=Paper=
{{Paper
|id=Vol-1292/ipamin2014_paper1
|storemode=property
|title=System Thinking: Crafting Scenarios for Prescriptive Analytics
|pdfUrl=https://ceur-ws.org/Vol-1292/ipamin2014_paper1.pdf
|volume=Vol-1292
|dblpUrl=https://dblp.org/rec/conf/konvens/WeberCLSGJ14
}}
==System Thinking: Crafting Scenarios for Prescriptive Analytics==
https://ceur-ws.org/Vol-1292/ipamin2014_paper1.pdf
System Thinking: Crafting Scenarios for Prescriptive
Analytics
Jens Weber Minhee Cho Mikyoung Lee
Heinz Nixdorf Institute (HNI) Korea Institute of Science and Korea Institute of Science and
University of Paderborn Technology Information (KISTI) Technology Information (KISTI)
Fuerstenallee 11 245 Daehak-ro, Yuseong-gu, Daejeon, 245 Daehak-ro, Yuseong-gu, Daejeon,
D-33102 Paderborn, Germany 305-806, Korea 305-806, Korea
jens.weber@hni.upb.de mini@kisti.re.kr jerryis@kisti.re.kr
Sa-kwang Song Michaela Geierhos Hanmin Jung
Korea Institute of Science and Heinz Nixdorf Institute (HNI) Korea Institute of Science and
Technology Information (KISTI) University of Paderborn Technology Information (KISTI)
245 Daehak-ro, Yuseong-gu, Daejeon, Fuerstenallee 11 245 Daehak-ro, Yuseong-gu, Daejeon,
305-806, Korea D-33102 Paderborn, Germany 305-806, Korea
esmallj@kisti.re.kr geierhos@hni.upb.de jhm@kisti.re.kr
ABSTRACT 1. INTRODUCTION
This paper focuses on the first step in combining prescriptive The software application InSciTe developed by the Korea Institute
analytics with scenario techniques in order to provide strategic of Science and Technology Information (KISTI) uses prescriptive
development after the use of InSciTe, a data prescriptive analytics analytic methods in order to develop strategies and provide
application. InSciTe supports the improvement of researchers recommendations in order to improve research performance. For
‘individual performance by recommending new research example, it calculates measures intended to increase the actual
directions. Standardized influential factors are presented as a number of academic contributions or recommend in extreme cases
foundation for automated scenario modelling such as the a change of research topic. InSciTe is a mobile and web based
prototypical report generation function of InSciTe. Additionally, a application that uses text mining techniques and methods for Big
use-case is shown which validates the potential of the Data analysis to support researchers in their activities [6]. The
standardized influential factors for raw scenario development. output of InSciTe describes, amongst other things, measurements
to achieve a defined number of published academic contributions
or describes which future collaborations the researcher should
Categories and Subject Descriptors pursue. The overall goal for applying prescriptive analytics is to
K.6.1 [Management of computing and information systems]:
achieve continuous improvement in research performance. An
Project and People Management – strategic information systems
additional benefit is the extension of methods of forecasting and
planning, systems analysis and design.
identification of opportunities to detect future trends in Research
and Development (R&D) [6]. However, the generated
General Terms recommendations at the end of the analysis process are quite static
Design, Management, Measurement, Verification. and the opportunities for developing the suggested measures
during the post-analysis process are only partially examined. The
Keywords InSciTe report [5] may describe metrics for increasing the number
of publications, recommend a potential strategic cooperation, or
Standardized Influential Factors, Prescriptive Analytics, Role
an increase in the current number of conference visits. The
Model Group, Scenario Technique.
possible failures that have to be considered are, for example, the
personal and individual difficulties in joining collaborations
which occur between the researchers or the failure of a
cooperation as well as an unreached number of published
contributions. Furthermore, there is no analysis of the
consequences if research performance decreases or stagnates
during a cooperation. For that reason, further research in the
context of forecasting and strategy development is required.
Copyright © 2014 for the individual papers by the papers' authors. This paper describes an initial approach for using the output of
Copying permitted for private and academic purposes. InSciTe for scenario planning and scenario techniques limited
This volume is published and copyrighted by its editors. especially to scenario field analysis [4]. In this way, possibilities
Published at Ceur-ws.org for the derivation and formulation of strategies for future
Proceedings of the First International Workshop on Patent Mining and scenarios based on the InSciTe results are provided. This
Its Applications (IPAMIN) 2014. Hildesheim. Oct. 7th. 2014. approach should finally support researchers in identifying invalid
At KONVENS’14, October 8–10, 2014, Hildesheim, Germany.
or infeasible results in the list of InSciTe recommendations and Currently, the application InSciTe can be summarized in the
suggest alternative courses of action. following four steps (see [11]).
(1) Step 1: Measuring research performance
2. BACKGROUND
Literature provides a variety of information on different scenario (2) Step 2: Finding role model researcher or group
techniques as well as other methods of forecasting for strategy (3) Step 3: Planning research activities
development including prescriptive analytics.
(4) Step 4: Evaluating and applying feedback and reports
2.1 Prescriptive analytics
Prescriptive analytics is “a set of mathematical techniques that 2.2 Towards scenario techniques
computationally determine a set of high-value alternative actions Scenario techniques have been proven in the field as a method for
or decisions given a complex set of objectives, requirements, and forward thinking in the areas of changing markets, business fields,
constraints, with the goal of improving business performance” [9]. and technological development as well as in research and
From the business perspective, a pioneer in the field of development [13]. This has been shown by the successful usage of
prescriptive analytics is the enterprise Ayata (USA), founded in these techniques by companies such as UNITY AG as well as
2003. This company offers software solutions which allow the Sinus GmbH (Germany). [10, 12]
usage of hybrid data. Model synergies, data and rules are applied
The usage of scenario techniques is based on two principles. It
and mathematical models are then combined with hybrid data and
promotes lateral and cross-functional thinking, which means that
business process rules. In this manner, problems in the field of
linked influential factors must be considered. It also furthers
operational research, optimization, decision support and Big Data,
understanding of the considered system within the context of its
can be solved with the support of prescriptive analytics. [1]
surrounding environment and helps to make these kinds of
systems both recognizable as well as manageable. Scenario
2.1.1 InSciTe techniques are also based on multiple potential futures, in which
InSciTe stands for “Intelligence in Science and Technology” and focus needs to remain on more than one influential factor [4].
has been in development by the Korea Institute of Science and
Technology Information (KISTI) since 2010. It is a software The scenario developed on these mentioned principles is known
solution for areas pertaining to “Technology Intelligence as a generally comprehensible description of a possible future,
Services”, “Intelligent Decision Support Services“, “Intelligent which arises from a complex network of influential factors. [4]
Technology Analysis Services“ and “Prescriptive Analytics for The representation of a development which could lead from the
Researchers”. It contains semantic text mining techniques, a present circumstances to this future situation could be also
reporting function for technologies and organizations, described. [4]
representation of technology trends, roadmaps, role model Scenario techniques may be generally divided into several steps.
recommendations and prescriptive analytics based on 5W1H1 [6, The first step is the preparation of a scenario in which the target is
7]. The current status of the software tool in 2013 was InSciTe identified and a general project goal setting is defined. Then the
Advisory and the goal in 2014 is to adapt it to an improved system second step, called scenario analysis, starts, which determines the
supporting prescriptive analytics. The overall goal is to extend the influential factors. The identification of certain influential factors
intelligence of InSciTe further. A partial goal is to provide the as well as their relevance requires the use of a variety of analytical
basic knowledge acquired over the course of this project as part of methods in this step. The third step derives multiple prognoses
a useful and applicable business intelligence system. [6] based on the key factors detected during the scenario analysis.
The described system does not initially support the solution from Each key factor enables the identification of several projections,
Ayata described in Section 2.1, which provides the analysis and each representing a different development direction. The
improvement of business processes and future decisions, but projections are then described in precise and understandable terms
determines instead the current position of research progress and and result in a so-called projection catalog. [4]
performance within a chosen field, comparing existing researchers These projections are then examined and compared in pairs for
as well as deriving measures that enhance research capacity in a consistency within the scenario building context and the result is a
direction that the identified role models have demonstrated in collection of characteristics of influential factors that determine a
order to attempt to generally improve and even exceed the similar consistency level. These bundled projections are clustered
performance of a given role model researcher. The relevant role in order to provide the basis for creating raw scenarios. The raw
model researchers are grouped together and with the support of scenarios can be tested and finally formulated as detailed, verbally
these groups, measurements can be derived into a quantifiable expressed, future scenarios. Opportunities and threats can then be
form in order to strengthen overall research capacity and analyzed during the scenario transfer process. A proposed general
performance. The role model researcher could be one or more strategic direction arises from the analyzed result. [4]
individual researchers or a research organization pertaining to one
or more research fields. [2, 6]
1 5W1H: KAIZEN-technique to improve organization by the
question-answering method of what, when, where, who, why
and how [7]
Some literature such as [3, 13, 14] refers to extensive explanations 3.2 Standardized influential factors for raw
of how scenario techniques can be generated. Although those
approaches differ, the results, processes, and goals of the scenario scenarios
techniques are identical to a great extent. The automatically generated report by InSciTe is always
structured identically and contains identical topics so that the
Figure 1 illustrates the basic phases and milestones in brief. derived factors can be adapted to each analysis step.
Consequently, the described process for identifying key factors
[4] can be stored and the analysis process is more streamlined.
The adaptable (standardized) influential factors were identified by
detailed structural and text analysis as well as within discussions
with the InSciTe development team (expert interviews). Table 1
illustrates these influential factors.
Table 1. Influential factors for the scenario analysis based on
the InSciTe report
Influential factors
1 Role Model Group (RMG) 7 H-index
2 Research Power Index (RPI) 8 Cooperation
3 Number of research fields 9 Scholar activities [5]
4 Currentness of the research fields 10 Career activity [5]
5 Consumption of resources 11 Industrial activity [5]
6 Expansion of the research field
Figure 1. Development process of scenarios [4]
In addition to the influential factors’ adaptability on the analysis
results and reports there is the possibility of extension, in
3. IDENTIFICATION OF INFLUENTIAL particular all possible development directions can be adjusted
after more detailed application tests are performed. The following
FACTORS BASED ON INSCITE REPORT list presents the influential factors and the current developmental
The results of prescriptive analytics via InSciTe are described in directions.
detail in an automatically generated report for recommendations.
These recommendations should improve the research performance (1) Role Model Group (RMG): The RMG includes several
of the target person and include, for example, which cooperation researchers or organizations that – due to analysis by InSciTe
should be joined, in which journals the researcher should publish – have certain similarities to the target researcher and
papers, or how the research field should be organized [5]. In therefore recommend the next steps, activities, or
practice, difficulties can occur for all the recommendations due to cooperation [5, 6].
limited resources, failure to establish the suggested cooperation or o Limited overlap of research fields: There is little
inability to publish papers due to scheduling restrictions. In order intersection in the research focus in an RMG
to promote static recommendations as well as to support strategic compared to the analyzed researcher and his
development, the idea came up to expand prescriptive analytics research fields.
via InSciTe using scenario techniques. It then seems useful to o High-level overlap of research fields: There is a
structure the scenario technique according to the automatically large intersection in the research areas of the
generated InSciTe report. researchers in the RMG and the analyzed
researcher and his research topics.
o No overlap of research fields: The researchers in
3.1 Feasibility of scenario methods the RMG have totally different research areas than
It will generally be necessary to first check whether a potential the target researcher.
scenario analysis is technically feasible. Furthermore, it must be (2) Research Power Index (RPI): The RPI is a compilation of
proven whether a scenario field analysis will result in the nine evaluation indicators called “Scholarity”,
generation of standardized influential factors which could be “Influentiality”, “Diversity”, “Durability”, “Emergability”,
individually implemented for other InSciTe reports. Interfaces “Partner Trend”, “Market Share”, “Supply Demand”, and
between InSciTe and scenario techniques allow researchers to “Commerciality”. It indicates the strength of the research
create raw scenarios from the generated report. The influential performance for the analyzed researcher. The merits and
factors predict different developmental directions. The demerits of a researcher are evaluated by the RPI. [5, 6]
standardization requirement results from a high amount of data, o The RPI for RMG members and the analyzed
based on the InSciTe application and used for the report researcher is on the same level.
generation process. It would be too much work to manually create o The RPI for RMG members is lower than the
individual local influential factors and they could be incompatible analyzed researcher’s RPI.
with the predefined process. o The RPI for RMG members is higher than the
analyzed researcher’s RPI.
(3) Number of research fields: Number of research fields which o
Fewer activities in total compared with other
the analyzed researcher focuses on. periods
o Many (more than 5) o No activities
o Standard/average (3 to 5) (11) Industrial activity²: Industrial activity is related to
o Few (1 to 3) commercial actions such as publishing patents, etc. [5].
(4) Currentness of the research fields: The factor includes o Increase activities in total
currentness or popularity as well as rarity of his or her o Activities in total stay constant
research fields. o Fewer activities in total compared with other
o Very current: Often presented in the media periods
o Current, but timeless research fields o No activities
o Normal: Mostly basic research or non-popular
research fields
(5) Consumption of resources in the research fields: What 4. FIRST USE-CASE
level of human resources and technical equipment are The use-case should show which characteristics of the raw
necessary for conducting research in a special field. scenarios are possible in general. Moreover, the use-case shows
o High level of resource deployment: High expense how it is possible to prepare the development of scenarios by
in the research field – difficult to change the using standardized influential factors. We focus on the impact and
research field or difficult to find and maintain consistency analysis. The consistency analysis guarantees that the
cooperation raw scenarios contain only influential factors on a high
o Low level of resource deployment: low expense in consistency level for further development directions. Here, the
the research field – easy to change the research impact analysis is necessary to select some important influential
field as well as easy to find and maintain factors, because the usage of all eleven influential factors requires
cooperation high computational effort. Furthermore, a handful of influential
(6) Expansion of the research field: Willingness to enter into factors are generally sufficient for the presentation of the use-case.
an additional research field to follow the RMG or to start The influential factor analysis is performed according to [4, 10].
cooperating. The IT scenario software Szeno-Plan developed by Sinus GmbH
o Enter a new additional research field (Germany) supported the implementation of the use-case.
o No new additional research field
(7) H-Index: H-index is used to measure the impact and 4.1 Impact analysis for the use-case
quantity of the research performance of an individual The (individual) influential factors are evaluated with regard to
researcher [9]. their mutual influence on each other. Indirect influential factors
o Increase were also considered in [4]. The rating scale was from 0 (no
o Remain constant influence) to 4 (very high influence). The results of the analysis
(8) Cooperation: With whom (out of the RMG members) are presented in the matrix in Figure 2. The results are normalized
should the researcher cooperate in order to increase his or her and plotted as a percentage. The quadrants of the matrix are
research performance? divided into four sections: “Critical factors”, “Driving factors”,
o Cooperation with all members of the role model “Buffering factors” and “Driven factors”.
group
o Cooperation with several members of the role Graphical distribution (ranking presentation)
model group Driving factors Critical factors
o Cooperation with none of the role model group
Indirect active sum in percentage
o Cooperation with researchers/organizations outside
of the role model group
(9) Scholar activity2: Scholar activity is related to scientific
research actions such as publishing papers, books, etc. [5].
o There should be more conference and journal paper
publications and conference visits, etc.
o Number of conference and journal paper
publications, conference visits, etc. should stay
constant
o The number of conference and journal paper
Buffering factors Driven factors
publications, conference visits, etc. should be
reduced.
o No publishing of papers and other research results. Indirect passive sum in percentage
(10) Career activity²: Career activity is related to human actions Figure 2. Graphical Distribution of indirect impact analysis
such as receiving awards, building careers, obtaining degrees
etc. [5].
o Increase activities in total For the use-case, the influential factors in the quadrant “driving
o Total activities stay constant factors” were selected (see red marked zone in Figure 2), because
these factors consist of a relatively high active sum and small
2 The influential factors no. (9) to (11) are addressed as specific passive sum. However, the factor “cooperation” from the quadrant
“activities” by the author; the names in the InSciTe report differ. “critical factors” was also selected because the InSciTe reports
often recommended it explicitly. As a result, six influential factors
were taken. The description of these factors and direction
developments can be seen in Section 3.2. Table 2 shows the
selected influential factors.
Table 2. Selected influential factors
S elected Influential factors
1 Role Model Group (RMG)
3 Number of research fields
4 Currentness of the research fields
5 Consumption of resources
6 Expansion of the research field Figure 4. Scenario distribution and consistency level
8 Cooperation
As seen in Figure 4, the raw scenarios no. 2 to no. 15 seem to be
interesting and are selected for further analysis analogous to [4,
4.2 Consistency analysis 10]. Raw scenario no. 1 was excluded; despite having the highest
The consistency analysis offers the possibility to identify which of consistency value there were no other alternative characteristics at
the development directions from the influential factors occur in the same consistency level. There is only one developable
the several raw scenarios. From each influential factor only one scenario and consequently no possible alternative strategy
development direction in one raw scenario is represented. The development. Table 3 therefore illustrates briefly four possible
results were discussed with the InSciTe developers at KISTI. The configurations of the raw scenarios (see Section 3.2 for any
highest consistency level is defined by the value 26 and the lowest details). Three of them show a consistency level of 24 and the
consistency level is 0. The six influential factors offer 319 excluded scenario which has the consistency value 26 is also
different raw scenarios in total. The allocation of the number of illustrated.
raw scenarios to the various consistency levels is shown in Figure
Table 3. Selected influential factors
3.
S cenario No. consistency S cenario No. consistency
measure measure
1 26 2 24
Influential Development Influential Development
factors directions factors directions
RM G: limited overlap RM G: high overlap
Currentness of current Currentness of very current
research fields: research fields:
Consumption of low Consumption of high
resources: resources:
Expansion: enter new Expansion: no new
Cooperation: outside Cooperation: with all
number of standard number of many
researchfields: researchfields:
S cenario No. consistency S cenario No. consistency
Figure 3. Frequency of consistency level of the several
measure measure
scenarios
5 24 7 24
Influential Development Influential Development
Most of the scenarios have a consistency level value ranging from factors directions factors directions
8 to 12. The fewest scenarios show the highest consistency level RM G: high overlap RM G: high overlap
values. This is an advantage for the analysis process because the Currentness of normal Currentness of current
fewer the number of scenarios determined with a high consistency research fields: research fields:
level the lower the analysis effort. A total of 14 raw scenarios Consumption of low Consumption of high
were determined in order to provide a consistency level value of resources: resources:
24 (near to the maximum value) and one raw scenario which Expansion: enter new Expansion: no new
represents the maximum value (26). Figure 4 shows an overview
Cooperation: with several Cooperation: with several
of the described situation.
number of many number of many
researchfields: researchfields:
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