MITS 6002 Colorado State University Life Cycle of Prescriptive Analysis Paper Learning Outcomes The following learning outcomes have been covered in this a

MITS 6002 Colorado State University Life Cycle of Prescriptive Analysis Paper Learning Outcomes
The following learning outcomes have been covered in this assessment:
LO3. Conduct research on a collection of business cases and perform statistical analysis as also
interpret these outcomes to recommend appropriate business directions.
LO4. Critically analyse a variety of business domains and adopt business analytics models
appropriate to the domain that requires quantitative techniques for decision making.
LO5. Recommend appropriate analytic tools and techniques to resolve complex business
analytics problems in various industry sectors. See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/330085905
Prescriptive Analytics: A Survey of Approaches and Methods: BIS 2018
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DOI: 10.1007/978-3-030-04849-5_39
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Prescriptive Analytics: A Survey
of Approaches and Methods
Katerina Lepenioti1(&), Alexandros Bousdekis1,
Dimitris Apostolou1,2, and Gregoris Mentzas1
1
Information Management Unit (IMU), Institute of Communication and
Computer Systems (ICCS), National Technical University of Athens (NTUA),
9 Iroon Polytechniou Street, 157 80 Zografou, Athens, Greece
{klepenioti,albous,gmentzas}@mail.ntua.gr
2
Department of Informatics, University of Piraeus,
80 Karaoli & Dimitriou Street, 185 34 Piraeus, Greece
dapost@unipi.gr
Abstract. Data analytics has gathered a lot of attention during the last years.
Although descriptive and predictive analytics have become well-established
areas, prescriptive analytics has just started to emerge in an increasing rate. In
this paper, we present a literature review on prescriptive analytics, we frame the
prescriptive analytics lifecycle and we identify the existing research challenges
on this topic. To the best of our knowledge, this is the ?rst literature review on
prescriptive analytics. Until now, prescriptive analytics applications are usually
developed in an ad-hoc way with limited capabilities of adaptation to the
dynamic and complex nature of today’s enterprises. Moreover, there is a loose
integration with predictive analytics, something which does not enable the
exploitation of the full potential of big data.
Keywords: Prescriptive analytics
Big data Literature review
Business analytics Data analytics
1 Introduction
Big data technologies and algorithms along with their applications have attracted
signi?cant attention over the past few years. An increasing number of enterprises invest
on big data analytics and try to exploit their potential in order to obtain useful insights
about their performance and gain a competitive advantage [1]. To this end, the scienti?c ?eld of data analytics has emerged, going beyond a simple raw data analysis on
large datasets [1]. Analytics, as a multidisciplinary concept, is de?ned as the means to
acquire data from diverse sources, process them to elicit meaningful patterns and
insights, and distribute the results to proper stakeholders [2].
Data analytics is categorized to three main stages characterized by different levels
of dif?culty, value, and intelligence [3]: (i) descriptive analytics, answering the questions “What has happened?”, “Why did it happen?” and “What is happening now?”.
(ii) predictive analytics, answering the questions “What will happen?” and “Why will it
happen?” in the future. (iii) prescriptive analytics, answering the questions “What
© Springer Nature Switzerland AG 2019
W. Abramowicz and A. Paschke (Eds.): BIS 2018 Workshops, LNBIP 339, pp. 449–460, 2019.
https://doi.org/10.1007/978-3-030-04849-5_39
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K. Lepenioti et al.
should I do?” and “Why should I do it?”. The maturity of the ?rst two stages has been
substantiated by the large amount of research works, associated platforms and business
solutions. The current paper investigates the literature on prescriptive analytics and
identi?es the existing research challenges on this topic. To the best of our knowledge
this is the ?rst literature review on prescriptive analytics.
The rest of the paper is organized as follows. Section 2 presents an overview of
prescriptive analytics along with three use cases in order to explicitly show the differences between the three stages of analytics. Section 3 describes our methodology for
the literature review, while Sect. 4 presents the results of the literature review. Section 5 provides a discussion of the results and identi?es the research challenges, while
Sect. 6 concludes the paper.
2 Towards Prescriptive Analytics
Prescriptive analytics is able to suggest (prescribe) the best decision options in order to
take advantage of the predicted future and illustrates the implications of each decision
option [3]. It incorporates the predictive analytics output and utilizes arti?cial intelligence, optimization algorithms and expert systems in a probabilistic context in order to
provide adaptive, automated, constrained, time-dependent and optimal decisions [4–6].
Prescriptive analytics has two levels of human intervention: decision support, e.g.
providing recommendations; decision automation, e.g. implementing the prescribed
action [6]. It is the most sophisticated type of business analytics and can bring the
greatest intelligence and value to businesses [3]. The effectiveness of the prescriptions
depends on how well these models incorporate a combination of structured and
unstructured data, represent the domain under study and capture impacts of decisions
being analysed [3, 5]. In order to show the potential of prescriptive analytics, we
illustrate the following motivating scenarios from three different application domains.
Industry 4.0
Industry 4.0 indicates the current trend of automation and data exchange in manufacturing technologies in order to facilitate manufacturing. For example, consider the
case of predictive maintenance in which sensors generate a multitude of data dealing
with indicators of equipment’s degradation. Descriptive analytics algorithms monitor
the current condition of the manufacturing system and provide alerts in cases of
abnormal behaviours. This is achieved by comparing the actual measurements of
several parameters that constitute indicators of degradation. When they vary from the
normal values, an alert triggers the predictive analytics algorithms. The alert is evaluated and, if it indicates a potentially hazardous state of the manufacturing equipment,
the predictive analytics algorithms generate predictions about the future health state of
the manufacturing system, e.g. a prediction about the time-to-failure. On the basis of
this prediction, prescriptive analytics algorithms are able to provide recommendations
about the optimal mitigating actions and the optimal time for their implementation in a
way that the expected loss and the risk are minimized. The Industry 4.0 scenario is
based upon the research works of [7] and [8].
Prescriptive Analytics: A Survey of Approaches and Methods
451
Transportation
The traf?c congestion control concerns more and more modern, crowded cities. To this
end, there are attempts to release the city centers from the traf?c jams. Currently, sensors
can detect vehicles that pass corresponding areas. This data along with historical data
from traf?c monitoring networks can be utilized for further analysis by descriptive
analytics algorithms. These algorithms can derive outcomes such as induction loop
information and vehicle location information in an aggregated form. These results feed
into the predictive analytics algorithms which provide predictions about the traf?c flow
(congestion level) of the system. To do this, they also exploit predictive models that
have been developed based on historical data and that take into account contextual
information (e.g. peak times). The predictions trigger the prescriptive analytics algorithms which execute actions with the aim to reduce the congestion level proactively
(e.g. traf?c lights control). The actions will change the current states of the system and
affect the future states in order to maximize the total reward (reduction of congestion).
The transportation scenario is based upon the research work of [9].
Healthcare
Healthcare is a key domain that can bene?t from data analytics due to the regulatory
requirements and the large amounts of data that have the potential to improve the
quality of healthcare delivery. In several cases, reliable analytics can mean the difference between life and death (e.g. trauma monitoring for blood pressure, operating
room monitors for anesthesia). For example, capturing real-time large volumes of data
from in-hospital and in-home devices can feed into descriptive analytics algorithms for
safety monitoring. When hazardous correlations of streams of physiological data
related to patients with brain injuries are detected, an alert is received by predictive
analytics algorithms, which provides a prediction about a bleeding stroke from a
ruptured brain aneurysm. On this basis, prescriptive analytics algorithms provide
medical professionals with critical and timely prescriptions in order to aggressively
treat complications. The healthcare scenario is based upon the research work of [10].
3 Literature Review Methodology
In this Section, we outline the methodology of the literature review which is based
upon the methodology proposed by [11]. We searched the following scienti?c databases: ACM, ArXiv, Elsevier, Emerald, IEEE and Springer. Since prescriptive analytics is a new and emerging research ?eld, we used only the query term “prescriptive
AND analytics”. For the ?rst phase, we queried the scienti?c databases to ?nd papers
that contain the query in their full record, including the full text of the publication. As
shown in Fig. 1, there is almost an exponential growth of the use of the term “prescriptive analytics” in publications throughout the last years. This trend outlines an
increase of interest for this domain and constitutes a motivation for our literature
review.
The ?rst phase of our search resulted in 2,971 papers. Since the ?rst phase of the
search includes works that do not necessarily contribute to the ?eld of prescriptive
analytics, we conducted a second phase in order to look for research works with the
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K. Lepenioti et al.
Fig. 1. The trend for the “Prescriptive Analytics” term
query term in their metadata, i.e. title, abstract, keywords or other metadata of their
record. The second phase resulted in 107 papers. The third phase of our search was
conducted according to the following inclusion criteria: (i) The papers contribute to the
?eld of prescriptive analytics; (ii) the publication date is between January 2010 and
February 2018; (iii) the publication type is journal, book or conference. The third phase
resulted in 44 papers, consisting of 13 journal articles and 31 conference papers. The
results of the three phases are shown in Table 1.
Table 1. The three phases of search
Source
First phase Second phase Third phase
ACM
26
3
3
ArXiv
529
3
3
Emerald
288
11
0
IEEE
511
42
17
ScienceDirect 552
27
4
SpringerLink 1065
21
17
TOTAL
2,971
107
44
Prescriptive Analytics: A Survey of Approaches and Methods
453
4 Analysis
4.1
Classi?cation of Reviewed Papers
We classi?ed the reviewed papers in four categories according to their contribution:
(i) conceptual models, frameworks and architectures; (ii) algorithms and methods;
(iii) information systems; (iv) algorithms and methods along with information systems.
This classi?cation along with the number of papers in each category and the speci?c
references is shown in Table 2. The fact that most of the reviewed papers propose
prescriptive algorithms or/and platforms indicates that the potential of prescriptive
analytics is already recognized from the research community. Therefore, the
researchers focus on exploring the aspects of its applicability and utilization.
Table 2. Classi?cation of papers
Type of contribution
Conceptual model/framework/architecture
Algorithm/method
Information system
Algorithm/method and information system
#
6
18
13
7
References
[2, 8, 12–15]
[16–33]
[34–46]
[47–53]
The most prominent application domains of prescriptive analytics in the reviewed
literature are shown in Table 3, while individual approaches for other domains, e.g.
aerospace, travelling and computer industry, are also proposed. Moreover, we found
that nineteen (19) out of the forty four (44) papers deal with generic approaches for
prescriptive analytics, while twenty ?ve (25) papers deal with domain-speci?c
approaches. This classi?cation points out that the research interest to address speci?c
topics with a prescriptive solution is almost equal with the quest for widely applicable
prescriptive solutions.
Table 3. Application Domains
Application domain
Manufacturing
Sales
Education/research
Retail
4.2
References
[8, 13, 14, 17, 20, 22, 24, 46, 49, 50]
[16, 17, 38, 44, 47]
[2, 39–41, 48, 52]
[23, 27]
Methods and Techniques for Prescriptive Analytics
A broad coverage of the reviewed literature proposes optimization methods and techniques. Optimization has been considered to be the most appropriate approach for
addressing prescriptive analytics [14, 17]. Indicative methods and approaches include:
linear optimization, including mixed-integer, binary integer and fractional programming
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K. Lepenioti et al.
[16, 19, 26–28, 37, 44, 47], non-linear optimization methods like binary quadratic and
mixed integer non-linear programming [23, 24], stochastic optimization for handling
uncertainty in the decision making process [38], distributionally robust optimization and
statistical bootstrap of Efron [33]. In addition, simulation methods and approaches have
been developed as an enabler of prescriptive analytics [2, 13, 14, 17, 20, 50, 51, 54].
Since the business data may be non-numeric, their business solutions may rely on
qualitative analysis, logic, reasoning, collaboration and negotiation [55]. This
encourages the utilization of decision rules and decision trees in the decision-making
process. Relevant research works include: decision rules for continuously improving
business processes using real-time predictions and recommendations [22]; business
rules in combination with a simulation and optimization prescription mechanism [2]; an
information system for prescriptive maintenance in which the decision is derived
according to rules in combination with mathematical functions [50]; an architecture
with the use of proactive event processing rules by combining complex event processing (CEP) engines with predictive analytics [31].
Although, the role of machine learning in predictive analytics is well-established,
the research works dealing with machine learning in prescriptive analytics is rare. Four
(4) of the reviewed papers deploy machine learning techniques: decision trees and realtime Random Forests (RF) to support production maximization and cost minimization
of natural gas and hydrocarbon liquids [49]; k-Nearest Neighbors (k-NN), kernel
methods, trees and ensembles in order to construct the weights of a prescription
problem [38]; Random Forest, Bayesian Belief Network (BBN) and Auto-Regressive
Integrated Moving Average (ARIMA) in combination with stochastic simulation in
order to identify signi?cant KPIs and estimate the earnings per share in computer
industry [18]. Finally, twelve (12) papers propose more sophisticated solutions that
consist of combinations of optimization, simulation, custom ratings and measures,
search policies and other heuristic techniques [8, 20, 25, 27, 32, 34, 39–41, 48, 52, 53].
4.3
Prescriptive Model Lifecycle
Based on a synthesis of the literature review, a prescriptive model lifecycle consists of
three conceptual steps: model building, model solving and model adapting. These steps
are further described below.
Model Building. Model building may rely on expert knowledge, on data or on a
combination of both. The literature review reveals a clear interest on modelling the
problem in the best possible way. The ?rst approach refers to the manual building of
the model from an expert based on domain knowledge. The second approach is based
on the statement that the optimization problem can be inferred or learned from previously observed decisions taken by an expert [56]. In this sense, the model can be built
based on the collected data involved in past cases in a data-driven way without any user
interference. The third approach has to do with learning and mining the model
parameters/weights from data and provide them as input into a static model prede?ned
by the domain expert. In the last two approaches, machine learning and rule-based
techniques have been used.
Prescriptive Analytics: A Survey of Approaches and Methods
455
Model Solving. Model solving takes place after model building and provides the
expected prescription. This step is a well-studied area. The majority of model solving
approaches deal with optimization algorithms. Examples include: a modi?cation of the
Goemans-Williamson’s MAX-CUT approximation algorithm for solving a binary
quadratic programming problem related to price optimization [23] and the gradientprojection algorithm for solving a mixed-integer non-linear optimization problem
related to industrial maintenance [24]. Other approaches have been developed during
the last years, such as the evaluation and ?ltering of rules for recommendation-based
business processes [22].
Model Adapting. Model adapting is conducted in two different ways according to the
approach followed for model building: rebuilding and training the model based on the
observed data from prescriptions; updating the parameter values of a static model after
mining and analyzing the gathered data. Model adapting usually includes model validation with the aim to assure reliability of the model. For example, feedback and
adaptation mechanisms can be utilized in order to validate the accordance of the
prescriptions with the system objectives [2].
Table 4 classi?es the reviewed papers according to whether model building is
conducted solely by the domain expert, solely in a data-driven way or in combination
of both as well as according to whether it incorporates the step of model adapting. It
should be noted that thirty (30) papers recognize and attempt to exploit the era of big
data. In thirteen (13) out of the forty four (44) reviewed papers model building is
conducted based on the knowledge of the domain expert, while in twenty four
(24) papers, the domain knowledge is combined with the collected data. There are six
(6) papers that provide a fully data-driven solution for model building. Moreover, six
(6) papers consider the development of a mechanism for model adapting. One (1) of
them deals with adapting a model that is fully built in a data-driven way, while ?ve
(5) of them deal with adapting models that have been built based on both domain
knowledge and data.
Table 4. Model building and model adapting in the reviewed papers
References
[21, 25, 27, 28, 36, 37, 39–41, 44, 45, 47, 52]
[22, 31, 32, 42, 48]
[8, 13–16, 18–20, 23, 24, 26, 29, 30, 34, 35, 38,
46, 50, 53]
[33]
[2, 12, 17, 49, 51]
Model building
Domain
Dataexpert
driven
X
N/A
N/A
X
X
X
N/A
N/A
N/A
N/A
X
X
X
X
X
Model
adapting
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K. Lepenioti et al.
5 Discussion and Research Challenges
Due to the emergence of big data technologies, there is the need for methodologies and
algorithms capable of analyzing all these data and deriving useful insights. To this end,
during the last years, there is an increasing amount of research works dealing with
prescriptive analytics. However, the power of data is not yet fully incorporated in the
prescriptive analytics solutions proposed in the literature. Currently, the ?eld of prescriptive analytics is still immature due to several challenges.
Prescriptive analytics applications are usually developed in an ad-hoc way with…
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