August 10, 2003

Calculating for Uncertainty

In today's economy, the only thing we know for certain is that more uncertainty lies ahead. Scenario-based planning could help you strategize for the unknown, plan technology investments — and even save your company

by Frank J. Bernhard

Continued from Page 1

How much does this sound like an economics problem? Every organization produces something of value, which in turn generates an economic profit that propels further investment in customer markets, process improvements, and expanded production capacity. Technology investments are no different from any other move to allocate capital and labor to enhance the organization's ability to deliver goods and services. Why don't we simply treat technology decisions as we do decisions regarding other enterprise functions? An organization's decisions about hiring and production goals also take into perspective unknown contingencies and events yet to materialize.

If we look at technology through the lens of a typical investment equation, we can apply methods of scenario-based planning (SBP) for evaluating the investment's ability to achieve a desired outcome. The concept of SBP has existed throughout modern times, but the results have never before had more potential to be effective than they do now, thanks to increasing data resources. SBP simply creates a set of probabilities that are aligned with a particular cost or production circumstance. This blend of economic relevance (choice) and a set of statistical measures (chance) lets us take full advantage of information's context. We're given greater flexibility, which allows us to define multiple conditions on which to base our business decisions.

Putting mathematics center stage for a moment, you can consider SBP as blending together, within an organization's production environment, a set of statistics with the linear algebra of behavior among variables. SBP requires that the business manager or analyst understand how the "variance" from a set of defined outcomes will behave given the conditions and constraints that exist within an organization's business processes. The path to understanding variant behavior begins by staging experiments and gathering external data that can tell you the defined outcome's occurrence at specified frequencies. By combining these probability measurements with known economic conditions, you can arrive at a graphic overview of your organization's ability to perform within a specified future period. You'll see how adding technology to production capability will change the variance in revenue or output. The mathematics, therefore, are about joining statistics and economic variables to exactly how and why change happens.

Setting Priorities

Consider a domestic airline carrier that had to examine its CRM application investment to determine whether to expand its scope to include trade partner relationships. The business goal was to improve overall transportation services marketing. The carrier knew the immediate costs of extending the CRM system's functionality. What was unclear was whether the potential investment would make the carrier's effort more efficient at earning revenue through the output of its core services. A fierce battle over scarce dollars ensued, with the added dimension of whether to maintain the "sovereignty" of all those incremental revenue sources. All sides put forth their views of the options, but answers about how much to spend and where to allocate effort remained elusive.

When contemplating an IT project, every organization questions how to prioritize staff, plan implementation, and choose a vendor solution among many possibilities. When it all boils down to making the enterprise profitable or not, the choices narrow quickly. Taking the known parts of the equation (such as cost provisions and days of implementation) and solving for the unknown variables (such as total cost reduction and marginal revenue productivity) lets you evaluate an optimal mix; that is, just the right amount to spend to produce a feasible and felicitous set of results. How you determine the impact of technology is just as important as how you assign traditional project costs. You want to reduce imbalances in the equation where possible.

Coming back to the airline carrier example: The CIO's team knew that, inevitably, including trading partners would differentiate the organization from the competition. Plus, the carrier had to reach out to any potential revenue streams to try to counteract the effect of diminishing profits in other areas. Success was not assured; in fact, prospects were fuzzy given the costs of expanding the CRM system and the stack of projects that would be moved to the sidelines. The CIO's team began with an inventory of the top five projects that could increase revenue within 90 days of implementation and hold a total cost allowance equal to or less than $5 million. Arriving at this final list required considerable pencil sharpening.

The team's next move was to establish the total economic envelope (TEE) factor for each of the projects on the list. The TEE factor represented a dimension of the incremental dollars that would be produced as a consequence of choosing to implement each project. Alongside each project on the list, the team supplied a weighting value that gave a probability estimate of achieving this TEE factor. Risk assessment led the team to put a lesser probability value next to some while putting a higher number next to others based on past experience. In the end, the goal of the process was to answer the question of whether to move forward with the CRM expansion or recommend alternative solutions. The airline was hemorrhaging millions of dollars every day, with no relief in sight. Success had to be in the outcome.

• Privacy
• Your California Privacy Rights
• Copyright © 2008 United Business Media LLC