ABC Process-Based Capital Budgeting.

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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