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Multi-Criteria Decision Making for Innovation Decisions
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A New Perspective in Social Sciences
EDITED BY
Doğa Başar Sarıipek
Bora Yenihan
Valentina Franca
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First published 2019
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Multi-Criteria Decision Making
for Innovation Decisions
Tuğba Sarı
INTRODUCTION: DECISION PROBLEMS
Decision making is typically described as choosing among alternatives. It seems
pretty simple but not all the times. As organisations grow, decision making
becomes a complex activity in which managers make choices among various
alternatives which are often conflicting or uncertain and choose wisely in order
to benefit the organisation and its key stakeholders (Nutt & Wilson, 2010).
Organisations’ sustainability and long-term success depend on making the right
and accurate decisions.
A typical decision problem has the following characteristics: inconsistency
between the desired situation and the current one and two or more alternative
actions to achieve the desired situation. We call problems aimed to choose the
optimum alternative based on at least two criteria, as multi-criteria decision
problems. Virtually decision making process having multi-criteria has several steps
(Robbins, Coulter & DeCenzo, 2017): (1) Identify the problem, (2) Identify the
decision criteria, (3) Allocate the weights to predefined criteria, (4) Develop the
alternatives, (5) Analyse each alternative, (6) Select the most appropriate alternative,
(7) Implement selected alternative, (8) Evaluate the decision effectiveness at the
end.
MULTI-CRITERIA DECISION MAKING
Multi-criteria decision making (MCDM) is a world of methods, concepts, models
and approaches to help the managers and the decision makers to define, evaluate,
rank, sort, and select among objects (such as products, services or projects) on
the basis of an evaluation (expressed by values or scores) according to several
predefined criteria (Colson & Bruyn, 1989). MCDM methods help decision
makers and managers as a mathematical computational tool in making subjective
evaluation and selection between alternatives. MCDM is considered as a complex
decision making aid involving both qualitative and quantitative criteria. By this
methodology, complex decision problems are broken into smaller pieces. After
making evaluations and judgements related to these small parts, they are combined
together to present an overall picture. There exist different MCDM techniques
Multi-Criteria Decision Making for Innovation Decisions
159
depending on the type of problem and the purpose of decision makers. Table 1
lists several MCDM methods.
TABLE 1. MCDM methods with explanations
METHOD
EXPLANATION
DEVELOPED BY
AHP
Analytic Hierarchy Process
Saaty, 1971
ANP
Analytic Network Process
Saaty, 1996
Preference Ranking Organisation
PROMETHEE
Mareschal, Brans, & Vincke, 1985
Method for Enrichment Evaluations
VIKOR
Multi-criteria Optimisation and
Compromise Solution
Opricovic, 1998; Opricovic &
Tzeng, 2002
TOPSIS
Technique for Order Performance
by Similarity to Ideal Solution
Hwang & Yoon, 1981
ELECTRE
Elimination and Choice Expressing
the Reality
Roy, 1968, Roy & Bertier, 1972
DEA
Data Envelopment Analysis
Charnes, Cooper, & Rhodes, 1978
TODIM
Interactive and Multi-criteria
Decision Making
Gomes & Lima, 1991
DEMATEL
Decision Making Trial and
Evaluation Laboratory
Fontela & Gabus, 1976
GRA
Grey Relational Analysis
Deng, 1982
UTA
Utility Additive Method
Jacquet-Lagreze & Siskos, 1982
MOORA
Multi-Objective Optimisation on
basis of Ratio Analysis
Brauers & Zavadskas, 2006
PAPRIKA
Potentially All Pairwise Rankings of
All Possible Alternatives
Hansen & Ombler, 1990s
MACBETH
Measuring Attractiveness by a
Categorical Based Evaluation
Technique
Costa, Vansnick & De Corte, 1990s
Of course, all MCDM methods are not limited to those described herein. Table
1 is intended to give general information to the readers and decision makers,
and new techniques that are developed or to be developed will not prevent the
extension of the table.
MCDM problems can be categorised into three parts: Choice problems, sorting
problems and ranking problems (Turan, 2015).
Choice problems aimed to select the best alternative among the others. For
decision makers in a manufacturing company to compare all the alternative
projects and select the best one which meets the expectations of the company is
an example to choice problems.
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A New Perspective in Social Sciences
AHP, ANP, UTA, MACBETH, PROMETHEE, ELECTRE I and TOPSIS,
VIKOR methods are most frequently used techniques for the solution of choice
problems.
Sorting problems aimed to sort alternatives based on specific criteria or
preferences. Sorting the performances of employees in a company as strong,
medium and weak and evaluating the employees based on this classification is
an example of this kind of problems.
AHP, ANP, ELECTRE-TRI, DEA are the commonly used methods in this
area.
Ranking problems rank the alternatives from the best alternative to the worst.
Hence, a decision maker may evaluate the results easily. Ranking the bank branches
based on their relative performances can be an example of the ranking problem.
Some of the techniques used for the solution of ranking problems are: AHP,
PROMETHEE, UTADIS, ELECTRE II, III and IV, VIKOR, TOPSIS
The most commonly used MCDM methods in product and service innovation
are explained with their brief definitions, purposes, and usage areas.
AHP-ANP
Analytic Hierarchy Process expresses complex decision problems into a simple
hierarchic structure. Since the weights of criteria are derived by means of pair-wise
comparisons in AHP methodology, a basic 1-9 scale is used to make a comparison
between criteria and between alternatives. If there are any interdependencies in
attributes of two or more criteria, ANP methodology is used in order to include
them in the model.
PROMETHEE
Preference Ranking Organisation Method for Enrichment Evaluations method is
an outranking method which is used to rank the alternatives and select the best
one based on previously set criteria. The method is based on the deviation degree
between assessments of two alternatives on a specific criterion. Larger deviation
degree represents a higher preference on the best alternative. Each criterion is
handled with a discrete preference function (Brans & Mareschal, 2005).
VIKOR
Multi-criteria optimisation and compromise solution method (Vise Kriterijumska
Optimizacija I Kompromisno Resenje) was developed to solve the discrete multicriteria problem with no commensurable and conflicting criteria. The VIKOR
Multi-Criteria Decision Making for Innovation Decisions
161
approach proposes a compromise solution, providing a minimum of an individual
regret for the opponent and a maximum group utility for the majority (Opricovic
& Tzeng, 2004). VIKOR is particularly powerful tool under such situations where
the decision maker is unable, or does not know how to express her/his preference
at the early stage of product development (Zhu, Hu, Qi, Gu & Peng, 2015).
TOPSIS
The basic principle of TOPSIS methodology is that the best alternative solution
should have the shortest distance from the positive-ideal solution (the solution
maximising the profit criteria and minimising the cost criteria) and the longest
distance from the negative-ideal solution (the solution maximising the cost criteria
and minimising the profit criteria) (He Wang, He & Xie, 2016). Since the
TOPSIS method needs a few inputs and it is easy to evaluate outputs, it becomes
very popular in both product innovation and process innovation analysis.
ELECTRE
ELimination Et Choix Traduisant la REalité: ELimination and Choice Expressing
the REality approach includes a group of methods designed for aiding all three
types of MCDM problems: choosing, ranking, and sorting. The first version
ELECTRE I was developed for choice problems, while ELECTRE II, III, and
IV are used for the solution of ranking problems. To sort alternative solutions
in MCDM problems, ELECTRE-TRI series were developed (Figueira, Greco &
Słowiński, 2013).
MCDM AND INNOVATION
In every stage of product and service innovation process, new decisions are made
continuously. These decisions can be considered as key factors in every successful
innovation process. In this sense, decision making and particularly multi-criteria
decision making is very important in choosing, selecting and sorting the relevant
data with the help of technology in developing new products, new processes,
and new services.
AHP method and ANP as an extension of AHP are the most popular MDCM
techniques used in the innovation process in both manufacturing and service sector,
because of the ability to include personal judgements (customer expectations) into
the model and to integrate successfully with other MCDM methods. AHPANP approaches are followed by TOPSIS which is an easy to apply compromise
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A New Perspective in Social Sciences
method. PROMETHEE, VIKOR, ELECTRE, DEMATEL, GRA, and TODIM
are the other methods preferred in innovation studies.
MCDM IN PRODUCT INNOVATION
In new product development, the importance of customer needs is crucial. MCDM
methods are useful tools for transforming customer requirements and demands
into the technical characteristics of a new or improved product or service. New
product development has several phases such as concept design, detail design,
prototype design and final product design. Li, Mobin and Keyser (2016) proposed
a model in which they decrease the large Pareto optimal solutions in the early
product development phase of next generation engine with DEA.
A hybrid methodology with analytic network process and quality function
deployment is used to better rank and choose the technical characteristics of a
new equipment development to squeeze the polyethylene pipes to stop the gas
flow without damaging the pipe (Zaim, Sevkli, Camgöz-Akdağ, Demirel, Yayla,
& Delen, 2014).
Zhang and Xu (2015) introduced a model for a well-known watch manufacturing
firm in Asia to select the best supplier in new product development. The model
which is based on fuzzy TOPSIS and goal programming helps firm managers to
achieve their strategic goals under fuzzy environmental conditions.
Sustainable manufacturing and operation practices such as green supply
chains, eco-designs, process designs, product recovery, lean practices, and cleaner
production applications have become crucial issue in today’s manufacturing firms.
Gupta, Dangayach, Singh and Rao (2015) designed a model to evaluate sustainable
manufacturing in the Indian panel industry by using AHP method.
In another study, Gupta and Barua (2018B) focused on the evaluation and
selection of the criteria for manufacturing firms to achieve green innovation
production process. The criteria were selected by using Grey DEMATEL theory.
Gupta and Barua (2018A) used a fuzzy TOPSIS methodology to rank the
alternative green innovation barriers. Case studies were held in four manufacturing
suppliers to evaluate and overcome these barriers.
Another green selection process with TOPSIS technique was proposed by Tian,
Wang, Wang and Zhang for environmentally sustainable product design in 2017.
They used hybrid TOPSIS and QUALIFLEX approach in the development of
personal electronic devices.
Soota (2016) interpreted an integrated approach by AHP and quality function
deployment to develop the best product (bike) by prioritising engineering
requirements based on customer needs.
Multi-Criteria Decision Making for Innovation Decisions
163
Design concept evaluation is one of the most critical stages in development
of a new product. Zhu, Hu, Qi, Gu and Peng (2015) determined the weight of
each evaluation criterion via AHP and evaluated the design concept alternatives
with the use of VIKOR methodology. Thus they enhanced the objectivity in
design concept evaluation.
Identifying the root causes of product infant failure has become recently an
important topic in the improvement of product quality. He, Wang, He and Xie
(2016) focused on the problem of noise vibration harshness in cars as an infant
product failure. They developed a new model to analyse functional, physical and
process oriented fault symptoms to improve qualitative and quantitative attributes
of product quality. A case study was accrued out in a car manufacturing company
by using rough set and fuzzy TOPSIS MCDM methodology.
MCDM IN SERVICE INNOVATION
Service innovation is based on value creation for customers. Reconfiguration of
service processes and adding value in each step involves the cultivation of new
skills, behaviours and competencies for the co-creation and passing of the value
to the customer (Horng, Liu, Chou, Tsai & Hu, 2017). Reducing decisionmaking uncertainty systematically contributes to the success of innovation process.
Furthermore, innovation success is directly and positively related to how well
informed and knowledgeable decision-makers are, how well organisations decrease
the uncertainty surrounding innovation managers influences their decision-making
effectiveness directly (Van Riel, Lemmink & Ouwersloot, 2004).
Service innovation is a critical source of competitive advantage, since it
both increases customer loyalty and attracts new customers and hence results
in improving market share and profits (Horng, Liu, Chou, Tsai & Hu, 2017;
Huang, Basanta, Kuo & Huang, 2014).
Horng, Liu, Chou, Tsai and Hu (2017) used DEMATEL and ANP
methodology in order to develop a sustainable service innovation framework for
the hospitality industry. They combined qualitative and quantitative dimensions
of expert opinions in the Taiwanese hospitality industry and used DEMATEL to
identify the relationships and causality between dimensions. And lastly, relative
weights of each innovation criterion were determined by ANP. Tseng, Lin, Lim
and Teehankee (2015) follow a similar approach to evaluate service innovations in
the hot spring hotel industry in Taiwan via a TODIM based model. The study of
Akıncılar and Dağdeviren (2014) presents a hybrid AHP-PROMETHEE model
to evaluate websites of hotels in Ankara, capital of Turkey.
Analysing customer cloud service choices help providers to improve their
software and its innovative functions. That’s why Yang, Su and Wang (2018)
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A New Perspective in Social Sciences
proposed a hybrid MCDM model by using techniques of DEMATEL, ANP and
VIKOR in evaluation for cloud service application.
MCDM applications are also used in health service improvements. Huang,
Basanta, Kuo and Huang (2018) created a health system for elderly people to
detect probable reasons for diseases and to help them in deciding when to counsel
a health centre. Their solution design depends on fuzzy AHP weights to deal with
the uncertainty resulting from various disease factors.
MCDM techniques are relatively new in the finance sector. Angilella and
Mazzu (2015) introduce a multi-criteria credit risk model by using ELECTRETRI methodology for financing of innovative SMEs.
There is a considerable usage of multi-criteria based approach for selection
process in the service innovation. Selecting information technology outsourcing
strategy for e-banking through fuzzy TOPSIS methodology (Hanafizadeh &
Rvasan, 2018); selecting the best enterprise resource planning system for SMEs
by using ANP and PROMETHEE techniques (Kılıç, Zaim & Delen, 2015)
and selection of the best new telecom service by an ANP based model (Oh,
Suh, Hong & Hwang, 2009) are some examples from service sectors in various
countries.
CONCLUSION
Innovation is key to adding value in each step in business processes both for
production and service sector in today’s fast changing business life. The value
created via innovation results in customer satisfaction and hence a great competitive
advantage in the market. MCDM methods in general help decision makers to
make more concrete and analytic decisions in every step of the innovation process
and to transform customer needs and expectations into criteria in the model.
There exists a variety of MCDM methods used for product or service innovation.
While the most popular ones are AHP, ANP, TOPSIS, PROMETHEE, VIKOR
and DEMATEL, the MCDM methods are not limited to those used in the
innovation analysis included in this study. Many such methods serve as a tool
in decision problems in literature. MOORA, MACBETH, UTA, PAPRIKA are
other MCDM approaches that can be used in future studies.
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