=Paper=
{{Paper
|id=Vol-1102/amino2013_submission_6
|storemode=property
|title=(Multi-) Modeling Enterprises for Better Decisions
|pdfUrl=https://ceur-ws.org/Vol-1102/amino2013_submission_6.pdf
|volume=Vol-1102
|dblpUrl=https://dblp.org/rec/conf/models/SunkleKR13a
}}
==(Multi-) Modeling Enterprises for Better Decisions==
https://ceur-ws.org/Vol-1102/amino2013_submission_6.pdf
(Multi-) Modeling Enterprises for Better Decisions
Sagar Sunkle, Vinay Kulkarni, and Hemant Rathod
Tata Research Development and Design Center
Tata Consultancy Services
54B, Industrial Estate, Hadapsar
Pune, 411013 INDIA
{sagar.sunkle,vinay.vkulkarni,hemant.rathod}@tcs.com
Abstract. Enterprises typically depend on past experience and human expertise
for responding to changes. This is no longer cost effective in the increasingly
dynamic world of enterprises. Enterprise models are required that describe the
enterprise as well as prescribe courses of action in the face of change. Owing to
complexity of enterprises, multiple models need to be employed when addressing
specific problems. Toward this end, we present an approach that uses number of
specialized models focused on decision making. Our contributions are- first, we
show how these models can be used in concert and second, we present how these
models can be used in a real world case study of merger of two enterprises. Our
ongoing research suggests that in spite of several challenges, this approach pro-
vides promising first steps toward enabling enterprises in responding to changes
with more certainty.
Keywords: Enterprise Models, Multi-Models, Decision Making, Enterprise Anal-
ysis
1 Introduction
For the past 17 years, we have helped 70+ enterprises in their IT automation efforts. In
recent times, we have witnessed that the need for automation and its actual realization
in an enterprise is driven by multiple change drivers in various dimensions. Modern
enterprises display a characteristic connectedness between these dimensions; change in
any one dimension affects the others. Our experience suggests that lack of enterprise-
wide context that encompasses all dimensions, is the key cause of enterprises’ inability
to change cost effectively.
This led us to posit that a model of enterprise is needed that is capable of cater-
ing to all relevant dimensions of enterprise while being machine processable and ana-
lyzable [1, 2]. Furthermore, specialized modeling languages could be used with these
enterprise models for decision making in enterprises [3, 4]. Our past experience and
ongoing investigations suggest that we essentially need both descriptive [5] and pre-
scriptive models of enterprise [6]. In this paper, we describe various models that we
think are needed for better decision making. Our specific contributions are twofold-
first, we systematically elaborate on how these models can be used in-concert, and sec-
ond, we put the proposed research in the context of Mergers and Acquisitions (M&A),
a special case of enterprise transformation. By elaborating how our approach can be
used for solving prevalent M&A problems in general and M&A of two large banks in
specific, we bring out further facets of our proposed research.
The paper is arranged as follows. Section 2 motivates the need for multiple kinds
of models of an enterprise and based on our experience and investigation puts forth the
kinds of models needed for better decision making. Section 3 describes how we are
applying the proposed approach to a real world M&A case study. Section 4 concludes
the paper.
2 Enterprise (Multi-) Models
2.1 Motivation
Enterprises are complex systems of systems and responding to change is extremely
cost-, time-, and effort- intensive [2]. This is felt both when aiming at sustainable en-
terprises, i.e., maintaining business-as-usual in the face of change [7] and tackling en-
terprise transformation scenarios, i.e., changing enterprises substantially in response to
change [8]. If we consider business, IT, and infrastructure to be roughly the key di-
mensions of any enterprise [5], we can see that changes in one dimension are affected
by change drivers in other dimensions. Business change drivers such as economic and
socio-political drivers, and more specifically drivers like dynamic supply chains, merg-
ers, acquisitions, and divestitures, and globalization and regulatory compliances, etc.,
eventually lead to changes in IT and infrastructure. Similarly, changes demanded by
mobile and cloud technology eventually result in changing the way business is per-
formed.
As far as IT systems are considered, enterprises use them in particular to derive me-
chanical advantage through automation of operational processes catering to their strate-
gic, tactical and operational goals [9]. Large enterprises traditionally operate in siloized
manner for ease of management and control. This results in IT departments knowing
only their local context. As a result, IT systems are developed independently and indi-
vidually to service globally mandated goal, be it transactional (i.e., business-as-usual)
or transformational in a very specific context. This leads to a plethora of IT systems ser-
vicing same goal from the global context. Furthermore, technological requirements in
specific contexts may vary, resulting in widely non interoperable technologies. Together
these problems result in highly escalated cost of IT to business.
In general, management relies solely on expert judgment about how to tackle changes
across dimensions of enterprise and in particular resolving issues pertaining to IT sys-
tems. Our take on this is it is better to make machine-processable and analyzable models
of enterprise as a whole as well as of its key parts that are respectively descriptive and
prescriptive in nature [10], so that decision making is automated to the extent possible,
yet always carried out in the context of all of an enterprise.
2.2 Proposed Models
In using models for better decision making in enterprises, we need models that act as
inventories of all relevant information, i.e., models that are descriptive. An ArchiMate-
based enterprise model that captures information about business, IT, infrastructure enti-
ties and relations while conforming to ArchiMate metamodels would be such a model.
We also need models that can use such information and prescribe course(s) of actions
based on some criteria. This core distinction is at the basis of several kinds of models
we are proposing for better decision making in enterprises as shown in Figure 1.
1 2 3 4 5
EA-based EA-related Non EA-related Non EA-related IT IT Plant
Enterprise Model Enterprise Goal Decision Making Plant-specific Implementation
Model Model Decision Making Model
Model
Interconnected Dimensions of Goals and sub-goals of
Enterprise [what and how] Enterprise [Whys]
Fig. 1: Different Kinds of Models Required for Better Decision Making in Enterprises
EA-based Enterprise Model(s) In Figure 1, 1 shows an enterprise model that
should be based on an enterprise architecture (EA) framework. This is a descriptive
model that inventories the information about key dimensions of an enterprise. This
model can be queried for stakeholder-specific information and can also be used for
some informative analyses pertaining to given EA framework. Our initial results in cre-
ating such a model were presented in [1].
Concepts + Relations
Enterprise Model
Business
Real Enterprise
using Ontology
Manipulable +
Necessary and
Analyzable
EA-based
Sufficient Details
Information
Structure
Behavior
IT
Other
Analysis Results
Reasoning
Infrastructure
Consistency Concept Queries Rules
Checking Satisfiability
Fig. 2: EA-based Enterprise Model using Ontological Representation [1]
Figure 2 shows how an EA-based ontology representation, in this case based on
ArchiMate, can essentially make enterprise models machine-processable and analyz-
able. Note that 1 kind of models capture what and how of an enterprise but not whys.
EA-related Enterprise Goal Model 2 in Figure 1 shows a prescriptive model
focused on capturing whys or intentions behind decisions taken in enterprise. For this,
we suggested first steps using intentional modeling language i* for capturing goals in
enterprise models based on ArchiMate. In contrast to goal descriptions provided by the
EA framework, such as ArchiMate motivation extensions, our approach enables solving
problems faced by enterprise, and select the optimum strategy from the available ones
[3, 4]. This is illustrated in Figure 3. We used a bidirectional metamodel mapping to
represent problems faced by our own model-driven development unit in the concerned
case study.
Enterprise (Architecture) Strategic Dependencies [SD]
Model Via and Rationale [SR] models in i*
EA-
Intentional
Metamodel
Revise Enterprise
Mapping Organizational
(Architecture)
Impact
Model via
Analysis
Mapping
Alternatives
[SD]` + [SR]` As-is -> To-be
Fig. 3: Intentional Model Related to the Enterprise Model [3]
Non-EA-related Decision Making Model The prescriptive models of Figure 3 are
relatable with enterprise models, i.e., based on similarity in concepts of actor, behavior,
resources one could relate these models [3, 4]. But we are currently also investigating
the utility of prescriptive models that are not relatable to enterprise models in the sense
that there is no metamodel to which these models conform that could be mapped to
enterprise metamodels in this case ArchiMate metamodels. 3 in Figure 1 illustrates
such models. We describe one such model using an example of workforce planning.
Generally, spreadsheets are used to encode workforces planning parameters to mon-
itor and to take actions based on parameter values. Essentially, operational decisions are
represented using formulaic language supported by spreadsheets suitable for quantifi-
cation which is cryptic and spread over number of spreadsheets. The information about
why certain formulae are used remains largely unclear. What-if and if-what analyses,
i.e., evaluation of alternate strategies from as-is state of enterprise to its to-be state and
search of strategy that might have led the enterprise to its current as-is state respectively,
are difficult to explicate with such representation. These analyses need to be conducted
frequently as for a service company such as ours, workforce situation remains in flux.
We are therefore investigating the use of system dynamics (SD) models to address the
problem of workforce planning. The detailed description of how we propose to use SD
models to capture issues of attrition, on roll skills, and on the job headcount, and how
recruitment from campus, lateral hiring, on-boarding process etc. affect these is out of
the scope of this paper.
(a) (b)
Fig. 4: (a) Using Non-relatable Prescriptive Models with Enterprise Models (b) IT Plant-specific
Prescriptive Models with Enterprise Models
The core concepts of stocks, flows, and influencing variables in SD represent drasti-
cally different abstractions than those found in enterprise metamodels. Instead of meta-
model mapping therefore, we propose to use these models in-concert by explicating the
analysis results in the form of strategies to achieve goals which are represented with
bidirectional mapping to enterprise models. This is shown in Figure 4 (a). One obvious
example where the problems of workforce planning are directly related to enterprise-
wide context is finding the steps required for an organization to increase revenue by
10% quarter on quarter. Cost benefit analysis of workforce planning would need to see
how revenue earned changes as a function of on roll manpower, project mix to bid, and
bid success ratio etc. The results of this analysis need to be put in the overall enterprise
context, in the ArchiMate sense, at the business layer, perhaps in terms of actions that
resource management department may take to satisfy the strategic goal of increased
revenue.
Non-EA-related IT Plant-specific Decision Making Model In Section 2.1 we re-
ferred to the problems of IT systems of an enterprise that must be addressed in both
business-as-usual and transformational situations. The set of interacting IT systems of
an enterprise, and technology and hardware infrastructures underneath them is what we
refer to as an IT plant. We believe that to address the problems of local optimality with
respect to a given property, functionality overlapping, and non-interoperability between
IT systems, we need to model IT systems and their interactions with specialized mod-
els. These kinds of models are shown as 4 in Figure 1. Their usage with enterprise
models is shown in Figure 4 (b).
We expect these models to be graph-like where IT systems are nodes and the edges
are interactions such as depending on one another, accessing same set of data, simply
relaying data, and so on between them. The analysis essentially focuses on interaction
patterns of the systems of an IT plant. Such models may be constructed by observing
in automated manner how IT systems use one another and how changes in underly-
ing technology platforms and hardware infrastructure of an IT system affects other IT
systems of given IT plant. By constructing models based on interaction patterns of IT
systems and refining them over a period of time, we think that problems of optimal-
ity with respect to given criteria, non-interoperability, and overlapping functionalities
could be addressed effectively. This constitutes part of our ongoing work. Like models
illustrated earlier in Figure 4, results of IT plant-specific model analysis will have to be
put in the context of enterprise as shown in Figure 4 (b).
(a) (b)
Fig. 5: (a) Relating IT Plant Implementation Model with Enterprise Model (b) Using (Multi-)
Models of Enterprise in Concert
IT Plant Implementation Model While strategic and tactical goals focus on the
long-term orientation of the organization and short-range planning respectively, opera-
tional goals focus on implementing tactical goals at the ground level. This is also more
relevant in the case of IT as business rather than IT as support function of business. We
are proposing that there should be a bidirectional traceability between enterprise model
and IT plant implementation models via operational models that translate strategic level
requirements of data, services, processes, user experience, and non-functional proper-
ties to implementation level specifications from which IT plant may be generated. 5
in Figure 1 shows such models which are illustrated in overall context in Figure 5 (a).
We are aware of stark differences in levels of abstraction and granularity of concepts
between enterprise models and IT plant implementation models and the operational
models with data, service, process, non-functional properties, and user experience re-
quirements in between are expected to bridge this gap. Earlier in Figure 1, we did not
show the operational model, because we are not certain of what will be the nature of
these models or if these models are required at all. We believe that it should be possible
to automate to the extent possible translation of results of various analyses obtained
with the rest of the models illustrated in Figure 1 into an actionable form.
As-is To-be
Fig. 6: Using (Multi-) Models of Enterprise in Concert for Enterprise Transformation
Figure 5 (b) shows various models illustrated so far together. While the enterprise
model remains the single version of truth, results of analyses using non-EA-related
decision making models of IT- and non-IT-specific models are to be explicated in con-
junction with enterprise goal models for better decision making. Since ultimately our
focus is on IT systems, we have proposed to connect enterprise models to IT plant
implementation models via operational models (of kinds of requirements). In the next
section we review further issues pertaining to multi-modeling of enterprises.
While Figure 5 (b) can be used straight away for decision making in business-as-
usual situations, Figure 6 shows how transformational situations can be addressed using
proposed models. Note that Figure 6 does not show as-is IT plant implementation mod-
els for the want of space. The as-is and to-be EA-based enterprise models are connected
via EA-related goals models. Other decision making models may be used for specific
problems in transformation and eventually IT plant implementation of to-be enterprise
model may be obtained.
In the next section, we show how the proposed models may be applied to a case
study of M&A of two wealth management companies taking into account various issues
outlined above.
3 Proposed Application to M&A
Background The case study concerns two large independent Wealth Management (WM)
(retail brokerage) companies (WM1 and WM2) which came together to form WM3.
The combined retail brokerage house has 10000+ Financial Advisors, managing multi-
billion dollars in client assets across 700+ locations in country X. WM1 and WM2 both
provide WM products such as credit, lending, annuity, insurance, banking, etc. and ser-
vices like brokerage, advisory, financial planning, wealth planning, retirement planning,
and trust etc., to wealthy individuals and small-to-medium size businesses across X.
WM3 was formed the expressed strategic goal of tripling WM1’s revenue and gross
margin in 5 years. WM3 also has strategic growth viewpoint where it needs to provide
new and innovative products and services to its clients. This requires a renovated state-
of-the-art IT platform to compete with its more aggressive peers and ever increasing
tech-savvy clients. In the following, we discuss how various decision making models
may be used to address specific problems in conjunction with the enterprise model.
Using non EA-related Decision Making Models for Business Aspects Several
tactical goals were devised that would contribute positively to key strategic goal. Three
of these tactical goals were optimize/rationalize branch and back-office operations, ra-
tionalize wealth management products and services, and of course, integrate workforces
of WM1 and WM2. Each of these three goals can be achieved using SD or similar mod-
els, which are essentially examples non EA-related decision making models.
For the tactical goals mentioned above, particularly for workforce integration of
WM1 and WM2, SD models could be used in a manner similar to the way they are used
in workforce planning as discussed earlier in Section 2.2. For optimization/rationaliza-
tion of branch and back-office operations, an important fact to consider is that WM1
and WM2 were competitors prior to the merger with several branches in the same lo-
cality. Consolidating the branches and their operations would result in recurring cost
savings every year. The decision to which ones to keep as-is, which ones to merge,
and which ones to let go of depends on branch operations related parameters such as
whether it is owned/leased, cost and duration of the existing lease, importance of the
location from WM3 perspective, floor capacity, terms and conditions, future growth po-
tential at that location, etc. Decisions on optimal branch structure, e.g., how many/which
branches should form a complex, how many/which complexes should be part of a re-
gion (western/eastern/northern/southern), etc., need to be taken to better manage WM3.
The WM1/WM2 back-office operations also need to be optimized along similar lines.
WM1 and WM2 operate in similar business domain and hence have similar and even
overlapping product and service portfolios. For rationalization of wealth management
products and services, it is necessary to look at these products and services portfolios
from WM3 business model perspective and take decision on keeping as-is, enhanc-
ing (modifying/merging/re-branding), decommissioning (retiring) the mix of products
and services. The parameters that would be of relevance in taking decisions include
product capabilities, channels, WM3 requirements, integration with other products/ser-
vices, 3rd party (product/bank/vendor) involvement, etc. Also, it makes sense to look
at cross-selling opportunities within WM1 and WM2 in terms of existing clients for
products/services that were otherwise not available before the merger.
Using non EA-related IT Plant-specific Decision Making Models With regards
IT platforms of WM1 and WM2, key goals were to integrate IT platforms of WM1 and
WM2 so as to obtain optimum IT platform functionality in WM3, optimizing WM3 IT
platform capacity, and come up with optimum data conversion/migration from WM2 to
WM1 in WM3.
In case of optimum WM3 IT platform functionality, the alternatives are to build a
new IT platform or enhance one of the existing WM1 or WM2 IT platforms to support
the WM3 target operating model arrived at separately. In order for the target IT platform
to support WM3 operating model, decisions need to be made on which applications to
keep as-is from WM1 or WM2, which ones to enhance (modify/merge-functionality),
which ones to build from scratch (nothing can be reused from WM1/WM2), which ones
to decommission and when. Note that problems of IT systems of enterprise described in
Section 2 get accentuated in this case because of the size and legacy of WM1 and WM2
combined together and therefore need further efforts in capturing interaction patterns of
IT systems of WM1 and WM2 themselves and possible interactions between IT systems
of WM1 and WM2.
To resolve the problem of optimizing WM3 IT platform capacity, current size of
WM1 + WM2 needs to be considered along with the future growth plans for WM3
which is that the capacity of the WM3 platform needs to be doubled. It means the exist-
ing applications (selected for WM3) should be able to handle 3 times their current vol-
ume (# of transactions, clients, branches, financial-advisors, employees, etc.) without
(negatively) impacting performance. This would require changes such as optimizing/
re-writing database queries, re-architecting / re-designing, adding more hardware, etc.
The changes would cut across multiple layers and applications. There could be prob-
lems similar to Y2K that need to be addressed. For example, 3 digits were sufficient
to accommodate WM1 Branches, but WM3 is going to have more # of branches and 3
digits may no longer be sufficient to accommodate WM3 branches.
For data migration problem, existing and historical data needs to be converted from
the source to the target platform and applications. This includes both business critical
and non-critical data. Since there is terabytes of data involved and limited conversion
(live cut-over) time-window available because of the nature of the business, there is very
limited scope for making an error (in speed and quality of the conversion) during the
entire process. The converted data should also comply with the regulatory requirements
applicable for WM3. We are currently investigating how above mentioned problem
descriptions can be represented using decision making models of IT plant as described
in Section 2.2.
Decision making in M&A Problems in Enterprise Context Figure 7 extends the
transformational situation captured in Figure 6, and shows the merger of WM1 and
WM2. This merger was initiated by WM1, therefore the as-is and to-be states are de-
picted as that of WM1. EA-based enterprise models of WM1 and WM2 are descriptive
models which capture all relevant information about WM1 and WM2 in a manner ex-
plained in [1]. The EA-related enterprise goal model using intentional modeling repre-
sents the strategic goal of revenue increment and its further breakdown into sub-goals
as in [3, 4]. The decomposition of goals and sub-goals here needs to continue till re-
sults of analysis on non EA-related decision making models including those that are
IT plant-specific can be plugged in suitably in the EA-related enterprise goal model.
Chosen alternatives to resolve specific problems are eventually implemented in the IT
plant implementation of WM3 via bridge provided as discussed in Section 2.2.
…
Workforce Products and
Branches
WM1 Integration Services
WM3 IT Plant
Triple revenue and gross
margin in 5 years WM3 Implementation
IT Plant
WM2 IT Plant
Capacity
Optimum Data
Integration Migration
Enhancement
…
Fig. 7: Using (Multi-) Models of Enterprise in Concert for M&A
As we proceed with our approach, we found some issues that need to be addressed
which we enlist below-
– Integrating multi-models of enterprise As discussed, multi-(level and formal-
ism)models are needed to address problems faced by enterprises. Using common
ontology to map concepts from different modeling languages to each other [11] and
level-agnostic metamodeling [12] may provide some help to address this issue.
– Keeping models and reality in sync Reality may already have changed till the
time various models are completed. We think agile concepts applied to enterprise
modeling would help along the lines discussed in [13]. Furthermore, multi-models
focused on specific concerns may help in keeping models and aspects of reality
they capture in sync.
– Relating strategic goals with properties of operational elements Strategic goals
and desirable properties of operational elements like IT systems are at different
level of abstraction. We need ways of computing properties [14] and deliberating
their tradeoff before plugging them in various decision making models.
– Treating various uncertainties Some of the uncertain aspects of enterprise model-
ing are variation in meaning of concept based on modeling language, modeling the
reality completely and accurately, reconciling modeling information spread over
multiple sources and multiple levels of abstraction, and ways in which enterprise
phenomena affect each other. Probabilistic methods are suggested for this [15], but
further research is needed for application to specific kind of uncertainty.
Many of the above issues have been recognized already by several researchers in
different contexts. Complexity and size of enterprise models make addressing them as
effectively as possible even more pertinent.
4 Conclusion
We proposed combination of EA-based enterprise model and set of models special-
ized for decision making pertaining to specific aspects of enterprise as descriptive and
prescriptive models respectively. Initial treatment of real world merger of two large
enterprises using our multi-model approach indicates that problems faced by enterprise
that demand specific ways of solving can be modeled and solved in separation yet main-
taining the overall enterprise context. Furthermore, modeling enterprise concerns down
to IT plant itself means that both business-as-usual and transformational situations can
be tackled. Initial results suggest that multi-models of enterprise help in separating and
localizing decision making in enterprise.
References
1. Sunkle, S., Kulkarni, V., Roychoudhury, S.: Analyzing Enterprise Models Using Enterprise
Architecture-based Ontology. In: MoDELS. (2013) Accepted.
2. Kulkarni, V., Sunkle, S.: Next Wave of Servicing Enterprise IT Needs. In: IEEE Conference
on Business Informatics. (2013) Accepted.
3. Sunkle, S., Kulkarni, V., Roychoudhury, S.: Intentional Modeling for Problem Solving in En-
terprise Architecture. In: Proceedings of International Conference on Enterprise Information
Systems (ICEIS). (2013) Accepted.
4. Sunkle, S., Roychoudhury, S., Kulkarni, V.: Using Intentional and System Dynamics Mod-
eling to Address WHYs in Enterprise Architecture. In: ICSOFT-EA. (2013) Accepted.
5. Lankhorst, M.: Enterprise Architecture at Work: Modelling, Communication and Analysis.
Springer (2005)
6. Saat, J., Winter, R., Franke, U., Lagerström, R., Ekstedt, M.: Analysis of IT/Business Align-
ment Situations as a Precondition for the Design and Engineering of Situated IT/Business
Alignment Solutions. In: HICSS, IEEE Computer Society (2011) 1–9
7. Laverdure, L., Conn, A.: SEA Change: How Sustainable EA Enables Business Success in
Times of Disruptive Change. Journal of Enterprise Architecture 8(1) (feb 2012)
8. Rouse, W.B., Baba, M.L.: Enterprise transformation. Commun. ACM 49(7) (2006) 66–72
9. Ross, J.W., Beath, C.M.: Sustainable IT Outsourcing Success: Let Enterprise Architecture
Be Your Guide. MIS Quarterly Executive 5(4) (2006)
10. Greefhorst, D., Proper, E.: Architecture Principles: The Cornerstones of Enterprise Archi-
tecture (The Enterprise Engineering Series). 2011 edn. Springer (June 2011)
11. Roque, M., Vallespir, B., Doumeingts, G.: Interoperability In Enterprise Modelling: Trans-
lation, Elementary Constructs, Meta-modelling And Ueml Development. Computers in In-
dustry 59(7) (2008) 672–681
12. Henderson-Sellers, B.: On the Mathematics of Modelling, Metamodelling, Ontologies and
Modelling Languages. Springer Briefs in Computer Science. Springer (2012)
13. McGovern, J., Ambler, S.W., Stevens, M., Linn, J., Sharan, V., , Jo, E.: The Practical Guide
to Enterprise Architecture. Prentice Hall (2003)
14. Lagerström, R., Saat, J., Franke, U., Aier, S., Ekstedt, M.: Enterprise Meta Modeling Meth-
ods - Combining a Stakeholder-Oriented and a Causality-Based Approach. In Halpin, T.A.,
Krogstie, J., Nurcan, S., Proper, E., Schmidt, R., Soffer, P., Ukor, R., eds.: BMMDS/EMM-
SAD. Volume 29 of Lecture Notes in Business Information Processing., Springer (2009)
381–393
15. Johnson, P., Lagerström, R., Närman, P., Simonsson, M.: Enterprise Architecture Analysis
With Extended Influence Diagrams. Information Systems Frontiers 9(2-3) (2007) 163–180