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Adaptive fuzzy model for determination of quality assessment services in supply chain

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Adaptive fuzzy model for determination of quality assessment services in supply chain

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The problem that is being addressed in this paper is to improve the services provided by company and achieve better communication among companies in the supply chain. Therefore, a qualitative assessment of the service has been required. This service is characterized by a group of parameters, which are often inaccurately estimated values as well as its importance for the evaluation system. This is often the result of assessor´s uncertainty, variability of conditions, etc. Therefore, in the context of AM4SCM (Adaptive Model for Supply Chain Management) a mathematical model for evaluating the quality of services has been developed (FAM4QS - Fuzzy Aggregation Method for Quality Service) which is based on the fuzzy arithmetic. By selecting different values for the degrees of fuzzy power mean, which are used for evaluation of parameters or groups of parameters of the system and the service also, contribute to a better assessment and it is due to the varying nature of the parameters. The observed model was simulated on 17 supply chains on the territory of the Republic of Serbia. Service quality assessment is carried out on the basis of data from the user requirements - participants of supply chains binding the so-called fuzzy aggregation function.
Service desk LEVEL 7 represents FAM4QS method that differs from the previous one [34] in the way that the parameter estimation and weight coefficients of the team of experts are presented as fuzzy triangular numbers due to their imprecision. As a result there is a review of the system in the form of fuzzy numbers, respectively the interval as its α-cut. If desired, the response can be defuzzyficated by the method of gravity. Due to better understanding of the method that deals with imprecise data, some of the concepts from the theory of fuzzy sets and properties associated with them should be considered. The theory of fuzzy sets generalizes traditional theory, so that instead of the characteristic function (which takes a value of 1 for the given element x if x ∈ A, and a value of 0 if x ∉ A) we observe the so-called membership function μA of this set, which determines the grade of membership of the element x to the set A that is no longer just 0 and 1 but it can take any value from the interval [0, 1], i.e. μ A (x) ∈ [0, 1]. In this study special fuzzy sets will be used-fuzzy numbers and the so-called triangular fuzzy numbers: A = (l, m, r) where l is called the left boundary of triangular fuzzy number, m is the value which belongs to the core of fuzzy number (membership function is 1), and r is the right boundary of the triangular fuzzy number. Depending on the nature of the data, i.e. our estimate (whether accurate or not) shall modify the aforementioned formula (see (1)) for certain imprecise ei or inaccurately estimated weights w i as follows.
… 
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1690 Technical Gazette 25, 6(2018), 1690-1698
ISSN 1330-3651 (Print), ISSN 1848-6339 (Online) https://doi.org/10.17559/TV-20170705130711
Original scientific paper
Adaptive Fuzzy Model for Determining Quality Assessment Services in the Supply Chain
Milovan TOMAŠEVIĆ, Nebojša RALEVIĆ, Željko STEVIĆ, Vidan MARKOVIĆ, Zdravko TEŠIĆ
Abstract: The problem that is being addressed in this paper is to improve the services provided by company and achieve better communication between companies in the
supply chain. Therefore, a qualitative assessment of service has been required. This service is characterized by a group of parameters, which are often inaccurately estimated
values, as well as their importance for the evaluation system. This is often the result of assessor´s uncertainty, variability of conditions, etc. Therefore, in the context of
AM4SCM (Adaptive Model for Supply Chain Management) a mathematical model for evaluating the quality of services has been developed (FAM4QS - Fuzzy Aggregation
Method for Quality Service) which is based on the fuzzy arithmetic. Selection of different values for the degrees of fuzzy power mean, which are used for evaluation of
parameters or groups of parameters of the system and the service, contributes to a better assessment and it is due to the varying nature of the parameters. The observed
model was simulated on 17 supply chains on the territory of the Republic of Serbia. Service quality assessment is carried out based on data from the user requirements -
participants of supply chains binding the so-called fuzzy aggregation function.
Keywords: AM4SCM (Adaptive Model for Supply Chain Management); Software quality; Software services; Supply Chain Management
1 INTRODUCTION
The concept of supply chain changes over time. It is
gaining in importance. During the first decade of this
century, according to [1], supply chain management and
control were the strategic focus of the leading
manufacturing companies. This is caused by rapid changes
of environment in which companies operate, the
globalization of markets and very high customers
requirements where high quality products and services are
becoming a priority. The aim of for today's supply chain is
to model the supply chain in a way that will provide
profitable outputs for all parts of the supply chain and its
participants. Looking at one of the supply chain definitions,
according to [2], it is a set of three or more organizations
that are directly connected with one or more flows of
products, services, finance and information flows from a
source to the end user in contemporary supply chains, and
very often it is necessary to coordinate activities and flows
to the extent that goes beyond the current limits. Supply
chain management has a high impact on the quality of
products and services, which according to [3] increases the
importance of the relationship between procurement,
suppliers and quality. With the increasing importance of
these relationships, the aim is to optimize the supply chain
which, according to [4], aims to successfully control the
different elements within the chain, which include the
participants, their mutual contacts and relationships, and
the way of organizing certain internal activities.
In addition to cost optimization, the aim of supply
chain management is to improve the flow of information
between the suppliers, companies and distributors. As one
of the important aims of supply chain management, which
has lately been emphasized, is to increase the quality of
service and flexibility in order to achieve the satisfaction
of the end users. This is confirmed by Christopher in his
book [5], "the whole purpose of supply chain management
and logistics is to provide customers with level and quality
of service that they required and to do so less cost to the
total supply chain".
When it comes to supply chain the flow of information
in a real time is one of the global problems. Many
researches are focused on solving this problem and
ensuring the flow of information in real time in the supply
chain, so that participants are more satisfied and do
business better. Problems often arise due to poor
connectivity of subsystems that are independently
developed and used as global integrators of all company
processes. Within the subsystem, solutions for individual
functions are given only as a set of fixed partial solutions
without generalization. It is often possible to find a system
whose structure is not specially projected; however, the
solution is sought in the merger (purchase) of subsystems
where the partial solutions occurred during the time of
need. The subjects of this study are model, method and
tools for supply chain management used to the greatest
extent possible using the concepts of responsibility for the
flow of information, increasing the quality of service in real
time in the supply chain.
According to Cheng [6] due to its complexity and
uncertainty, quality control of supply chain represents
great challenges to practitioners and researchers, so the
problem considered in this article is to improve the service
provided by the company and to achieve better
communication between the companies in the supply
chain, in order to accelerate their business and deliver more
profit, as well as to exert greater cooperation with
customers and to continue good business relation with
them.
In addition, the investigated problem is the service
qualitative assessment, when it is characterized by a group
of parameters which are often inaccurately estimated
values, as well as its importance for the evaluation system.
This imprecision is often the result of assessor´s
uncertainty, variability of conditions, etc. Since imprecise
data will be employed, the goal of this study is to introduce
acceptable methodology or assessors (functions) for
evaluating the quality of service. The assessor should be
able to deal with imprecise data.
This paper is structured as follows. In section 2 is
shown literature review and the need for research. Section
3 presents description of the model, while section 4
presents verification and simulation of the model with
discussion. Section 5 derives conclusions and directions
for future research.
Milovan TOMAŠEVIĆ et al.: Adaptive Fuzzy Model for Determining Quality Assessment Services in the Supply Chain
Tehnički vjesnik 25, 6(2018), 1690-1698 1691
2 LITERATURE REVIEW
This is the time when companies cannot rely on their
own inventive and productive abilities [7]. So, nowadays
the center of gravity is not a competition among the
organizations it has been shifted toward the supply chain
[8,9].
In addition to facing global competition, companies
are faced with customers who change their requirements
very quickly, but are also dealing with the technological
changes that influence reduction of critical reactions when
it comes to competence [10-12].
Therefore, firstly special attention should be paid to
the supply chain at the first place, supply partners,
improvement and acceleration of products and services [7].
These competencies are of particular importance for the
firms with identified market changes. Therefore, they
should turn towards integrated supply chain in order to
positively and effectively respond to these changes [13,
14]. Adequate management of the modern supply chain
requires quality inputs, further reflected on its full flow. To
provide that the purpose of the supply chain is satisfying
user needs and requirements, the essential aim of the
modern supply chain is the integration of all possible
activities and processes that need to bring greater value to
the end user. Supply chain integration (SCI) has positive
impact on performances of companies [15-17] and helps
firms to reconfigure their resources and capabilities
internally and externally [18]. Supply chain integration
may be more crucial in early stages and when that process
is completed, a company can focus on SCM practice and
competition capability [19]. Also supplier integration has a
strong and positive impact on schedule attainment and
customer satisfaction [20].
According to Nagurney [21] quality is one of the most
essential factors for the success of supply chains, but also
quality of service according to [22] is still one of the major
problems with consumers. Consequently, due to ensuring
the continuous improvement of quality service that leads to
customer satisfaction, the study investigated the effect of
external knowledge and knowledge chain to quality of
service. Companies should use the chain of knowledge to
collect the external knowledge from the customers,
suppliers and competitors, as well as transformation of
knowledge to improve their quality of service.
The need for research and improvement of the system
for solving user´s problem arises from the current situation
which companies are faced with, due to continual increase
of users who need IT (information technology) services.
According to [23] one of two approaches for improvement
of business performance is integrated information
technology. Pieces of information and their quality have a
high impact on the whole supply chain, because poor
information quality according to [24] may lead to
organizational losses such as losing customers, missing
opportunities, and making incorrect decisions. The
significant role and impact of information sharing in supply
chains have been extensively studied [25-28]. Apart from
better information sharing, the connectivity among partner
firms that enables information integration is crucial for
firms to realize customer service performance gains [29].
The main objective of the research is the development
of models that can respond to as many user requests as
possible. It is a system that will serve companies to
converge towards continuous quality improvement in the
delivery of their IT services. The system that has emerged
from this study, with the given specification, is a part of the
model, which is subjected to changes and upgrades, which
means that it will eventually improve over time. In order to
stay competitive, it is important to constantly improve the
quality of services and software as well as to respond to the
latest needs faster than it is now being done, i.e. to be more
agile.
3 THE MODEL AND METHOD
To assess the parameters of service it is advisable to
take an arithmetic mean of the phenomenon with a normal
distribution, but if it is not the case, then it is often better to
take a different assessment as aggregation functions
specially degree environments [30, 31]. Diversity of choice
values of degrees of that environment implies more or less
disjunctively or conjunctively of forms selected
aggregation functions (higher r disjunctive form, less r
conjunctive form). In the paper [32], quality of services
was improved by using the aggregation functions in the
LSP method.
Taking the mid-stage of the aggregation instead of the
typical one, due to the inaccuracy of data which are
handled and which look like some of the triangular fuzzy
numbers, the same as when evaluating the overall system,
the fuzzy number, i.e. the interval of values with different
values of the membership function, is received.
Defuzzification provides better value in comparison with
conventional method. To avoid harsh conclusion i.e. the
answer for the quality assessment system is a number, in
that case the response was an interval of values that is
actually alpha-section of the stage as the number of output
where alpha belongs to the desired degree of aggregation
functions [33].
In this chapter, SSSI (the six-step service improvement
method used lsp) method has been presented. Its main
feature is that the power mean has been used for quality of
service with weight coefficients in which the degree
changes, if necessary, depending on whether more or less
characteristics of conjunctive or disjunctive form are
required. The parameters that appear in this formula which
are characteristics of the system as well as the weight
coefficients are a matter of judgment of the team of experts.
The results are presented.
Algorithm of the SSSI method for assessing the quality
of the software consists of the following steps [32]:
1. Select a group from the category of services (same
rank) in the catalog of services;
2. Use the lsp method. The formula for calculating the
estimates for each of these criteria is given by [34]:
[ ] [ ]
1
1
, 0 1, 1, 0 1 , 01 , 2
1
k
i
i
kr
r
E we w w e ,E ,k
iiii
i
=

= ≤≤ = ∈


=

(1)
where wi the coefficients weight, r value based on the
expectation of the combined impact of taking into account
the priority level of the group. r takes values from −∞ (full
conjunction) to + (full disjunction);
3. Identification of the criteria comparisons;
4. Computation preference (priority) for each service
selected rank;
Milovan TOMAŠEVIĆ et al.: Adaptive Fuzzy Model for Determining Quality Assessment Services in the Supply Chain
1692 Technical Gazette 25, 6(2018), 1690-1698
5. Analysis of the results and selection of the best ranked
in the group- UCL (upper control limit) and LCL (lower
control limit) [35];
6. If it is possible perform understandable conclusion and
recommendation to improve service on the basis of
knowledge gained in the previous step, and if not we will
continue with another tour cycle.
Adaptive model for supply chain management is a
complex system that connects the functional and inter-
functional business processes and allows participants in the
supply chain management of processes in real time (see
Fig. 1). It consists of:
- Model for supply chain management (BSCMS)
- Model for managing user requirements (Service Desk)
- Model for assessing the quality of services provided
(FAM4QS).
The hierarchical structure of the adaptive model for
supply chain management (AM4SCM) is shown in Fig.1
with seven levels of activity and feedback interfaces that
enable continuous improvement of AM4SCM.
Figure 1 AM4SCM
LEVEL 1 requires that the conducted system analysis
enables, defines and coordinates the following activities:
- Determination of the current state of IT systems used
(if applicable)
- Defining the town of generating information and
control procedures and data entry,
- Definition of potential users,
- Requirements for potential beneficiaries,
- Determining the level of access to projected
information,
- Define and generate the necessary level of
information,
- Harmonization of information with other participants
participating in the chain
- Post analysis system to the project team to create
software,
- Control of the draft software,
- Set up links to all relevant actors,
- Evaluation of the project and definition of any changes
in the concept and flow of information based on the real
needs of relevant stakeholders called in the supply chain.
LEVEL 2 A general model for supply chain
management makes use of the case with its activities
covering the vast majority of premium features for business
and they are presented in the following diagram. At
LEVEL 3 the model has been adjusted to company
requirements by choosing from the previous processes if
they exist or otherwise they are created. LEVEL 4 or
process consists of four steps. The first step includes
defining the partners, defining data and documents needed
for the operation after which the rules on the exchange of
information and their availability are set out. At LEVEL 5,
the selected processes are implemented and adapted by the
company. LEVEL 6 is connecting with the Service Desk
system which is shown in Fig. 2.
Figure 2 Service desk
LEVEL 7 represents FAM4QS method that differs
from the previous one [34] in the way that the parameter
estimation and weight coefficients of the team of experts
are presented as fuzzy triangular numbers due to their
imprecision. As a result there is a review of the system in
the form of fuzzy numbers, respectively the interval as its
α-cut. If desired, the response can be defuzzyficated by the
method of gravity. Due to better understanding of the
method that deals with imprecise data, some of the
concepts from the theory of fuzzy sets and properties
associated with them should be considered.
The theory of fuzzy sets generalizes traditional theory,
so that instead of the characteristic function (which takes a
value of 1 for the given element x if x A, and a value of
0 if x A) we observe the so-called membership function
μA of this set, which determines the grade of membership
of the element x to the set A that is no longer just 0 and 1
but it can take any value from the interval [0, 1], i.e. μA(x)
[0, 1].
In this study special fuzzy sets will be used - fuzzy
numbers and the so-called triangular fuzzy numbers: A =
(l, m, r) where l is called the left boundary of triangular
fuzzy number, m is the value which belongs to the core of
fuzzy number (membership function is 1), and r is the right
boundary of the triangular fuzzy number. Depending on the
nature of the data, i.e. our estimate (whether accurate or
not) shall modify the aforementioned formula (see (1)) for
certain imprecise ei or inaccurately estimated weights wi as
follows.
Milovan TOMAŠEVIĆ et al.: Adaptive Fuzzy Model for Determining Quality Assessment Services in the Supply Chain
Tehnički vjesnik 25, 6(2018), 1690-1698 1693
( )
1
11
rr
r
nn
ˆˆˆ ˆ ˆ
E w e ... w e= ⋅ ++
(2)
Fuzzy numbers that represent the weight and
evaluation of individual parameters were marked with
i
ˆ
w
and
i
ˆ
e
, i = 1,…, n.
It is given that an intervalvalued estimate for each
individual service (as α-cut) is as follows:
[ , ],
ij
EE
∗∗
i =
1,…, n. After receiving the results of interval values in
methods FAM4QS ranking service from the lowest C,
medium B and the highest A will be done according to the
following criteria:
The mean value of the intervals
[ , ],
ij
EE
∗∗
i = 1,…, n:
11
11
,
nn
ii
ii
E EE
nn
∗ ∗∗
= =

=

∑∑
(3)
Interval value obtained by previous adding, for
example
(or
10%
±
), on the left and right boundary
of interval
11
11
1 05 , 1.05 ,
nn
ii
ii
UCL . E E
nn
∗ ∗∗
= =

= ⋅


∑∑
(4)
11
11
0 95 , 0.95 ,
nn
ii
ii
ICL . E E
nn
∗ ∗∗
= =

= ⋅


∑∑
(5)
provides a selection criterion whether a service belongs to
the highest (A) or the lowest rank (C). Those services that
have a core (peak) or α-cut for α = 1 greater than the right
border UCL have the highest rank, and the services whose
core is less than the left border LCL have the lowest rank.
Firms whose core is within left boundary of LCL and UCL-
right boundary are mid-level services (B).
Diagram of activities for FAM4QS is shown in Fig. 3.
Note: The number r is also a real number different
from zero and does not have to be the same as values rj
(from the formula for assessment ej that is analog to
formula (2)). By changing the value of r (respectively rj) it
has obtained the characteristics of the disjunctive or
conjunctive forms for evaluation services (parameters). By
increasing r(r..+∞) disjunctively grows and
conjunctively decreases, by reducing r(r..−∞),
disjunctively declines, and conjunctively grows. Due to
assessment of relevant parameters, the assessment of rj and
r depends on whether it is to be more disjunctive or
conjunctive. Characteristic of conjunctive form is that a
bad score of at least one parameter gives a bad score of the
whole service, and only good reviews of all parameters
provide a good assessment of an entire service; while for
the disjunctive form a bad score for the entire service
results when all the parameters are evaluated as poor. The
service is rated good if at least one parameter is evaluated
as good. The values for r can be found in [36].
Figure 3 Diagram of activities for FAM4QS
4 VERIFICATION AND SIMULATION OF THE MODEL
Application of a system of 17 supply chains in Serbia
by fuzzy method has been made in the head of FAM4QS
application. The first step of FAM4QS implementation is
to organize services of the same group. Below services are
grouped according to certain criteria set by the definition.
The service grouping as a first step of identification was
done based on the identified service class group attributes
[32]:
1. Technology group is represented by technical
attributes that better describe influence of applied
technology tools on service development and
operations.
2. Complexity group represented observed level of
complexity in creating solution. More tiers in the
solution implementation in most cases represent more
complexity in service operation.
3. Development process group represented the possibility
to lever the influence on the service by applied
development process. Some development processes
created very stable service, but had a problem with low
level of flexibility towards change.
4. Development of team group team experiences, skills,
team cohesion, in house and outsourcing options that
affect the ability for quality maintenance of specific
service.
5. Business support domain group relates to the end user
profile, number, location, and a type of application that
is being used (for example, OLTP, reports, etc.).
6. In this case study we identified the following value
domains for the above group attributes:
a. For technology dependent group attribute TDi, the
study identifies two-tier, three-tier and four-tier client
server architecture, Web platform on Open Source,
Web platform on proprietary (Oracle) platform, and
Milovan TOMAŠEVIĆ et al.: Adaptive Fuzzy Model for Determining Quality Assessment Services in the Supply Chain
1694 Technical Gazette 25, 6(2018), 1690-1698
programming languages: Java, VB6, C++, and Oracle
PL/SQL.
b. Complexity group attribute Ci, takes high, medium and
low values.
7. Different services were developed using different
development process DPi. These processes in this case
were: Procedural SSA (Structured Systems Analysis),
8. RUP, Agile (Scrum), Hybrid.
9. Development of team group TDi was different for
different services. The services were developed and
maintained internally (IH), externally (OH), and mixed
teams (MX).
10. Business support domain group BDi was described by
values (Yes/No) for the following attributes front-end
support, back-end support, internal user’s domain,
external user’s domain, OLTP, reporting facilities.
Based on these group attributes definition, each
instance of service class Si from the catalog was assigned
the values as the following:
( )
,, , ,
,
i ii i i i
S TD C DP DT BD=
(6)
where:
( )
123
,,
i
TD td td td=
where
{ }
1
2 ,3 ,4td T T T
,
{ }
2
,,td WO WP DC
,
{ }
3
, ,;td J VB C D
,
()
i
Cc=
,
where
{}
,,c HI MI LO
,
( )
i
DP dp=
, where
{ }
,,,dp S R A H
,
()
i
DT dt=
, where
{}
,,
dt IN OH MX
,
()
123
,,
i
BD bd bd bd=
, where
{}
1
,bd FE BE
,
{ }
2
,bd OL RE
,
{ }
3
,bd IN EX
Value domains are:
23 4
TD : T Two Tier, T Three Tier , T Four Tier ,
WO Web OpenSource,
WP Web Pr oprietary, DC DesktopClient( FatClient ),
J Java, VB VisualBasic , C C , D DotNet ;
C : HI High,MI Medium , LO Low;
DP : S SSA, R RUP, A Agil
−− −
−−
− ++
−− −
−− −
 
lity, H Hybrid ;
DT : IH In House , OH Out House ,MX Mixed ;
BD : FE FrontEnd , BE BackEnd , OL OLTP, RE Re ports ,
IN InternalUser, eX ExternalUser
−− −
− −−
−−

() ( ) ( )
( ) ( )
( ) ( )
( ) ( ) ( )
0.45 0.5 0.6 0.05 0.15 0.2 0.20 0.35 0.45
12 3
0.25 0.3 0.4 0.65 0.7 0.8
11 12
0.4 0.5 0.55 0.45 0.5 0.6
21 22
0.25 0.3 0.4 0.45 0.5 0.6 0.15 0.2 0.35
31 32 33
w ; ; ; w ; ; ; w ; ; ,
w ; ; ; w ; ; ,
w ; ; ; w ; ; ,
w ;;; w ;;;w ;; .
= = =
= =
= =
= = =
The parameters to be used to complete evaluation of
services are shown in Tab. 1. Tab. 2 shows the grouping of
the same rank.
Due to better coordination quality of service observed
it is suggested to define measurement period that is as long
as possible (one year) with all the data collected during this
time.
Table 1 Parameters for evaluation
Groups
Subgroups
P1 = QS (Quality of service)
P11 =Number of incidents
P12 = The average time of solving
P2 = Documentation
P
21
= The documentation inside
the code
P
22
= The documentation outside
the code
P3 = Responsibility of the customer
P31 = Flexibility
P32 = Expense
P33 = Stability
Table 2 Clustering service
Product ID
TD
C(H/M/L)
DP
DT
BD
SCM 1
3T, WO, J
H
A
OH
FE, OL, IN
SCM 2
3T, WO, J
H
A
IN
FE, OL, IN
SCM 3
3T, WO, J
H
A
IN
FE, OL, IN
SCM 4
3T, WO, J
H
A
OH
FE, OL, IN
SCM 5
3T, WO, J
H
A
IN
FE, OL, IN
SCM 6
3T, WO, J
H
A
OH
FE, OL, IN
SCM 7
3T, WO, J
H
A
IN
FE, OL, IN
SCM 8
3T, WO, J
H
A
OH
FE, OL, IN
SCM 9
3T, WO, J
H
A
OH
FE, OL, IN
SCM 10
3T, WO, J
H
A
IN
FE, OL, IN
SCM 11
3T, WO, J
H
A
IN
FE, OL, IN
SCM 12
3T, WO, J
H
A
IN
FE, OL, IN
SCM 13
3T, WO, J
H
A
IN
FE, OL, IN
SCM 14
3T, WO, J
H
A
IN
FE, OL, IN
SCM 15
3T, WO, J
H
A
OH
FE, OL, IN
SCM 16
3T, WO, J
H
A
OH
FE, OL, IN
SCM 17
3T, WO, J
H
A
IN
FE, OL, IN
Table 3 Number of user requirements by service
Month/
SCM
1 2 3 4 5 6 7 8 9 10 11 12 13
14 15 16 17
1
60
2
33
0
2
2
0
0
1
0
0
1
0
3
7
1
1
2
12
3
35
2
0
6
0
0
3
0
2
1
0
5
7
2
1
3
29
8
60
1
0
7
0
0
1
0
5
4
2
1
6
0
1
4
13
4
43
1
0
9
0
2
5
1
1
4
0
2
16
0
0
5
15
3
22
1
0
1
0
0
6
1
1
2
0
2
1
0
1
6
21
3
53
2
0
5
0
1
2
0
2
2
0
4
11
2
2
7
11
4
38
2
0
3
0
0
1
0
1
0
0
1
4
0
0
8
13
2
25
2
1
4
0
3
6
2
2
2
1
2
4
2
0
9
22
9
33
2
1
2
0
1
8
2
2
0
0
3
10
1
1
10
16
4
29
2
0
6
0
4
9
0
2
3
0
2
9
1
2
11
9
9
21
2
1
4
0
0
6
0
4
0
0
3
1
0
0
12
16
4
9
2
1
5
1
2
2
0
3
2
1
6
4
0
1
Table 4 Estimates for the number of service user requirements
Rank
Score
SCM
User req.
Scored
0-25
0.9
1
237
0.1
26-40
0.8
2
55
0.7
41-60
0.7
3
401
0.1
61-75
0.6
4
19
0.9
76-90
0.5
5
6
0.9
91-120
0.4
6
54
0.6
121-150
0.3
7
1
0.9
151-190
0.2
8
13
0.9
191+
0.1
9
5
0.9
10
6
0.9
11
2
0.9
12
21
0.9
13
4
0.9
14
34
0.8
15
80
0.5
16
9
0.9
17
10
0.9
Estimates were presented with the following criteria
for services according to the user requirements that are
shown with crisp values.
Estimates for the average time of solving the problem
is rendered according to the following criteria - see Tab. 5.
Milovan TOMAŠEVIĆ et al.: Adaptive Fuzzy Model for Determining Quality Assessment Services in the Supply Chain
Tehnički vjesnik 25, 6(2018), 1690-1698 1695
Table 5 Estimates of services for the average time of solving customer requests
Rank
Score
SCM
Time
Scored
0-3
0.9
1
7.19
0.7
3-6
0.8
2
13.32
0.4
6-9
0,.7
3
3.40
0.9
9-11
0.6
4
25.46
0.1
11-13
0.5
5
2.86
0.9
13-15
0.4
6
4.14
0.8
15-17
0.3
7
18.13
0.2
17-20
0.2
8
32.30
0.1
20+
0.1
9
6.58
0.7
10
6.30
0.7
11
14.92
0.4
12
8.80
0.7
13
11.31
0.5
14
11.07
0.5
15
6.49
0.7
16
9.04
0.6
17
5.77
0.8
The average time (h) of resolving customer requests
for services (17) over a period of 12 months Tab. 6.
Figure 4 Graphical representation of service results
In Fig. 5 it is shown that the best service (5) does not
have any problems in the period from the second to the
eighth month, even in the tenth month it was functioning
smoothly. Regarding service 14, which is the worst, it can
be seen that it has higher oscillations in the beginning
compared to the later period. In Fig. 6, it can be observed
that the service 5 almost has no serious problems in its
functioning until the end of the year.
Figure 5 Comparative analysis of the best and the worst rated service by
number of received users requests by months.
Figure 6 Comparative analysis of the best and the worst rated services
according to an average time for solving users requirements
Table 6 The average time of solving customer requests
Month/SCM
1
2
3
4
5
6
7
8
1
1.82
0.56
2.68
0
1.19
0.11
0
0
2
2.94
2.07
5.12
31.57
0
0.44
0
0
3
9.48
4.23
3.28
25.10
0
1.94
0
0
4
16.67
2.54
5.36
26.04
0
9.21
0
3.48
5
4.17
1.12
2.28
36.02
0
10.52
0
0
6
11.19
9.73
2.01
58.30
00
5.83
0
0.10
7
4.30
144.33
1.52
15.10
0
14.32
0
0
8
14.91
0.74
4.20
23.83
0.28
0.80
0
45.22
9
6.01
1.13
4.69
12.01
0.44
0.63
0
1.02
10
12.65
6.46
4.30
24.36
0
3.60
0
68.78
11
5.73
3.35
2.83
15.06
13.05
3.45
0
0
12
8.88
0.89
1.23
18.04
1.04
0.38
18.13
0.55
9
10
11
12
13
14
15
16
17
1.09
0
0
3.11
0
1.72
1.20
1.23
0.32
0.88
0
18.05
11.01
0
1.61
1.92
1.06
0.57
2.14
0
8.70
6.28
19.80
1.03
10.61
0
17.61
1,28
0.33
15.53
5.50
0
1.07
8.66
0
0
0.91
1.92
30.02
2.51
0
1.03
1.02
0
1.05
0.93
0
14.29
1.21
0
29.13
8.42
27.09
0.20
1.06
0
10.02
0
0
1.07
7.56
0
0
1.18
10.22
12.06
16.03
3.45
1.08
7.32
0.93
0
1.15
7.56
11.57
0
0
1.53
2.24
21.65
1.06
7.08
0
14.58
15.55
0
9.10
7.26
0.29
3.30
1.02
0
20.25
0
0
3.72
1.06
0
0
1.53
0
17.28
18.73
2.20
34.06
13.34
0
30.06
The averaged cross section is [0.729 0.772] and for
5%±
LCL = [0.693 0.733] (formula (5)), and UCL =
[0.765 0.811] (formula (4)), the service will be ranked
according to these criteria. So, for example, for SCM 4 the
core is (0.653 + 0.688)/2 = 0.6705 < 0.693 so it has the rank
Milovan TOMAŠEVIĆ et al.: Adaptive Fuzzy Model for Determining Quality Assessment Services in the Supply Chain
1696 Technical Gazette 25, 6(2018), 1690-1698
C, for SCM 8 the core is (0.677 + 0.714)/2 = 0.6955 > 0.693
so it has the rank B, for SCM 10 the core is (0.778 +
0.825)/2 = 0.8015 < 0. So it has the rank B, for SCM 17 the
core is (0.797 + 0.851)/2 = 0.824 > 0.811 so it has the rank
A.
Table 7 Ranking services in terms of quality
Product ID
TD
C(H/M/L)
DP
DT
BD
FAM4QS
SCM 14
3T, WO, J
H
A
OH
FE, OL, IN
[0.620, 0.656]
SCM 4
3T, WO, J
H
A
IN
FE, OL, IN
[0.653, 0.688]
SCM 8
3T, WO, J
H
A
IN
FE, OL, IN
[0.677, 0.714]
SCM 1
3T, WO, J
H
A
OH
FE, OL, IN
[0.678, 0.715]
SCM 2
3T, WO, J
H
A
IN
FE, OL, IN
[0.697, 0.736]
SCM 13
3T, WO, J
H
A
OH
FE, OL, IN
[0.713, 0.755]
SCM 15
3T, WO, J
H
A
IN
FE, OL, IN
[0.720, 0.760]
SCM 11
3T, WO, J
H
A
OH
FE, OL, IN
[0.725, 0.765]
SCM 3
3T, WO, J
H
A
OH
FE, OL, IN
[0.726, 0.765]
SCM 7
3T, WO, J
H
A
IN
FE, OL, IN
[0.728, 0.768]
SCM 16
3T, WO, J
H
A
IN
FE, OL, IN
[0.737, 0.781]
SCM 9
3T, WO, J
H
A
IN
FE, OL, IN
[0.745, 0.793]
SCM 12
3T, WO, J
H
A
IN
FE, OL, IN
[0.757, 0.804]
SCM 6
3T, WO, J
H
A
IN
FE, OL, IN
[0.765, 0.809]
SCM 10
3T, WO, J
H
A
OH
FE, OL, IN
[0.778, 0.825]
SCM 17
3T, WO, J
H
A
OH
FE, OL, IN
[0.797, 0.851]
SCM 5
3T, WO, J
H
A
IN
FE, OL, IN
[0.866, 0.921]
Table 8 Service estimates
Product ID
TD
C(H/M/L)
DP
DT
BD
FAM4QS
RANK
SCM 14
3T, WO, J
H
A
OH
FE, OL, IN
[0.620, 0.656]
C
SCM 4
3T, WO, J
H
A
IN
FE, OL, IN
[0.653, 0.688]
C
SCM 8
3T, WO, J
H
A
IN
FE, OL, IN
[0.677, 0.714]
B
SCM 1
3T, WO, J
H
A
OH
FE, OL, IN
[0.678, 0.715]
B
SCM 2
3T, WO, J
H
A
IN
FE, OL, IN
[0.697, 0.736]
B
SCM 13
3T, WO, J
H
A
OH
FE, OL, IN
[0.713, 0.755]
B
SCM 15
3T, WO, J
H
A
IN
FE, OL, IN
[0.720, 0.760]
B
SCM 11
3T, WO, J
H
A
OH
FE, OL, IN
[0.725, 0.765]
B
SCM 3
3T, WO, J
H
A
OH
FE, OL, IN
[0.726, 0.765]
B
SCM 7
3T, WO, J
H
A
IN
FE, OL, IN
[0.728, 0.768]
B
SCM 16
3T, WO, J
H
A
IN
FE, OL, IN
[0.737, 0.781]
B
SCM 9
3T, WO, J
H
A
IN
FE, OL, IN
[0.745, 0.793]
B
SCM 12
3T, WO, J
H
A
IN
FE, OL, IN
[0.757, 0.804]
B
SCM 6
3T, WO, J
H
A
IN
FE, OL, IN
[0.765, 0.809]
B
SCM 10
3T, WO, J
H
A
OH
FE, OL, IN
[0.778, 0.825]
B
SCM 17
3T, WO, J
H
A
OH
FE, OL, IN
[0.797, 0.851]
A
SCM 5
3T, WO, J
H
A
IN
FE, OL, IN
[0.866, 0.921]
A
From calculation using FAM4QS as can be seen in Fig.
5, it can be concluded that the best result for the number of
user requests and the average time of solving them
according to the adopted criteria has been
achieved,regarding supply, for the chain 5 (SCM 5), and
the worst result for the chain 14. If the chain 14 (SCM 14)
isanalyzed as the worse performance regarding the supply
it can be concluded that the reasons are the following:
- Analysis and specification requirements were done
badly and they are incomplete.
- Unavailability of business users for developers.
- Insufficient team confidence that develops the
application programmers, and occasional absence from
the team.
- The lack of interaction between the requirement
specifications and the end users (the impact of user towards
the requirements specification is negligible).
- Non-dynamics of system (rate of change of the system
or bad system update).
Due to increase in the success rate and reduced tendency of
negative trend in SCM 14 and chains with similar
characteristics the following steps are suggested:
- A detailed analysis of requirements and greater
flexibility of the model or system (easy and quick
adaptability of new requirement specifications towards
new requests).
- Improvements in communication between business
users and developers (larger number of direct meetings,
more frequent communication by e-mail, Skype, telephone
...).
- Raising the quality of human relations, working
environment and greater control over nonattendance.
- Establishment of direct link between end users and
service providers.
- Increasing the speed and level of system update.
5 CONCLUSION
Within AM4SCM, a mathematical model was defined
for evaluating the quality of the service provided which
solves the problem of pre-existing models with imprecise
estimates of parameters. Evaluations of the team of experts
have been used while assessing weight coefficients and
other parameters relevant to the system. (Progression)
arithmetic mean - mean estimates of experts is usually
taken for the assessment of weight element in six-step
method for improving the quality of service which was
upgraded.
Milovan TOMAŠEVIĆ et al.: Adaptive Fuzzy Model for Determining Quality Assessment Services in the Supply Chain
Tehnički vjesnik 25, 6(2018), 1690-1698 1697
If the phenomenon of observed evaluation has a
normal distribution, then it is good to take the mean of such
assessment parameter, but if not then it is often better to
take different assessment. Distribution of the assessment of
the team of experts was regarded as fuzzy number and for
easy calculation unsymmetrical triangular fuzzy number.
Thus, it has provided the opportunity to get fuzzy number
instead of a number of such estimates of the entire system,
i.e. interval of values with different values of the
membership function. Defuzzification provided better
value than the standard procedure. To avoid stiff
conclusion, i.e. the answer for the system quality
assessment is a number, the response interval value is taken
which is actually alpha-section stage as the number of
output where alpha desired grade of membership is taken.
Fuzzy aggregation environments used for assessing the
quality of supply chains are degree environments where we
have taken different values for degrees which were
conditioned by different nature of parameters. Due to these
differences, it follows that there are more or less
disjunctive or conjunctive forms of selected aggregation
functions. By applying our method on 17 selected
homogeneous supply chains, the analysis of the best and
the worst chain provided the conclusion that it is necessary
to analyze the requirements and increase the flexibility of
the model, improve communication between business
users and developers, raise the quality of interpersonal
relationships, exercise control of absenteeism, establish
direct connection between end users with service providers
and increase the speed and level of system update.
Working with large systems facilitates and accelerates
process of finding new methods such as working with
neural networks in combination with FAM4QS.
Likewise the traditional method, this method can also
use software packages so the user can automatically
receive, on the basis of given criteria, assessment of the
quality of service in order to facilitate further decision-
making. The software that we developed is written in C #
and allows commercial use of FAM4QS. It will be further
developed, i.e. for large systems, where FAM4QS will be
combined with neural networks.
Acknowledgements
The research for this article was conducted under the
project "Development of software to manage repair and
installation of brake systems for rail vehicles", Ministry of
Science of Serbia, no. 035050, for the period 2011-2017.
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Contact information:
Milovan TOMAŠEVIĆ, Research Associate, PhD student
University of Novi Sad, Faculty of Technical Sciences
Trg Dositeja Obradovica 6, 21 000, Novi Sad, Serbia
mt@uns.ac.rs
Nebojša RALEVIĆ, PhD, Full Professor
University of Novi Sad, Faculty of Technical Sciences
Trg Dositeja Obradovica 6, 21 000, Novi Sad, Serbia
nralevic@uns.ac.rs
Željko STEVIĆ, Assistant, PhD student
University of East Sarajevo,
Faculty of Transport and Traffic Engineering Doboj
Misica 52, 74 000, Doboj, Bosnia and Herzegovina
zeljkostevic88@yahoo.com
Vidan MARKOVIĆ, PhD, Associate Professor
University of Novi Sad, Faculty of Technical Sciences
Trg Dositeja Obradovica 6, 21 000, Novi Sad, Serbia
vmarkovic@uns.ac.rs
Zdravko TEŠIĆ, PhD, Full Professor
University of Novi Sad, Faculty of Technical Sciences
Trg Dositeja Obradovica 6, 21 000, Novi Sad, Serbia
ztesic@uns.ac.rs
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Article
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During an aggravated economic situation many companies have to deal with various situations that present demand distortion and changes in production processes. As a result orders to suppliers fluctuate upstream of the supply chain in amplified form. This phenomenon is called the bullwhip effect, which is one of the more interesting and developing problems within supply chain management. This undesirable effect produces excess regarding inventory, problems during production planning and poor customer services. In this paper we experimented with two special cases in a simple four stage supply chain with the level constraints represented by the overall equipment effectiveness (OEE) level: Case 1 – stable demand with single 5 % change and ideal OEE level, and Case 2 – stable demand with single 5 % change and OEE level changes upstream of the supply chain. The results of spreadsheet simulation are shown in the tables and charts. The impact of slight demand distortion and level constraints within the supply chain on the bullwhip effect was evident. The comparison of the results showed that when deviations in production processes are present the higher bullwhip effect occur at different stages within the supply chain and depending on the situation do not have to occur at stages within the supply chain with the lowest OEE levels.
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Information sharing serves as an essential approach for the survival of enterprises and enabler of supply chain integration. Nowadays, with the advancement in information and communication technology, information sharing has become more conceivable. Furthermore, information sharing in supply chains has become more efficient by the global introduction of long- term cooperation and coordination which leads ultimately to the improvement of companies’ competitive advantages. There is a lack of information sharing within companies nowadays, which results in inefficiency of coordinating actions within the units in the company or organization. The purpose of this study is to investigate and overview the effectiveness of information sharing in supply chain management, in order to increase the efficiency of the organizational performance in the manufacturing sector. This study elaborates the benefits and barriers of information sharing leading to enhanced supply chain integration among enterprises, as a result.
Conference Paper
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This paper outlines the critical role of information sharing in the link between information quality and Supply Chain performance. Supply chain partners coordinate their processes through information sharing, in order to facilitate supplier-customer interactions. Since proprietary and confidential information is usually communicated along the supply chain, the preservation of the quality of the exchanged information is a crucial issue. A research framework is developed in which information sharing acts as the mediator between information quality and supply chain performance. The empirical findings from a survey of 61 manufacturing firms in Greece confirmed the mediating role of information sharing. The main implication of the findings for managers is that information sharing among partners along the supply chain facilitates higher overall performance, as a result of enforced Supply Chain Management practices elevating information reliability and quality.
Book
Although the notion triangular norm appeared already in Karl Menger’s paper Statistical metrics in 1942, the interest in probabilistic metric spaces in general and in these operations in particular started to increase only after Berthold Schweizer and Abe Sklar published the final set of axioms for t-norms in 1958 in Espaces métriques aléatoires. In the sequel, triangular norms turned out to be basic tools not only in probabilistic metric spaces (as they are called today), where they allowed the classical triangle inequality to be properly generalized, but also in several other parts of mathematics, initiating a deeper investigation of t-norms from different points of view. Starting from the first Linz Seminar on Fuzzy Set Theory in 1979, when Ulrich Höhle suggested to base the intersection and union of fuzzy subsets on triangular norms and conorms, respectively, t-norms continued to play an important, sometimes even dominant role in the annual “Linz” Seminars. The 24th Linz Seminar on Fuzzy Set Theory, which took place in the Bildungszentrum St. Magdalena in Linz from February 4 till 8, 2003, was entirely devoted to the topic Triangular Norms and Related Operators in Many-Valued Logics. The present volume is composed of selected and extended versions of the presentations during this stimulating week, with one notable exception: although he could not come personally to Linz, Bert Schweizer agreed to provide a chapter for this volume, highlighting the development of triangular norms from the very beginning and giving an outlook at the potential of triangle functions (acting on probability distributions) for the future. This present volume complements the monograph Triangular Norms co-authored in 2000 by E. P. Klement, R. Mesiar & E. Pap, as well as the forthcoming book Associative Functions on Intervals: A Primer of Triangular Norms, co-authored by C. Alsina, M. J. Frank & B. Schweizer, by giving, from the point of view of our authors, an up-to-date account of the existing knowledge about triangular norms and related operations, as well as of the applications of these operations in many-valued logics, measure theory, probabilistic metric spaces, multivariate stochastic analysis, etc. As a consequence, the book is divided into three main parts: Part I Introduction starts with Bert Schweizer’s chapter, followed by an overview of the basic notions and properties of t-norms. Part II Theoretical aspects of triangular norms presents not only algebraic and analytic properties of t-norms and their relationship to functional equations, but also discusses t-norms on specific lattices and the problem of selecting an appropriate t-norm fitting a given set of data. Finally, Part III Applications of triangular norms and related operations exemplifies the role of t-norms and related operators (in particular, t-conorms, residual implications and copulas) in logic and algebra, in the theory of non-additive measures, in probability theory and statistics, in preference modelling, and in probabilistic metric spaces. Our special thanks go to our authors for their willingness to contribute to this volume, and to our colleagues Ulrich Bodenhofer, Andrea Mesiarová, Endre Pap, and Susanne Saminger for their support in many respects. We also were supported by our universities, the Johannes Kepler University in Linz and the Slovak University of Technology in Bratislava, as well as by Project 42 s2 of the Action Austria-Slovakia and by the grants VEGA 1/0273/03 and GACR 402/04/1026. The professional work and the hospitality of the staff of the Bildungszentrum St. Magdalena in Linz during the 24th Linz Seminar on Fuzzy Set Theory were greatly appreciated, as well as the support of the co-sponsors of this conference, the Linzer Hochschul-fonds, the Software Competence Center Hagenberg, and the European Society for Fuzzy Logic and Technology (EUSFLAT). Finally, we are indebted to our publisher Elsevier, in particular to Edith Bomers, Andy Deelen, and Joyce Happee for their assistance in the editorial process.
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Purpose This paper aims to investigate the antecedents of supply chain information integration (SCII) and their consequences on company performance from the perspective of resource-based view (RBV). Design/methodology/approach Based on empirical survey data collected from 202 Australian manufacturers, this study examines the effects of strategic supply chain relationship (SCR) and supply chain technology (SCT) internalization on external and internal information integration (II) and the effects of external and internal II on operational (operational efficiency and service quality) and financial performance. Structural equation modeling and the maximum-likelihood estimation methods are used to test the proposed relationships. Findings The results indicate that both strategic SCR and SCT internalization are positively related to external and internal II. Moreover, strategic SCR has a stronger positive relationship with external II than with internal II, and SCT internalization has a stronger positive relationship with internal II than with external II. Internal II is positively related only to service quality, and external II is positively related only to operational efficiency. Both operational efficiency and service quality are positively related to financial performance. Originality/value This study contributes to the SCII literature and provides significant managerial implications for manufacturers to leverage their supply chain resources and capabilities by establishing a resources-capabilities-performance framework for the antecedents and consequences of SCII.
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A number of studies suggest that making correct decisions depends on high-quality information; how information quality affects decision-making is still not fully understood. Following the multi-dimensional view of information quality, this paper investigates the effects of information accuracy, completeness, and consistency on decision-making. Results show that information accuracy and completeness affect decision quality significantly. Although the effect of information consistency on decision quality appears to be non-significant, consistency of information may intensify the contribution of accuracy, indicating that information accuracy and consistency influence decision quality jointly.
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Recent advances in the field of computer networks and Internet have increased the significance of electronic commerce. Through electronic networks, companies can achieve integration by tightly coupling processes at the interfaces between each stage of the value chain. Electronic linkages in the value chain have been fundamentally changing the nature of inter-organizational relationships. Organizations are redesigning their internal structure and their external relationships, creating knowledge networks to facilitate communication of data, information, and knowledge, while improving coordination, decision making, and planning. This study is devoted to examining the types of knowledge flow in collaborative supply chain, and proposing a knowledge management architecture to facilitate knowledge management in collaborative supply chain. Three cases are presented to outline how different industries build their e-business model under different architectures. Also, knowledge flows are discussed in these e-business models. These case studies reveal the benefits that organizations can achieve through the implementation of electronic commerce technologies in collaborative supply chain. The results also show that different network type of supply chain, the amount of transaction, and the main collaborative function in the supply chain will lead to different type of knowledge flow and the tools adopted, and ultimately different knowledge management system.