic & Lecic-Cvetkovic, 2018). The production process
consists of four phases (Omerbegovic-Bijelovic, 2006; Gupta & Starr, 2014; Todorovic, 1993): planning, or-
ganizing, implementation and control. A contemporary business environment implies a constant develop-
ment of products/services, improvement of production processes, supply and distribution, maintenance of
stocks of raw materials and (semi) products, as well as the improvement of the management process by
using new methods and techniques of enterprise management (Atanasov, Rakicevic, Lecic-Cvetkovic, &
Omerbegovic-Bijelovic, 2014).
Performance indicators are a powerful tool for understanding the adequacy of business and identifying prob-
lematic areas, which presents an integral part of proper management and professional practice (Simeunovic,
2015). They are used to report on results concerning enterprise objectives and targets (Gudmundsson,
Marsden, & Josias, 2016). Performances identification comprises identification of performance indicators,
measurement methods, benchmarks for comparison of results, as well as the source and reliability of the
data used. For the performance identification process, it is essential to define adequate performance indi-
cators. The decision-making process and actions are greatly influenced by indicators nature, use and time
horizon (short or long-term) (Franceschini, Galetto, & Maisano, 2019). The selection of adequate perform-
ance indicators is a critical point in the process of translating an enterprise mission into a business strategy
and relevant objectives, which means that performance indicators are an integral part of a production man-
agement strategy (Atanasov, 2016). Adequate performance indicators are used to measure benefits for en-
terprise networks and should correspond to its size, processes and activities (Graca & Camarinha-Matos,
2017). The complexity of defining the adequate performance indicators stems from the necessity of their re-
liability and the meaningfulness of the information they provide (Dajic & Todic, 2017). The adequate per-
formance indicators should be sensitive to changes, adequate for the current state of a system process, and
easily calculated. The authors (Villa & Taurino, 2018) consider that one of the most critical problems for man-
agers in SMEs is related to production. The reason is that multiple tasks need to be accomplished with lim-
ited resources and machines, a lack of technology, resources, awareness, and knowledge of production
management.
For the purpose of this paper, the adequate performance indicators are considered as a group of the most
important performance indicators for the planning phase. Selected performance indicators should be those
with the highest total adequacy for the observed phase. The total adequacy represents a cumulative value
of all weights of performance indicators defined for the planning phase. The objective is to achieve the high-
est value of the total adequacy which means that the selected performance indicators are entirely adequate
for the observed phase. This value indicates that a set of adequate performance indicators has an impor-
tant impact on the observed phase and enables continuous improvement to achieve a more efficient and
effective production management. One of the objectives of a manufacturing company is to ensure a stable
production management. This objective can be achieved by the application of a spreadsheet control mod-
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Teodora Rajković, Lena Ðorđević Milutinović, Danica Lečić-Cvetković 2020/25(3)
els. The usability and overall rules for creating spreadsheet models depend on many factors, such as the
purpose of the model, the complexity and dimensions of the problem being modelled, the knowledge and
experience of the creator, etc. (Antic & Djordjevic, 2018).
A spreadsheet model can be represented as a set of algebraic equations implemented in a spreadsheet en-
vironment. Spreadsheet control models implement a mathematical model, which reflects the domain of an
expert's knowledge and experience (Djordjevic, 2016). Like any other, a spreadsheet model uses a set of in-
puts and calculates a set of outputs (Antic & Djordjevic, 2018). Spreadsheet programmes are probably the
most successful example of end-user software development tools, used for a variety of purposes (Jannach
& Schmitz, 2016). Users can analyze decision alternatives before having to choose a specific plan for im-
plementation in an enterprise (Ragsdale, 2018). According to (Djordjevic, 2017) modern spreadsheets are
categorized as end-users programming languages (i.e., End-User Computing - EUC) for non-professional
developers. In order to develop and ensure the quality of spreadsheet models and applications that are ef-
ficient, effective, portable, and adequately documented, it is necessary to apply spreadsheet engineering
knowledge. Spreadsheet engineering can be explained as the application of software engineering principles
to spreadsheets. It deals with various aspects of spreadsheet design and implementation, i.e., the life cycle
of spreadsheet models and applications (Djordjevic, 2017). Djordjevic (2017) defines software engineering
as a broad field that covers various techniques, for example: design before programming, modular struc-
ture, data referencing, documentation, etc. Many of these techniques are directly applicable to spreadsheets
and define spreadsheet engineering. Adoption and application of the spreadsheet engineering principles
have the potential to solve some of the basic problems related to spreadsheets: poor design, poor modu-
larity, mixed data and calculations, lack of documentation, etc.
Although spreadsheets have countless advantages, they are also characterized by numerous errors (Djord-
jevic, 2017). Errors occur due to the omission of the testing phase and the lack of professional programming
knowledge of the spreadsheet creators (Djordjevic, 2016). The authors (Hermans, Jansen, Roy, Aivaloglou,
Swidan, & Hoepelman, 2016) define the following issues related to spreadsheets: long life span, many dif-
ferent users performing a variety of tasks, lack of documentation and quality issues (substantial impact of
errors involving spreadsheets). The authors (Broman & Woo, 2018) define basic principles for organizing
spreadsheet data in order to reduce errors and facilitate analyses: be consistent, do not leave any cells
empty, put just one thing in a cell, organize the data as a single rectangle (with subjects as rows and vari-
ables as columns, and with a single header row), create a data dictionary, do not include calculations in the
raw data files, do not use font colour or highlighting as data, make backups, use data validation to avoid data
entry errors, and save the data in plain text files. According to (Djordjevic & Antic, 2014) each spreadsheet
model should comprise: modular and structural design, information flows, the range of field for input and
output data, calculation module, and a reporting part.
3. A Spreadsheet Model Based on Adequate Performance Indicators
Most users do not apply spreadsheet engineering principles because of the lack of experience or training
(Roy, Hermans & van Deursen, 2017). Consequently, it is important to develop a standardized model that
can be easily implemented in everyday business activities. The model should be easy to understand and
quickly accepted by end-users.
3.1 Problem description
The problem of selecting a set of adequate performance indicators for production management is a com-
plex task, which can be solved by choosing a set of adequate performance indicators for each stage of pro-
duction management. The model presented in this paper assumes a set of performances for the first stage
of production management, defined to monitor the success of this phase. For the purpose of this research,
four different performances and eight performance indicators of the planning phase are defined. Selected
performances and performance indicators are chosen in accordance with the problems usually appearing
and mostly affecting everyday business activities of SMEs. The selection is grounded on the research pre-
sented in (Franceschini, Galetto, & Maisano, 2006) and encompasses the following: the indicators should
not be too complex, in order to avoid overloading the employees with work, the indicators should be easily
understood and effectively used, they should have a clear distinct feature to be largely accepted by the em-
ployees, and necessary information should be available. The main objective is to select the group of ade-
quate performance indicators of the highest importance for the observed phase. Additionally, the
performances and performance indicators used for the model development are defined based on the liter-
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Management: Journal of Sustainable Business and Management Solutions in Emerging Economies 2020/25(3)
ature review (Amrina & Vilsi, 2015; Bauer & Hayessen, 2015; Bhatti, Awan, & Razaq, 2014; Gomes, Yasin, &
Lisboa, 2011; Gosselin, 2005; Kang, Zhao, Li, & Horst, 2016; Muchiri, Pintelon, Gelders, & Martin, 2011;
Tewari, Singh, & Tewari, 2016). The most cited ones are selected as the most monitored in the planning
phase in SMEs operating in the manufacturing industry.
The most significant performances for the observed phase used in the simulation are the following: Coop-
eration with suppliers, Cooperation with customers, Machine performances, and Cost performances. Per-
formance indicators defined for considered performances are presented as follows. For performance
Cooperation with suppliers, performance indicators are: Delivery time, Defect of delivered raw materials, and
Mistakes and omissions; for performance Cooperation with customers, performance indicators are: Delivery
time and Service; for performance Machine performances, performance indicators are: Mistakes and omis-
sions, Service, and Available capacity of machines; for performance Cost performances, performance indi-
cators are: Mistakes and omissions, Planned production costs (per product unit), Planned labour costs, and
Planned material/raw material costs. It is important to note that one performance indicator can be an indi-
cator of more performances.
Table 1 shows the weight coefficients of performance indicators per performance. The weights are defined
according to the importance of performance indicators for the observed performance. In practice, these
weights are defined by a production manager or a planner, who monitors performance indicators of SMEs.
In this paper, the values were defined by the authors of the paper and entered in columns Cooperation with
suppliers, Cooperation with customers, Machine performances and Cost performances. The Porter scale (0-
5) is used for the performance indicator weight estimation. Value 0 presents the lowest significance of the in-
dicator for the observed performance, and value 5 represents the highest significance of the indicator for the
observed performance. For example, performance indicator Delivery time has a value 3 for the performance
Cooperation with suppliers and a value 5 for the performance Cooperation with customers, but a value 0 for
Machine and Cost performances. For the performance Cooperation with customers, this performance indicator
has a great significance, but it is not a performance indicator for Machine and Cost performances. The weights
of performance indicators per performance differ and clearly define the priority of performance indicators.
Performance indicators per performance are ranked according to their weights in descending order.
Table 1: Weights of performance indicator per performance for the planning phase and their ranks
3.2 Mathematical formulation
In order to explain the spreadsheet simulation model for the performance indicators adequacy maximization,
the following notations are used:
• Pn: performances of the planning phase, n=1,...,4;
• PIkPn:performance indicator per performance, n=1,...,4; k=1,...,4;
• PIPn:number of performance indicators per performance, n=1,...,4;
• WPIkPn: weight coefficient of performance indicator per performance, n=1,...,4; k=1,...,4;
• C: minimum weight of performance indicator;
• APImin: minimum number of adequate performance indicators for the phase (where APIminPn is for Pn,
n=1,...,4);
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Teodora Rajković, Lena Ðorđević Milutinović, Danica Lečić-Cvetković 2020/25(3)
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• APImax: maximum number of adequate performance indicator for the phase (where APImaxPn is for Pn,
n=1,...,4);
• APIPn: number of adequate performance indicators for observed performance according to the constraint
C, n=1,...,4;
• uPn:number of adequate performance indicators APIPn;
• XPn:state variable defining total adequacy of performance indicators for the observed phase;
• YPn:adequacy of performance indicators per performance according to the number of defined uPn.
The user of this model defines constraint C. The minimum and maximum value of C is shown by inequality
(1), according to the Porter scale.
0 ≤ C ≤ 5 (1)
The value of PIPn is defined in advance and entered as an input value (Table 2). Values for APIPn are defined
as a number of performance indicators PIPn that fulfill the defined constraint C. The value APIPn must be be-
tween the minimum and the maximum of a defined number of adequate performance indicators for the plan-
ning phase (2). The minimum and maximum values are defined by the user.
APImin ≤ APIPn ≤ APImax (2)
The control variable (uPn) represents the number of adequate performance indicators for the observed per-
formance. This value must be between the minimum and the maximum number of adequate performance
indicators for considered performance. The control domain is defined by three constraints (3-5).
uPn - APImin ≥ 0 (3)
APImax- uPn ≥ 0 (4)
APIPn ≥uPn (5)
Variable YPn represents the sum of weights of adequate performance indicators per performance, accord-
ing to the uPn.For example, rank 1 implies a performance indicator with the highest weight value, rank 2 has
a performance indicator with the next weight value (in descending order), etc. This affects the change of
order of performance indicators per performance, where the performance indicator takes place according
to the ranked value (equation 6).
PIkPn = PIrPn , r = 1,...,4; k = 1,...,4 (6)
The control variable uPn defines the number of performance indicators per performance for which the weight
will be summed and presented as YPn.Variable YPn sums weights of performance indicators starting from rank
1 until the rank that has the same value as uPn (equation 7).
YPn =W
PI Pn , n=1,...,4 (7)
Variable XPn represents a system state, i.e., total adequacy of performance indicators for the planning phase,
shown in equation (8).
XPn = XPn-1 + YPn , n=1,...,4 (8)
The variable XPn reflects the adequacy level of the performance indicators. The value of this variable represents
the total adequacy of performance indicators for the planning phase, as the cumulative value of all weights
of performance indicators defined by the control variables. Based on this value, it can be deduced whether
the chosen performance indicators are adequate for the observed performance. If not, the production man-
ager, or a planner, will have to define performance indicators that are more significant for the observed per-
formance. The objective function Jrepresents the sum of variables XPn and it should be maximized for all
performance indicators (9). The total value of this objective function should achieve the maximum value,
which implies that the observed set of performance indicators is adequate for the planning phase.
(max) J = , n=1,...,4 (9)
For the observed model, the minimum adequacy value is 0 (if APImin=0), whereas the maximum adequacy
value is 47 (if APImax=4).
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Management: Journal of Sustainable Business and Management Solutions in Emerging Economies 2020/25(3)
3.3 The model implementation and simulation results
The described performance indicators adequacy model is implemented in a spreadsheet environment Mi-
crosoft Excel (Figure 1). The legend of terms and abbreviations used in the spreadsheet model is the fol-
lowing: PI(P) is the number of performance indicators for the planning phase; PI(Pn) represents the number
of performance indicators per performance; API(P) is the number of adequate performance indicators per
planning phase and API(Pn) relates to the number of adequate performance indicators per performance. The
developed spreadsheet model is characterized by a modular design presented through three top-level mod-
ules: input, calculation, and output modules. The input module is intended for storing input data (circum-
stance and control variables), entered by the user. The calculation module is used for the mathematical
model implementation. The report module represents the results reflected through the objective function,
which determines the quality of control.
Figure 1: The spreadsheet model for performance indicators adequacy maximization
The application of structural design affects the readability (separated input, output and calculation mod-
ules), accuracy (provided by translating the verbal model into mathematical and mathematical into a spread-
sheet model, applied conditional formatting and data validation), reusability (it can be used again and with
different data), and modification of the model (each module can be modified to reflect the newest facts,
without influencing other modules) (Grossman, Mehrotra, & Sander, 2011). The model structure comprises
the logic of the input and output modules and organized information flows. The inputs and outputs of the
model are separated, without the distractions caused by the calculation, and represent sets of connected
blocks. Information flows are direct and non-intersectional. At the highest level, information flows start from
the input data (PIPn, APIPn, APImin, APImax, C, uPn), continue through the calculation section, and end with the
output values (XPn, JPn). This structure, based on the principles of spreadsheet engineering, reduces the
likelihood of errors and improves the functionality of the created model. Arranged information flows allow the
model to be easily modified by changing the part for calculations, with the input and output parts isolated.
The simulation is conducted for the planning phase of production management and four performances of that
phase. The control variable values are changed according to the constraint (5). Three scenarios with differ-
ent input data were analyzed (Table 2). According to the value of the objective function, the user of this model
can deduce whether the set of performance indicators is adequate for the observed phase. A low value of the
objective function implies that new performance indicators should be defined for the observed phase.
The simulation result will be explained for the second scenario (Table 2). The performance indicators per per-
formance were defined and explained in section 3.1. Based on the constraints, the minimum observed weight
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Teodora Rajković, Lena Ðorđević Milutinović, Danica Lečić-Cvetković 2020/25(3)
is C=3, the minimum number of adequate performance indicators for the phase is APImin=1 and the maxi-
mum number of adequate performance indicators for the phase is APImax=3. It is needed to define the num-
ber of adequate performance indicators per performance that meets those constraints and strives to maximize
the adequacy of performance indicators for the planning phase. Results of the model simulation are:
• Performance Cooperation with suppliers has three (uP1=3) adequate performance indicators (De-
fect of delivered raw materials, Mistakes and omissions and Delivery time), where the weight of per-
formance indicators per this performance is YP1=12;
• Performance Cooperation with customers has two (uP2=2) adequate performance indicators (De-
livery time and Service), where the weight of performance indicators per this performance is YP2=9;
• Machine performance has three (uP3=3) adequate performance indicators (Available capacity of
machines, Mistakes and omissions and Service), where the weight of performance indicators per
this performance is YP3=12;
• Cost performance has three (uP4=3) adequate performance indicators (Planned material/raw ma-
terial costs, Planned labor costs and Planned production costs (per product unit)), where the weight
of performance indicators per this performance is YP4=12.
The total adequacy of these performance indicators for the planning phase is 45.
Table 2: Input data and simulation results of the implemented spreadsheet model
The objective function will achieve the maximum value when the control domain takes the largest possible
number of adequate performance indicators by performance (Simulation 3). This simple, flexible, low-cost,
and adjustable model is suitable for SMEs that do not have enough financial resources for expensive soft-
ware solutions used for defining a set of adequate performance indicators for production management im-
provement. The spreadsheet model can be implemented in all manufacturing SMEs, for a particular phase
of production management, with a significant impact on its effectiveness. The full efficiency and effectiveness
of the model imply adjustment to the needs and capabilities of the particular manufacturing company and
its business environment.
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Management: Journal of Sustainable Business and Management Solutions in Emerging Economies 2020/25(3)
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Teodora Rajković, Lena Ðorđević Milutinović, Danica Lečić-Cvetković 2020/25(3)
The basic contribution of this paper is the model developed for measuring the adequacy of selected performance indica-
tors in the first phase of production management – planning. The performances and their indicators are defined accord-
ing to the literature review, selecting the adequate ones for the planning phase. The model is developed for the production
management of SMEs.
Future work can be directed toward a more systematic research of the model applicability to real-life problems of larger
dimensions, i.e., the problem with an increased number of performances and performance indicators and additional con-
straints. Furthermore, the implementation of the model for all production management phases should be considered. Con-
sequently, the adequacy of performance indicators for the whole production management process can be measured.
Another interesting question comprises an adaptation of the model for large enterprises, where performances and per-
formance indicators differ from those defined for SMEs.
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Received: 2020-02-06
Revisions requested: 2020-03-23
Revised: 2020-05-04 (2 revisions)
Accepted: 2020-06-09
21
Management: Journal of Sustainable Business and Management Solutions in Emerging Economies 2020/25(3)
22
Teodora Rajković, Lena Ðorđević Milutinović, Danica Lečić-Cvetković 2020/25(3)
Teodora Rajković
University of Belgrade, Faculty of Organizational Sciences, Serbia
teodora.rajkovic@fon.bg.ac.rs
Teodora Rajković, MSc, is a teaching assistant at the Department for Production and
Services Management of the Faculty of Organizational Sciences, University of Belgrade.
Her research interests are related to operations management, production and services
management, key performance indicators, e-manufacture and e-service. The author has
published more than 10 papers in national and international conference proceedings.
Lena Ðorđević Milutinović
University of Belgrade, Faculty of Organizational Sciences, Serbia
lena.djordjevic.milutinovic@fon.bg.ac.rs
Lena Ðorđević Milutinović, PhD, is an assistant professor at the Department for
Production and Services Management of the Faculty of Organizational Sciences,
University of Belgrade. Her research interests include operations management,
production and services management, spreadsheet engineering, spreadsheet
management, material flow management, control systems, enterprise information
systems and business applications. She has published over 70 papers in national and
international scientific journals and conference proceedings, national and international
scientific journals and conference proceedings chapters in national and international
monographs, textbooks, and auxiliary teaching literature.
Danica Lečić-Cvetković
University of Belgrade, Faculty of Organizational Sciences, Serbia
danica.lecic-cvetkovic@fon.bg.ac.rs
Danica Lečić-Cvetković, PhD, is a full professor of Production and Services
Management and e-Manufacturing at the Department for Operations Management,
Faculty of Organizational Sciences, University of Belgrade. Her research interests and
fields are related to operations management, production and services management, key
performance indicators, e-manufacture and e-service. The author has published more
than 100 papers in journals and conference proceedings in international and national
journals and conferences. Since 2008, she is a member and leader of the MESA
(Manufacturing Enterprise Solutions Association) group for the Republic of Serbia.
About the Authors
