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How Does The Video And Presentation On Software Engineering Relate To Project Management And Software Development?

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IT Capital Budgetting
Indiana University of Pennsylvania
Dr. James Rodger
Fall 2010 Ankur Dhakal
Justin Joseph
Seok Joo Kim Introduction What is the smart data? (Ankur) What is the Capital budgeting? (Justin) What is the difference between IT and traditional capital budgeting? (Kim) Can you tell us something about Situational Model? (Ankur) Would you explain about the overall structure and results of experiments? (Kim) Tell us something more precisely about 4 methods?(Ankur) Would you tell me about the Simulation results of 4 Methods?(Justin) Do you think there is any potential improvements and extensions of this
research?(Kim) Do you think there is any potential improvements and extensions of this
research?(Ankur) Tell the audience something about our conclusion?(Justin) What is the smart data?(Kim ask)
Ankur answer
Smart data is the comprehensive concept to use data smartly to manage the organization more efficiently
In relation to the MIS, the use of smart data concept can be
a great tool for solving the organization problem because
making data smart enables the organization to have certain
superior characteristics
By smart data, the data are interoperable and readily
exchangeable among qualified members of an enterprise
And it also contributes to optimizing enterprise performance
by providing a strategy that will vastly improve enterprise
data resource management
So, we will use this smart data concept to select the
effective methods to solve the ITCB problem What is the Capital budgeting ?(Ankur ask)
Justin answer
The capital budgeting problem is the problem of selecting a set of capital expenditures that
satisfy certain financial and resource
constraints
And, the type of capital budgeting problem
considered in our research is sometimes
called a hard capital rationing problem
Because a decision-maker has to select the
best project mix among several competing
projects under the budget constraint such as What is the difference between IT and traditional
capital budgeting?(Justin ask)
Kim answer
Capital budgeting in IT is slightly different from the traditional capital budgeting problem
Because a certain investments are mandatory, the
investments size is large and only certain investments
can be depreciated
About the depreciation issue, IRS provides a
maximum upper limit on the IT depreciation expense
deduction
So, in this study, we will consider these constraints
about finding the optimal solution of the ITCB problem Can you tell us something about Situational
Model? (Kim ask)
Ankur answer
This method used e-based antilogarithm transformation of revenue and IT budgeting variable to compute the
actual values of revenue and IT budget for analysis
A general ITCB problem can be mathematically
represented as the binary variable knapsack
optimization problem
where objective function is a non-linear function that
maximizes certain managerial criterion such as after-tax
profit of a set of IT investments Would you explain about the overall structure
and results of experiments? (Justin ask)
Kim answer
Using several simulations, this research empirically compared the performance of two SA heuristic
procedures with the performance of two well-known
traditional ranking methods for capital budgeting
For the purpose of benchmarking, this research
used two simple ranking methods that can be used
to solve the ITCB problem
According to the experiment result, the heuristic
approaches was best suited for solving the ITCB
problem Tell us something more precisely about 4
methods?(Kim ask)
Ankur answer
4 Different approches were used A Rank D Rank FRSA (Feasibility Restoring Stimulate Annealing) SSA (Simple Stimulate Annealing) CONTINUED Projects ranked in the acending order of their
investment value ,before their selection using
the budget constrai, then the ranking is called
A-RANK.
Projects ranked in the decending order of
their investment value ,before their selection
using the budget constrai, then the ranking is
called D-RANK. CONTINUED
And, we developed two simulated annealing (SA) procedures for solving the ITCB problem
Our first SA procedure(FRSA) utilizes a feasibility
restoration component. The feasibility restoration always
maintains a feasible solution
In our second procedure(SSA), we eliminate the
feasibility restoration step from the first SA procedure.
In the event when a solution is not feasible, failure occurs
in our second SA procedure and annealing schedule is
omitted
This simulated annealing can deal with arbitrary systems
and cost functions and statistically guarantees finding an
optimal solution. This method is relatively easy to code,
even for complex problems Would you tell me about the Simulation results of 4
Methods?(Ankur ask)
Justin answer
About the After-Tax Profit, FRSA, SSA outperformed A-Rank, D-Rank in the maximization of after-tax
profit. But, difference of profits was not significant
between A-Rank and D-Rank because of t he existence of common projects And, the difference of profits was not significant between FRSA and SSA. It resulted from the weak effects of feasibility restoration continued
About the Number of Projects selected, Ranking methods selected more projects than
2 SA methods. It was caused by the Ranking
methods are biased toward smaller or larger
projects. But, SA methods have preference for
the projects which increase the after-tax profit
And, about the pairwise differences in means
comparison, we couldn’t find no significant
difference in FRSA and SSA, but, found the
significant difference in A-Rank and D-Rank Would you make the summary of simmulation
results then?(Justin ask)
Kim answer
Conclusively, Two Smart Data SA heuristic methods(FRSA, SSA) outperformed traditional
methods(A,D-rank) Given that 0-1 knapsack optimization problem is NPhard, Simulation model for 500 companies proved
these facts
And, we can argue that this research contribute the
optimization of IT capital budgeting Because the use of corporate taxation and
depreciation for selecting the profitable IT investments
can improve the organizational profitability Do you think there is any potential improvements
and extensions of this research?(Kim ask)
Ankur answer
First, The objective function can be changed into the maximization of NPV if the management don’t
want the tax-induced interaction
Second, other heuristic methods such as Genetic
Algorithm and Tabu Search can be used because
single investment budgeting period is not realistic
And, finally, for more practical application, future
research is needed for solving the multi-period
ITCB problem Tell the audience something about our conclusion?(Ankur ask)
Justin answer
This case showed that how Smart Data approach could improve the efficiency and
effectiveness of capital budgeting in IT
industry
The utilization of smart data AI methods such
as SSA and FRSA could outperform the
traditional capital budgeting methods such as
A-Rank and D-Rank methods
Conclusively, the organization can select the
IT projects which lead to the highest after-tax
profits and optimizing enterprise performance,
by applying the Smart Data approach Management