Azadeh
Dabbaghi
Research
Institute of Petroleum Industry (RIPI), Iran
E-mail: dabbaghi@ut.ac.ir
Submission: 8/19/2019
Revision: 9/18/2019
Accept: 1/30/2020
ABSTRACT
Technology assessment help managers to accomplish an
overall evaluation of technologic options and to identify investment
priorities. Making such priority has become of great importance owing to
ever-increasing costs of technological research and development and resource
scarcity. Technology Attractiveness Assessment, as a primary step of Technology
assessment process, has been considered in this paper. Based on the
multi-criteria decision-making approach and because of the inherent uncertainty
in the preference information on attributes, a Grey-MADM based methodology has
been utilized in this paper to assess the technology attractiveness and rank the upstream industry
technological options. Its application to a
real case problem has been described step by step. The results of the case
study showed that "Nano Coating for Drilling
Tools", "Petroleum Systems Model Building" and "Integrated
Asset Modelling" are the most attractive upstream technologies.
Keywords: Technology Attractiveness Assessment, Grey Theory, Grey Possibility Degree, Upstream Oil and Gas Industry
1.
INTRODUCTION
The enterprise-level
technology assessment process is usually performed on key technologies, i.e.
technologies that play an essential role in achieving corporate strategic
goals. In the field of technology management, the technology assessment process
is divided into two major parts: technology auditing and technology
attractiveness assessment; in this paper, the technology attractiveness
assessment is studied.
The technology
attractiveness assessment process is an intellectual framework and an
appropriate tool for a better understanding of the state of the technology. A
continuous process, which is an essential part of enterprise technology
management, supports the enterprise competitiveness with reinforcing input
information in the strategic process of choosing the best technology. The
results of the recent researches indicate the technology management consultants
and academic researchers' deep attention in this field (TRAN; DAIM, 2008).
The emphasis is on the
fact that the process of technology attractiveness assessment is not only for
high-technology companies but also for all companies that use technology to
deliver their products and services. Such companies must assess the technology
of the product and the process used in the value chain and also the other
technologies that contribute to the development of technological capabilities.
The importance of
assessing enterprise-level technology attractiveness can be found in
determining the research and technological development policies, attracting new
technology, development of existing technology in the enterprise, deciding on
purchasing or manufacturing technology, and determining the level of optimal
investment in technology. Such an assessment also contributes to determining
the value chain and market benefits.
Various methods have been presented for assessing technology, so far. In
this paper, a step by step methodology has been provided for the technology
attractiveness assessment using grey theory which is appropriate to the
analysis of incomplete data under uncertainty situation.
2.
Technology Assessment
The concept of
technology assessment was first introduced in the late '60s with the
establishment of the Office of Technology Assessment (OTA) in the United
States. The purpose of the technology assessment at that time was to understand
the social, economic, political, moral, and the other possible implications
arising from introducing a new technology or developing an existing technology
that was used to assist US public policy.
From the beginning of
introducing the concept of technology assessment, the views of executive
directors of industries and businesses were attracted to it. Coats & Fabian
(1982), concluded that many companies regard the technology assessment as an
attempt to predict the impact of external environment on their activities, rather
than predicting the consequences of their activities on the outside
environment.
Maloney (1982)
indicated that the concept of the enterprise-level technology assessment is
different from the public-level technology assessment. The research revealed
that the purpose of the private sector from technology assessment studies was
to maximize the corporate profits in the short and medium time. In contrast,
the public sector in a long-term perspective regards the technological
implications on the dimensions of society.
3.
Technology Attractiveness Assessment
At the enterprise level
and from the perspective of the executive directors in the field of technology
and R & D management, the technology attractiveness assessment means the
determination of related technology attractiveness of the products and
processes that the enterprise is using or intends to use it in the near future.
The technology attractiveness assessment is used to increase the effectiveness
of financial and operational analyses of the various technology options, the
selection, and acquisition of critical technologies and strategic planning of
enterprise technology (TRAN; DAIM, 2008).
In developing a
technology strategy based on the technology portfolio analysis approach,
technology attractiveness assessment is one of the key dimensions of the
attractiveness-capability matrix that has been used by the various researchers
in the field of academic researches as well as leading technology advisors to
develop the enterprise's technological capabilities (VITTORIO, 2001). Azzone and Manizni (2008) showed
that according to the purpose of the application, the type of application and
study context (industry level, corporate level, the section of private and
public R & D companies, specific industries, etc.), there are various
methods to assess the attractiveness of technology.
Technology assessment
has been of great importance to researchers and managers of public and private
sectors since 1969. Different TA methods have been developed and utilized over
the past four decades. Several studies summarized and categorized these
techniques and methods (KRICHMAYER et al., 1975; HENRIKSEN, 1997; TRAN; DAIM
2008). A list of technology assessment methods is shown in table 1 Based on the
classification conducted by Tran and Daim (2008).
Table 1: technology assessment techniques
Technology assessment method |
Usage in public
decision-making domain |
business
and non-governmental uses |
Structural
modeling and system dynamics |
þ |
|
Impact
analysis |
þ |
|
Scenario
analysis |
þ |
|
Risk
assessment |
þ |
|
Decision
analysis |
þ |
þ |
Environmental
concerns and integrated TA |
þ |
|
Emerging
technologies |
þ |
|
Cost-benefit
analysis methods |
|
þ |
Decision
analysis |
|
þ |
Measures
for technology |
|
þ |
Roadmapping |
|
þ |
Scenarios
and Delphi |
|
þ |
Surveying
and information monitoring |
|
þ |
As shown in table 1, decision
analysis method has been applied in a wide range of technology assessment
problems in both the private and public sectors.
4.
Technology Attractiveness Assessment
Using Multi-Criteria Decision Making
By adopting the
"Multi-Criteria Decision Making" approach in technology
attractiveness assessment, the process of assessing the technological options
based on the various attributes can be fulfilled in a systematic methodology.
Different technological options based on the decision-making attributes are
assessed compared with each other and within the framework of the proposed
method.
Nowadays, the
achievement of competitive advantages and the effectiveness of production
enforce the companies and businesses to choose one or more identified
technologies to develop the technological capabilities and technology
portfolio, due to the time, financial, and management limitations. The results
of the technology attractiveness assessment are considered as the key inputs of
the strategic process of choosing technology.
Some researches
utilized AHP as a multi criteria decision making technique for assessing the
attractiveness or selecting the appropriate technologies. Prasad and Somasekhard (1990) used a combination of the Delphi method
and AHP for choosing the technologies in Indian telecommunications. Raju et al.
(1995) applied AHP to rank five technology alternatives in toilet soap-making. Aloini et al. (2018) applied an Intuitionistic Fuzzy
multi-criteria group decision making with the TOPSIS method for technology
assessment in the advanced underwater system sector.
Many attributes are
adopted and utilized in the literature for assessing the technology
attractiveness. Jolly (2003) collected a number of criteria identified in the
literature and classified them into the four groups, as shown in table 2.
Table 2: Technology Attractiveness Assessment attributes
Competition factors |
Market factors |
Number of stake-holders |
Market volume opened by technology |
Competitors’ level of involvement |
Span of applications opened by technology |
Competitive intensity |
Market sensitivity to technical factors |
Impact of technology on competitive issues |
Technical factors |
Barriers to copy or imitation |
Position of the technology in its own life-cycle |
Dominant design |
Potential for progress |
Other criteria |
Performance gap vis-a` -vis alternative
technologies |
Societal stakes |
Threat of substitution technologies |
Public support for the development |
Ability to transfer the technology from one unit
to another |
From the strategic perspective, some
researches have also indicated that the use of high technology significantly
increases the enterprise's competitive position (ESBATI et al., 2009).
Therefore, the impact of technology on achieving the enterprise goals, changing
the conditions and creating compatibility and ultimately creating competitive
advantage, is an essential factor in determining the attractiveness of the
technology (ARASTI, 2001).
In the past years, the
approaches based on fuzzy theory have been much applied for multi-criteria
decision-making problems (WANG, 2005), especially in the technology
attractiveness assessment which is under uncertainty situation in real-world
cases. Prabhu and Vizayakumar
(2001) utilized a multi-criteria decision-making approach in the case of
assessing iron-making technologies considering non-crisp (fuzzy) values. Jakubczyk and Kamiński
(2017) introduced fuzziness into the decision-making process in health
technology assessment. Chuu (2009) developed a fuzzy
multiple attribute group decision-making to improving advanced manufacturing
technology selection process. Tavana and Sodenkamp (2010) applied a fuzzy multi-criteria decision
analysis model to advanced technology assessment at Kennedy Space Center.
Another method that
enables the mathematical analysis and evaluation of systems with uncertain
information is the grey systems theory which is explained in section 5.
5.
Grey Systems Theory
5.1.
Grey Systems Theory and Fuzzy Set Theory
The grey system theory
was introduced by Deng in the early 1980s for the use in uncertainty situations
with incomplete data and inadequate information (DENG, 1989). In many systems,
such as social, economic, industrial systems, etc., the naming is because of
the branches and issues that are being investigated in these systems.
Accordingly, "grey systems" are based on the color of the subjects
under investigation. For example, in the theory of control, the darkness of
colors indicates the amount of information and data clarity. One of the best
examples is the "black box".
This term refers to a
box which is entirely encoded and unknown to all its internal relations and
structures. Here, the word "black" represents the unknown
information. White is used for well-known information and "grey" for
the information that is partly known and partly unknown. Accordingly, systems
with known information are called the "white system", the systems
with unknown information are called the "black system", and systems
with partly known and partly unknown information are called "grey
system" (LI; LIU, 2008).
The inherent
uncertainty in the various fields such as production, technology, industrial
management, etc. is rooted in two types of uncertainty. The first type of
uncertainty, "stochastic uncertainty", is due to the random nature of
the problem, which is described using statistics and probabilities, and
patterns and statistical distribution functions. The study of this aspect of
phenomena is based on high volume samples and considering the assumption that
these samples follow a specific pattern called probability distribution (LIU;
LIN, 2006).
The second type of
uncertainty, "recognitive uncertainties",
is due to the inherent complexity of the phenomenon and the lack of complete
information about it (DENG, 1985). In order to describe and study this aspect
of phenomena, the grey systems theory has been developed as the extension of
fuzzy theory in incomplete information situation. The advantage of grey systems
theory on the fuzzy set theory is that the grey theory involves fuzzy
situations (DENG, 1989).
In other words, grey
systems theory can work effectively in fuzzy situations. The use of fuzzy set
theory requires the recognition of the corresponding membership function based
on the experts' experience. However, the grey theory works without such a
requirement, also based on the available information range (LIU; LIN, 2006).
Grey system theory has been applied to various areas such as grey decision,
grey control, and grey prediction (LI et al., 2005). The "grey possibility
degree" method is considered in the grey decision area and is more
appropriate for solving many decision-making problems in uncertainty
situations, than the other methods (TSENG, 2009).
As indicated in section
1, the enterprises should be able to deal with the uncertainties in the
attractiveness assessment of technologies and process of determining the
relative impact of new technologies on competitiveness, effectiveness, and
efficiency of their activities. Also, there is not complete information
available to decision-makers (recognitive
uncertainties) to predict the functional characteristics of new technologies,
especially emerging new technologies and the consequences of their use in the
enterprise; therefore, the " grey possibility degree " technique has
been used as one of the most widely used and efficient techniques among grey
systems theory to assess the technology attractiveness and rank the
technological options of the enterprise.
5.2.
Grey Number and Possibility Degree
5.2.1.
Grey Set and Grey
Number
A grey set G of X (universal set ) is defined by
its two mappings and. Where and ; , and is the lower membership function and is the upper
membership function in G grey set (SU et al., 2016).
A grey number can be defined as a number with uncertain
information. There will be a numerical interval expressing it. This numerical
interval will contain uncertain information. Generally, the grey number is written
as
5.2.2.
Grey possibility Degree
in order to compare two grey numbers , the grey
possibility degree (GPD) can be utilized as follows (SHI et al.,2005):
.
The comparison between and is based on the
following four possible cases:
If =and , then is equal to , then .
If , then is larger than , then .
If , then is smaller than
, then .
If there is an intercrossing part in them, when, then we say
that is larger than.When , then we say
that is smaller than.
The length of a grey number () and the basic
operation laws of grey numbers can be calculated based on the definitions
presented by Moore (1966) and Wu et al.,(2005).
6.
Assessing Technology Attractiveness
Utilizing GPD
This paper follows the general approach proposed by Li et
al. (2007) and followed by Baskaran et al. (2012). This method is very suitable
for solving the multi-criteria decision-making problem in an uncertain
environment. Assume that is a discrete set
of m possible corporate technologies which is regarded as decision
alternatives. is a set of n
additively independent attributes for assessing the attractiveness of
technology. By adopting the Grey possibility degree approach, assessing and
ranking of the alternatives can be performed utilizing the following steps:
· Step 1: calculate the weights
of the attributes:
In order to identify the importance weights of technology
attractiveness assessment criteria, a group of k decision-makers (experts)
identify as the vector of n attribute weights. Each
attribute weight can be calculated as follows
|
|
Where is the attribute weight of Kth
decision maker which is identified by grey number based on table 3 (LI et al., 2007; DABBAGHI et
al., 2009).
Table 3: the 7 scale expression of experts'
preferences on the importance weight of attributes
scale |
Very
Low |
Low |
Medium
Low |
Medium |
Medium
High |
High |
Very
High |
VL |
L |
ML |
M |
MH |
H |
VH |
|
|
[0.01,0.1] |
[0.1,0.3] |
[0.3,0.4] |
[0.4,0.6] |
[0.6,0.7] |
[0.7,0.9] |
[0.9,1.0] |
In order to
decrease the number of attributes and facilitate the decision-making process,
the less important attributes in which can be deleted from the set of n attributes. is the lower limit of the " Medium High " scale according to Table 3.
·
Step 2: Establish the grey decision
matrix (MEHREGAN et.al., 2014; RAJAPRASAD, 2018):
Where are the ratings of the ith
technology with respect to the jth attribute. These ratings are
expressed in the form of grey numbers by the 1-7 linguistic scale as shown in
table 4.
Table 4: the 7 scale expression of experts'
ratings about alternatives respect to the attributes
scale |
Very
poor |
poor |
Medium
poor |
Medium |
Medium
good |
good |
Very
good |
VP |
P |
MP |
M |
MG |
G |
VG |
|
|
[0,1] |
[1,3] |
[3, 4] |
[4, 5] |
[5,
6] |
[6,
9] |
[9,10] |
· Step 3: Calculate the
weighted normalized grey decision matrix
Where , in which is calculated as follows (MEHREGAN et.al.,
2014) based on the attribute type:
In which and .
In the calculated weighted normalized grey decision
matrix, the attribute weights are considered in the decision matrix values.
Furthermore, the normalization process guarantees that the ranges of normalized
grey numbers are between 0 and 1.
· Step 4: Define a Reference
technology
For the set of technology alternatives, an assumptive
technology as the best alternative can be obtained by:
· Step 5: determine the ranking
of technologies
The grey possibility degree between each alternative
technology and the reference technology is calculated based on the definitions
presented in section 5-2-2. The less the value calculated for the ranking of the j th technology is better
and vice versa.
7.
Application and Analysis: Upstream
industry in Iran
In this part of the paper, the application of the proposed methodology (described in section 6) to the upstream industry is described step by step. National Iranian Oil Company as the only public company in the upstream oil industry, is headquartered in the research and technological development. So, the assessment and selection of attractive technologies and then preparation of a roadmap to attain the selected technologies, as one of the missions of this company, has been adopted to satisfy the technical and operational needs of the leading company and its subsidiaries.
In order to assess the technology attractiveness, a set of technologies were considered according to the upstream value chain and the operating conditions of this industry, as shown in table 5.
Table 5: the list of attractive upstream
technologies as the case problem alternatives
Technologies |
Alternative |
Full Wave
Inversion |
T1 |
Seismic
Sequence Stratigraphy |
T2 |
Wide
Azimuth Acquisition Time
Laps |
T3 |
Walk Away
and 3D Vertical Seismic Profile (VSP) |
T4 |
Petroleum
Systems Model Building |
T5 |
Coupled
Fluid-Structure Interaction Analysis |
T6 |
Cement And
Drilling Fluid Additives |
T7 |
Nano
Coating for Drilling Tools |
T8 |
Acidizing
Methods and Additives |
T9 |
Hydraulic
Fracturing |
T10 |
Integrated
Asset Modelling |
T11 |
Automated
History Matching and Data Integration |
T12 |
Smart Well |
T13 |
Water
Alternating Gas (Wag) Injection |
T14 |
Tight Core
SCAL Analysis |
T15 |
Integrated
Fracture Network Modelling using Seismic and Dynamic Data |
T16 |
Enhanced
Oil Recovery Pilot Design |
T17 |
Well
Production Enhancement |
T18 |
The attributes for the technology attractiveness assessment were collected based on the literature review presented in section 4 and were customized and approved based on the upstream experts' opinions and results are shown in Table 6. These experts were ten experienced managers and researchers in the upstream petroleum industry of the country, familiar with the operational issues and the process of technology management.
Table 6: the set of attributes
|
Attributes |
Attribute nature |
Q1 |
Up-to-dateness |
Technical |
Q2 |
Performance Vis-a -Vis other technologies |
|
Q3 |
Potential for transfer and development of the related technologies |
|
Q4 |
Span of application and demand level of stakeholders |
Competition |
Q5 |
Estimated development costs |
|
Q6 |
Impact of technology on quality and differentiation |
Market |
Q7 |
Potential for Commercialization |
·
Step 1
The experts' opinions about the significance of the attributes were collected. So, the calculations related to the determination of weights were conducted using equation 12; the results are presented in Table 7.
Table 7: calculated attribute weights based on
the experts' opinions
For all of the calculated attribute weights ; so all of the attributes were considered as important attributes in the remaining steps.
·
Step 2
The grey decision matrix was established as shown in Table 8.
Table 8: the Grey decision Matrix
·
Step3
The weighted normalized matrix was calculated as shown in Table 9.
Table 9: the weighted
normalized matrix
·
Step4
The reference technology can be defined as follows
·
Step5
The grey possibility degree between each alternative technology and the reference technology is calculated and results are shown in Table 10.
Table 10: the values of grey possibility degree
calculated for each alternative technology
|
|
|
|
i=1 |
0.684 |
i=10 |
0.736 |
i=2 |
0.846 |
i=11 |
0.645 |
i=3 |
0.753 |
i=12 |
0.646 |
i=4 |
0.859 |
i=13 |
0.854 |
i=5 |
0.624 |
i=14 |
0.840 |
i=6 |
0.776 |
i=15 |
0.871 |
i=7 |
0.687 |
i=16 |
0.773 |
i=8 |
0.621 |
i=17 |
0.808 |
i=9 |
0.832 |
i=18 |
0.755 |
According to the calculated grey possibility degree for each alternative, the ranking order of technologies are shown in Table 11.
Table 11: the ranking results of the alternative
technologies
8.
CONCLUSIONS
Nowadays, the organizations with the aim of achieving the competitive advantage and production effectiveness have to assess the attractiveness of technologies and choose a limited number of identified technologies due to the time, financial and managerial constraints. The identified attractive technologies can be considered as the critical inputs to the remainder strategic technology management process.
This paper applies the "Grey Possibility Degree" approach into a methodology for technology attractiveness assessment. The grey systems theory is appropriate for the assessment and analysis of systems under uncertainty situation. This methodology, which is explained in five steps, provides a systematic procedure for assessing a set of attractive technologies through a number of attributes based on the overall framework of "multi-criteria decision-making methods".
Finally, as a case study, the methodology has been utilized to assess the technology attractiveness in the upstream industry in Iran. A set of 18 upstream technologies were considered as alternatives. The experts' preferences and ratings about these alternatives were described by grey numbers to deal with the uncertainty inherent in their judgments. The calculations were accomplished based on the methodology and explained step by step.
The results showed that the most attractive upstream technologies are: 1. Nano Coating for Drilling Tools, 2. Petroleum Systems Model Building, 3. Integrated Asset Modelling, 4. Automated History Matching and Data Integration, 5. Full Wave Inversion. These selected technologies in the case problem have been considered for the upstream technological capability assessments and further development.
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