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1 | P a g e
Determining Key Attributes for Profiling the
Information Technology (IT) Portfolios
June 22, 2015
[Anonymized for review]
[Note: A graduate student has done the bulk of this work.]
Abstract
Prior studies have shown that the key concept of IT Portfolio Management (ITPM) is to improve
the performance of IT investment and to optimize business value for the entire enterprise. To
exploit the ITPM domain, we aim to integrate critical IT attributes with enterprise strategic goals
to address the fundamental concept of IT investment planning and decision-making in this paper.
Through incorporating the chosen IT portfolio attributes using Data Envelopment Analysis
(DEA)-based model, a firm will be able to optimize efficiency of organizational units driven by
IT portfolios and to systematically profile numerous IT portfolios via efficient frontiers, also
known as best-practice frontiers, to better articulate upcoming IT investment decisions.
Furthermore, since the conventional DEA model only measures efficiency at a sole
organizational level, we extend toward a new ITPM model, termed the DEA/Parallel (DEA/P)
model, to optimize efficiency across organizational levels in a multi-business unit firm. Our
methodology incorporates mathematical optimization and computational experiments, along with
combining real-world data using the Monte Carlo approach, to simulate a firm’s IT portfolios
and provide theoretical insights into the components of the optimal solution. Based on the
analysis of using two DEA-based models (conventional DEA model and the proposed DEA/P
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model), we compare the differences of these two models through three aspects: efficiency score,
efficient frontier, and distance function. Specifically, instead of being determined by a senior
executive’s intuition, weight scores produced by our proposed DEA/P model enable a firm to
create a rationale viewpoint of how much to invest in each strategic goal to improve investment
efficiency. Accordingly, our findings indicate that the conventional DEA model’s efficient
frontier could potentially be applied to a lower bound for investing in less risky business
objectives, while the DEA/P model’s efficient frontier could be applied to a higher bound for
managing IT resources before entering a risky market. Therefore, the two main contributions of
this paper are as follows: 1) a new methodology, which is used to demonstrate the optimality that
measures the efficiency of IT resource allocation driven by IT portfolios across multi-
organizational levels/units simultaneously and 2) strategies for locating efficient frontier and
distance function, which are used to illustrate graphical results profiling the chosen IT portfolio
attributes and to indicate the gap between the best-practice frontier and inefficient Decision
Making Unit (DMU) aimed at the follow-up efficiency improvement.
Keywords: IT Portfolio Management (ITPM), IT portfolio attributes, Data Envelopment Analysis (DEA),
efficient frontier
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I. Introduction
Typically, information technology (IT) is considered to have a cost saving role among various
business functions in a firm. Chan et al. (1997) found that the “fit” between Information System
(IS) and business objectives is significantly associated with the performance of a firm. According
to the latest forecast by the research firm Gartner, Inc. (2014), global IT spending grew by 3.2 %
to total $3.8 trillion U.S. dollars. Following the implementation of the Sarbanes-Oxley Act, many
enterprise investment decisions are strictly examined; as a result, investment issues have become
a great concern nowadays for many firms. To better position IT in terms of business value
creation, IT Portfolio Management (ITPM) aims to improve the performance of IT investment
and to optimize the business value of enterprise IT. In addition, compared to financial-related
investments, the relationship between inputs (e.g., budgeted cost) and outputs (e.g., expected
return) in the IT investment contexts may not be linear. Due to several specific features including
a non-parametric approach and linear fractional programming model, Data Envelopment
Analysis (DEA) is regarded as an appropriate methodology to cope with non-linear problems
related to IT investment issues to demonstrate IT resource allocation (Tanriverdi and Ruefi,
2004). On the other hand, DEA has been applied to solve multi-attribute decision-making
problems in several areas. Through incorporating multiple key IT portfolio attributes using DEA-
based model, the key motivation of our ITPM research is to assist a firm in systematically
profiling numerous IT portfolios that comprise the chosen IT portfolio attributes to better
accomplish its enterprise business objectives.
Following enterprise IT viewpoints, we consider an IT portfolio to be a production unit when
measuring the efficiency of IT resource allocation. Given the organizational benefits of IT
portfolios, making appropriate IT investment decisions to achieve optimal IT resource allocation
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across different organizational levels has been recognized as one of the critical issues for
enterprise executives. Thus, our research question is: “How can a Multi-Business Unit Firm
Incorporate Numerous IT Portfolio Attributes to Optimize its Efficiency across Organizational
Levels?” To address this research question correspondence to IT resource allocation within the
firm, the two main contributions of this study are as follows: 1) a new methodology, which is
used to demonstrate the optimality by measuring the efficiency of IT resource allocation driven
by IT portfolios across multi-organizational levels/units simultaneously and 2) strategies for
determining efficient frontier and distance function, which are used to illustrate graphical results
profiling the chosen IT portfolio attributes and to indicate the gap between the best-practice
frontier and inefficient Decision Making Unit (DMU) aimed at the follow-up efficiency
improvement. This paper is organized as follows: Section II reviews theoretical studies. Our
model is developed in Section III. In Section IV, the proposed methodology is illustrated with a
hypothetical example. Section V concludes the results and presents managerial interpretations.
II. Theoretical Development
2.1 IT Resources and IT Portfolio Management (ITPM)
Zhu and Kramer (2002) and Zhu (2004) point out that the Resource-Based View (RBV) in
Information System (IS) research has been widely applied to interpret how enterprises are able to
produce competitive value from IT assets. Meanwhile, firm performance, including sustainability,
will increase by leveraging IT. According to Melville, Kraemer and Gurbaxani (2004), the RBV
is used to resolve the productivity paradox and to explain how firms create business value in
connection with an organization’s competences. In accordance with Hitt and Brynjoifsson (1996),
the production theory can be used to evaluate IT investments regarding IT productivity.
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Regarding the enterprise IT point of view, an IT project is the main tactical level through which
IT activity translates to business results for the enterprise. IT project selection is an essential
business problem because most IT components are customized for an enterprise through project
implementation (Cho and Shaw, 2013). Integrating Zhu (2003) and Ray et al. (2005)’s concepts,
an IT portfolio level can be considered a bridge that connects project levels to the firm level
regarding strategic IT resource allocation. The IT portfolio of a firm is understood as its total
investment in computing and communication technology (Weill and Vitale 2002), or the sum
total of all IT projects. According to Jeffery and Leliveld (2004), the definition of IT portfolio
management (ITPM) is to manage IT as a portfolio of assets similar to a financial portfolio and
then strive to improve the performance of the portfolio by balancing risk and return. The key
motivation for doing ITPM is to find the most applicable IT portfolio to improve the
performance of IT investment and to optimize the business value of enterprise IT.
2.2 Productivity Theory and Data Envelopment Analysis (DEA)
Prior research has shown that production theory has been widely utilized to uncover how best to
combine resource inputs to achieve desired outcomes, and production theory can be used to
evaluate IT investments concerning IT productivity (Hitt and Brynjoifsson, 1996). Among
different production functions, one method, namely, Data Envelopment Analysis (DEA),
proposed by Charnes, Cooper and Rhodes (1978), does not require any particular characteristics
such as statistical distribution and functional form. In this respect, the DEA model is broadly
used to estimate productivity analysis to address the inputs consumed by the outputs produced,
and it also shows the tradeoffs in achieving various performance metrics (Banker et al., 2004,
2011). Compared to other approaches, the DEA model lessens the complexity of analysis by
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concurrently measuring the relevant attributes of multiple Decision Making Units (DMUs) and
then turning out a composite score, referred to as efficiency (Powers and McMullen, 2000).
2.3 Why use DEA to Solve IT Portfolio Management (ITPM) problems?
ITPM involves making applicable decisions to achieve a firm’s strategic objectives by fine-
tuning budgeted costs and returns as business conditions change; thus, the objectives of ITPM
are to plan, measure and optimize the business value of enterprise IT. With reference to financial
economics literature, the relationship between return and risk is positively linear, whereas the
relationship between return and risk regarding IT investments may be non-linear (Tanriverdi and
Ruefli, 2004). Motivated by the non-linear relationship between return and risk in IT investment,
the DEA model is known as a non-parametric approach and a linear fractional programming
model, and there is no need for DEA to include explicit mathematical forms between inputs (e.g.,
risk) and outputs (e.g., return). For these reasons, DEA is an appropriate methodology to uncover
hidden relationships among multiple inputs and outputs while incorporating the chosen IT
portfolio attributes in the ITPM field to demonstrate IT resource allocation. To put it simply,
DEA can be used with heterogeneous metrics of inputs and outputs in the ITPM context (Cho,
2010). Additionally, since both budgeted cost and benefit can be seen as key IT portfolio
attributes related to scarce firm resources most of the time, combining both of them with IT
function enhances IT productivity and, in turn, the organization’s growth. Accordingly, we
summarize the numerous main IT portfolio attributes in the Appendix.
2.4 How can DEA be applied to the IT Portfolio Management (ITPM) context?
Addressing portfolio problems is linked to the challenge of enterprise resource allocation to
maximize value in most cases (Dia, 2009); therefore, there is a need to develop an appropriate
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method that can combine various IT portfolios attributes to measure the productivity of IT
project portfolios in the ITPM domain. Briefly, IT productivity is regarded as the relationship
between a firm's IT-related investments and its associated efficiency gains, such as financial
returns. To tackle this issue, since IT function-based strategic goals are implemented by a set of
IT-related projects, called an IT (project) portfolio, we can apply DEA-based models to optimize
the relative efficiency of organizational units driven by IT portfolios and thereby demonstrate the
organizational performance. Further, while applying the ITPM context to better accomplish
enterprise business objectives, the DEA-related model is able to not only incorporate multiple IT
portfolio attributes to address IT resource allocation but also to generate the efficient frontier (the
best-practice frontier), which appears as a graphical outcome of optimal combination of inputs
and outputs for a firm systematically profiling numerous IT portfolios.
2.5 Parallel DEA Model and its contributions
Before the concept of parallel production model was applied to the DEA model, researchers
considered either a firm level or an organizational department level to be an individual DMU
without connecting them with lower organizational levels when measuring efficiency of resource
allocation, and, therefore, they did not devise the decomposition of a production system into
several sub-systems. Research on parallel production systems began with Färe and Primont
(1984) building on the conventional DEA model, and Kao (2009) applied the theory and
proposed the general parallel production system with multiple processes operating independently
from earlier work. Moreover, the contribution of the parallel DEA model is to decompose a
system into multiple separated processes through the concept of parallel production system and
then measure both the system and process efficiencies of each DMU in one linear program
framework. Particularly, when applying parallel DEA model to ITPM context built on Kao
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(2012), senior executives could construct a rationale viewpoint of how much to invest in each
strategic goal, which is implemented by the IT project portfolio, to improve investment
efficiency without the problem of resource duplication.
III. Model Development
3.1 A conventional DEA model for ITPM context
By taking into account the nature and complexity of the relation, the DEA model is a proper
multi-attribute model to estimate inputs (e.g., risk and budgeted cost) and outputs (e.g., expected
returns), while transforming the ratio of multiple inputs and outputs into an equivalent linear
program by a scalar measurement ranging from 0 (the worst) and 1 (the best) for each DMU
(Charnes et al., 1978). Since an IT project is the main level through which IT activity translates
to business results, IT (project) portfolios can be thought of as a pool of heterogeneous IT
projects within a firm. By importing this concept associated with DEA’s features, we will
prioritize the IT projects as DMUs by using the conventional DEA model applied in the ITPM
context as shown in Table 1A, along with variables and definition in Table 1B.
3.2 A new proposed DEA/P model for ITPM context in a multi-business unit firm
Milgrom and Roberts (1990; 1995) assume a one-level firm with resources and activities as its
elements. However, Barua, Lee and Whinston (1996) and Barua and Mukhopadhyay (2000)
contend that the firm should be conceptualized in multiple levels because investments into
resources and activities are converted into firm level performance outcomes through several
intermediate levels. The main reason is that IT plays a role in all levels of the firm, from
investments through intermediate levels to ultimate risk/return performance (Tanriverdi and
Ruefi, 2004). IT portfolios perform the particular IT-related functions not only link to enterprise
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strategic goals but also to support the associated business plans at each organizational level.
Typically, an enterprise consists of various business units (or organizational departments), and
each business unit may have its own strategic priority to accomplish enterprise IT-driven
strategic goals, which can be realized by a set of ongoing IT projects, known as the IT project
portfolio. For these reasons, we are interested in developing a good mechanism to assess the
productivity of IT resources driven by IT project portfolios to reach IT-driven strategic goals,
while allocating IT resources to different organizational levels in a multi-business unit firm.
Thus, we propose a new ITPM model called the Data Envelopment Analysis /Parallel-based
model, or DEA/P, which builds on the parallel DEA model to optimize efficiency of strategic
goals implemented by various IT portfolios in a parallel resource allocation for a multi-business
firm. Meanwhile, we illustrate the three main components of our proposed DEA/P model in
Figure 1: (1) Business Unit, (2) Strategic Goals implemented by IT Project Portfolio, and (3) IT
Projects. Referring to the DEA/P’s mathematical equations in Table 2A, higher organizational
levels distribute their strategic IT resources into several lower organizational levels via a parallel
approach. In section IV, we will demonstrate our DEA/P model with a hypothetical example
with reference to a Fortune 50 firm’s IT project portfolios. Likewise, how to calculate efficiency
scores for multiple organizational levels by using the DEA/P model can be found in Table 2B.
3.2.1 Parameter/Variable Definition
To address the strategic priority of each business unit or organizational department in a firm, a
weight score, 𝑤(𝐼𝑇𝑃𝑃), derived from the DEA/P model can be regarded as a percentage of IT
resources assigned to an IT (project) portfolio. The weight scores generated by our DEA/P model
is the percentage (%) of IT budget allocation to demonstrate the strategic priority of the
enterprise strategic goal. Therefore, instead of determined by senior executive’s intuitive or
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subjective experience, the contributions of the DEA/P model’s weight scores enable a firm to
create a rationale viewpoint of how much to invest in each strategic goal to improve investment
efficiency for each business unit. Further, the selection of input and output variables plays an
essential role in the DEA/P model since these variables reflect variations in IT-related resource
utilization across different organizational levels. Along with the DEA/P model components in
Table 2A and Table 2B, we summarize the DEA/P model’s parameters and variables in Table 2C.
3.2.2 Model Assumption and Managerial Interpretation
There are a few assumptions concerning our proposed DEA/P model while measuring the
efficiency of IT resource allocation across multiple business units. Firstly, when a firm assigns
its IT resource to different organizational levels, it will be through a top-down parallel approach
without considering the interdependency value for each organizational level (e.g., business unit,
IT portfolio, and IT project). Secondly, each business unit has its own strategic goals realized by
a number of IT (project) portfolio, and therefore different business units will not share the
assigned IT resources with each other. Thirdly, since the IT Portfolio Management (ITPM)
domain is defined as a continuous process to manage IT project, application, and infrastructure
assets and their interdependencies (Kumar et al., 2008), we will mainly address the IT project
portfolio in this paper. Based on our model assumptions, the summary of managerial
interpretations regarding our proposed DEA/P model in the ITPM context is shown in Table 2D.
IV. Hypothetical Example and Analysis by Using the DEA and DEA/P Models
4.1 Research Design and Simulation Data Description
According to Gartner’s estimate of average IT spending and IT budgeting key initiative overview
(McGittigan, 2014), the average enterprise IT investment is assumed to be 3.5% of revenue and
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projects are constructed based on an average project size of $4 million. In this study, we depend
on a computational model for simulating firm selection of IT project investment portfolio. Our
model design is based on the research context of this study, which is a US Fortune 50 enterprise
in the finance and insurance sector. The firm’s senior executives review IT project investments
periodically. With respect to research design, we will build a systematic approach to simulate a
large amount of IT project portfolio data based on actual operational parameters from a number
of Fortune 50 firms’ IT project portfolios. To combine the suggested IT portfolio attributes while
addressing ITPM-related issues, our proposed model includes important parameters/variables
that are common but may uniquely characterize the IT portfolio.
Along with the expected skewed distribution, the descriptive statistics for the simulated IT
portfolios are in Table 3 and our simulated data across multiple organizational levels can be
found in Figure 2. Thus, we demonstrate the two DEA models through simulated IT portfolio
data to address 3 business units, 9 IT Portfolios and 90 IT projects.
4.2 Analysis for a simulated multi-business unit firm using the DEA model and DEA/P model
The enterprise IT resources are connected with different organizational levels (i.e. business unit,
IT portfolio, and IT project); therefore, we next evaluate the efficiency of a multi-business unit
firm by using the conventional DEA and our proposed DEA/P model to differentiate these two
DEA-based models through three aspects: (1) Efficiency Score - the efficient score of Decision
Making Unit (DMU) is understood as the status of IT-related resource utilization, (2) Efficient
Frontier - the efficient frontier is the optimal ratio combination between inputs (e.g., budgeted
cost) and outputs (e.g., expected return), and (3) Distance Function associated with Efficient
Frontier – this can illustrate graphical results profiling the chosen IT portfolio attributes and
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indicate the gap between the best-practice frontier and inefficient DMU aimed at follow-up
efficiency improvement. Detailed discussions related to these three aspects are shown below.
4.2.1 Business Unit Level
(1) Efficiency Score and Efficient Frontier (Best-Practice Frontier)
Three business units are considered to be three DMUs on the subject of the business unit level
associated with on our simulated data, and the efficiency scores (E) produced by the
conventional DEA and our proposed DEA/P model are able to reveal efficiency of IT resource
allocation for three business units shown in Table 4A. Consequently, E = 1 means that an
organizational unit (e.g., business unit) achieves the optimal condition for its IT resource
allocation in comparison to the peer DMUs. Otherwise, the organizational unit would be thought
of as having inefficient status.
In addition, our simulated data in Figure 2 indicates that business unit-B (BU-B) has the lowest
IT investments and business unit-C (BU-C) has the highest IT investments. Based on the
graphical result in Figure 3, two business units (BU-B and BU-C) reach the efficient frontier
produced by the conventional DEA model; however, none of the business units meets the
efficient frontier generated by the DEA/P model. Referring to our results, the senior executives
may leverage the DEA/P model to make more economical IT resources allocation, compared to
the conventional DEA model.
(2) Distance Function associated with Efficient Frontier
Although there are numerous IT portfolio attributes, the efficient frontier in our hypothetical
example is comprised of two input variables (i.e. general spending and labor cost) and one output
variable (expected return). Thus, we found that the business unit-A (BU-A) is the only business
unit that is slightly distant from the efficient frontier. To build on the concept of distance
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function, the distance between two points of the xy-plane can use the distance formula; therefore,
the distance between (x1, y1) and (x2, y2) is given by: d = √(∆𝑥)2 + (∆𝑦)2
For example, according to the distance formula as shown above, the senior executives could
modify resource allocations to make the BU-A reach the efficient frontier generated by the
conventional DEA model, as the other two business units in Table 4B. Additionally, Table 4B
indicates that three business units have closer distance between the origin and the efficient
frontier produced by the DEA/P model. In line with this perspective, if a firm intends to embrace
an economical investment decision as shown in Figure 3, it may lower both ratios of Labor to
Expected Return and General Spending to Expected Return to reach the efficient frontier
generated by the DEA/P model, ranging from the efficient frontier generated by the conventional
DEA model.
4.2.2 IT (Project) Portfolio Level
(1) Efficiency Score, Weight Score, and Efficient Frontier (Best-Practice Frontier)
In terms of IT-related resource utilization, each business unit may have multiple ongoing IT
portfolios to realize enterprise strategic goals, and we consider IT portfolios to be DMUs in this
section. Compared to the conventional DEA model, our DEA/P model is able to measure the
efficiency of IT portfolios across multiple business units simultaneously and to generate an
additional managerial reference, called a Weight Score, which is defined as how much to invest
in each strategic goal to improve investment efficiency.
In our hypothetical example, both IT portfolio – E (a small-sized IT portfolio focusing on
innovation management in BU-B) and IT portfolio – I (a large-sized IT portfolio focusing on
customer management in BU-C) reach the optimal condition by applying the conventional DEA
model, but these two IT portfolios show there is still some room for efficiency improvement by
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using DEA/P model. Additionally, the results from the DEA/P model show that these two IT
portfolios belonging to two separated business units reveal very different weight scores (i.e. w =
0.188 for IT portfolio – E and w = 0.71 for IT portfolio – I); therefore, the weight scores can be
used to emphasize their strategic focus as shown in Table 5A. In Figure 4, two IT portfolios (E
and I) reach the efficient frontier produced by the conventional DEA model, but none of the IT
portfolios meets the efficient frontier generated by the DEA/P model. Thus, we mark point E’
and I’ to represent the two optimal points on the efficient frontier generated by the DEA/P model,
and these two points can be seen as targets regarding the upcoming investment decisions.
(2) Distance Function associated with Efficient Frontier
We summarize the distance from origin to each IT portfolio in Table 5B. Since there are only
two IT portfolios that achieved the efficient frontier generated by the DEA model, we further
estimate these two IT portfolios’ distance from origin to the efficient frontier produced by the
DEA/P model. In terms of resource focus, these two IT portfolios may make some changes to
improve their efficiency as follows: IT portfolio - E (a small-sized IT portfolio focusing on
innovation management in BU-B) may need to lower General Spending but increase the Labor
Cost; IT portfolio - I (a large-sized IT portfolio focusing on customer management in BU-C)
may need to reduce the Labor Cost but increase General Spending.
Based on our example, the weight scores from the DEA/P model can be seen a complement
reference for the senior executives to find out the strategic focus, while linking IT resource
allocation to the enterprise strategic goals. As such, a higher weight score can be considered a
more influential strategic focus connected to a certain organizational level. In this regard,
because IT portfolio – I has the highest weight score among others, it will be the first priority to
accomplish a particular strategic goal in our hypothetical example.
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4.2.3 IT Project Level
(1) Efficiency Score, Weight Score, and Efficient Frontier (Best-Practice Frontier)
The IT portfolio level is considered to be the bridge connecting the firm level to the IT project
level, and IT projects are implemented at a primary level to demonstrate a specific IT-driven
business functions within the enterprise. After analyzing our simulated data as shown in Figure 2,
there are only three IT projects (i.e. A26, A32, and C25) that achieve the optimal condition
among all the 90 IT projects by using the conventional DEA model in Table 6A, whereas there is
not any IT project that appears in the optimal condition by using the DEA/P model in Table 6B.
Also, the efficient frontiers in Figure 5 could be a reference for the senior executives to better IT
resource allocation for the IT projects. Briefly speaking, this shows that the DEA/P model has a
more economic viewpoint concerning the efficiency measurement rather than the conventional
DEA model. Specifically, weight scores produced by the DEA/P model can bring up the
emphasis of resource allocation to better complement the efficiency score.
(2) Distance Function associated with Efficient Frontier
Referring to our analysis along with simulated data, when IT resources are distributed across the
organizational levels (from business unit level to the IT project level), both DEA and DEA/P
model show that the subordinate organizational levels may gain lower efficiency scores due to
the increasing number of production units. For those IT projects that are relatively close to x axis,
we suggest that the senior executives may either reduce the investments in the General Spending
or lift up the Expected Return in order to lower the ratio of General Spending to Expected Return.
On the other hand, for those IT projects that are relatively close to the y axis, we recommend that
the senior executives may either reduce the investments in the Labor Cost or raise the Expected
Return in order to lower the ratio of Labor Cost to Expected Return.
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V. Discussion
5.1 Conclusion
Along with combining real-world data using the Monte Carlo approach to simulate firms’ IT
portfolios, our methodology incorporates mathematical optimization and computational
experiments to optimize IT resource utilization. Thus, based on the analysis by using two DEA-
based models (conventional DEA model and the proposed DEA/P model), we compare the
differences, including efficiency scores, efficient frontiers, and distance functions. Based on our
hypothetical example, the results indicate that the small-sized IT portfolio focusing on
innovation management and the large-sized IT portfolio focusing on customer management may
easily reach the optimal condition in the customer-oriented business unit by applying the
conventional DEA model, but these two IT portfolios show there is still some room for
efficiency improvement by using DEA/P model. In this regard, the senior executive may better
improve the efficiency of IT resource utilization by using DEA/P model because it can
incorporate both input and output variables across multiple organizational levels that are
connected with each other. Specifically, based on the weight score generated by our proposed
DEA/P model, the “customer management” appears as the most critical strategic goal compared
to others. This could be that our research context of this study is a US Fortune 50 enterprise in
the finance and insurance sector.
5.2 What do the results mean to IT Portfolio Management?
Our findings indicate that the conventional DEA model’s efficient frontier could potentially be
applied to a lower bound for investing in less risky IT portfolios, while the DEA/P model’s
efficient frontier could be applied to a higher bound for managing IT portfolios before entering a
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risky market. Both DEA and DEA/P model show that the subordinate organizational levels may
gain lower efficiency scores due to the increasing number of production units, and the advantage
of using the DEA/P model over conventional DEA to solve ITPM problems is to have a more
economic viewpoint concerning the efficiency measurement. To address the limitation of the
conventional DEA model, our proposed DEA/P model is able to incorporate all inputs and
outputs related to IT investments across organizational levels that are connected with each other.
Specifically, instead of being determined by a senior executive’s intuition, weight scores
generated by the proposed DEA/P model enable a firm to create a rationale viewpoint of how
much to invest in each strategic goal to improve investment efficiency for the organizational unit.
We aim to assist a firm in comprising the chosen IT portfolio attributes among alternative
choices to better achieve enterprise business objectives when making upcoming investment
decisions. Thus, the two main contributions can be found as follows: 1) a new methodology,
which is used to demonstrate the optimality that measures the efficiency of IT resource allocation
driven by IT portfolios across multi-organizational levels/units simultaneously and 2) strategies
for determining efficient frontier and distance function, which are used to illustrate graphical
results profiling the chosen IT portfolio attributes and to indicate the gap between the best-
practice frontier and inefficient Decision Making Unit (DMU) aimed at the subsequent efficiency
improvement. Regarding our future work, we will run a large-scale simulation to complement
our initial illustrative example, and accordingly, the results from the simulated data may serve as
strong references when applying our proposed ITPM model to better analyze empirical data.
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Tables
Table 1B – Parameter/Variable and Definition for DEA model
Variable Definition
𝐸𝑗
𝑥1𝑗
The efficiency of IT project j
Estimated cost of IT project j
𝑥2𝑗 Estimated risk of IT project j
𝑦𝑗 Estimated return of IT project j
𝑢 The weight on the return
𝑣1 The weight on the cost
𝑣2 The weight on the risk
Table 1A – DEA model in ITPM context
Max 𝐸𝑗 =𝑢𝑦𝑗
𝑣1𝑥1𝑗 + 𝑣2𝑥2𝑗
Subject to 𝑢𝑦𝑗
𝑣1𝑥1𝑗 + 𝑣2𝑥2𝑗 ≤ 1
j = 1… n
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Table 2A – DEA/Parallel Model (mathematical equation) in ITPM context
Table 2B – Efficiency Score for multiple organizational levels via DEA/P Model
𝐸𝐵𝑈 = 1 - 𝑠𝑘
(𝐵𝑈)
𝐸𝐼𝑇𝑃𝑃 = 1 - 𝑠𝑘
(𝐼𝑇𝑃𝑃)/ ∑ 𝑣𝑖
𝑚𝑖=𝐼(𝐼𝑇𝑃𝑃) 𝑋𝑖𝑘
(𝐼𝑇𝑃𝑃)
𝐸𝐼𝑇𝑃 = 1 - 𝑠𝑘
(𝐼𝑇𝑃)/ ∑ 𝑣𝑖
𝑚𝑖=𝐼(𝐼𝑇𝑃) 𝑋𝑖𝑘
(𝐼𝑇𝑃)
𝑤(𝐼𝑇𝑃𝑃) = ∑ 𝑣𝑖𝑋𝑖𝑘
(𝐼𝑇𝑃𝑃)i∈𝐼(𝐼𝑇𝑃𝑃)
∑ 𝑣𝑖𝑋𝑖𝑘𝑚𝑖=1
20 | P a g e
Table 2C – Parameter/Variable and Definition for DEA/Parallel Model
Parameter Definition
𝐸𝐵𝑈
𝐸𝐼𝑇𝑃𝑃
𝐸𝐼𝑇𝑃
Efficiency scores of IT-related resource allocation across multi-organizational
levels; that is, firm level, business unit (BU), IT project portfolio (ITPP), and
IT project (ITP), can be generated by our proposed DEA/P model.
v𝑖 Weight on the ith input (IT resource) variable
u𝑟 Weight on the rth output (expected return) variable
𝑋𝑖𝑘(𝐵𝑈)
A certain amount of the ith input (IT resource) is assigned to the specific
Decision Making Unit (DMU) k; therefore, DMU k is considered as a specific
business unit k with regard to the business unit (BU) level.
𝑌𝑟𝑘(𝐵𝑈)
The rth output (expected return) is produced by the specific Decision Making
Unit (DMU) k; therefore, DMU k is considered as a specific business unit k
with regard to the business unit (BU) level.
𝑋𝑖𝑗(𝐵𝑈)
A certain amount of the ith input (IT resource) is assigned to the Decision
Making Unit (DMU) j; therefore, DMU j is considered as a business unit j with
regard to the business unit (BU) level.
𝑌𝑟𝑗(𝐵𝑈)
The rth output (expected return) is produced by the Decision Making Unit
(DMU) j; therefore, DMU j is considered as a business unit j with regard to the
business unit (BU) level.
𝑠𝑘(𝐵𝑈)
The buffer IT resources related to the specific Decision Making Unit (DMU) k;
therefore, DMU k is considered as a specific business unit k with regard to the
business unit (BU) level.
𝑋𝑖𝑘(𝐼𝑇𝑃𝑃)
Amount of input (resource) i required for the IT project portfolio from the
specific Decision Making Unit (DMU) k
𝑌𝑟𝑘(𝐼𝑇𝑃𝑃)
Given certain input-based resource allocations, an amount of output (return) r
expected for the IT project portfolio from the specific Decision Making Unit
(DMU) k
𝑋𝑖𝑗(𝐼𝑇𝑃𝑃)
Amount of input (resource) i required for the IT project portfolio from
Decision Making Unit (DMU) j
𝑌𝑟𝑗(𝐼𝑇𝑃𝑃)
Given certain input-based resource allocation, an amount of output (return) r
expected for the IT project portfolio from Decision Making Unit (DMU) j
𝑠𝑘(𝐼𝑇𝑃𝑃)
The buffer IT resources for the IT project portfolio level under the specific
Decision Making Unit (DMU) k
𝑋𝑖𝑘(𝐼𝑇𝑃)
Amount of input (resource) i required for the IT project from Decision Making
Unit (DMU) k
𝑌𝑟𝑘(𝐼𝑇𝑃)
Given certain input-based resource allocations, an amount of output (return) r
expected for the IT project from Decision Making Unit (DMU) k
21 | P a g e
𝑋𝑖𝑗(𝐼𝑇𝑃)
Amount of input (resource) i required for the IT project from Decision Making
Units (DMU) j
𝑌𝑟𝑗(𝐼𝑇𝑃)
Given certain input-based resource allocations, an amount of output (return) r
expected for the IT project from Decision Making Units (DMU) j
s𝑘(𝐼𝑇𝑃)
The buffer IT resources for the IT project level under the specific Decision
Making Unit (DMU) k
Table 2D – Summary for Parameter/Variable associated with Managerial Interpretation
Parameter/Variable Range Managerial Interpretation
EBU: E-score for Business
Unit or Org. Department
EITPP: E-score for IT
project portfolio level
EITP: E-score for IT
project level
E = 0 (worst) ~ E = 1 (optimal)
The higher efficiency score can be
understood as a better strategic resource
allocation in connection with an
organizational level, as such; E = 1
means the optimal condition for IT-
related strategic resource allocation.
S: Slack score The slack score is associated
with E-score
Utilized resources – a lower score
indicates high utilization and a higher
score indicates organizational slack.
W: Weight score
(strategic option focus) W = 0 (worst) ~ W = 1 (optimal)
The weight score can be seen as how
much to invest in each strategic goal to
improve investment efficiency.
A higher weight score can be considered
a more influential strategic focus
connected to a certain organizational
level.
X1: Input Variable 1
Each hierarchical organizational
level has its amount of resources
related to Labor Cost
Labor Cost
X2: Input Variable 2
Each hierarchical organizational
level has its amount of resources
related to General Spending
General Spending
Y1: Output variable 1
Each hierarchical organizational
level has its amount of resources
related to Expected Return
Expected Return
22 | P a g e
Table 3 – Descriptive Statistics for the Simulated IT Portfolio
Variable Mean Std. Dev.
Budgeted Cost 1 – Labor Cost (X1) $2.96 Million $12.22 Million
Budgeted Cost 2 – General Spending (X2) $8.8 Million $28.72 Million
Expected Return (Y) $12.38 Million $42 Million
Table 4A – Efficiency Score for Business Unit (BU)
DEA model DEA/P model
Business Unit Efficiency score BusinessUnit Efficiency score
BU – A E = 0.969 BU – A E = 0.683
BU – B E = 1 BU – B E = 0.743
BU – C E = 1 BU – C E = 0.728
Table 4B – Distance Function for Business Unit (BU)
DEA model DEA/P model
Business
Unit
Distance from origin
to each business unit
Business
Unit
Distance from origin
to each business unit
BU – A OA = 0.721 BU – A OA’ = 0.537
BU – B OB = 0.694 BU – B OB’ = 0.515
BU – C OC = 0.781 BU – C OC’ = 0.569
Range
for the
better
resource
allocation
The optimal condition for the BU-A is that
the ratio of Labor to Expected Return goes
down to 0.25 and ratio of General Spending
to Expected Return goes down to 0.676;
therefore, we suggest multiple ways for the
BU-A to get closer the DEA model’s efficient
frontier as follows:
- Labor Cost could go down to 3.15%
- General Spending could go down to 3%
- Expected Revenue could go up to 3.07%
Range for
the better
resource
allocation
The efficient frontier generated by the
DEA/P model can be considered to a more
economical best-practice frontier, which
represents a shorter distance from origin to
the efficient frontier.
23 | P a g e
Table 5A – Efficient Score for IT (Project) Portfolio
DEA model DEA/P model
IT Portfolio Efficiency score IT Portfolio Efficiency score Weight score
IT Portfolio – A (BU-A1) E = 0.885 IT Portfolio – A (BU-A1) E = 0.679 0.177 (17.7%)
IT Portfolio – B (BU-A2) E = 0.914 IT Portfolio – B (BU-A2) E = 0.683 0.589 (58.9%)
IT Portfolio – C (BU-A3) E = 0.947 IT Portfolio – C (BU-A3) E = 0.686 0.234 (23.4%)
IT Portfolio – D (BU-B1) E = 0.934 IT Portfolio – D (BU-B1) E = 0.691 0.169 (16.9%)
IT Portfolio – E (BU-B2) E = 1 IT Portfolio – E (BU-B2) E = 0.796 0.188 (18.8%)
IT Portfolio – F (BU-B3) E = 0.932 IT Portfolio – F (BU-B3) E = 0.741 0.643 (64.3%)
IT Portfolio – G (BU-C1) E = 0.889 IT Portfolio – G (BU-C1) E = 0.570 0.167 (16.7%)
IT Portfolio – H (BU-C2) E = 0.960 IT Portfolio – H (BU-C2) E = 0.638 0.123 (12.3%)
IT Portfolio – I (BU-C3) E = 1 IT Portfolio – I (BU-C3) E = 0.776 0.710 (71.0%)
Table 5B – Distance Function for IT (Project) Portfolio
DEA model DEA/P model
IT Portfolio
Distance from
origin to each IT
portfolio
IT Portfolio Distance from origin to
DEA/P’s E-frontier
IT Portfolio – A (BU-A1) OA = 0.752 IT Portfolio – A (BU-A1)
Since both IT portfolio E and IT
portfolio I achieve the optimal
efficiency via the conventional
DEA model, we intend to
further uncover the more
economical resource allocation
by using the DEA/P model.
Thus, OE’ = 0.515 and OI’ =
0.622
IT Portfolio – B (BU-A2) OB = 0.744 IT Portfolio – B (BU-A2)
IT Portfolio – C (BU-A3) OC = 0.736 IT Portfolio – C (BU-A3)
IT Portfolio – D (BU-B1) OD = 0.733 IT Portfolio – D (BU-B1)
IT Portfolio – E (BU-B2) OE = 0.647 IT Portfolio – E (BU-B2)
IT Portfolio – F (BU-B3) OF = 0.698 IT Portfolio – F (BU-B3)
IT Portfolio – G (BU-C1) OG = 0.734 IT Portfolio – G (BU-C1)
IT Portfolio – H (BU-C2) OH = 0.711 IT Portfolio – H (BU-C2)
IT Portfolio – I (BU-C3) OI = 0.802 IT Portfolio – I (BU-C3)
24 | P a g e
Table 6A – Efficiency Score for IT Project
DEA model
IT Project Efficient Score Efficient Score Efficient Score
Aij
A: Org. Dept. A
i: IT Portfolio
j: IT Project
Bij
B: Org. Dept. B
i: IT portfolio
j: IT project
Cij
C: Org. Dept. C
i: IT portfolio
j: IT project
A11 0.697
A12 0.701
A13 0.816
A14 0.692
A15 0.794
A16 0.844
A17 0.688
A18 0.838
A19 0.786
A10 0.855
B11 0.705
B12 0.769
B13 0.711
B14 0.702
B15 0.723
B16 0.687
B17 0.700
B18 0.830
B19 0.795
B10 0.886
C11 0.684
C12 0.693
C13 0.702
C14 0.716
C15 0.696
C16 0.809
C17 0.694
C18 0.724
C19 0.693
C10 0.749
A21 0.732
A22 0.785
A23 0.859
A24 0.656
A25 0.701
A26 1.000
A27 0.839
A28 0.689
A29 0.771
A20 0.693
B21 0.869
B22 0.731
B23 0.710
B24 0.702
B25 0.737
B26 0.693
B27 0.824
B28 0.799
B29 0.782
B20 0.687
C21 0.686
C22 0.711
C23 0.825
C24 0.791
C25 1.000
C26 0.777
C27 0.696
C28 0.863
C29 0.692
C20 0.718
A31 0.731
A32 1.000
A33 0.728
A34 0.747
A35 0.830
A36 0.803
A37 0.771
A38 0.732
A39 0.793
A30 0.800
B31 0.746
B32 0.732
B33 0.699
B34 0.708
B35 0.693
B36 0.713
B37 0.756
B38 0.758
B39 0.726
B30 0.733
C31 0.726
C32 0.712
C33 0.702
C34 0.801
C35 0.801
C36 0.654
C37 0.816
C38 0.708
C39 0.702
C30 0.766
25 | P a g e
Table 6B –Efficiency Score for IT Project
DEA/P model
IT Project Efficient Score Weight Efficient Score Weight Efficient Score Weight
Aij
A: BU- A
i: IT Portfolio
j: IT Project
Bij
B: BU-B
i: IT portfolio
j: IT project
Cij
C: BU-C
i: IT portfolio
j: IT project
A11 E = 0.697
A12 E = 0.701
A13 E = 0.816
A14 E = 0.692
A15 E = 0.794
A16 E = 0.844
A17 E = 0.688
A18 E = 0.838
A19 E = 0.786
A10 E = 0.855
W = 6.70%
W = 2.90%
W = 9.00%
W = 21.4%
W = 2.80%
W = 3.90%
W = 45.5%
W = 14.0%
W = 0.40%
W = 1.50%
B11 E = 0.705
B12 E = 0.769
B13 E = 0.711
B14 E = 0.702
B15 E = 0.723
B16 E = 0.687
B17 E = 0.700
B18 E = 0.628
B19 E = 0.602
B10 E = 0.671
W = 32.8%
W = 2.30%
W = 24.5%
W = 3.00%
W = 1.20%
W = 9.00%
W = 2.50%
W = 6.50%
W = 3.50%
W = 25.5%
C11 E = 0.681
C12 E = 0.533
C13 E = 0.540
C14 E = 0.551
C15 E = 0.536
C16 E = 0.807
C17 E = 0.534
C18 E = 0.721
C19 E = 0.533
C10 E = 0.577
W = 1.50%
W = 4.50%
W = 39.0%
W = 0.10%
W = 9.30%
W = 1.20%
W = 7.30%
W = 9.00%
W = 2.90%
W = 25.2%
A21 E = 0.732
A22 E = 0.785
A23 E = 0.650
A24 E = 0.656
A25 E = 0.701
A26 E = 1.000
A27 E = 0.839
A28 E = 0.689
A29 E = 0.643
A20 E = 0.693
W = 4.20%
W = 2.10%
W = 10.7%
W = 34.3%
W = 17.2%
W = 1.00%
W = 1.90%
W = 20.2%
W = 5.80%
W = 2.60%
B21 E = 0.869
B22 E = 0.731
B23 E = 0.710
B24 E = 0.702
B25 E = 0.737
B26 E = 0.693
B27 E = 0.624
B28 E = 0.605
B29 E = 0.782
B20 E = 0.654
W = 55.9%
W = 20.6%
W = 5.00%
W = 1.60%
W = 2.70%
W = 4.60%
W = 1.00%
W = 2.80%
W = 1.80%
W = 8.50%
C21 E = 0.528
C22 E = 0.547
C23 E = 0.636
C24 E = 0.610
C25 E = 1.000
C26 E = 0.774
C27 E = 0.536
C28 E = 0.861
C29 E = 0.533
C20 E = 0.552
W = 1.80%
W = 25.6%
W = 14.9%
W = 6.10%
W = 8.30%
W = 12.5%
W = 0.3%
W = 2.60%
W = 7.90%
W = 20.0%
A31 E = 0.731
A32 E = 0.749
A33 E = 0.728
A34 E = 0.747
A35 E = 0.629
A36 E = 0.608
A37 E = 0.771
A38 E = 0.732
A39 E = 0.601
A30 E = 0.606
W = 3.20%
W = 2.40%
W = 8.90%
W = 30.5%
W = 1.90%
W = 6.10%
W = 1.80%
W = 13.1%
W = 28.7%
W = 3.40%
B31 E = 0.746
B32 E = 0.732
B33 E = 0.699
B34 E = 0.708
B35 E = 0.693
B36 E = 0.713
B37 E = 0.756
B38 E = 0.758
B39 E = 0.726
B30 E = 0.733
W = 75.5%
W = 2.00%
W = 3.30%
W = 1.20%
W = 3.40%
W = 1.40%
W = 3.60%
W = 2.60%
W = 4.80%
W = 2.20%
C31 E = 0.559
C32 E = 0.548
C33 E = 0.540
C34 E = 0.798
C35 E = 0.798
C36 E = 0.611
C37 E = 0.814
C38 E = 0.545
C39 E = 0.540
C30 E = 0.590
W = 0.4%
W = 2.30%
W = 1.00%
W = 88.2%
W = 0.90%
W = 0.10%
W = 1.60%
W = 2.80%
W = 0.80%
W = 1.90%
26 | P a g e
Figures
Figure 1 – A simulated multi-business unit firm (enterprise) by using the DEA/Parallel model
Frim
(Enterprise)
Business Unit A (BU-A) Business Unit B (BU-B) Business Unit C (BU-C)
27 | P a g e
Figure 2 – Cost Allocation and Expected Return for different organizational levels
(Business Units, IT Portfolios, and IT Projects)
28 | P a g e
Labor cost
Expected Return
Figure 3 – Efficient Frontier for Business Unit through DEA and DEA/P approach
Labor Cost
Expected Return
Figure 4 – Efficient Frontier for IT Portfolio level via DEA and DEA/P approach
General Spending
Expected Return
General Spending
Expected Return
29 | P a g e
Labor
Expected Return
Figure 5 – Efficient Frontier for IT Project level via DEA model and DEA/P model
General Spending
Expected Return
30 | P a g e
Appendix
Table 7 – IT Portfolio Attributes associated with Parameter(s) and Variable(s)
IT Portfolio
Attribute Parameter/ Variable Description Primary Reference Data Source
Benefit
Expected Return The definition of expected return is related to on a
corresponding ROI.
Ilmanen, 2011; Eisfeldt
and Papanikolaou, 2013
Focal Firm IT portfolio data
along with simulated data
Cost Saving
The savings from the business process improvement,
the reduction in inventory, or gathering payables
more quickly after implementing IT project(s).
Kim and Chhajed, 2000;
Lee and Kim, 2000
Focal Firm IT portfolio data
along with simulated data
Budget Cost
Capital Expenditure
The definition of capital expenditure is funds
invested in a firm for the purposes of furthering its
business objectives.
Shim et al., 2012;
Bodmer, 2014
Focal Firm IT portfolio data
along with simulated data
Operating Expense
Operating expense is defined as what a business
incurs as a result of performing its normal business
operations.
Rahman, 1998;
Promislow, 2010
Focal Firm IT portfolio data
along with simulated data
Labor Cost Labor cost (including direct and indirect labor) is
defined as the salaries and wages paid to employees.
Triplett, 2007
Han and Mithas, 2013
Focal Firm IT portfolio data
along with simulated data
Project Type
Must Do
This type of IT project addresses a critical
compliance or controllership issue; these IT projects
receive first priority in terms of funding or resources.
Ross and Beath, 2002;
Crawford et al., 2005;
Kumar et al., 2008
Focal Firm IT portfolio data
along with simulated data Long Term Growth
This type of IT project usually adds new capabilities
for the business; these IT projects have the
significant impact to the existing business process.
Operating Margin
This type of IT project is the ROI-driven IT project
that allows the business to do the same process faster
or at lower cost.
31 | P a g e
Risk Risk
Risk value (risk score) is defined as the probability
that the return falls under the manager’s managerial
expectation for each IT project associated with its
utility function.
Lientz and Larssen, 2006;
Dewan et al., 2007;
Wang et al., 2010
Focal Firm IT portfolio data
along with simulated data
Technical
Complexity
High Technical
Complexity
This category of IT project is to extend applications
to customers or vendors for the first time, introduce a
new business process or new technology that is not
in the standard tech stack.
Tatikonda and Rosenthal,
2000;
Muller and Turner, 2007
Focal Firm IT portfolio data
along with simulated data Med Technical Complexity
This category of IT project is related to major
functionality enhancement, new custom developed
application using non-standard offerings, or new
technology that is not in the standard tech stack but
exists in the current infrastructure.
Low Technical Complexity
This category of IT project is simple functionality
upgrade with standard technologies, new custom
developed application, or significant
capacity/infrastructure expansion.
Portfolio
Distribution
Dominant IT Portfolio
Dominant IT portfolio is defined as a firm that
concentrates its IT investment on one or a very small
number of large IT projects.
Prahalad and Bettis, 1986 Focal Firm IT portfolio data
along with simulated data
Uneven-distribution-based
IT Portfolio
Uneven distribution-based IT portfolio is defined as
a firm that allocates its IT investment to a portfolio
composed of diversified IT projects (e.g., varying
project types and project sizes).
Even distribution-based IT
Portfolio
Even distribution-based IT portfolio is defined as a
firm that allocates its IT investment to all the IT
projects with similar sizes.
32 | P a g e
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