107
Organizacija, Volume 52 Issue 2, May 2019Research Papers
Evaluation of Product Competitiveness: 
A Case Study Analysis
Nestor SHPAK1, Nadiya SELIUCHENKO1, Viktoriya KHARCHUK, Nataliya KOSAR1, Włodzimierz 
SROKA2
1Lviv Polytechnic National University, Bandery 12, Lviv, Ukraine 
2WSB University, Faculty of Applied Sciences, Cieplaka 1c, 41-300 Dąbrowa Górnicza, Poland, wsroka@wsb.edu.pl 
(corresponding author)
Background and Purpose: The existing frameworks provide a superficial approach to the evaluation of product 
competitiveness which reveals the linkage between the level of product competitiveness and quantitative as well as 
qualitative factors that have the most significant impact thereon. Given this fact, the purpose of this paper is to elab-
orate a model for evaluating the competitiveness of sunflower packaged oil, considering both quantitative and qual-
itative factors that may alter it. Further, this model is being implemented to examine the most demanded Ukrainian 
sunflower oil brands in order to reveal possibilities for enhancing competitiveness.
Design/Methodology/Approach: The general methodology of the research includes elements of theoretical, em-
pirical, qualitative and quantitative analyses. The theoretical analysis aims to shed light upon a different under-
standing of “the evaluation of competitiveness”, as well as approaches and tools for analysing it. Empirical analysis 
focuses on observing official statistical data of the export of sunflower oil and future trends. Qualitative analysis 
consists in the identification, systematization and description of factors that affect the competitiveness of sunflower 
oil packaged. In turn, quantitative analysis is based on usage of the Fuzzy logic tool in order to evaluate the impact 
of complex and partial factors on the level of product competitiveness.
Results: This paper provides a business case for product competitiveness evaluation of a particular sunflower oil 
brand. Based on usage of the Fuzzy logic toolkit, the impact of complex and partial factors on competitiveness level 
was analysed. As a result, simulation of the competitiveness sensitivity of a particular oil brand on relevant complex 
factors that determine it competitiveness level was presented. This business case may help managers to channel 
their efforts and resources in the proper particular direction to increase product competitiveness and product posi-
tioning on the market.
Conclusion: The results of this research would be useful to practitioners in their assessment of product competitive-
ness, modelling future levels, and understanding hidden possibilities for enhancing product competitiveness. The 
framework offered might be adopted for other types of products.
Keywords: product competitiveness, general and partial factors, fuzzy logic.
DOI: 10.2478/orga-2019-0008 
1 
Received: March 9, 2019; revised: April 8, 2019; accepted: May 12, 2019
1 Introduction
Nowadays, the highly competitive entrepreneurial envi-
ronment - as well as market fluctuations - are noticeably 
observed in modern conditions of uncertainty, imbalance 
and disproportion between the expected and actual state 
of the market (Shpak et al., 2016). It demands business 
be focused on a number of aspects, e.g. CSR in its op-
erations (Bhana, 2018; Sroka & Vveinhardt, 2018; Kli-
estikova et al., 2018; Meyer, 2018), inter-organisational 
cooperation, both bilateral and multilateral (e.g. Kozma, 
108
Organizacija, Volume 52 Issue 2, May 2019Research Papers
2017, Šebestová et al., 2017), as well as coopetition, i.e. 
simultaneous cooperation and competition with competi-
tors (Mohalajeng & Kroon, 2016; Cygler & Sroka, 2017; 
Cygler et al., 2018). The issue which is more and more 
important, especially in the developed countries, is also 
employment of older generation employees (Kolářová et 
al., 2017; Kubíčková et al., 2018). However, a matter of 
crucial importance for businesses is the production of a 
strongly competitive product. At the same time, product 
competitiveness is a broad phenomenon which is being 
considered from numerous points of view and related 
interdisciplinary areas. It is worth mentioning that differ-
ent areas of business or industry branches create specific 
conditions and hence have a significant impact on product 
competitiveness. Considering such peculiarities, it is logi-
cal to focus research on a certain product. 
One of the most important directions for economic de-
velopment is to enhance product exporting and to promote 
national trademarks on international markets (Meyer & 
Meyer, 2017; Meyer & De Jongh, 2018). On the domes-
tic market, each industry has in-demand and well-known 
products or trademarks. Nevertheless, Ukrainian compa-
nies are struggling on the global market. Such a situation 
is considered to be related to the level of product competi-
tiveness for national trademarks in particular.
Observing the results of Ukrainian product exporting 
revealed that sunflower oil is one of the most in-demand 
products on the international market (SSSU, 2018). In par-
ticular, the share of Ukrainian oil production on the global 
market exceeds 50%. However, sunflower oil contribut-
ed only one-third of the total Ukrainian exports (UCAB, 
2018). The lack of this type of product in the export struc-
ture is caused by certain constraints.
In the face of increasing competition on the Ukraini-
an market, producers should pay considerable attention to 
improving the competitiveness of their products. From this 
perspective, the aim of the research is to investigate the 
relationship between the competitiveness of a particular 
product and numerous factors that affect the given prod-
uct. Regarding all the above mentioned factors, assessing 
the competitiveness of sunflower oil allows researchers to 
compare different brands and to distinguish the most sig-
nificant factors.
Our paper is structured as follows: Firstly, we pres-
ent the theoretical background. Secondly, we present the 
methodology that was applied in this research. The next 
part of our paper presents the research results. Finally, we 
present the conclusions and limitations of our study.
2 Theoretical background
Product competitiveness discourse is the subject of heated 
debates by academics and practitioners (Oral & Kettani, 
2009; Roostika et al., 2015; Androniceanu, 2017; Popp et 
al., 2018a, 2018b). Nonetheless, there are some aspects 
that have been left untouched due to different purposes 
of investigation. The present paper proposes an overview 
of Ukrainian and international academics’ scientific work 
pertaining to an evaluation of product competitiveness. 
The question of quantitative assessment of product com-
petitiveness is always relevant for producers in order to de-
termine strategy, namely to increase and / or expand their 
positions on both domestic and foreign markets. 
An analysis of scientific publications reveals numerous 
models and frameworks for the evaluation of product com-
petitiveness. The main approaches may be summarised as 
follows (Pastushchin, 2013: 232-240):
1) Methodology of the evaluation of product com-
petitiveness through calculating its rating. According to 
this approach, product rating is dependent on product qual-
ity indicators (Kobilyatsky, 2003; Pomffyová et al., 2017; 
Dvorsky et al., 2018). Such a point of view brings some 
limitations because, in this case, managers neglect other 
product characteristics and internal and external environ-
mental conditions (Androniceanu & Popescu, 2017).
2) Methodology of the evaluation of product com-
petitiveness through the volume of sales. This approach as-
sumes that the volume of sales reflects consumer demand, 
which is why it might be the most significant criteria for its 
competitiveness. This approach focuses only on one prod-
uct competitiveness characteristic which makes it rather 
narrow. Under modern conditions, a high volume of sales 
might be the result of a weak competitive environment and 
the absence of similar products on the market. 
3) Methodology of the evaluation of product com-
petitiveness through a complex index with multiple foci 
(Fatkhutdinov, 2000; Chepurnoy, 2005). According to this 
approach, a complex index of product competitiveness 
should include a set of partial indicators which generalise 
the following characteristics of product competitiveness: 
consumer requirements, technical requirements, enterprise 
expenses.
4) Methodology of the prediction index of com-
petitive strength of alcohol brands based on Fuzzy logic 
(Shtovba, 2007). The authors offered a fuzzy model of 
brand competitiveness index which  is based on expert 
knowledge bases (quality of the brand product, image of 
the brand product, and service connected with the brand 
product) and so on.
109
Organizacija, Volume 52 Issue 2, May 2019Research Papers
On the basis of the given literature analysis, one can make 
a sweeping assumption that today there is an absence of 
an acknowledged approach that suits all aspects of the 
evaluation of product competitiveness. Scholars often use 
individual qualitative indicators which reflect different pa-
rameters of product competitiveness (e.g. Ivanenko, 2012; 
Stavenki & Zhurilo, 2009), or group and integral indica-
tors which generalise different characteristics of product 
competitiveness – consumer needs, competitor products 
that best meet consumer needs, the image of the enterprise, 
a hypothetical sample, and a group of analogue products 
(Litvinova, 2013; Massey et al., 2018). 
It should be noted that models and frameworks for the 
evaluation of product competitiveness developed, first of 
all, separately for different industry branches. Such a vari-
ety of different approaches brought about specific peculiar-
ities of each business area or market. Thus, it is hard to of-
fer a unique model or framework that suits all peculiarities 
of the Ukrainian domestic market and might be adopted 
for different company/product characteristics.
In contrast, different holistic approaches for the eval-
uation of product competitiveness have been proposed in 
the literature. For example, Fumio (1985) introduced the 
scaling method based on rivalry comparison. He proposed 
the use of the maximum correlation ratio method for se-
lecting the most significant characteristics of product com-
petitiveness. In turn, Chang & Yeh (2001) developed an 
approach to evaluating airline competitiveness based on 
the utilisation of the multiattribute decision-making mod-
el. This approach addressed the issue of identifying five di-
mensions of competitiveness and appropriate performance 
measures. To solve the problem of inconsistency during 
the validation procedure, the authors decided to use the ad-
ditive weighting method, weighted product method and to 
allocate preferences according to similarity to the ideal re-
sult (providing the minimum expected loss of value). Such 
a combination of methods helps to reveal the competitive 
advantage of a particular company in comparison to its 
competitors. Oral & Kettani (2009) proposed the industri-
al competitiveness model highlighting key points such as:
- adopting “scientific models” and “practical 
frameworks” in order to improve modelling of firm com-
petitiveness for strategy formulation,
- developing a formal model – Integrated compet-
itiveness model (ICM), which consists of four sub models 
and six indices, 
- developing sub-models and indices: actual out-
put sub-model, comparative actual sub-model, potential 
sub-model, comparative potential sub-model, actual mas-
tery index, actual cost superiority index, potential indus-
trial mastery index, potential cost superiority index, actual 
competitiveness index, and potential competitiveness in-
dex,
- forming a competitive strategy according to links 
between companies’ actual and potential competitiveness.
In general, one may conclude that many authors have high-
lighted the necessity of combining different methods in or-
der to obtain a relevant evaluation of product competitive-
ness. Nevertheless, the models or frameworks considered, 
whether taken separately or in combination, do not com-
prehensively reflect the characteristics of product competi-
tiveness. Along with complications in the Ukrainian econ-
omy, an evaluation of product competitiveness should be 
based not only on assessing set metrics that reflect peculiar 
characteristics, but should also include the internal and ex-
ternal environment of the particular companies.
3 Methodology
Existing dilemmas in the evaluation of product compet-
itiveness provide the basis for a further contribution to 
solving this problem, in particular, developing a model for 
its assessment which might be applicable under changing 
environmental conditions. The conducted literature anal-
ysis has shown that the usage of economic-mathematical 
and expert methods, separately or in combination, for the 
evaluation of product competitiveness does not allow re-
searchers to consider all product features (both quantita-
tive and qualitative characteristics) and to eliminate the 
dependence of expert assessments on the subjective judg-
ments of experts. So, it is logical to apply such a method 
that allows researchers to:
- simulate complex systems under conditions of insuf-
ficient information and randomness of processes, 
- solve problems of aggregation of ambiguous, subjec-
tive and inaccurate expert judgments about the state of a 
particular parameter,
- reflect a complex nature of the evaluation of product 
competitiveness,
- consider numerous factors that affected product com-
petitiveness.
As noted by a number of scholars (e.g. Pedrycz, 2011; 
Xianbo, et al., 2013; Marcos Duarte Jr., 2018; Moravcik-
ova et al., 2017), the fuzzy logic toolkit provides an op-
portunity to obtain a fairly objective assessment as it takes 
into account all factors (both quantitative and qualitative), 
as well as the level of confidence of the experts who carry 
out the evaluation. Given this fact, this method was ap-
plied in our paper. 
The methodology structure consists of two parts. The 
first stage was to choose the subject of research, in particu-
lar, the type of product for the evaluation of its competi-
tiveness. For this purpose, the empirical analysis of pack-
aged sunflower oil was conducted. In addition, factors that 
had the most significant impact on product competitive-
ness were identified and described. The second part was to 
construct a model for the evaluation of the competitiveness 
of sunflower oil by means of fuzzy logic tools. MATLAB 
software was used for modelling. 
110
Organizacija, Volume 52 Issue 2, May 2019Research Papers
The construction of the model using the Fuzzy Logic Tool-
box package consisted of the following steps: 
1. the definition of linguistic variables and their 
corresponding term sets, 
2. the construction of the fuzzy set adjective, which 
includes: determining the range of changes in the data per-
taining to competitiveness factors and the output variable 
– product competitiveness; choosing the type of fuzzy set 
adjective for each variable and justifying its parameters,
3. the formation of fuzzy knowledge bases,
4. assessing the adequacy of the model based on the 
training sample, adjusting its parameters and making a de-
cision on the final version.
4 Empirical analysis of sunflower oil 
exporting
According to secondary financial data obtained from the 
State Statistics Service of Ukraine (SSSU, 2018), oil pro-
duction and exports have been one of the most significant 
such items on the global market over the past few years. 
For instance, sales volumes of sunflower oil on interna-
tional markets increased by 16% (597 million $) in 2017 
(Table 1).
In the course of the study, it was established that 
Ukraine has significant potential for increasing oil exports 
as it lacks restrictions on the export of various sunflower 
oil processing products to the foreign market. Meanwhile, 
the consumption of high quality, healthy food (including 
sunflower oil) has grown significantly. Taking into account 
the results of the study, packaged sunflower oil was select-
ed for practical implementation of the process of evaluat-
ing product competitiveness.
4.1 Factors determining the 
competitiveness of packaged 
sunflower oil
To ensure that all factors which influence product competi-
tiveness were included in the evaluation process, an expert 
survey was conducted. The main aim of the survey was 
to identify a group of factors that determine product com-
petitiveness. This survey reached 35 Ukrainian sunflower 
oil manufacturers, independently operated with different 
sizes and market shares, to obtain a relevant result. For the 
purposes of data collection, questionnaires were sent by 
e-mail to different groups of employees who have exper-
tise connected with the company or product competitive-
ness. In particular, the respondents were directors, CEOs, 
sales managers, marketers, and social media marketers. 
Interviewees answered the following questions:
• which of the following factors affects the compet-
itiveness of sunflower oil?
• what is the significance of each partial factor (sig-
nificance was described verbally: important and 
irrelevant) ?
• what is the nature of the relationship between each 
factor and the level of competitiveness: direct or 
inverse?
• what are possible combinations of factors based 
on their levels (levels were described verbally: 
Table 1: Input and output variables for the fuzzy inference system
Year Total CIS coun-tries EU Asia Africa America
Australia and 
Oceania
2006 1628820,61 188594,15 861562,49 415846,54 162504,95 312,21 0,28
2007 1923240,61 22947,81 982733,04 366905,45 342025,55 2098,28 0,17
2008 1339557,44 205308,41 527647,63 398777,09 167907,69 39915,96 0,66
2009 2333842,54 153718,89 629739,06 1052483,37 478161,11 19714,68 25,43
2010 2688562,35 273224,57 716578,71 1175242,87 522797,75 587,92 130,53
2011 2333842,54 153718,89 629739,06 1052483,37 478161,11 19714,68 25,43
2012 3589606,45 112015,75 699046,84 2025268,68 736049,93 17221,23 3,99
2013 3199506,79 108935,62 403363,34 2181825,95 479139,88 26134,50 107,49
2014 4339490,34 87530,30 817372,41 2990921,31 433976,18 7039,77 2650,37
2015 3938555,37 58851,57 744343,01 2818297,43 303438,00 12259,79 1365,57
2016 4842064,81 44372,45 1396170,77 3071259,42 305086,75 21085,65 4089,70
2017 5757344,05 35297,74 1812147,70 3615398,20 264153,71 24834,30 5512,39
111
Organizacija, Volume 52 Issue 2, May 2019Research Papers
low, medium, high) ?
• what is the range of change in quantitative factors 
(the range was determined based on the secondury 
data analysis) ?
Questionnaires included the title and description of nu-
merous factors with possible combination of all complex 
and partial factors. It is worth mentioning that during 
preparation of the survey questionnaires (and complex 
factors identification in particular), existing Ukrainian 
frameworks on a given issue were used (Shtovba, 2007; 
Shtovba & Shtovba, 2005) and secondary data analysis. 
Therefore, among these factors, only price might be pre-
sented in quantitative terms. Quality, image, and service 
belong to qualitative factors. In addition, they are complex 
(aggregated), since each of them depends on a number of 
partial factors.
After collecting all filled-in questionnaires, the re-
searchers revised the interviewer’s responses. 
As a result of the analysis of the questionnaires, we identi-
fied complex and partial factors that influence the level of 
competitiveness of sunflower oil. 
On the basis of the survey results, partial factors, both 
quantitative and qualitative ones, were established. Table 
2 illustrates the final list of the complex and partial factors 
of the competitiveness of packaged sunflower oil, together 
with their description.
4.2 Definition of linguistic variables and 
their corresponding term sets
Quality, image and service are complex factors and each 
of them depends on a number of partial factors. Consider-
ing the important for decision making to have assessments 
of complex factors, we decided to build a separate fuzzy 
knowledge base for each complex factor and fuzzy knowl-
edge base for assessing the level of competitiveness. In 
addition, Shtovba (2005) and Miller (1956) argue that in 
the case of a large number of expert input it is difficult to 
describe causal relationships with fuzzy rules. Therefore, 
they recommend building fuzzy knowledge bases that 
have no more than 5-7 input parameters. As a result, the 
Complex factors Partial factors Factors characteristics
Quality (F1) Genetic seed purity
(F11)
Sunflower seeds characterise genetic and physiological information, 
primarily about the ratio of spare and biologically active substances. The 
genetic purity of the seeds is determined not only by its genetic compo-
nent but also by the conditions of cultivation and cultivation (cleansing, 
calibration, protrusion, and packing) of seeds.
Primary product quality (F12) It depends not only on the seeds but also on the conditions of growing 
sunflowers, the time of harvesting, transportation, further storage, and 
processing. 
All products must comply with the regulatory requirements of the 
National Standards of Ukraine 4694-2006. Depending on the criteria for 
the quality of sunflower seeds, it may be categorised as higher, first or 
second class. Olive factories buy sunflower, the humidity of which varies 
from 6% to 8%, with a content of garbage impurity - 3%, which is not 
infected with pests, except infection of the second mite. The sunflower 
seeds must not be bitter, are discarded by colour and odour, and the 
maximum allowable dose of residual quantities of pesticides, chemicals, 
and heavy metals is determined.
Innovation of production and 
logistics technologies (F13)
Production assets renovation funds the usage of new recipes, modern 
storage methods and means for transportation of oils
Staff quality (F14) Qualification, staff motivation, discipline in the production process
Degree of burnout during 
usage(F15)
Determined by how much oil is needed, especially when cooking prod-
ucts that require frying
Table 2: Factors that affect the level of competitiveness of packaged sunflower oil. Source: own
112
Organizacija, Volume 52 Issue 2, May 2019Research Papers
Image(F2) Implementation of socially re-
sponsible projects tend towards 
external company environment 
(F21)
Participation in charity events;
Level of care for packaging recycling
Implementation of socially re-
sponsible projects tend towards 
internal company environment 
(F22)
Taking care of staff, their development, and general working conditions
The level of distribution of 
negative reviews about a par-
ticular trademark (F23)
Spreading negative information about the company and its products on 
the Internet and at retail intermediaries
The level of green technologies 
used in production (F24)
The technological process must be safe for the environment and antici-
pate the use of alternative energy sources
Foreign investment share in 
registered capital (F25)
Allows firms to invest more in socially responsible activities, green 
technologies, and improve sales and logistics
Number of international quali-
ty certificates (F26)
Certificate of Quality Management System, Certificates of Compli-
ance with International Requirements Kosher P1 and P2, Certificate of 
production according to European organic requirements, Certificates for 
the food safety system, namely ISO 22000:2005, ISO/TS 22002-1:2009 
and additional requirements FSSC 22000, availability of Eco standard. 
Management systems should be certified in accordance with internation-
al standards such as ISO 9001 (quality) and ISO 14001 (environmental 
protection)
Service (F3) Product availability in retail 
trade (F31)
The number of retail intermediaries involved in product sales; distribu-
tion geography
Frequency of shares held by 
the producer together with the 
trade intermediaries for the 
final consumer (per year) (F32)
Carrying out actions to stimulate sales - providing discounts on products; 
drawings among consumers for the best dish recipes
Information support to inter-
mediaries and final consumers 
(F33)
Providing intermediaries with information about their products, their 
quality parameters and outdoor advertising media; advertising in mass 
media, and on the Internet, presence in social networks with the offer of 
original recipes; information content of the site; informing intermediar-
ies and producers about sales promotion incentives, producer participa-
tion in exhibitions and fairs
Price (F4) Retail price Determined by the cost of oil production, the profitability of production 
and trade margins of intermediaries, as well as consumer demand
Table 2: Factors that affect the level of competitiveness of packaged sunflower oil. (continued)
113
Organizacija, Volume 52 Issue 2, May 2019Research Papers
fuzzy model for assessing the competitiveness of oil will 
include four expert knowledge bases. 
Firstly, complex factors are evaluated on the basis of par-
tial factors, and then, based on the obtained estimates of 
complex factors, the level of competitiveness oil was es-
timated (the fuzzy values of the estimates of complex fac-
tors are defuzzificationed and their crisp values are already 
given to the fuzzy system of the next level). The output 
tree of the evaluation of the competitiveness of oil is pre-
sented in Figure 1.
Linguistic variables were defined on the basis of the 
output tree. Hence, the hierarchical model includes the fol-
lowing linguistic variables: at the 1st level – 14; at the 2nd 
level – 4; on the 3rd level – 1. We assigned the titles corre-
sponding to the names of the highlighted competitiveness 
factors to the linguistic variables of the 1st and 2nd level. 
The output linguistic variable will be called “Oil Compet-
itiveness Level”. All linguistic variables may have three 
meanings: low; medium; high. 
For each variable in the model, three terms were select-
ed. This is due to the fact that a greater number of fuzzy 
terms with a large number of input variables would com-
plicate the process of knowledge base formation.
In the future, the term set of all linguistic variables will be 
denoted accordingly {L, M, H}.
4.2.1 Construction of membership functions 
of fuzzy terms
In order to describe the linguistic terms “low”, “medium” 
and “high”, it is necessary to build membership functions. 
The construction of membership functions of fuzzy terms 
involves solving problems including (Shtovba, 2007; Mat-
viychyk, 2005): 
• determining the range of changes in the values of the 
input variables (competitiveness factors) and the out-
put variable (the competitiveness of sunflower oil),
• selection of membership function type for each varia-
ble and the justification of its parameters.
Figure 1:  Graphic visualization of the relationship between factors of the competitiveness of sunflower oil. Source: own
114
Organizacija, Volume 52 Issue 2, May 2019Research Papers
The criterion pertaining to the level of competitiveness 
of oil is to choose the real number C, which belongs to the 
interval [0; 100]. In terms of an assessment of the quali-
tative factors of competitiveness (partial and complex), it 
is also advisable to choose actual numbers from the range 
[0; 100]. 
Among the highlighted competitiveness factors are the 
following quantitative ones, in particular: the share of for-
eign investment in the registered capital (F25), the number 
of international quality certificates (F26), the frequency of 
shares held by the manufacturer together with the inter-
mediary for the final consumer (F32), and price (F4). Evi-
dently, the share of foreign investment in registered capital 
(expressed as a percentage) may vary within [0; 100]. The 
range of changes in other factors we determined on the 
basis of a survey of experts: F26 ϵ [0; 10]; F32 ϵ [0; 12]; 
F4 ϵ [25; 40].
The higher the value of the number, the higher the oil 
competitiveness level or the factor affecting it. However, 
it is necessary to take into account that the relationship 
between factors and the level of competitiveness can be 
either direct or inverse. Most of the factors have a direct 
impact on oil competitiveness. Factors reducing the lev-
el of competitiveness include the following: the level of 
burnout during usage (F15), the level of negative feedback 
about the particular trademark (F23), and price (F4).
The fuzzy logic toolkit in the MATLAB package con-
tains built-in membership functions. To assess the compet-
itiveness of the oil, we used a number of qualitative vari-
ables that can only be evaluated by an expert. Therefore, 
we could not form a classic statistical sample. We have 
selected a symmetric Gauss curve. The advantage of this 
function is that only two parameters need to be specified 
for its construction, and it is sufficiently flexible. 
In the Fuzzy Logic Toolbox, Gaussian membership 
functions are given as follows: (x, params), where x is the 
vector for which the membership levels are calculated; 
params is a vector of the membership function parameters 
that are set in such order [c b], where c is the curve’s span, 
and b is the coordinate of the maximum of the membership 
function. The parameters of the membership functions are 
set automatically in such a way as to evenly cover the 
range of values of the linguistic variable. Taking into ac-
count the nature of the linguistic variables of our model, 
we leave the given parameters unchanged (Table 3).
4.2.2 Formation of fuzzy knowledge bases
In order to simulate the complex factors (quality, image, 
service) and the competitiveness of oil, fuzzy knowledge 
bases of the Mamdani type were formed (Mamdani & As-
silian, 1975) (Tables 4-7). 
All the rules contain only logical “And” operations and 
have weights equal to one.
Fuzzy rules were built as a result of expert’s survey. Possi-
ble combinations of partial and complex factors regarding 
their levels were specified, as a result of expert’s survey.
In Table 7, we took into account the risk of oil quality de-
Linguistic variables Membership functions parameters
Quality
Genetic seed purity
Primary product quality 
Innovation of production and logistics technologies 
Staff quality 
Degree of burnout during usage
Range of all variables values change [0; 100]
Params:
«Low»: [21.23; -4.441e-16]
«Medium»: [21.23; 50]
«High»: [21.23; 100]
Image 
Implementation of socially responsible projects tend towards 
external company environment 
Implementation of socially responsible projects tend towards 
internal company environment 
The level of distribution of negative reviews about a particu-
lar trademark 
The level of green technologies used in production
Foreign investment share in registered capital 
Number of international quality certificates
Range of all variables values change [0; 100]
Params:
«Low»: [21.23; -4.441e-16]
«Medium»: [21.23; 50]
«High»: [21.23; 100]
Variable value change range [0; 10]
Params:
«Low»: [1.699; 6.939e-17]
«Medium»: [1.699; 5]
«High»: [1.699; 9.974]
Table 3: Membership function parameters for the evaluation of oil competitiveness. Source: own
115
Organizacija, Volume 52 Issue 2, May 2019Research Papers
Service
Product availability in retail trade, Information support to 
intermediaries and final consumers
Frequency of shares held by the producer together with the 
trade intermediaries for the final consumer (per year)
Range of all variables values change [0; 100]
Params:
«Low»: [21.23; -4.441e-16]
«Medium»: [21.23; 50]
«High»: [21.23; 100]
Variable value change range [0; 12]
Params:
«Low»: [2.548; 1.11e-16]
«Medium»: [2.548; 6]
«High»: [2.548; 12]
Price
Variable value change range [25; 40]
Params:
«Low»: [3.185; 25]
«Medium»: [3.185; 32.5]
«High»: [3.185; 40]
Oil competitiveness level
Variable value change range [0; 100]
Params:
«Low»: [21.23; -4.441e-16]
«Medium»: [21.23; 50]
«High»: [21.23; 100]
The graph of fuzzy terms membership functions to the linguistic variable “Oil competitiveness level” is shown in Figure 
2.
Figure 2: Fuzzy terms membership functions for the prediction of oil competitiveness level. Source: own
116
Organizacija, Volume 52 Issue 2, May 2019Research Papers
creasing, even when the manufacturer has a high image 
and provides a high level of service (although experts ar-
gue that such manufacturers carry out strict quality con-
trol, and the likelihood of non-identification of oil quality 
mismatches to established national standards is very low).
No. If Then
F11 F12 F13 F14 F15 Quality (F1)
1 H H H H L H
2 H H H M L H
3 H H M H L M
4 H M H H L M
5 M H H H L M
6 H H H H M M
7 H H H M M M
8 H H M M M M
9 H M M M M M
10 H M M M M M
11 M M M M M M
12 M M M H M M
13 M M H M M M
14 M H M M M M
15 M M M L M M
16 M M M M H L
17 M M L M M L
18 M L M M M L
19 L M M M M L
20 L L M M M L
21 L L L M M L
22 L L L L M L
23 L L L L L L
Table 4: Fuzzy knowledge base for the evaluation of oil quality. Source: own
No. If Then
F21 F22 F23 F24 F25 F26 Quality (F2)
1 H H L H H H H
2 H H L H H M H
3 H H L H M H H
4 H M L H H H H
5 M H L H H H M
6 M M L H H H M
7 M M M M H M M
8 M M M M M M M
9 M M H M M M L
Table 5: Fuzzy knowledge base for image evaluation. Source: own
117
Organizacija, Volume 52 Issue 2, May 2019Research Papers
10 M M H L M M L
11 M L H L M M L
12 L M H L M M L
13 M M H L L M L
14 M M H L L L L
No. If Then
F31 F32 F33 Quality (F3)
1 H H H H
2 H M H H
3 M H H M
4 H H M M
5 M M H M
6 M H M M
7 H M M M
8 M M M M
9 H L H M
10 H L M M
11 M L M M
12 L M M L
13 L L L L
Table 6: Fuzzy knowledge base for service evaluation. Source: own
Table 7: Fuzzy knowledge base for the evaluation of sunflower oil competitiveness. Source: own
No. If Then
Quality (F1) Image(F2) Service (F3) Price (F4) Sunflower oil competitive-
ness level (C)
1 H H H L H
2 H H H M H
3 H H M L H
4 H H M M H
5 H M H L H
6 H M M L H
7 H H H H M
8 H H M H M
9 H M H M M
10 H M M M M
11 M M M M M
12 M M M L M
13 M M L L M
14 M H H H M
Table 5: Fuzzy knowledge base for image evaluation. (continued)
118
Organizacija, Volume 52 Issue 2, May 2019Research Papers
For the proposed hierarchical knowledge base, the max-
imum number of rules is: 35+36+33+34=1080 (Shtov-
ba, 2007). However, one important peculiarity of fuzzy 
knowledge bases has to be taken into account – the search 
for all possible rules deprives the system of flexibility, the 
ability to adapt to real conditions. Therefore, the number 
of rules in each knowledge base should be less than the 
number of all possible combinations of the values of the 
input variables. If the knowledge base does not have a rule 
that corresponds to a particular situation, the system offers 
the solution that is most suitable for this situation, namely, 
a solution for which the membership function will have 
the highest value. In addition, the lack of certain combina-
tions of input variables is explained by economic content. 
In practice, it does not always have any combination of 
values of input variables. For example, in Table 4, if “Ge-
netic purity of seeds” is high, then all other factors cannot 
be low (high or medium only), because genetically pure 
seeds are an expensive resource and can be used only by 
the manufacturer who provides the proper level of all other 
factors. The same explanation can be given in the absence 
of certain combinations of input variables in all other ta-
bles.
Fuzzy logical output of Mammadi is realized by the fol-
lowing operations:
- And method – by operation of the minimum;
- Implication – by operation of the minimum;
- Aggregation – by operation of the maximum;
- Defuzzification – by the method of the centre of 
   gravity (centroid). 
In the Fuzzy Logic Toolbox, implementation of logical op-
erations, methods of implication, aggregation, and dephas-
ing is programmed. They can be s set by using the follow-
ing menu (Fig. 3).
15 H M H H M
16 M H M H M
17 M M M H L
18 M M L M L
19 M M L H L
20 M L M M L
21 M L L M L
22 M L M H L
23 M L L L L
24 L L L L L
25 L L L M L
26 L L L H L
27 L M M H L
28 L M L H L
29 L L M H L
30 L M M M L
31 L M M L L
32 L H H H L
33 L M L M L
34 L L M M L
35 L H M M L
36 L H M H L
37 L H H M L
38 L H M L L
39 L M L L L
40 L M H M L
Table 7: Fuzzy knowledge base for the evaluation of sunflower oil competitiveness. (continued)
119
Organizacija, Volume 52 Issue 2, May 2019Research Papers
4.2.3 Formation of fuzzy knowledge bases
To assess the adequacy of the model, we used a training 
sample containing 120 “input-output” observations, which 
were generated randomly. Taking into account the results 
obtained, the knowledge bases were adjusted (Tables 4-7 
present its final version). Figure 4 shows a three-dimen-
sional graph presenting the dependence of the level of oil 
competitiveness on quality and price (level of the image is 
75 and level of service is 65). 
The analysis on the basis of a given graph is approxi-
mate. Due to the importance of usage accurate data about 
the dependence of competitiveness on the level of complex 
factors (quality, image, service) and price, an analysis of 
sensitivity was carried out in the work.
4.3 Simulation of the competitiveness 
sensitivity of a particular oil brand
In the next step, three major oil brands were chosen to an-
alyse their competitiveness levels. They were selected on 
the basis of analysis of official financial statements (SSSU, 
2018) and their market share (UCAB, 2018) for the last 
five years. Due to obtained data, such brands as “Chumak”, 
“Korolivskyj smak”, and “Majola” are the most demanded 
ones (Bakertilly, 2017) and considered as main competitor 
at domestic market.
The research conducted allowed the researchers to 
evaluate and compare the level of competitiveness of 
major Ukrainian sunflower oil trademarks. Using the de-
veloped model, the competitiveness levels of three major 
sunflower oil brands were calculated (Table 8). 
Figure 3: Operations and methods of fuzzy logical inferance of Mamdani. Source: own
120
Organizacija, Volume 52 Issue 2, May 2019Research Papers
As judged by the data given in Table 3 and Table 8, it is 
possible to make some conclusions about the level of com-
petitiveness of different brands of sunflower oil and the 
factors influencing it.
“Chumak” oil is characterised by high quality, and the 
manufacturer has a fairly strong image. The service lev-
el can be referred to as high rather than average, but the 
price is high. Therefore, the level of competitiveness of 
this product is approaching the average level.
In the case of “Korolivskyj smak”, one can state that the 
Figure 4. Three-dimensional graph presenting the dependence of the level of oil competitiveness on quality and price (obtained 
after setting the model on the training sample). Source: own 
quality of its sunflower oil and its image is considered high 
rather than average. In contrast, the service level tends to 
be above average and the price is clearly average (the coor-
dinate of the maximum of the membership function for the 
average price is equal to 32,5 UAH. Taking into account 
the points garnered in terms of all aggregated factors, the 
level of competitiveness of this sunflower oil is equal to 
57,8.
Regarding sunflower oil produced by “Majola”, one 
can declare an average level of quality, manufacturer im-
age, and service. We can say with a greater degree of cer-
tainty that the price is definitely average. Thus, as a con-
sequence, Myola’s level of sunflower oil competitiveness 
can be described as average.
A comparison of the estimations obtained allows us to 
conclude that “Korolivskyj smak” sunflower oil has a high 
level of competitiveness, characterised by favourable rela-
tions of price and non-price factors.
Taking into account the level of all non-price competi-
tiveness factors, it can be noted that the price of “Mayola” 
oil is quite high in comparison with “Korolivskyj smak” 
sunflower oil. “Chumak” sunflower oil has a high level of 
Trademark Name Quality
(F1)
Image
(F2)
Service
(F3)
Average retail price, 
UAH/L
(F4)
Sunflower oil com-
petitiveness level (C)
Chumak 88 91 76 37 53.3
Korolivskyj smak 78 76 69 32 57.8
Majola 53 48 51 29 50
Table 8: Indicators for assessing the level of competitiveness of different brands of packaged sunflower oil. Source: own
121
Organizacija, Volume 52 Issue 2, May 2019Research Papers
non-price factors, but a relatively high price reduces its 
competitiveness compared to “Korolivskyj smak” oil.
The developed model allows us to carry out an analysis of 
the sensitivity of the resultant index to the change of values 
of input variables. 
We have analyzed the sensitivity of the level of compet-
itiveness of the “Chumak” sunflower oil to the following 
factors: price reduction; increase of service level; decrease 
in oil quality. The level of image remains the same. It was 
supposed that in case of decreasing oil quality, producer 
with high image has either inform consumers on a given 
issue or remove oil from the retail chain. Consequently it 
could trigger the spread of negative reviews of trademark 
and in more remote period - reduced producers’ image.
The values of the factors we changed as follows: the price 
was reduced in step size of UAH 0,10 to average price 
level since it is important for the producer to keep the price 
at least at the average level (32,5 UAH is the coordinate of 
the maximum of the membership function for the average 
price); service increased in step 1 to the maximum level of 
100; the quality was reduced in step 1 to the average level 
(50 is the coordinate of the maximum of the membership 
function for the average quality).
The next step of our research is to analyse the sensitivity 
of the level of “Chumak” sunflower oil competitiveness to 
changes in price and service (Table 9).
According to the results, the following solutions can be 
proposed for “Chumak”:
• decreasing the price by 4,05% (up to UAH 35,5 per 1 
litre) under the condition of invariability of all other 
factors will allow producers to increase the level of 
competitiveness of “Chumak” sunflower oil by 8,6% 
(C = 57.9). As the result competitiveness of “Chu-
mak” sunflower oil will surpass the competitiveness 
level of “Korolivskyj smak” sunflower oil (C = 57.8);
• a 31,6% improvement of the service level (up to the 
maximum possible value of F3 = 100), provided that 
all other factors remain unchanged, will increase the 
level of competitiveness of “Chumak” brand oil by 
only 0,38% (up to the level of C = 53,5), which indi-
cates the inexpediency of placing significant empha-
sis on service only;
• a 20,45% decline in oil quality (up to F1 = 70) doesn’t 
provoke a decline in oil competitiveness, which could 
be explained by the high confidence of buyers in the 
brand (this is possible only in the short term);
• reducing the quality of oil by 21.59% (to F1 = 69) 
already leads to reduction in the competitiveness level 
by 0,75% (up to C = 52.9); further deterioration in 
quality causes a constant decline in the competitive-
ness of oil;
• it is rational to reduce the price and improve the ser-
vice simultaneously. In particular, if price decreases 
by 3,24% (up to UAH 35,8 per 1 litre) and service 
level improves by 15,8% (up to F3 = 88) then the 
level of oil competitiveness will increase by 9% and 
will make C = 58,1, which will exceed the level of 
competitiveness of “Korolivskyj smak” sunflower oil 
(C = 57,8).
Fac-
tors 
Options
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Dependence of competitiveness level on the price at the current level of all other factors
Price 
(F4) 37 36.9 36.8 36.7 36.6 36.5 36.4 36.3 36.2 36.1 36,0 35.9 35.8 35.7 35.6 35.5 35.4
C 53.3 53.6 53.9 54.2 54.5 54.9 55.2 55.6 56.1 56.6 56.7 56.9 57.1 57.4 57.6 57.9 58.2
Price 
(F4) 35.3 35.2 35.1 35.0 34.9 34.8 34.7 34.6 34.5 34.4 34.3 34.2 34.1 34.0 33.9 33.8 33.7
C 58.5 58.8 59.1 59.5 59.9 60.3 60.7 61.1 61.6 62.0 62.5 63.0 63.5 64.0 64.5 64.9 65.0
Price 
(F4) 33.6 33.5 33.4 33.3 33.2 33.1 33.0 32.9 32.8 32.7 32.6 32.5
C 65.2 65.3 65.5 65.6 65.7 65.7 65.8 65.8 65.9 65.9 65.9 65.9
 Dependence of competitiveness level on the service at the current level of all other factors
Ser-
vice 
(F3) 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92
Table 9: Results of the sensitivity analysis of the level of competitiveness of “Chumak” packaged sunflower oil to changes in 
price and service level. Source: own
122
Organizacija, Volume 52 Issue 2, May 2019Research Papers
C 53.3 53.3 53.3 53.3 53.3 53.3 53.3 53.3 53.3 53.3 53.3 53.3 53.3 53.3 53.3 53.3 53.3
Ser-
vice 
(F3) 93 94 95 96 97 98 99 100
C 53.3 53.3 53.4 53.4 53.4 53.5 53.5 53.5
 Dependence of competitiveness level on the quality at the current level of all other factors
Qual-
ity 
(F1) 88 87 86 85 84 83 82 81 80 79 78 77 76 75 74 73 72
C 53.3 53.3 53.3 53.3 53.3 53.3 53.3 53.3 53.3 53.3 53.3 53.3 53.3 53.3 53.3 53.3 53.3
Qual-
ity 
(F1) 71 70 69 68 67 66 65 64 63 62 61 60 59 58 57 56 55
C 53.3 53.3 52.9 52.5 52.1 51.7 51.4 51.1 50.8 50.6 50.4 50.2 50.0 49.9 49.7 49.6 49.6
Qual-
ity 
(F1) 54 53 52 51 50
C 49.5 49.4 49.4 49.4 49.4
Changes in the competitiveness level in case of service improvement and price reduction at the existing level of all other factors
Price 
(F4) 37 36.9 36.8 36.7 36.6 36.5 36.4 36.3 36.2 36.1 36,0 35.9 35.8 35.7 35.6 35.5 35.4
Ser-
vice 
(F3) 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92
C 53.3 53.5 53.7 54.0 54.4 54.8 55.2 55.6 56.1 56.8 57.1 57.6 58.1 58.7 59.3 59.8 60.6
Price 
(F4) 35.3 35.2 35.1 35.0 34.9 34.8 34.7 34.6 34.5 34.4 34.3 34.2 34.1 34.0 33.9 33.8 33.7
Ser-
vice 
(F3) 93 94 95 96 97 98 99 100
C 61.3 61.8 62.6 63.5 64.2 64.9 65.6 66.3
This business case may help managers to channel their 
efforts and resources in the proper particular direction to 
increase product competitiveness and product positioning 
on the market.
5 Conclusions 
The paper has addressed the question of evaluating com-
petitiveness considering determining factors. Based on 
this, a fuzzy model of brand competitiveness and its practi-
cal application for Ukrainian sunflower oil were proposed. 
The aim of this model was to outline essential qualitative 
and quantitative competitiveness factors (complex and 
partial) and to evaluate the level of competitiveness re-
garding their significance. The main idea behind using the 
fuzzy logic toolkit is that all factors determining the level 
of competitiveness (both quantitative and qualitative) were 
considered. The implementation of this model allows to es-
timate the level of complex factors of oil competitiveness, 
which depend on the values of partial factors, to estimate 
the level of sunflower oil competitiveness considering the 
values of complex factors. In addition, another important 
feature of the model is the possibility for researchers to 
analyse the sensitivity of oil competitiveness to changes in 
the values of factors.
Our study contributes to the knowledge base in several 
ways. Firstly, although this research adopts a single-coun-
try approach, it gives us the possibility of comparing the 
results with other sectors of the economy. Secondly, this 
Table 9: Results of the sensitivity analysis of the level of competitiveness of “Chumak” packaged sunflower oil to changes in 
price and service level. (continued)
123
Organizacija, Volume 52 Issue 2, May 2019Research Papers
detailed analysis may become a substantial advantage 
allowing us to formulate hypotheses to be verified in the 
context of other industries and countries. 
One should also add that the model for the evaluation of 
the level of competitiveness of sunflower oil developed in 
this paper has several practical applications. First of all, 
it allows researchers to forecast the level of oil competi-
tiveness taking into account the values of complex factors 
and price. In addition, it also allows for the assessment of 
the level of complex factors, while simultaneously taking 
into account the values of particular factors. As a result it 
allows researchers to conduct an analysis of the sensitivi-
ty of product competitiveness to the impact of significant 
factors (partial and complex). The results of such analysis 
could provide a starting point for decision-making in order 
to improve product competitiveness. In addition, the mod-
el can be used for case studies to predict the competitive-
ness of other food products produced from agricultural raw 
materials by adjusting its parameters (linguistic variables 
and their corresponding term sets, functions of member-
ship of fuzzy terms, and fuzzy knowledge bases).
Our study has several limitations, the first (and most 
important) of which was the analysis of only one product 
produced by one country. Despite this limitation, we be-
lieve that the results achieved allowed us to obtain a true 
picture of the situation of the product analysed, thus con-
tributing to academic debates of the comprehensive evalu-
ation of product competitiveness.
Literature
Baker Tilly Ukraine LLP. (2017, August). Vyrobnytstvo 
vysokooleinovykh kultur v Ukraini: perspektyvy ta 
rozvytok [Production of high-oleic cultures in Ukraine: 
prospects and development]. Retrieved March 2, 2019, 
from https://bakertilly.ua/news/id41132
Bhana, N. (2018). Corporate social responsibility initia-
tives and its impact on firm share price performance: 
Evidence from South Africa, Journal of Contemporary 
Management, 15(1), 100-126.
Chang, Y.-H. & Yeh, Ch.-H. (2001). Evaluating airline 
competitiveness using multiattribute decision making, 
29(5), 405-415, 
 http://doi.org/10.1016/S0305-0483(01)00032-9  
Chepurnoj, I.P. (2005). Konkurentosposobnost` pro-
dovol`stvenny`kh tovarov [Competitiveness of Food 
Products]. Moskva: Dashkov i K.
Cygler,  J., Sroka, W., Solesvik, M. & Dębkowska, K. 
(2018). Benefits and drawbacks of coopetition: the 
roles of scope and durability in coopetitive relation-
ships, Sustainability, 10, 2688, 
 http://doi.org/10.3390/su10082688 
Cygler, J. & Sroka, W. (2017). Coopetition disadvantages: 
The case of the high tech companies, Engineering Eco-
nomics, 28(5): 494–504, 
 http://doi.org/10.5755/j01.ee.28.5.16421 
Duarte, A.M. Jr. (2018). Applying the TODIM Fuzzy 
Method to the evaluation of Brazilian banks, Pesquisa 
Operacional, 38(1), 153-171, 
 http://doi.org/10.1590/0101-7438.2018.038.01.0153 
Dvorsky, J., Popp, J., Virglerova, Z., Kovács, S. &  Oláh, J. 
(2018). Assessing the importance of market risk and its 
sources in the SME of the Visegrad Group and Serbia, 
Advances in Decision Sciences, 22(A) 22nd Anniver-
sary Special Issue, 1-25. 
Fatkhutdinov, R.A. (2000). Konkurentosposobnost’: 
ekonomika, strategiya, upravlenie [Competitiveness: 
Economy, Strategy, Control]. Moskva: Infra-M.
Fumio, T. (1985). Product competitiveness evaluation - 
quantitative analysis for development strategy, Tech-
nological Forecasting and Social Change, 28(2), 123-
139, http://doi.org/10.1016/0040-1625(85)90010-1 
Ivanenko, A.V. (2012). Alhorytm otsinky konkurentospro-
mozhnosti produktsii [Algorithm of an estimation of a 
production competitiveness]. Visnyk ekonomiky trans-
portu i promyslovosti, 38, 182-185.
Kliestikova, J., Krizanova, A., Corejova, T., Kral, P.  & 
Spuchlakova, E. (2018). Subsidies to increase remote 
pollution?, Science and Engineering Ethics, 24(2), 
755-767, http://doi.org/10.1007/s11948-017-9908-0 
Kobyliatskyi, L.S. (2003). Upravlinnia 
konkurentospromozhnistiu [Management of competi-
tiveness]. Kyiv: Zovnishnia torhivlia.
Kolářová, I., Bédiová, M. & Rašticová, M.  (2017). Job 
opportunities for people over 50 in the Netherlands, 
Forum Scientiae Oeconomia, 5(1), 121-128, 
 http://doi.org/10.23762/fso_vol5no1_10 
Kozma, T. (2017). Cooperation in the supply chain net-
work, Forum Scientiae Oeconomia, 5(3), 45-58, 
 http://doi.org/10.23762/FSO_vol5no3_17_3 
Kubíčková L., Hajko, V., Rašticová, M. & Hazuchová, 
N. (2018). Active Work Participation of the Czech El-
derly. Acta Universitatis Agriculturae et Silviculturae 
Mendelianae Brunensis, 66(5), 1287 – 1293, 
 http://doi.org/10.11118/actaun201866051287 
Litvinova, V.O. (2013). Porivnialnyi analiz rozrakhun-
kovykh metodiv vyznachennia rivnia konkurento-
spromozhnosti produktsii [Comparative analysis of 
computational methods for determining the level of 
competitiveness of products], Visnyk sotsialno-ekono-
michnykh doslidzhen, 4, 81-87. 
Mamdani, E.H. & Assilian, S. (1975). An Experiment in 
Linguistic Synthesis with Fuzzy Logic Controller. Int. 
J. Man-Machine Studies, 7(1), 1-13.
Massey, G., Kliestikova, J., Kovacova, M. & Dengov, V.V. 
(2018). The perceived accuracy of fake news: Mecha-
nisms facilitating the spread of alternative truths, the 
crisis of informational objectivity, and the decline of 
trust in journalistic narratives, Geopolitics, History, 
and International Relations, 10(2), 37-43, 
 http://doi.org/10.22381/GHIR10220184 
124
Organizacija, Volume 52 Issue 2, May 2019Research Papers
Math Works (2018). Fuzzy Logic Toolbox at MATLAB. 
Retrieved from 
 https://www.mathworks.com/products/fuzzy-logic.
html  (2 March 2019).
Matviichyk, A.V. (2005). Analiz ta prohnozuvannia roz-
vytku finansovo-ekonomichnykh system iz vykorystan-
niam teorii nechitkoi lohiky [Analysis and forecasting 
of the development of financial and economic systems 
using the theory of fuzzy logic]. Kyiv: Tsentr navchal-
noi literatury.
Meyer, N. & De Jongh, J.J. (2018). The importance of 
entrepreneurship as a contributing factor to economic 
growth and development: The case of selected Euro-
pean countries, Journal of Economics and Behavioral 
Studies, 10(4), 287-299, 
 http://doi.org10.22610/jebs.v10i4.2428  
Meyer, N. & Meyer, D.F. (2017). An econometric anal-
ysis of entrepreneurial activity, economic growth and 
employment: The case of the BRICS countries, In-
ternational Journal of Economic Perspectives, 11(2), 
429-441.
Meyer, N. (2018). South African female entrepreneurs’ 
intention to remain in business, Vanderbijlpark: North-
West University (Doctoral thesis).
Miller, G.A. (1956), The magic number seven plus or mi-
nus two: some limits on our capacity for processing 
information, Psychological Review, 63, 81-97.
Mityushkin Yu.I., Mokin, B.I., & Rotshtejn, A.P. (2002). 
Soft Computing: identifikacziya zakonomernostej 
nechetkimi bazami znanij [Soft computing: identifica-
tion of the regularities by the fuzzy knowledge bases]. 
Vinnytsia: UNIVERSUM-Vinnytsia.
Mohalajeng, L.E. & Kroon, J. (2016). Perceptions by users 
of an Open Innovation platform regarding the innova-
tion value chain, Journal of Contemporary Manage-
ment, 13(1), 892-925.
Moravcikova, D., Krizanova, A., Kliestikova, J. & Rypa-
kova, M. (2017). Green marketing as the source of the 
competitive advantage of the business, Sustainability, 
9(12), 2218, http://doi.org/3390/su9122218 
Oral, M. & Kettani, O. (2009). Modelling firm competi-
tiveness for strategy formulation, CIRRELT 2009, 52, 
1-39. Retrieved from 
 https://www.google.com/url?sa=t&rct=j&q=&es-
rc=s&source=web&cd=1&ved=2ahUKEwj3x-
MXZy7fiAhUKLlAKHZ7IBB8QFjAAegQIA-
B A C & u r l = h t t p s % 3 A % 2 F % 2 F w w w. c i r r e l t .
ca%2FDocumentsTravail%2FCIRRELT-2009-52.pd-
f&usg=AOvVaw1-Vx50ZB2d89xCRi906Il3 
Pastushchyn, V. (2007). Analiz metodiv otsinky konkuren-
tospromozhnosti produktsii pidpryiemstva [Analysis 
of methods for assessing the competitiveness of enter-
prise products]. Ukrainska nauka: mynule, suchasne, 
maibutnie, 12, 232-240.
Pedrycz, W. (2011). Fuzzy multicriteria decision-making: 
Models, methods and applications, Chichester: Wiley.
Pomffyová, M., Kožárová, M., & Krajčík, V. (2017). Inno-
vative and information perspectives of business man-
agement, Polish Journal of Management Studies, 16(2), 
221-232, http://doi.org/10.17512/pjms.2017.16.2.19 
Popp, J., Oláh, J., Fári, M., Balogh, P. & Lakner, Z. (2018). 
The GM - regulation game – The case of Hungary, In-
ternational Food and Agribusiness Management Re-
view, 21(7), 945-968, 
 http://doi.org/10.22434/IFAMR2017.0065 
Popp, J., Kiss, A., Oláh, J., Máté, D., Bai, A. & Lakner, 
Z. (2018). Network analysis for the improvement of 
food safety in the international honey trade, Amfiteatru 
Economic, 20(47), 84-98, 
 http://doi.org/10.24818/EA/2018/47/84 
Roostika, R., Wahyuningsih, T., & Haryono, S. (2015). 
The impacts of external competitiveness factors in the 
handicrafts industry, Polish Journal of Management 
Studies, 12(1), 166-176.
Shpak, N., Kyrylych, T., & Greblikaitė, J. (2016). Diver-
sification models of sales activity for steady develop-
ment of an enterprise, Sustainability, 8(4), 393, 
 http://doi.org/10.3390/su8040393 
Shtovba, S.D. (2007). Proektirovanie nechetkikh sistem 
sredstvami MATLAB [Design of fuzzy systems by 
means of MATLAB]. Moskva: Goryachaya liniya – 
Telekom.
Shtovba, S. & Shtovba, O. (2006). Prediction of compe-
titive position of brand product by fuzzy knowledge 
base. Journal of Automation and Information Scien-
ces, 38(8), 69–80, 
 http://doi.org/10.1615/J Automat Inf Scien.v38.i8.70
Sroka, W. & Vveinhardt, J. (2018). Nepotism and favourit-
ism in the steel industry: A case study analysis, Forum 
Scientiae Oeconomia 6(1), 31-45, 
 http://doi.org/10.23762/FSO_VOL6NO1_18_4 
State Committee of Ukraine on technical regulation and 
consumer policy (2006). National Standards of Ukraine 
4694-2006 “Sunflower. Olive raw materials. Techno-
logical conditions”. Retrieved from http://ua-info.biz/
legal/basedt/ua-qmppbr.htm (2 March 2019).
State Statistics Service of Ukraine (SSSU) (2018). Foreign 
trade in selected products by country Retrieved from 
http://www.ukrstat.gov.ua/operativ/operativ2018/
zd/e_iovt/arh_iovt2018.htm. (2 March 2019).
Šebestová, J., Šperka, R., Małecka, J. & Łuczka, T. (2017). 
Co-working centres as a supportive network for cross 
border business cooperation, Forum Scientiae Oeco-
nomia, 5(4), 23-34, http://doi.org/10.23762/FSO_
VOL-5NO4_17_2
UCAB (2018). Main indicators of foreign trade of 
Ukraine. Statistics Service of Ukraine. Retrieved from 
http://ucab.ua/ua/doing_agribusiness/zovnishni_rinki/
osnovni_pokazniki_ zovnishnoi_torgivli_ukraini (2 
March 2019).
Walsh, V., Roy, R., Bruce, M. & Potter, S. (1992). Winning 
by design: technology, product design and internation-
125
Organizacija, Volume 52 Issue 2, May 2019Research Papers
al competitiveness, Oxford, UK: Blackwell Publishers.
Xianbo, Z., Bon-Gang, H. & Pheng, L.S. (2012). Imple-
menting enterprise risk management in a Chinese con-
struction firm based in Singapore, Proceedings from 
the World Construction Conference 2012 – Global 
Challenges in Construction Industry (pp. 434-444), 
28-30 June 2012, Colombo, Sri Lanka.
Zhao, X., Hwang, B.-G., Low, S.P. (2013). Developing 
fuzzy enterprise risk management maturity model for 
construction firms, Journal of Construction Engineer-
ing and Management, 139(9), 1179–1189,  
 http://doi.org/1061/(ASCE)CO.1943-7862.0000712
Nestor Shpak, Dr.Sc., is a professor at the Management 
and International Business Department, Educational 
and Scientific Institute of Economics and Management, 
Lviv Polytechnic National University. Scientific activities: 
10 scientific monographs, professional publications, 
6 books, more than 200 national and international 
contributions, and presentations. Research interests: 
management of innovations, management of info-
communications, modeling of innovative economic 
systems, marketing activity.
Viktoriya Kharchuk, Ph.D., is an assoc. professor 
at the Management and International Business 
Department, Educational and Scientific Institute 
of Economics and Management, Lviv Polytechnic 
National University. Scientific activities: 2 scientific 
monographs, professional publications, 4 books, 
more than 40 national and international contributions, 
and presentations. Research interests: financial 
management, risk management, sustainable 
development, corporate social responsibility.
Nadiya Seliuchenko, Ph.D., is an assoc. professor at 
the Department of Business Economics and Investment, 
Educational and Scientific Institute of Economics and 
Management, Lviv Polytechnic National University. 
Scientific activities: scientific monograph, professional 
publications, 7 books. Research interests: financial 
management, risk management.
Nataliya Kosar, Ph.D., is an assoc. professor at the 
Department of Marketing and Logistics, Educational 
and Scientific Institute of Economics and Management, 
Lviv Polytechnic National University. Scientific activities: 
7 scientific monographs, professionalpublications, 3 
books. Research interests: marketingmanagement, 
marketingresearches, strategic marketing, consumer 
policy.
Włodzimierz Sroka, Ph.D., is an associate professor 
in the Management Department of WSB University in 
Dąbrowa Górnicza. He specializes in theoretical and 
practical issues relating to management and strategic 
management. He is the author of numerous scientific 
papers about strategic alliances, network organizations, 
strategy, company restructuring, CSR and business 
ethics, as well as innovations and entrepreneurship. 
His research activities mostly include cooperative 
strategies (e.g., alliances, network organizations), co-
opetitive relations, M&A, business ethics and CSR, 
innovations and entrepreneurship. He merges his 
scientific activity with being the CEO of a medium-sized 
engineering company. He was previously employed in 
different managerial positions in both the steel and 
machine industries.
126
Organizacija, Volume 52 Issue 2, May 2019Research Papers
Ocena konkurenčnosti izdelkov: analiza primera
Ozadje in namen: Obstoječi okviri zagotavljajo le površinski pristop k vrednotenju konkurenčnosti izdelkov, ki upo-
števa povezavo med stopnjo konkurenčnosti izdelkov in kvantitativnimi ter kvalitativnimi dejavniki, ki imajo največji 
vpliv na to. Ker je namen tega članka izdelati model za ocenjevanje konkurenčnosti pakiranega sončničnega olja, ob 
upoštevanju tako kvantitativnih kot kvalitativnih dejavnikov, ki vplivajo na konkurenčnost. Razviti model smo upora-
bili tudi za preučevanje najbolj zahtevnih ukrajinskih blagovnih znamk sončničnega olja, da bi ugotovili možnosti za 
povečanje konkurenčnosti.
Zasnova / metodologija / pristop: Metodologija raziskave vključuje elemente teoretičnih, empiričnih, kvalitativnih 
in kvantitativnih analiz. Namen teoretične analize je osvetliti različno razumevanje »vrednotenja konkurenčnosti« ter 
pristopov in orodij za njegovo analizo. Empirična analiza se osredotoča na opazovanje uradnih statističnih podatkov 
o izvozu sončničnega olja in prihodnjih trendov. Kvalitativna analiza je sestavljena iz identifikacije, sistematizacije 
in opisa dejavnikov, ki vplivajo na konkurenčnost pakiranega sončničnega olja. Kvantitativna analiza pa temelji na 
uporabi orodja mehke logike za oceno vpliva kompleksnih in delnih dejavnikov na raven konkurenčnosti izdelkov.
Rezultati: V prispevku je predstavljen poslovni primer ocenjevanja konkurenčnosti posamezne blagovne znamke 
sončničnega olja. Na podlagi uporabe orodja mehke logike smo analizirali vpliv kompleksnih in delnih dejavnikov 
na raven konkurenčnosti. Kot rezultat je bila predstavljena simulacija konkurenčne občutljivosti posamezne oljne 
blagovne znamke na relevantne kompleksne dejavnike, ki določajo njeno konkurenčnost. Ta poslovni primer lahko 
pomaga upravljavcem usmeriti svoja prizadevanja in sredstva v pravo smer, da bi povečali konkurenčnost izdelkov 
in pozicioniranje izdelkov na trgu.
Zaključek: Rezultati teh raziskav bi bili koristni za strokovnjake pri ocenjevanju konkurenčnosti izdelkov, modeliranju 
prihodnjih ravni in razumevanju skritih možnosti za povečanje konkurenčnosti izdelkov. Ponujeni okvir bi se lahko 
uporabljal tudi za druge vrste izdelkov.
Ključne besede: konkurenčnost izdelkov, splošni in delni dejavniki, mehka logika