|Title||Spreadsheet development and 'what-if' analysis - quantitative versus qualitative errors|
|Authors||Thompson S. H. Teo & Margaret Tan|
|Publication||Accounting, Management and Information Technologies|
|Series||Volume 9, Number 3, September, pages 141-160|
Past research has shown that errors are relatively common in all types of spreadsheets. As spreadsheets are used widely by executives in analyzing and supporting their decision making, especially in financial analysis, budgeting and forecasting applications, it is important for spreadsheets to be accurate.
Errors undetected in spreadsheets may have undesirable consequences. For example, errors may adversely impact the firm's competitiveness or profitability when the costing of projects is prone to incorrect computation.
For this purpose, we investigate the types of errors that may occur even for simple domain-free spreadsheet problems. In addition, we also show that spreadsheet errors are difficult to detect during 'what-if' analysis (i.e. when some design parameters are changed) when spreadsheets are not properly designed.
The results show that most students do not take due care in designing spreadsheets. It appears that the techniques in teaching spreadsheets should really focus on how to design a comprehensive spreadsheet that is both easy to maintain and debug rather than just demonstrating the many features of spreadsheets.
Of the 168 spreadsheets developed, 70 spreadsheets had errors in the first exercise and 84 had errors in the second exercise, giving an error rate of 41.7% and 50%, respectively. These error rates are generally consistent with previous studies.
Although the fraction of spreadsheets with errors was high, there were actually very few errors, i.e. only 0.5 and 0.8 errors per model for the first and second spreadsheet exercises, respectively.
It appears that the problem with spreadsheet development is not the absolute number of errors. Rather, it is that minor errors can cascade down into errors in bottom-line values. This can be quite serious as managers do not verify or validate the values and often rely on bottom-line values in the model to make decisions.