What is a DSS cost benefit analysis?
by Dan Power
Editor, DSSResources.com
Decision support system cost benefit analysis is one technique used to determine if a proposed DSS should be built and implemented. A cost benefit analysis identifies and quantifies all the positive factors associated with the DSS over its planned period of use. These are called the benefits. Then one identifies, quantifies, and subtracts all the negative factors for the same time period, called the costs. The net amount in dollars is usually discounted and a positive return suggests the DSS project is desirable. Some of the costs and benefits are difficult to quantify in financial terms. For example, the primary benefit of DSS should be improved decisions. This intangible benefit is very hard to quantify and claiming the benefit assumes that managers will use the decision support system. Let's examine the analytical process.
Cost benefit Analysis is grounded in finance and accounting disciplines and is closely tied to budgeting. Typical summative measures in cost benefit analysis (CBA) are ROI, NPV, and discounted cash flow. CBA facilitates the allocation of capital. Sadly CBA provides the appearance of accuracy and precision, and people forget the assumptions that must be made. CBA is useful for evaluating cost-savings projects and automation of current processes. CBA is more difficult to use for decision-support, infrastructure, and strategic information systems projects. For example, cost models for data warehouses are not available. Benefits are tough to measure and benefits are not quantifiable or easily converted to dollars.
Examples of DSS cost factors include direct hardware and software costs, project personnel costs, support services (vendors or consultants), process change costs (people, material), and incremental infrastructure costs. Examples of DSS benefit factors include improved access to data, improved accuracy and consistency of data used in decision making, faster access to decision support, and cost savings from process improvements.
In a Sentry Market survey, 30% of respondents identified "access to data" as the biggest benefit of a data warehouse. Other important benefits of DSS include: 1) improved data accuracy; 2) better control of data; 3) better data consistency; 4) decentralization of data; 5) cost savings; and 6) less reliance on legacy systems. Few managers think that DSS will result in cost savings.
We can identify both tangible and intangible costs and benefits. We call a cost or benefit tangible if we can quantify the consequences. Intangible costs and benefits are difficult and sometimes impossible to quantify. Intangible results need to be considered in an evaluation, but too many intangibles limit the validity of the cost benefit analysis.
In summary, cost benefit analysis is a systematic, quantitative method for assessing the life cycle costs and benefits of competing DSS project alternatives. It involves explicitly stating assumptions, disregarding sunk costs and prior results, estimating direct and indirect costs and benefits, discounting costs and benefits, and performing sensitivity analysis. Discounting involves calculating how much a dollar of costs or benefits is worth today, even though it will be realized in the future. Discounting calculates the time value of money.
We perform a cost benefit analysis by following six steps:
- Define alternatives to the proposed project
- Collect Cost and Benefit Data
- Document assumptions
- Estimate Costs and Benefits (direct, indirect, tangible, intangible)
- Establish Measurement criteria (especially for benefits)
- Evaluate all alternatives using NPV, Benefit/Cost Ratio or Payback
DSS cost benefit analysis is an analytical tool that can assist in decision making. It is a general decision support tool that can and is used to evaluate DSS projects.
References
Power, D. J. Decision Support Systems Hyperbook. Cedar Falls, IA: DSSResources.COM, HTML version, 2000, accessed on (today's date) at URL http://dssresources.com/subscriber/password/dssbookhypertext.
Power, D. J., Decision Support Systems: Concepts and Resources for Managers, Westport, CT: Greenwood/Quorum Books, 2002.
Last update: 2010-07-18 10:22
Author: Daniel Power
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