Scenario Planning The consultant organisational interaction I: setting goals Custom Essay

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Discussion point: New risks put scenario planning in favour
By Awi Federgruen and Garrett van Ryzin 19 August 2003
Awi Federgruen is the Charles E. Exley professor of management at Columbia Business School. Garrett van Ryzin is the Paul M. Montrone professor of private enterprise at Columbia Business School.
Who could have predicted the horrific events of September 11, 2001?
A 1999 US congressional commission led by former senators Gary Hart and Warren Rudman came close. It warned that the US was `increasingly vulnerable to attack on our homeland’ and that `rapid advances in information and biotechnologies will create new vulnerabilities’.
But perhaps more important than the commission’s prophetic messages was its approach. Instead of forecasting a specific future, it set out a collection of possible attack scenarios. It then evaluated national security by analysing possible policies to prepare for, or respond to them.
This approach — known as scenario planning — has gained renewed popularity among public and private decision-makers.
In January this year, the New England Journal of Medicine published a scenario planning analysis on whether US health workers or the whole nation should be vaccinated against smallpox to counter the threat of bio-terrorism. President George W. Bush decided to inoculate 500,000 military personnel and 439,000 health workers.
But what is scenario planning? How does it differ from conventional planning? It is based on a different notion. Rather than adopting a single, `most likely’ outcome, it advocates describing the future by a collection of possible eventualities. It encourages ‘contingent thinking’ rather than plans based on single predictions.
Introduced in the 1940s by the futurist Herman Kahn, it was used to analyse cold war threats. Since the work of academics Duncan Luce and Howard Raiffa in the 1950s, MBA students have been taught decision trees as a pictorial way of representing problems. And Richard Bellman showed how `decision tree analysis’ could be undertaken algebraically.
In the context of investment decisions, scenario planning has more recently re-emerged as real options analysis.
This shift leads to different decisions. Consider the example of a product launch. The product’s price is $1, but demand is uncertain. Different scenarios for macroeconomic and competitive factors produce demand of between 200 and 1,400 units. Generating the necessary production capacity requires developing land at a cost of 5100. The plant can be built either immediately for $600, or later — after sales volumes are known — for $660.
There are three possible strategies: (1) forego the venture; (2) develop the land and build the plant immediately; or (3) develop the land, but postpone the plant decision until demand is known.
Traditional planning starts with a forecast of demand. The midpoint of the range, 800, is a natural choice. This forecast is then used to evaluate the three options.
The second strategy looks best, yielding a projected profit of $100 ($800 in revenue, less the $100 land cost and $600 plant costs). But $700 is a lot to wager on a guess about future demand. So a careful planner performs a complete `sensitivity analysis’ to determine-the best option under different forecasts. This shows the second strategy is optimal if demand is more than 700, but the first (do nothing) is best if demand falls below this break-even value. While the recommendation is no longer clear-cut, at least the third strategy can be discarded as inferior, irrespective of future demand.
But while sensitivity analysis does determine the best response to each scenario, it fails to account for the fact that some options are available only before demand is known. That is the primary difference between scenario and traditional planning.
Consider the outcomes of each of the strategies. Doing nothing (strategy 1) always results in zero profits. The second and third strategies generate some profit. Under strategy 3, the $100 development cost is a sunk cost. The plant should therefore be built as long as revenue exceeds the $660 building cost, that is, demand (D) exceeds 660, with a resulting profit of D-660-100. If D falls below 660, we do not build the plant and profit equals -$100. In contrast, strategy 2’s profit is always D-600-100.

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