![](bg6.png)
Zachman (Zachman, 1987) TOGAF (TOGAF,
1995), Archimate (Archimate, 2012), that provide
holistic blueprints for the organizational and
architectural models. However, a key aspect that is
missing is machine processability analyzability,
which is the core contribution of this paper. MEMO
(Frank, 2002) provides a method to support the
development of enterprise models. Abstractions for
various interrelated aspects like corporate strategy,
business processes, organizational structure and
information models are provided, but, with limited
support for automated analysis. Other key topics like
Business-IT alignment, landscape mapping etc, are
covered in detail over the past (Schekkerman, 2006),
however the focus of this paper is more on
automated machine-dependent (i.e., minimum
human dependency) decision making using a variety
of appropriate modeling techniques. From a tooling
perspective, various tools exist for enterprise
architecture and business process modeling (Scheer,
1996; IBM RSA, 2014; iGrafx, 2014; MEGA,
2014), however analysis support is limited to
simulation of business processes so as to identify
process bottlenecks and suggest optimization in
terms of resources, time and cost. These tools do not
provide support for taking forward analysis results
of one model onto another. Moreover, analysis
capability of these tools is limited to business
process models only. Existing literature on
enterprise modeling research (Schekkerman, 2006)
also does not include evidence of use of multiple
modeling techniques in conjunction, or of model
checking to verify multiple modeling paradigms. To
this respect, our previous work on mapping
Intentional models with System Dynamic models in
the context of EA (Sunkle et al., 2013) was an early
start. In this paper, we have extended that work by
introducing the concept of modeling across various
layers of the enterprise with suitable techniques that
are appropriate for that layer and finally we propose
to orchestrate them in concert to get a holistic view
of the enterprise.
5 CONCLUSIONS
In this paper, we discussed a model-centric approach
to enable enterprises improve their agility and
prepare them for better adaptive responsiveness. We
proposed a layered architecture for modeling
enterprises wherein the adjoining layers have a well-
defined relationship and each layer addresses a set of
coherent concerns as seen from the perspectives of a
set of stakeholders. The key idea is to specify each
layer in terms of a model which can be viewed as a
set of relatable models each constituting an intuitive
and closer-to-problem-domain specification of a
concern – as advocated by separation of concerns
principle. We argued the case for these models to be
relatable, analyzable and simulatable. We illustrated
the rationale behind the proposed model-centric
approach through a motivating example. We
described several modeling techniques (e.g.,
intentional, stock-n-flow, agent-based) that best
match an underlying problem scenario. We
described how each one of the models caters to
specific goals and how they relate to and
complement each other. We further described how
our proposed solution percolates analysis results
from one model to another model either in the same
or in a different enterprise layer. Until now, we have
found very little evidence of such an approach in the
existing literature and believe that the enterprise
engineering community can largely benefit from the
investigations and position taken in this paper.
REFERENCES
ArchiMate 2. 0 Specification 2012. Van Haren Publishing
Series. Bernan Assoc.
Bresciani, P., Giorgini, P., Giunchiglia, F., Mylopoulos, J.,
Perini, A., 2004. Tropos: An Agent-Oriented Software
Development Methodology,In Autonomous Agents
and Multi-Agent Systems, Vol. 8, No. 3, pp. 203–236,
Springer.
Clark, T., Barn, B., 2011. Event driven architecture
modelling and simulation, In SOSE’11, pp. 43-54,
IEEE Computer Society.
FEA 2006. Federal Enterprise Architecture Program EA
Assessment Framework 2.1.
Frank, U., 2002. Multi-perspective enterprise modeling
(MEMO) conceptual framework and modeling
languages," In HICSS., pp.1258-1267. IEEE
Computer Society.
Forrester, J., 1958. Industrial Dynamics--A Major
Breakthrough for Decision Makers, In Harvard
Business Review, Vol. 36, No. 4, pp. 37–66.
IBM RSA 2014. http://www-03.ibm.com/software/
products/us/en/ratisystarchfami.
iGrafx 2014. http://www.igrafx.com.
Kulkarni, V., Roychoudhury, S., Sunkle, S., Clark, T.,
Barn, B., 2013. Modeling and Enterprises - The Past,
the Present, and the Future, In MODELSWARD’13,
SCITEPRESS.
Lankhorst, M., 2005. Enterprise Architecture at Work:
Modeling, Communication and Analysis, Springer.
MEGA EA Tools, 2014. http://www.mega.com/en/
solution/enterprise-architecture.
ICEIS2014-16thInternationalConferenceonEnterpriseInformationSystems
470