ABC Process-Based Capital Budgeting.
This article describes and analyzes a field study at a division of a telecommunications company (one of the thirty largest domestic companies in the United States), that had recently established a new division to investigate and develop new business opportunities on the information highway. The initial opportunity was a $50 million investment in an electronic mall or "cybermall" to be developed on an interactive television network. The project concept was to generate two major revenue streams: 1) rent revenue, by charging fees for the electronic mall sellers to use the cybermall on the interactive television network to sell their products and services, and 2) advertising revenue, by charging fees for companies to advertise in this cybermall. In order to deploy and operate this cybermall, the company would have to incur various capital and operating costs identified with the following four major business processes: network operations, product research and development, marketing, and content production.
When the division of the company was created in 1995, there were no operational electronic malls on an interactive television network in the United States for this division to benchmark and analyze. The division had to develop its own approach to investigate business opportunities in a new industry (i.e., the "information highway"). This approach included the following innovations: 1) development of a new business process for cybermall revenues (See Figure I) and for the related costs needed to deploy and operate this cybermall (See Figure II), 2) development of a pro-forma activity-based costing (ABC), process-based capital budgeting model to investigate such new business opportunities (See Table 1), and 3) development of a simulation model to analyze the uncertainties in key revenue and cost variables (See Tables 2-4 and Figures III and IV). The following sections of this article describe and analyze these innovations.
ADVANTAGES OF ABC WITH PROCESS-BASED MANAGEMENT
Activity-based costing (ABC) systems were developed to provide more accuracy in assigning indirect and support costs to activities, business processes, products, services, and customers (Kaplan and Atkinson, 1998; Krumwiede 1998). ABC systems have recognized that organizational resources are needed both for direct production of goods and services and for indirect or support activities. The goal of ABC is to measure and then price out all the resources used for activities that generate the production of goods and services for customers.
An ABC system first traces costs to support activities and then to products. Traditional product costing has also involved two stages; however, in the first stage, costs are traced to departments, not activities. In both traditional and ABC cost systems, the second and last stage consists of tracing costs to the products or services. The principal difference between the two methods is the number and type of cost drivers used. The traditional product costing systems used allocation bases that may or may not have been cost drivers. For example, many companies have found that direct labor was not a cost driver and may never have been a cost driver, especially in highly automated production environments. The ABC system uses a much larger number and variety of cost drivers than the one or two volume-based cost drivers typical for a traditional cost system. As a result the ABC method has increased accuracy.
A traditional cost system uses only volume-based cost drivers, such as direct labor and machine hours, and ignores the key role of support activities, such as number of setups and design changes, in producing many modern products and services. Such volume-based cost drivers often lead to one group of products subsidizing another group of products. These subsidies often create the appearance that one group of products is highly profitable, adversely impacting the pricing and competitiveness of another group of products. In a highly competitive environment with complex products and services, accurate cost information can be critical to sound planning and decision making.
ABC also facilitates the use of process-based management that represents an evolving management strategy for highly competitive environments, as opposed to the traditional, departmental management focus (Hammer and Champy, 1993; Rummler and Brache, 1990). Process-based management focuses upon the broader control span of cross-functional processes or the "hidden factory" of how work really gets done in organizations, as opposed to the narrow control span of individual departments or "chimneys" or "turf areas" of organizations. Business processes have been discussed as a series of activities that are cross-functionally linked to achieve specific organizational objectives. In fact, almost every business has at least two core processes: the new product/service development process and the customer order fulfillment process (Ramanathan and Schaffer, 1995). The first core process brings together research and development, marketing, and product/service development. The second core process encompasses the sales, purc hasing, production, and accounting organizations. A big challenge for this cybermall project was how to use ABC to facilitate process-based management for a new business opportunity that involved completely new business processes for the information highway.
An integrated process-based management approach has been advocated for success, and even survival, in today's increasingly competitive and rapidly changing business environment (Daly and Freeman, 1997). This approach breaks down functional or departmental "silos" or "chimneys" and focuses upon optimizing customer value and supplier relationships. It requires an integrated performance planning, analysis, and reward system and enhances the effective management of the interrelationships among both processes and functions. It also helps increase the focus on both the internal and external value chains and makes it easier to eliminate non-value added work which enables the organization to use capacity more efficiently. It also facilitates the creation of intellectual capital through teaming and warehousing information on skills and provides a consistent framework for managing potentially diverse initiatives.
It is equally applicable to both manufacturing and service businesses and also facilitates the understanding of cost relationships (Euske et al., 1998). It is compatible with Porter's value chain framework and activity-based costing as well (Selto, 1995). Changes in the business environment have caused many companies to modify their strategic objectives and redesign their existing business processes (Kershaw and Mahenthiran, 1998). Thus, it may be viewed as an approach to initiate and manage changes in existing business processes. Such change may be either a radical re-engineering of all the business processes or incremental changes indicated by benchmarking.
A process-based management approach has also been advocated to replace traditional functional budgeting with an activity-based or process budget (Sharman, 1996; Schmidt, 1992). The next step would be applying a process-based management approach to capital budgeting and developing this approach with actual company applications which has been advocated as "innovation action research" (Kaplan, 1998). Such an innovative approach to capital budgeting may be needed due to the limitations of traditional capital budgeting, such as failure to recognize all the costs and benefits of the new investment. The typical approach to simulation analysis of capital budgeting projects assumes that such variables as market size, market share, and fixed and variable operating costs are uncertain. The analyst selects a probability distribution for each uncertain variable and performs the simulation analysis (Clark et al., 1989). The problem with this approach is that it does not model the underlying revenue and cost drivers. Breal ey and Myers (1996) offer an excellent critique of the traditional approach to simulation analysis. They note the difficulty of specifying the relationships between the variables in a simulation model. This difficulty is caused in part because analysts use such broadly defined variables in their models. The model developed in this article is built upon the uncertainty in the revenue and cost drivers themselves.
Another major limitation of traditional capital budgeting is the inappropriate adjustment for risk by using excessively high discount rates, especially for new technology projects, and requiring payback over arbitrarily short time periods (Kaplan and Atkinson, 1998).
Analyzing and managing risks in capital investments has been argued to be an identified knowledge void (Klammer, 1994). Contributing to this void was the use of a "black box" approach where computed data were accepted without knowing exactly how they were determined which often led to unexplained results. Accordingly, a disciplined, staged approach to risk analysis has been advocated using sensitivity analysis with probability distributions, as we have done in this study.
These capital budgeting limitations may be addressed by using our process-based management approach with staged risk analysis for a potential capital investment in a new industry, like the cybermall opportunity. Our approach provides a structured analysis of the risks and returns associated with a potential investment so that they may be considered in reaching an investment decision. It provides insight into the relative importance of various risks in the business processes related to the potential investment and thus provides priorities for risk management. By addressing these capital budgeting limitations, our approach has the potential to be generalizable for analyzing risky capital investment opportunities.
CYBERMALL BUSINESS PROCESS FOR REVENUES
Since the cybermall represented a new business opportunity in a new industry, there were no existing revenue business processes to use. Large cable television system operators, both outside and from within the telecommunications company, were consulted in constructing the business process which relied upon the development and deployment of an interactive television network. Also, traditional television marketers, Home Shopping Network (HSN) and the QVC System, were consulted as indirect or "out-of-market" benchmarks for the cybermall on interactive television. Thus, both external and internal types of benchmarking information were used in developing the new cybermall business process for revenues.
Figure I represents the business process used for the cybermall revenues in the ABC process-based capital budgeting model. The starting point or first input is the number of cable subscribers passed by the new interactive television network (that is, homes connected to the network via cable TV wire). Pro-forma benchmarking information was obtained from both external and internal cable television system operators. The ten largest cable operators were projected to build and deploy broadband infrastructures for interactive television over the next five years with all other operators deploying over the subsequent seven years. This deployment was projected to start in the fifty largest cities or suburbs. These same sources were used to obtain the interactive adoption percentage that was multiplied by the number of cable subscribers passed by this new interactive television network to obtain the number of interactive television subscribers.
The avenue or cybermall shopping percentage was obtained from the HSN and QVC "out-of-market" benchmarks and multiplied by the number of subscribers to obtain the number of avenue shoppers. This generated one of the two major revenue sources for the cybermall project: advertising computed as an annual amount per avenue shopper. The HSN and QVC indirect benchmarks were also used for the following three inputs: purchase frequency, average purchase amount, and purchase return percentage. Multiplying the first two inputs with the purchase retention percentage and with the number of avenue shoppers generated the net shopping revenue for the cybermall. To obtain the company revenue for this project, the net shopping revenue was multiplied by the rental percentage charged to the retail sellers. This was the second major revenue source for the cybermall project: rent revenue from these sellers to use this electronic mall. In estimating these two revenue sources, other inputs (annual advertising amount per shopper, n umber of retail sellers and rental percentage per seller) were also provided by these external and internal sources.
CYBERMALL BUSINESS PROCESSES FOR COSTS
Since the cybermall represented a new business opportunity in a new industry, there were no existing business processes for the project costs. Thus, external and internal cable television system operators and the HSN and QVC television marketers were again consulted in developing business processes for the development, deployment, and marketing of an electronic mall on an interactive television system. Four major cybermall business processes were identified: network operations, product research and development, marketing, and content production. Figure II relates the key portions of the revenue business process to the business process costs
A pro-forma ABC approach was used to develop the capital and ABC costs listed in Figure II. Cost drivers, such as the number of networks, video servers, and fiber loop systems, were based upon the interactive television deployment generated for the revenue process. The capital and ABC costs for this deployment schedule of interactive television were only applicable to the first two business processes: network operations and product research and development.
The traditional ABC approach relies upon historical cost pools, historical cost drivers and historical transaction volumes to calculate ABC historical costs. None of these types of historical information existed for this new cybermall business opportunity. Accordingly, a pro-forma ABC approach was used to analyze this new business opportunity. This approach relied upon both external and internal benchmarks to develop new unit costs, new cost drivers, and new transaction volumes. These new unit costs for the new cost drivers were then multiplied by the new transaction volumes for the new cost drivers to derive both capital and operating (ABC) pro-forma costs as noted in Figure II.
As an example of the scheduled deployment of interactive television, the network operations process can be used to demonstrate this pro-forma approach for both capital and ABC operating costs. A key activity in this process was to deploy interactive video servers that connected to the interactive television network in order for the company to provide electronic mall services. The new cost driver was the number of networks needed. From consulting with external and internal cable television operators, the new unit cost was estimated to be $200,000 for one video server needed for each network deployed to 500,000 homes. If the new transaction volume was determined to be four networks, then capital costs would be $800,000 (=$200,000 X 4).
For an ABC operating cost example, a key activity was to adapt and maintain the deployed, post-production distribution network in order to provide ongoing electronic mall services. The new cost driver was the number of fiber loop systems. The new unit cost was estimated to be four full-time equivalent (FTE) employees for one fiber loop system that connected with each video server. Linking this to the previous capital cost example, there were four video servers that would require four fiber loop systems and sixteen FTEs ( 4 X 4).
For the network operations process, the major capital costs were the deployment of interactive video servers, workstations, and head-end equipment, including fiber loops. The major operating (ABC) costs were for maintaining the deployed equipment and software, adapting to post-production networks, compiling databases, and serving customer contacts. For the product research and development process, the major capital costs were for adapting video servers to emerging technology, and the major ABC operating costs were for maintaining server technology configurations, developing new network head-ends, and maintaining network programming for the deployed interactive television systems.
In Figure II, three variables-- the number of avenue shoppers, the net shopping revenues, and the number of retail sellers-- are used to develop variable cost relationships with this revenue business process. For the network operations process, there were three different types of variable cost relationships with the revenue process: 1) retail services per number of sellers or retailers, 2) access fees as a percentage of revenue, and 3) order processing per number of shopping orders. Also, there were three types of variable cost relationships for the marketing process: 1) mailings per number of total shoppers, 2) specific advertising per number of new shoppers, and 3) general advertising as a percentage of revenue. There were three types of variable cost relationships for the content production process: 1) graphics per number of total sellers, 2) new content production per number of new sellers, and 3) core content production per number of existing sellers.
There were also fixed costs for all four business processes. There were depreciation expenses for the capital costs from the network operations and the product research and development processes. There were full-time equivalent (FTE) labor costs for all four processes. There were various start-up costs for each process and there were other annual costs for the marketing and content production processes.
Uncertainties were noted for many capital and operating costs. Since these costs were tied to various volume levels in the revenue process as shown in Figure II, the related uncertainties can be analyzed by simulating the revenue process. For example, the interactive television deployment schedule is linked by various cost drivers to all the capital and ABC costs designated in Figure II. Also, a link was empirically derived between the number of cable subscribers passed by the network and the interactive deployment schedule as shown in Figure II. Thus, the capital and ABC cost driver volumes and variable costs can also be simulated from the revenue process simulation.
ABC PROCESS-BASED CAPITAL BUDGETING DECISION MODEL
All the revenue and cost processes in Figures I and II were linked together in a series of related Excel spreadsheets. The overall model has been designated as an ABC process-based capital budgeting decision model. Table 1 summarizes the base case projections for the project. Using the company's current cost of capital of 10%, the project had a net present value (NPV) of $405.3 million, including a residual value for selling the business at the end of the project life. Managers expressed concern about the impact of both the choice of discount rate and the assumptions underlying the residual (business sale) value calculation. Since the project was a new line of business, they felt that a larger (but not excessively high) discount rate would be more appropriate than the company's current cost of capital. Table 1 also summarizes NPVs for various discount rates to facilitate management's choice of a discount rate. With a residual value included, NPV was positive for the various discount rates. Concerning the res idual value assumptions, the model assumed perpetual cash flows after the last year of the project in 2004 and discounted those cash flows, resulting in an estimated residual value of $944.9 million in year 2004. However, without any residual value, the NPV at a 10% discount rate was only $40.9 million, and was negative for discount rates above 20.7%.
Managers were very concerned about the fact that net cash flows were negative for the first six years as shown in Table 1. Even though the NPV was positive when considering low discount rates and including the residual value, managers felt that the choice of a discount rate did not adequately capture the project risk because of the distant payoffs.
ABC -- BASED SIMULATION OF THE CYBERMALL PROJECT
Thus, due to the limitations noted above, a simulation approach was used to analyze the major uncertainties in the base case. The cash flow model in Table 1 is a deterministic approach with a single set of estimates. As such, the managers cannot study the risks they saw in the project. A probabilistic model is more powerful than a deterministic one because uncertain cash flow items are assigned probability distributions. Using Monte Carlo simulation, distributions of uncertain cash flows are generated and an output variable such as NPV is sampled for each iteration of the simulation. This process is continued for each iteration of the simulation.
In a typical simulation study (Meimban et. al., 1992) original estimates of cash flow items are varied by some amount and variation in NPV is examined. The items being varied are the revenue and expense items from a typical income statement. The drawback to using these items is that one is studying the result of many underlying decisions and processes and not the processes themselves. Although the ABC approach was developed to analyze historical cost relationships, it also provides a very powerful framework for conducting a risk analysis of proposed capital investments. The ABC approach allows the manager to vary the underlying activity in order to study the impact of different levels of the activity itself. Managers have the potential to learn much more about the inherent risks of their decision when they study the uncertainty in the business processes, rather than highly aggregated items such as total revenues, labor costs, and material costs.
Accordingly, a model was constructed to study the underlying risks of the cybermall decision. First, the various Excel spreadsheets in the model were linked together in order to run effective simulations on the major uncertainties previously cited. In a few instances, the base case was found not to be fully integrated. Thus, the original model was extended into a general simulation model with appropriate linkages to all of the underlying activity drivers as shown in Figures I and II. Interviews with managers determined which activity drivers to simulate and the bounds on the variability of the drivers. Table 2 summarizes these major sources of uncertainty, the linkages in Figures I and II, and the assumptions about the probability distributions assigned to them.
There were three general problems to deal with in developing the inputs to the simulation model. First, probability distributions for the various input variables were needed. Going from point estimates to probability distributions is difficult for managers, and managers at this company were no exception. Specifying a range of possible values is one thing, but choosing between a normal distribution and (say) a geometric probability distribution is another. For example, the normal distribution requires the analyst to specify both the mean and standard deviation of the distribution, as well as assume symmetry about the mean. Since managers generally were willing to estimate pessimistic, most likely, and optimistic levels for the inputs, triangular probability distributions were specified. A triangular distribution is defined by three values: the minimum, the most likely and the maximum. This distribution is often used in simulation studies when the analyst does not know anything more about the probability distr ibution of a variable than these three values. In an ideal world, each distribution would be fit to real data using the method of maximum likelihood (Law et. al., 1994), but there was no data to collect for the cybermall project. It was an entirely new line of business. There was little choice but to use the triangular distribution, even though using it when the true distribution is of another type can lead to large errors in the simulation results (Law et. al., 1994).
The second problem concerned the modeling of the interactive adoption percentage, or rate, over time. In the base case model, analysts in the company estimated percentage amounts each year over the life of the project. In general, the pattern of adoption of new products tends to have the same general shape across a wide range of new products. Early adoption occurs slowly but then increases over time. In the middle part of the process, adoption occurs at a rapid rate, but then slows down during the final phase of the adoption cycle. When plotted in a graph, these estimates form an S-shape that is characteristic of the cumulative adoption rates associated with other new product introductions (Moore and Pessemier, 1993). The adoption rate was modeled over time using the Fisher-Pry equation which gives a very close fit for a wide range of new product introductions (Moore and Pessemier, 1993, and Meade and Towhidul, 1995). Because the adoption rate is so important to the revenue projections, parameter estimates f or the Fisher-Pry model developed by company planners were also varied in the simulation to investigate the sensitivity of the results to model specification.
The third issue in setting up the simulation model was to develop a model for forecasting the number of video servers. In the base case model, these estimates were simply inputted as individual numbers in the interactive deployment schedule that was not linked to the project. A regression model was developed for forecasting the number of video servers needed each year. In the regression, the number of video servers was regressed against the total number of subscribers. The coefficients from the regressions were used in the simulation model to link the number of servers projected in the interactive deployment schedule to the number of avenue shoppers or subscribers.
After the simulation model was developed, the next step was to decide how many of the input variables were important. The TopRank software from the Palisade Corporation (1996) was used to perform a what-if analysis on the variables of interest (see Table 3) to determine which ones should be studied further. Using built-in functions available in the TopRank software, each input in Table 2 was varied from its minimum to maximum value. The software computed a value for NPV for each different value and saved it for later analysis. After all of the inputs had been varied across the specified ranges, TopRank analyzed the impact of each variable on NPV using regression analysis. A graph in Figure III (called a Tornado diagram) of the standardized regression coefficients displayed (in descending order of magnitude) the relative importance of the input variables to NPV. The most important variables were those with the largest coefficients (in absolute value). The standardized regression coefficient showed the number of standard deviations by which NPV increases if the independent variable increases by one standard deviation. For example, increasing the number of cable subscribers by one standard deviation increased NPV by 1.25 standard deviations.
The Tornado diagram for NPV with the residual value is in Figure III. Two key inputs for the NPV were identified in order of importance: the number of cable subscribers and the rate at which the interactive adoption percentage grew over time. The next step was to conduct a full simulation study of the model with these two variables as the key revenue inputs.
Simulations were performed using the @Risk software, also from Palisade Corporation. Because simulation analysis incorporates risk in the variability of the cash flows, a risk-free interest rate was used as the discount rate to avoid double-counting risk (Brealey and Myers, 1996). The long-term Treasury Bond rate was used as an estimate of the riskless interest rate for the simulation analysis. At the time of the study, rates were about 8 percent. Since NPV is the net of several offsetting items, present values (PVs) were included to help interpret the simulation results for NPV, as shown in Panel A of Table 3. Inputs were modeled per Panel B of Table 3. NPV with a residual value, PV of Revenues, PV of ABC costs, PV of Variable costs, PV of Fixed costs, and PV of capital costs were selected as the output variables of the simulation.
It is clear that the residual value is a significant factor to the project. Without a residual value included, the mean NPV is statistically near zero at $28 million and a standard deviation of $70 million. On the other hand, including the residual value increases the mean NPV to $518 million and the standard deviation to about $348 million. One could conclude that the project has a positive net present value only if the business could be sold to generate a positive residual value. Also, including a residual value generated a 97.6 percent probability of a positive NPV. However, there was only a 41 percent probability of a positive NPV without a residual. Thus, there appears to be a large amount of risk related to the existence of a residual value.
Output from the @Risk software explored the uncertainty in cash flows in several different ways. First, the annual cash flows over time were plotted using what is known as a Summary Graph (Figure IV). The line through the middle of the graph shows the mean level of annual cash flow, the inner band shows an interval equal to plus/minus one standard deviations from the mean, and the outer band shows an interval equal to plus/minus two standard deviations from the mean. With a 95 percent probability, annual cash flows were negative for the first six years. Thus, the initial negative cash flows observed in Table 1 appeared to be a general pattern for this project and positive NPVs were generated by cash flows occurring over the last few years of the project. This in turn suggests that the number of cable shoppers and the growth in the percentage of avenue subscribers were the key revenue variables crucial to the success of this project, as analyzed in the Tornado diagram in Figure III. This type of scenario anal ysis has also been advocated as a key component of a business plan (Sahlman, 1997).
Although the Tornado diagram indicated two key revenue variables, it did not identify which combinations of the inputs lead to large or small NPVs. The @Risk software also identifies groupings of inputs which cause certain output values (referred to as scenario analysis). In performing the scenario analysis, @Risk saved only those iterations for which the value of the output variable met a certain criterion. It then analyzed values of the input variables in those iterations. @Risk then found the median of this subset and compared it to the median of the input for all iterations in the simulation. Significant inputs were those for which the median of the subset deviated from the overall median by at least one-half a standard deviation. The reported scenarios showed all inputs which were significant in meeting the stated criterion. For each output cell, @Risk allowed one to enter up to three scenarios (or criteria). The default scenarios were the 25th, 75th, and 90th percentiles for the output cell.
In Panel C of Table 3, a scenario analysis of the simulation results using the 25th and 90th percentiles as the scenarios is shown for NPV with a residual. The first three columns for "NPV [greater than] 90%" included three different pieces of information. The column labeled "Percentile" compared the median value of the various inputs in the subset with the median values of the inputs for the entire simulation. If this value is greater than 50%, then the subset median is greater than the median for the whole simulation. Thus, a median value of 84.75% for the input "Growth in Interactive Adoption %" indicated that large adoption rates contributed to large project NPVs. The column labeled "Actual" showed the actual median of the subset of iterations with NPV in the 90th percentile. The third column, labeled "Ratio of Median to Std Dev" was the difference between the subset median and the median for the entire simulation, divided by the standard deviation for the input for the whole simulation. Negative values would indicate that the subset median is smaller than the median for the entire simulation. The larger this value is, the greater the relative importance of the particular input in comparison to the others listed. Although growth in the interactive adoption rate over time was more important than the number of cable subscribers, both variables were the key ones in driving the cybermall project value. This was true whether residual value was included or not, and for large as well as low levels of NPV.
SUMMARY AND CONCLUSIONS
An ABC process-based capital budgeting model was developed to analyze a new $50 million business opportunity on the information highway. This approach used external and internal benchmarks in developing pro-forma business processes for this new investment opportunity. The model was summarized in Figures I and II and Table 1.
This ABC process-based capital budgeting model generated the following planning and control information for management decision making:
1) Linkages were established between emerging technology, new business processes and financial forecasts.
2) External and out-of-market benchmarks were used for revenue and cost forecasts.
3) A dynamic model was created to show how revenues, costs, and capital budgeting metrics change with various assumptions about uncertainties for new business opportunities and corresponding business plans (Sahlman, 1997).
4) Business process engineering could be applied for entering new industries (Cooper and Slagmulder, 1997).
5) Costs could be managed by business processes and related activities instead of managing processes and activities according to their costs (White, 1997; Daly and Freeman, 1997).
Since the business managers at the field study company were not satisfied with just an initial base case analysis, a simulation approach was developed to consider major uncertainties. In the initial simulation analysis, Top-Rank software was used to do "what-if" analyses to analyze the impacts of key variables (as listed in Table 2) upon the capital budgeting results. These results were summarized in Panels A and B of Table 3 and Figure III.
Then, @Risk software was used to further investigate the major uncertainties identified in the "what-if' analyses. This scenario analysis considered which combinations of model inputs led to large or small NPV results. These results were summarized in Panel C of Table 3 and Figure IV. This approach went beyond the traditional approach that only looks at aggregate impacts to investigate detailed breakdowns of key revenue and cost variables.
Thus, the ABC process-based approach systematically considered investment uncertainties in the following sequence in order to improve management decision-making information:
1) All the variables for the revenue process in Figure I were simulated as shown in Table 2.
2) These revenue simulations determined the interactive television deployment schedule which, in turn, simulated transaction volumes for the capital and operating (ABC) cost drivers as shown in Figure II.
3) The variable cost volumes were also automatically simulated from the revenue simulations as shown in Figure II.
4) Key revenue and Cost variables for the capital budgeting results were identified and simulated as shown in Figures III-IV and Tables 3-4.
In summary, this ABC process-based approach for capital budgeting allowed company managers to vary the underlying activity drivers in business processes in order to study the impact of specific revenue and cost variables. These managers learned more about the risks in the proposed $50 million cybermall investment because specific revenue and cost uncertainties in the business processes were analyzed. Thus, these managers had more robust information for capital budgeting decision making concerning investments in an emerging industry.
The ABC process-based approach opens up an entirely new avenue for risk analysis. By separately identifying the level of revenue and cost associated with process activities, the uncertainty surrounding such activities and related revenues and costs can be studied. This gives managers far more information than is possible from the traditional simulation of aggregated income statement items.
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|Author:||Cook, Thomas J.; Grove, Hugh D.; Coburn, Steve|
|Publication:||Journal of Managerial Issues|
|Date:||Sep 22, 2000|
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