=Paper=
{{Paper
|id=Vol-1164/PaperVision16
|storemode=property
|title=Anticipation-driven Architecture for Proactive Enterprise Decision Making
|pdfUrl=https://ceur-ws.org/Vol-1164/PaperVision16.pdf
|volume=Vol-1164
|dblpUrl=https://dblp.org/rec/conf/caise/MagoutasSBAMS14
}}
==Anticipation-driven Architecture for Proactive Enterprise Decision Making==
https://ceur-ws.org/Vol-1164/PaperVision16.pdf
Anticipation-driven Architecture for Proactive
Enterprise Decision Making
Babis Magoutas1, Nenad Stojanovic2, Alexandros Bousdekis1, Dimitris Apostolou1,
Gregoris Mentzas1, Ljiljana Stojanovic2
1
Information Management Unit, National Technical University of Athens, 9 Iroon
Polytechniou str., 157 80 Zografou, Athens, Greece
{elbabmag, albous, dapost, gmentzas}@mail.ntua.gr
2
FZI Research Center for Information Technologies, Haid-und-Neu-Str. 10-14, 76131
Karlsruhe, Germany
{nstojano, stojanov]@fzi.de
Abstract. In this paper we present a visionary approach about a new
architecture for supporting proactive decision making in enterprises. We argue
that a cognitive approach of continuous situation awareness can enable
capabilities of proactive enterprise intelligence and propose a conceptual
architecture outlining the main conceptual blocks and their role in the
realization of the proactive enterprise. The presented approach provides the
technological foundation and can be taken as a blueprint for the further
development of a reference architecture for proactive enterprise applications.
We illustrate how the proposed architecture supports decision-making ahead of
time on the basis of real-time observations and anticipation of future undesired
events by presenting a practical application in the oil and gas industry.
Keywords: proactivity, predictive analytics, enterprise, decision-making,
event-driven computing, condition-based maintenance.
1 Introduction & Motivation
Today’s enterprises are facing increasing pressure due to globalization, uncertainties,
and increased regulations, among others. These pressures are forcing the companies to
manage production at the margins of performance, achieving better control through
the whole of the production process. As an example companies need to know what
goods are in transit, what is about to enter the warehouse, what is being shipped from
suppliers, in order to dynamically route goods in-transit.
To cope with these challenges in general, dynamic business networks need to
enhance their monitoring capabilities within the network and across different levels.
To achieve this, real time monitoring can be used and such kind of real-time
monitoring have been already implemented in many enterprises in the form of
extensive sensor/IoT- systems. Indeed, sensing enterprises are a reality, starting from
manufacturers that can sense some deviations from the production plan as soon as
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they appear, till large logistics networks that sense delays about the delivery time in
real-time. The main driving concept in sensing enterprises is events and
correspondingly, the event-driven architecture (EDA) is underlying their realization.
However, event monitoring, is only the first, crucial step to manage problems in
complex, dynamic systems. Next step is enabling that event monitoring copes with
the scale and dynamics of the business context (internal and external). Indeed, change
is constant, therefore monitoring solutions must also change so they can adapt and
stay relevant. For example, changes in the business performances should be registered
as soon as they happen and taken as new monitoring goals.
This kind of dynamic monitoring is the basis for the new level of (sensing)
performance observing that is not only sensing the problems, but also sensing that the
problem might appear, i.e. focusing on a proactive approach. Indeed, observing a
delay is very useful information, but anticipating that there will be a delay is far more
important from the business point of view. Moreover, such anticipation will lead to
the possibility to act ahead of time, i.e. to be proactive in resolving problems before
they appear or realizing opportunities before they become evident for the entire
business community and be able to recover and support continuity. This ability to
support continuity in operations at the margins is called resilience [1], and is a key
strategy in today's and future industrial operation. From the architecture point of view
this requires reorientation from events as changes that happened in time to
anticipation as prediction that something will happen in near future.
In this paper we present a visionary approach about anticipation-driven sensing and
decision-making that will enable the transition from sensing to proactive enterprise.
One of the main novelties is the treatment of anticipation as the first class citizens in
our approach: it supports the whole life-cycle of the anticipation, from
sensing/generating anticipations till validating the reactions (proactions) based on
them. We argue that a cognitive approach of continuous situation awareness can
enable capabilities of proactive enterprise intelligence and propose a conceptual
architecture for proactive enterprises systems. We present an application scenario for
proactive decision-making in the area of condition-based maintenance.
The rest of the paper is organized as follows. Section 2 discusses our vision for the
proactive enterprise, while Section 3 outlines the proposed approach for realizing
such a vision. Section 4 presents our proposed architecture and Section 5 an
envisaged scenario where proactivity can be injected in enterprise decision-making.
Section 6 discussed related work, while Section 7 concludes the paper.
2 Vision
Our vision for the proactive enterprise compared to the current reality of sensing
enterprise is illustrated in Figure 1. Sensing enterprises are operating on the surface of
the possibilities (the tip of the iceberg), whereas a deeper diving into the endless
wealth of opportunities is required in order to enable the transition to the proactive
enterprise. Consequently, like the events are driving reactivity in the sensing
enterprise, anticipations (predictions) are driving proactivity in proactive enterprise
leading to increased situation awareness capabilities even ahead of time (cf. Figure 1).
This requires new methods and technologies that are responsible for dealing with
anticipations, which are part of the novel anticipation-driven architecture:
Anticipation-driven Architecture for Proactive Enterprise Decision Making 123
Anticipation based on Big data - Exploiting the power of big enterprise data,
by sensing the whole business ecosystem: shifting relevant business context
from internal processes to the ecosystem
Anticipation-based Actions - Extracting the actionable meaning from data,
by applying advanced big data predictive analytics: shifting the processing
capabilities from real-time into ahead-of-time processing
Anticipation-driven Optimization - Increasing the strategic value of data
analysis for decision making, by dynamically adapting patterns of interest
found in real-time big data streams and enabling proactive decisions: shifting
decision making focus from early warnings into business optimization
Figure 1: From Sensing till Proacting. (1-> 2, Space axis: From Processes to the
Business Ecosystem; 3-> 4, Time axis: From Real-time to Ahead of Time
Processing; 5 -> 6: Decision Making: From Early Hints to Business
Optimization)
This will lead to a new class of enterprise systems, proactive and resilient
enterprises, that will be continuously aware of that what „might happen“ in the
relevant business context and optimize their behavior to achieve what “should be the
best action” even during stress and balancing on demanding margins. Proactive
enterprise systems will be able to suggest early on to the decision makers the most
appropriate process adjustments to avoid singular system behavior and optimize its
performance.
This paper introduces a novel architecture for realizing proactive enterprise,
encompassing anticipations as the first class citizens and driver of the processing. The
architecture is based on the Observe, Orient, Decide, Act (OODA) loop of situational
awareness that has been recognized as one of main models for the big data supply
chain [2] - the key for continuous situational awareness. This model sees decision-
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making occurring in a recurring cycle of unfolding interaction with the environment,
oriented via cues inherent in tradition, experience and analysis. These cues inform
hypotheses about the current and emerging situation that, in turn, drive actions that
test hypotheses.
The first step towards continuous situation-aware proactivity is to enable
comprehensive observing of the relevant enterprise context/ecosystem through the
design and development of a smart sensing system able to cope with a huge amount of
heterogeneous (big data) in real-time, focusing on predictive sensing (sensing early
warnings – anticipations). Next, semantic understanding of acquired data in near real
time should be enabled (orient) by designing and developing an efficient management
framework for dynamic (proactive) and context-aware anticipation and detection of
the situations of interest on the basis of complex and predictive data analysis
algorithms and event-detection. This will provide the basis for supporting making
decisions and actions ahead of time through designing and developing mechanisms
for the proactive recommendations based on the dynamic situational awareness and
the predictive data analysis. An example of this dynamic mechanism is the use of
proactive indicators to support resilience. Finally, proactive handling that will result
in sustainable business improvements should be ensured, through designing and
developing methods for defining and dynamic monitoring of KPIs and corresponding
adaptation of the whole OODA cycle, closing the feedback loop and leading to the
continual proactive business optimization. Figure 1 illustrates how our approach for
realizing the OODA loop can be seen in the context of the proactive enterprise vision.
3 Conceptual Architecture
Based on the proposed continuous situational awareness approach presented above,
we outline the main conceptual blocks and their roles in the realization of the
proactive enterprise platform, an anticipation-based platform for integrating
heterogeneous real-time and dynamic streams created by hardware sensors, software
and external data used in enterprises. The proposed conceptual architecture is strongly
oriented on the OODA loop and combines services of smart sensing, anticipation
management, incremental proactivity and proactivity management (see Figure 2).
Smart sensing services include adapters, pub-sub middleware and the Scalable
Event Storage. Adapters enable communication with all necessary enterprise
information sources such as hardware sensors (which might include vibration and
temperature sensors, environmental sensors), software sensors from ERP and other
enterprise systems and external business context data. Pub-sub middleware is realized
as an event cloud, a scalable, P2P based repository that delivers RDF events to the
requesting parties (subscribers) and ensures the decoupling between components so
that the system can scale very easily. The Scalable Event Storage enables semantic
enrichment with background knowledge of real-time streams and allows storage of
events (in the form of RDF triplets) received from adapters for historical and
statistical purposes. It supports synchronous and asynchronous queries expressed in a
subset of the SPARQL language and accessible through corresponding APIs.
Services for anticipation management will enable the generation of real-time,
data-driven predictions, as well as the discovery of unusual situations, based on
events delivered by storage. Novel predictive analytics services will be realized as
Anticipation-driven Architecture for Proactive Enterprise Decision Making 125
intelligent services on the top of probabilistic stream processing technologies. The
Complex Event Processing (CEP) component has the role of dynamic definition and
detection of complex events and reasoning over events, supplied by Event Storage.
Complex Event Patterns can be defined and deployed dynamically or produced by
offline analytics. CEP allows goal-driven identification of relevant situations of
interest and leverages detection of anomalies in real-time providing the basis for
proactive actions.
Incremental proactivity service subscribes for predicted situations of interest
(pub/sub communication) and generates corresponding proactive recommendations,
by taking into account business context. It couples dynamic and uncertain decision
making methods and decision theoretic optimization models and proactively
recommends actions and activation time maximizing the utility for the enterprise,
while considering several criteria such as cost, time and safety.
Proactivity Management deals with defining and dynamic monitoring of KPIs
and corresponding adaptation of the whole OODA cycle, closing the feedback loop
and leading to the continual proactive business optimization.
By taking into account the complexity of the business environment the modern
enterprise is working in (Big Data, Dynamic Context, Critical Decision Making), we
argue that this architecture, by assuming that it will be validated through use cases,
can be taken as a blueprint for further development of a reference architecture for
Proactive enterprises.
Figure 2: Conceptual Architecture
4 Envisaged Scenario & System Walkthrough
In this section we present a practical application of the proposed framework for
anticipation-driven sensing and proactive decision-making, in the oil and gas industry.
We describe the practical role and use of the proposed framework focusing on how it
can support decision-making ahead of time on the basis of real-time observations,
126 Pre-proceedings of CAISE’14 Forum
predictions and anticipation of future undesired events, through an indicative scenario
of proactive condition-based maintenance (CBM).
CBM in the oil and gas industry employs various monitoring means to detect
deterioration and failure in some critical drilling equipment. Equipment failure
situations can be forecasted/anticipated based on observations of events related to this
equipment or the surrounding environment; e.g monitoring engine temperature
indicators, monitoring electric indicators (measuring change in the engine’s electric
properties) and performing oil analysis [3]. In reality, several different patterns will
imply various failure distributions; for the sake of the example, we will focus on
thermal indicators, adopting the approach presented in [4]. More specifically, in this
scenario we focus on the gearbox drilling equipment and consider as indicators the
rotation speed of the drilling machine’s main shaft in RPM, along with the lube oil
temperature of the drilling machine’s gearbox.
We distinguish between two different types of operations performed by
components of the proposed framework in order to support anticipation management;
offline and online operations performed at design time and real-time, respectively. At
design-time the offline analytics component extracts from historical data of oil
temperature, RPM events and gearbox equipment failure, the distributions associated
with gearbox breakdown along with their relation to monitored indicators and builds a
breakdown prediction model that will enable the generation/detection of the
anticipation of interests. Moreover, it identifies which complex event patterns indicate
that a drilling gearbox equipment failure starts to occur, on the basis of historical data,
and communicates the identified pattern to CEP for real time monitoring.
At real-time, the CEP component detects a complex pattern of simple oil
temperature and RPM events characterized by an abnormal oil temperature rise (10%
above normal) measured over 30% of the drilling period when drilling RPM exceeds
a threshold, caused by abnormal friction losses in the drilling gearbox during drilling.
This pattern, learned at the offline phase, is a strong indication that a gearbox
equipment failure starts to occur. Based on the pattern detected by CEP in real-time,
the online predictive analytics component analyzes the current trend of oil
temperature and RPM increases and drops for the most recent events and predicts
(anticipates) the occurrence of a future gearbox break down along with the associated
probability distribution function, based on trend analysis and the breakdown
prediction model learned at the offline phase.
Based on the predicted probability distribution for the occurrence of a future
gearbox breakdown, the online decision-making component provides proactive
recommendations of actions that either mitigate (i.e. reduce the probability of
occurrence) or completely eliminate the future gearbox breakdown, along with the
recommended activation time. This component applies dynamic and uncertain
decision making methods and decision theoretic optimization models that minimize
cost or maximize the utility for the oil drilling company. Examples of actions aiming
to optimize the maintenance policy according to cost criteria may be a) to take the
equipment down for full maintenance - an action that completely eliminates the
predicted gearbox breakdown - or perform less costly actions that only reduce the
probability of failure such as b) perform lubrication of metal parts, or c) shift drilling
to lower pressure mode. Actions could also be related to resource management and
organization of the resources needed to rectify the gearbox failure in case it occurs.
Anticipation-driven Architecture for Proactive Enterprise Decision Making 127
The business added value of anticipation-driven proactive decision-making in this
scenario is huge. With a typical day rate for a modern oil rig being around USD 500
000, reducing undesired downtime, with its associated high cost (one hour of saved
downtime is typically worth USD 20 000) is of outmost importance in the oil drilling
industry. Therefore, we expect that the proposed framework, which is able to provide
early notifications about equipment problems and proactive recommendations about
optimal decisions on the basis of utility, cost and other factors, will allow proactive
enterprises in the oil and gas industry to gain a strong competitive advantage based on
reduced downtimes and optimized performance.
5 Related Work
Although the idea of proactive computing may seem simple, the quantity and quality
of proactive applications is rather modest. Proactive applications have been developed
in an ad-hoc manner for several years; applications regarding proactive decision-
making include network management [5], supply chain management [6] scheduling of
manufacturing systems [7] and maintenance [8].
Especially maintenance has gathered significant research interest. Although there is
not a complete agreement in the literature about the classification of maintenance
types, they can generally be divided to three categories: breakdown maintenance
which takes places when a failure occurs, time-based preventive maintenance which
sets certain activities when a defined period of time passes and Condition Based
Maintenance (CBM) which recommends actions according to the health state of the
manufacturing system [9]. In CBM, real-time proactive decision support becomes
significant because a maintenance strategy, usually based on a prognosis model, needs
to be implemented [8]. Several techniques have been developed within the framework
of CBM by utilizing OR, AI, multiple criteria methods and several statistical
techniques accompanied with the appropriate architecture [10].
Despite these applications, the lack of a generic paradigm to develop proactive
event-driven applications makes it difficult for this capability to spread. Because of its
nature, proactive computing requires an integration of various technologies for
sensing, real-time processing and decision-making. The approach presented in this
paper provides the technological foundation and can be taken as a blueprint for the
further development of a reference architecture for proactive enterprise applications.
With respect to the maintenance domain, which is the application domain of the
presented envisaged scenario, our approach goes beyond time-based preventive
maintenance by extending stochastic preventive maintenance methods [11] and
integrating them in an innovative anticipation-driven ICT architecture.
6 Conclusions and Future Work
We argue that the proposed architecture for anticipation-driven decision-making is the
ultimate basis for realizing proactive enterprise. This has several implications for
practitioners. They need to be prepared both in technical terms and from a cognitive
perspective to take advantage of the novel business intelligence capabilities that will
be provided. On the one hand they need to design and implement physical (such as
smart sensors and actuators, location-aware sensors, cyber-physical systems) and
128 Pre-proceedings of CAISE’14 Forum
virtual sensors (such as agents in customer transaction and relationship systems) in
virtually every aspect of their enterprise that has an impact on the end result.
Regarding future work, we aim to follow a multi-aspect approach for validating the
main facets of the proposed research. We will pursue validation in diverse enterprise
settings with different technical constraints and user requirements so that the impact is
leveraged. Validation will be performed on a technical level (covering system-related
metrics such as performance) and on a business level, covering the benefits for end-
users of the proposed system. Specifically, on the business perspective, validation will
be focused based on performance in terms of decreased maintenance costs and
equipment deterioration and increased reliability. On the other hand, domain experts
will validate the results based on factors which are usually hard to measure such as
increase in safety, decrease of environmental impact and the added value of the
proactive business intelligence capabilities provided. Validation of the approach will
be performed in the context of the ongoing FP7 project ProaSense in two main use
cases: proactive manufacturing in the area of production of lighting equipment, and
proactive maintenance within the oil and gas sector.
Acknowledgments. This work is partly funded by the European Commission project
FP7 STREP ProaSense “The Proactive Sensing Enterprise” (612329).
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