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support footprint and costs, and (2) know how information is engineered in a smart data paradigm by contrast We want to provide a glimpse about what should be expected from smart data technology so that it can be used in decision making Costing systems that stress direct-labor performance measures reinforce the tendency for companies to invest primarily in short-term cost-reducing or laborsaving projects This dumb data approach biases against approaches that develop people or customer satisfaction without reducing current costs We postulate that smart data and a smart data strategy are essential elements for optimizing enterprise performance The adoption of the discounted cash ow approach for evaluating capital investment projects is a major innovation in optimizing cost management accounting practices In this example of how executives should use smart data, we describe an information technology capital budgeting (ITCB) problem, show that the ITCB problem can be modeled as a 0 1 knapsack optimization problem, and propose two different simulated annealing (SA) heuristic solution smart data procedures to solve the ITCB problem Using several simulations, we empirically compare the performance of two SA heuristic procedures with the performance of two well-known traditional ranking methods for capital budgeting Our results indicate that the information technology (IT) investments selected using the SA heuristics smart data approach have higher after-tax pro ts than the IT investments selected using the two traditional ranking methods The capital budgeting problem is a problem of selecting a set of capital expenditures that satisfy certain nancial and resource constraints and has been studied extensively in the nance and decision sciences literature The capital budgeting problem is often formulated as a binary integer programming formulation, where the decision variable takes a value of 0 indicating that a potential investment is not chosen for funding or 1 indicating that a potential investment is chosen for funding When the number of potential investments is large, solving the integer capital budgeting problem is time consuming Under such circumstances, researchers have used soft constrained programming, problem decomposition, and investment clusters to lower the time to solve the capital budgeting problem The type of capital budgeting problem considered in our research is sometimes called a hard capital rationing problem in the literature In a hard capital rationing problem, a decision maker has to select the best project mix among several competing projects such that a budget constraint is satis ed In the case of hard capital rationing, budget constraint is de ned such that no borrowing, lending, or forward carrying of cash from one period to another is allowed The hard rationing capital budgeting problem is a very common problem occurring in manufacturing, healthcare, and information technology industries Capital budgeting in IT is slightly different from the traditional capital budgeting problem as certain capital investments are mandatory; for example, a local telephone company is required to provide service to anyone in the area who desires a telephone service Additionally, the capital investments in IT are sizable; that is, more capacity or bandwidth can be added in a project, and only certain IT capital investments can be depreciated Most telecommunications and Internet service providers nd that it is
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necessary to invest in new technologies and expand their services to compete with their competitors Furthermore, these IT companies are also subject to the pressure of not borrowing heavily for these investments as any unused capacity is wasted, which leads to a high opportunity cost of not investing in competing interests within the company Considering small technology life cycles and mandatory capacity expansion, the total cash outlay in IT projects is usually greater than the budget available and not all the IT projects are always funded Different IT investments have various tax implications For example, in the United States, certain hardware and software investments can be depreciated, but personnel and outsourcing investments represent nondepreciable expense In a survey of various US companies, approximately 27% of IT budget is allocated to hardware, 42% is allocated to personnel, 7% is allocated to software, 10% is allocated to outsourcing, and the remainder to other IT activities In the case of multinational rms and the presence of market imperfections, a rm may be able to take tax advantages due to market imperfections Thus, considering the US tax code and its implications, the ITCB problem is exacerbated owing to nonlinear interactions between different IT projects The ITCB problem with tax interactions is slightly different from traditional non-IT capital budgeting problems with tax interactions The primary difference is due to the fact that the US Internal Revenue Service (IRS) provides a maximum upper limit on the IT depreciation expense deduction This upper limit requires that all depreciable IT projects be treated collectively and not as standalone depreciable investments For example, assuming an IRS upper limit of $25,000, if there are ve depreciable IT investments and the combined depreciation expense of four IT investments is $25,000 then the fth IT investment cannot be depreciated This upper limit restriction leads to a discrete objective function in a binary integer capital budgeting formulation, which cannot easily be solved using traditional integer programming software In this section, we study an ITCB problem with tax-induced interactions of IT investments and propose two simulated annealing heuristic procedures to solve the ITCB problem Because the ITCB problem considered in our research does not have a clearly de ned objective function, we cannot use conventional optimization procedures for benchmarking Thus for the purpose of benchmarking, we use two simple ranking methods that can be used to solve the ITCB problem Using the ranking method (RM), all potential projects can be ranked in either ascending order of their investment values or descending order of their investment values before the projects are selected in a particular order until the budget is exhausted The RM can be broken down into two submethods the A-Rank method and the D-Rank method If projects are ranked in the ascending order of their investment values, before their selection using the budget constraint, then the ranking method is called the ascending ranking method (A-Rank) If projects are ranked in the descending order of their investment values, before their selection using the budget constraint, then the ranking method is called the descending ranking method
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