Assessing Individual Fitness for Research and Development Position
using Fuzzy AHP and Pareto: Case Study in Manufacturing Industry
I Made Ronyastra
a
, Evy Herowati
b
, Rahman Dwi Wahyudi, Joniarto Parung
c
and Christanto Henadi
Industrial Engineering Department, Universitas Surabaya, Surabaya, Indonesia
Keywords: MCDM, Fuzzy AHP, Pareto, Research and Development, Assessment Model.
Abstract: Research and development function play significant role in the success of company’s venture and this function
has a strict set of recruitment criteria to ensure company can find a good candidate among applicants. The
strict recruitment criteria can be time and money consuming while still prone to wrong recruitment which can
lead to a high turnover for the company. To help companies in selecting competent candidates for the
workforce, there is a potential workforce self-assessment model made for industrial engineering students or
graduates. The assessment model is created in advance by identifying the criteria for research and
development job positions required by the manufacturing industry. The criteria that have been identified are
grouped based on categories and based on the same understanding. Furthermore, Pareto 80/20 method is used
to find out the most influential criteria and Fuzzy Analytical Hierarchy Process (FAHP) method is using
expert considerations whose consistency was tested using the Analytical Hierarchy Process (AHP)
consistency test. The expert used in this research is a professional from a manufacturing company in Indonesia.
The research identified 5 objective criteria where analytical capabilities has the most weight and 4 subjective
criteria where problem solving skill has the most weight, to be considered. The model provides fitness in
terms of suitability percentage for the R&D job.
1 INTRODUCTION
Research and development (R&D) function in a
business organization plays significant role in the
success of the company’s venture especially due to
the radical changes that happened since 1990s in
terms of competitive environment (Chiesa et al.,
2009). Rapid advancement in technology, shortened
product life cycle, and intensified competition have
led R&D to another challenge so that they could come
up with products or services innovations that will
satisfy the always changing customer needs. Hence,
R&D job is a suitable role for creative persons with
purpose of crafting solutions to problems in the
market and offered it better than the competitors do.
To be a good R&D person, one must have sound
knowledge regarding market trends and the technical
area.
a
https://orcid.org/0000-0002-6118-6094
b
https://orcid.org/0000-0002-9653-604X
c
https://orcid.org/ 0000-0002-3866-8132
Based on the Industrial Engineering Body of
Knowledge, Industrial Engineers (IE) are also taught
with knowledge that match with the R&D job role.
The engineers must take ergonomic and human
factors courses, and product design and development
courses which covers the topic of developing new
product or service. Aside from the technical aspect,
IE also equipped with knowledge regarding the
economic aspects of projects in engineering
economic courses. IE are also taught to become a
problem solver where they should be able to find
solution for problems. Thus, IE graduates can be
potential candidates to take the R&D jobs. However,
the scope of IE is quite wide which implies that not
every IE can become a successful R&D person.
It is necessary to construct a model to assess IE
fitness with the R&D role so that the IE can check
whether they are suitable for the role. If they are not
suitable, then they should be encouraged to apply for
Ronyastra, I., Herowati, E., Wahyudi, R., Parung, J. and Henadi, C.
Assessing Individual Fitness for Research and Development Position using Fuzzy AHP and Pareto: Case Study in Manufacturing Industry.
DOI: 10.5220/0010950000003260
In Proceedings of the 4th International Conference on Applied Science and Technology on Engineering Science (iCAST-ES 2021), pages 619-625
ISBN: 978-989-758-615-6; ISSN: 2975-8246
Copyright
c
2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)
619
other function or role and vice versa, so it would
improve the probability of being hired by companies.
From the company’s point of view, employee
recruitment is often a time consuming and costly
process that they must conduct to find the best
candidates so it would help companies when the
candidates can pre-screen themselves prior to
applying. The competition among companies in
finding the best talent are getting fiercer for it may
lead to operational excellence (Oshri & Ravishankar,
2014). There are multiple criteria used by companies
or human resources department to select the best
candidates among applicants. Thus, the selection
process can be considered as a multiple criteria
decision problem. This research aimed to construct an
assessment model to measure candidates’ fitness for
R&D job by considering the multiple criteria decision
problem. The criteria were derived from secondary
data analysis where selection criteria were collected
from various R&D job advertisements. To assign the
weight for each criterion, an expert in the field was
asked to give judgement using Fuzzy AHP method.
The result can be used to develop a talent pool
management especially for companies focusing on
R&D function.
2 LITERATURE REVIEW
Talent pool management is part of talent management
which in its application can have a positive impact on
individuals and organizations. Talent pool is a
collection of individuals with high potential and
performance that an organization can take advantage
of in filling important positions (Collings & Mellahi,
2009). Talent pool is a group of individuals with
broad abilities at a certain level who are considered
eligible to fill positions at a higher level. It can be
concluded that talent pool management is the process
of identifying a group of talented individuals who
have superior performance and quality than other
individuals. The process of putting an employee into
the talent pool usually involving multiple criteria.
Thus, the techniques of multi criteria decision making
are often used in the process.
Multi Criteria Decision Making (MCDM) is used
in solving a problem that has both objective and
subjective criteria that are contradictory and not
commensurate. Multi Criteria Decision Making
(MCDM) is a set of methods that deals with
evaluating a series of alternatives that are many, often
contradictory, and have various criteria (Mulliner et
al., 2016). In its use, MCDM is divided into Multi
Objective Decision Making (MODM) and Multi
Attribute Decision Making (MADM). MODM is a
decision-making method by designing a decision
alternative by taking many criteria as a basis, while
MADM is a decision-making method by selecting the
best alternative which uses many criteria as a basis.
Some popular techniques in MADM includes
Analytical Hierarchy Process (AHP), Weighted
Product Model (WPM) / Weighted Product Method
(WP), Fuzzy Analytical Hierarchy Process (FAHP).
In dealing with too many criteria, it is necessary
to reduce the number of criteria for further analysis.
The Pareto principles can be applied in the reduction
process. The Pareto diagram is a bar chart combined
with a line diagram to show the causes or dominant
factors of several causes of a problem. The use of the
Pareto diagram aims to evaluate the things that are the
dominant factors in the occurrence of a specific
problem based on the impact or frequency of
occurrence (Hashemi et al., 2021).
Analytical Hierarchy Process (AHP) is a decision-
making technique in MCDM developed by Thomas
L. Saaty. The AHP decision-making model describes
a complex multi-factor or multi-criteria problem into
a hierarchy (Chen & Dai, 2021). In the AHP hierarchy
there is a multi-level structure where the first level is
the goal, the next level is the criteria, and the last level
is the alternative. With a hierarchy, complex and
multifactorial problems can be divided into groups
arranged in a hierarchical form so that problems
become structured and systematic. The AHP are then
further developed into Fuzzy Analytical Hierarchy
Process (Fuzzy AHP) to solve fuzzy uncertainty
problems in AHP (Coffey & Claudio, 2021). The
main task of the AHP fuzzy method is to decide the
relative importance of each pair of factors in the same
hierarchy. In its use, fuzzy has a scale of importance
conversion as follows (Büyüközkan et al., 2008):
Table 1: Fuzzy conversion scale.
Linguistic Scale
for Importance
Level
Triangular
f
uzzy scale
Triangular fuzzy
reciprocal scale
Equally Important (1/2, 1, 3/2) (2/3, 1, 2)
Slightly more
im
p
ortant
(
1, 3/2, 2
)
(
1/2, 2/3, 1
)
More Im
p
ortant
(
3/2, 2, 5/2
)
(
2/5, 1/2, 2/3
)
Highly more
important (2, 5/2, 3) (1/3, 2/5, 1/2)
Extremely more
im
p
ortant
(
5/2, 3, 7/2
)
(
2/7, 1/3, 2/5
)
There are several steps in using fuzzy AHP as
followings:
1. Calculating fuzzy synthetic values, define as:
iCAST-ES 2021 - International Conference on Applied Science and Technology on Engineering Science
620
𝑆𝑖= 𝑀
𝑀
To get the value of
∑
𝑀
, conduct the fuzzy
summation for the value of area analysis m for a
certain matrix as:
𝑀
= 𝑙
,𝑚
,
𝑢
To find
∑
,
∑
,𝑚
,
, conduct the fuzzy
summation from the values of𝑀
(𝑗=1,2,…,𝑚) so
then
𝑀
= 𝑙
,𝑚
,
𝑢
And then conduct the vector inverse which will
results:
𝑀
=
1
∑
𝑢
,
1
∑
𝑚
,
1
∑
𝑙
2. Calculating the degree of possibility of M
2
≥ M
1
:
𝑉
(
𝑀
≥ 𝑀
)
=
𝑠𝑢𝑝
𝑦 ≥ 𝑥
min (𝜇𝑀
(
𝑥
)
,𝜇𝑀
(
𝑦
)
)
Since M
1
= (l
1
, m
1
, u
1
) and M
2
= (l
2
, m
2
, u
2
) are
convex fuzzy numbers, then:
𝑉(𝑀
≥ 𝑀
=ℎ𝑔𝑡(𝑀
∩ 𝑀
=
⎩
⎨
⎧
1 , 𝑚
≥ 𝑚
0, 𝑙
≥ 𝑢
𝑙
− 𝑢
(
𝑚
− 𝑢
)
−
(
𝑚
− 𝑙
)
,otherwise
3. Degree of possibility for a convex fuzzy number
greater than k convex fuzzy numbers M
i
(i = 1,
2,…, k) can be defined as:
V (M ≥ M
1
, M
2
, …, M
k
)
= V [(M ≥ M
1
) dan M ≥ M
2
dan … dan (M ≥ M
k
)]
= min V (M ≥ M
i
), I = 1, 2, 3, …, k
To assign weight vector mentioned as:
W’ = (d’(A
1
), d(A
2
), …, d’(A
n
))
T
Where A
i
(i = 1,2, …, n) are elements of n
4. Normalize the vector weights
W = (d(A
1
), d(A
2
), …, d(A
n
))
T
With W is not a fuzzy number.
3 METHODS
The first step in the research is to collect the criteria
for research and development job positions obtained
from the job vacancy website. The criteria obtained
are grouped into three categories of criteria, namely
objective criteria, subjective criteria, and absolute
criteria. To determine the most influential criteria
from each category of criteria, the criteria were
reduced using the Pareto 80/20 method. The criteria
that have been determined are then assessed for the
level of importance by professionals in the field of
research and development and the data for the level
of importance was also tested for consistency using
the Analytical Hierarchy Process (AHP) consistency
test before calculating the weight using the Fuzzy
Analytical Hierarchy Process (FAHP). The weights
that have been obtained for each criterion will be used
as the basis for the suitability assessment system. The
scoring system was created using the spreadsheet
application in which there are questions that must be
answered by the respondent to calculate the
percentage of fitness for the R&D position.
4 RESULTS AND DISCUSSION
Criteria Grouping.
The criteria obtained are 64 criteria, then the criteria
are grouped into 3 categories, namely objective,
subjective, and absolute criteria. The results of
grouping obtained the objective criteria group
consisting of 21 criteria, the subjective criteria group
19 criteria. In each group, the criteria are re-grouped
based on the similarity of the understanding they have
so that the objective and subjective criteria groups
each become 8 criteria.
Pareto Chart.
The criteria data have been grouped and will be
reduced using the Pareto 80/20 diagram to determine
the most influential criteria. The number of objective
criteria is reduced to 5 criteria, namely education
level, work experience, ability to analyse, ability to
do research, and ability to plan as shown in Figure 1.
The left y-axis is the frequency while the right side of
y-axis is indicating the percentage.
The subjective criteria were reduced to 4 criteria
namely interpersonal skills, mastery of software,
ability to solve problems, the ability to speak spoken
and written English as shown in Figure 2.
Assessing Individual Fitness for Research and Development Position using Fuzzy AHP and Pareto: Case Study in Manufacturing Industry
621
Figure 1: Pareto diagram for objective criteria.
Figure 2: Pareto diagram of subjective criteria.
Analytical Hierarchy Process (AHP) Consistency
Test.
The reduced criteria are assessed in advance for the
level of importance by an expert who is a professional
research and development practitioner in PT.
Mandom Indonesia. The data on the level of
importance of the criteria obtained through the
questionnaire was subjected to a consistency test
before being used in calculating the weight of the
criteria. The results of the consistency test showed
that the level of importance of the data was consistent
with the consistency ratio value of the objective
criteria was 0.07 and the consistency ratio value for
subjective criteria was 0.07 which still below the 0.1
threshold value.
Fuzzy Analytical Hierarchy Process (FAHP).
To determine the weight of the criteria based on the
level of importance of the criteria, the FAHP method
is used. The criterion level of importance data will be
converted using the previous Fuzzy conversion scale
before the calculation is carried out. Calculations
using the FAHP are carried out to assign weight of
each criterion. Table 2 and Table 3 summarize the
weights for objective and subjective criteria
respectively.
Table 2: Objective criteria weights.
Ob
j
ective Criteria Wei
g
ht
Education level 0,07
Ex
p
erience len
g
th 0,12
Analytical capabilities 0,32
Research capabilities 0,27
Planning capabilities 0,22
Table 3: Subjective criteria weights.
Sub
j
ective Criteria Wei
g
ht
Inter
p
ersonal skill 0,32
Software mastery 0,03
Problem solving skill 0,47
En
g
lish lan
g
ua
g
e skill 0,18
Scoring System.
The known weights become the basis of the system
for calculating the value of conformity. Each group of
criteria has sub criteria, in the scoring system each
sub criterion is represented by one question that must
be answered according to the answer choices given.
Each answer has its own value, which later the scores
of each sub-criterion question will be averaged and
become the value of the criteria group. The value of
each group of criteria are then multiplied by the
weight that has been determined and then the total
value for the categories of objective and subjective
criteria is sought. The total values of the objective and
subjective criteria categories are averaged to find the
percentage value of the respondent's fitness with the
R&D job position for PT. Mandom Indonesia. The
scoring system details are listed in Table 4 (objective
criteria) and Table 5 (subjective criteria).
The scoring system was created using the
Microsoft Excel application and contains an initial
section containing personal data, a content section
containing questions, and the final section containing
the percentage value of matches. Questions in the
content section are answered by selecting the answers
provided in the dropdown list. An example of filled
application is shown in Figure 3.
5 CONCLUSIONS
In this study, the criteria were obtained from the
website for job vacancies from 8 manufacturing
industry companies with a total of 64 criteria. The
criteria that have been collected are grouped based on
categories and understanding, so that the objective
and subjective criteria groups each amount to 8
groups of criteria. After grouping, the criteria were
reduced so that the objective criteria became 5 criteria
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Table 4: Objective criteria scoring system.
Criteria
Grou
p
Criteria Score Weight
Education
Level
Bachelor of Industrial En
g
ineerin
g
100
0,07
Bachelor of Chemical Engineering 100
Bachelor of Mechanical Engineering 100
Other Bachelor of Engineering 100
Bachelor of Food Technolo
gy
100
Bachelor of Mana
g
ement 100
Bachelor of Statistics 100
Bachelor of Bio Technolog
y
100
Bachelor of Pharmaceuticals 100
Other Bachelor Degree 100
Diploma in Industrial Engineering 0
Di
p
loma in Desi
g
n 0
Work
Experience
Fresh Graduate 25
0,12
Ex
p
erience 1
y
ea
r
50
Experience 2 years 75
Experience ≥ 3 years 100
Analytical
Capabilities
Data Analytics: Grade in Operational Research Course A= 100, AB=
80, B = 60,
BC = 40, C =
20, D-E = 0
0,32
Market and trend anal
y
sis:
g
rade in Marketin
g
Mana
g
ement Course
Numerical Interpretation: grade in Optimization Mathematics
Research
Capabilities
Research and ex
p
eriment:
g
rade in Ph
y
sical Practicu
m
A= 100, AB=
80, B = 60,
BC = 40, C =
20, D-E = 0
0,27
Creating research budget: grade in Cost Analysis
Research Methods: Grade in Industrial Statistics 2
Planning
Capabilities
Priorities settin
g
: Grade in Production Plannin
g
and Control Course A= 100, AB=
80, B = 60,
BC = 40, C =
20, D-E = 0
0,22
Effective
p
lannin
g
: Grade in Production Plannin
g
and Control Course
Project Management: Grade in Industrial Planning Course
Table 5: Subjective criteria scoring system.
Criteria Group Criteria Score Weight
Interpersonal
Skill
Abilit
y
to work with tar
g
et and under
p
ressure
1 = 0, 2 = 25, 3 =
50, 4 = 75, 5 =
100
0,32
Abilit
y
to coo
p
erate in teamwor
k
Innovative and Creative
Lo
g
ical thinkin
g
Energetic
Meticulous
Initiative
Software
mastery
SPSS
1 = 0, 2 = 25, 3 =
50, 4 = 75, 5 =
100
0,03 Ms. Pro
j
ect
Ms. Office
Problem
Solving Skill
Brainstorming
1 = 0, 2 = 25, 3 =
50, 4 = 75, 5 =
100
0,47 Workin
g
p
roblems with tar
g
et
Abilit
y
to create solution
Language skill Verbal and Written English Language Skill
1 = 0, 2 = 25, 3 =
50, 4 = 75, 5 =
100
0,18
Assessing Individual Fitness for Research and Development Position using Fuzzy AHP and Pareto: Case Study in Manufacturing Industry
623
Figure 3: Scoring system interface.
iCAST-ES 2021 - International Conference on Applied Science and Technology on Engineering Science
624
groups and the subjective criteria became 4 criteria
groups. Each criterion is weighted using the FAHP
based on the level of importance data obtained from
professionals of PT. Mandom Indonesia which has
been tested for consistency. The weights of the
criteria are used as the basis for making the scoring
system. The assessment system was built in the form
of a questionnaire using the Microsoft Excel
application. When fully filled, the system can
compute the fitness percentage for a candidate with
R&D Job position.
Since R&D is only one of many functions in a
company, this research can be further improved by
exploring the other functions as well such as
marketing, finance, production, and others.
Furthermore, once the models for the other functions
are developed, a complex talent pool selection can be
developed as well to group the employees based on
their suitability for each function.
ACKNOWLEDGEMENT
This research is fully funded by the Directorate of
Higher Education in Ministry of Education, Culture,
Research, and Technology Republic of Indonesia
under contract number: 005/SP-Lit/LPPM-
01/RistekBRIN/Multi/FT/III/2021.
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