## Uncertainty reduction in Open Design

So far, uncertainty reduction in Open Design has been dealt with as follows.

Initially, all stakeholders set the values for their constraints as they wish. With these values the LP model will, in general, not yield a positive solution space. The sensitivity analysis of the computer calculation will indicate, however, which constraints are more important than others. The owners of these crucial constraints can then negotiate amongst themselves, until a positive solution space is reached.

This procedure is, by nature, iterative and unsatisfactory in the sense that not all stakeholders are treated equally. Only a few have to give in. Where should we draw the line for crucial stakeholders' At three, four or five? The higher their number, the more complicated the negotiations will be. We therefore recommend asking each stakeholder to specify three values instead of one for the constraint assigned to the stakeholder:

1. An ideal value, as seen from the stakeholder's viewpoint;

2. An acceptable value, i.e. the value the stakeholder would accept without much discussion;

3. A walk-out value, i.e. the value the stakeholder would only accept when absolutely necessary and after thorough discussion.

This procedure is similar to estimating costs and revenues of investment projects in Monte Carlo simulations. For both costs and revenues three estimates are given:

1. A pessimistic value, defined as having a probability of 10% that reality will be worse than that;

2. A most probable value (best guess);

3. An optimistic value, defined as having a probability of 10% that reality will be better than that.

In general, the probability curve through these three points will not be symmetrical but skewed. The Monte Carlo simulation of the financial return calculation then provides the probability distribution for the financial return (Net Present Value or Internal Rate of Return).

Experience with this procedure shows that calculations with the most probable values as given by experts tend to provide a reasonable outcome, whereas the same calculations based on single values given by experts tend to give weird results. Apparently, when people are asked to give only one value, they will actually give their pessimistic estimate without saying so. A calculation based on such values will always give an unsatisfactory financial return. In practice, the financial analyst has then to challenge the experts or managers involved and ask them if they can do better. They ask the sales manager if he can sell better, ask the production manager if he can produce more efficiently, etc. This goes on until a financial return has been reached which is in line with the company's policy. Calculation with most probable values, by contrast, usually gives a satisfactory result straight away, with the important advantage that managers don't feel manipulated. By accepting their estimates at face value, they feel committed to the outcomes.

The conclusion is that people become a lot more genuine when asked to give three values of the variable concerned. If only one value is asked for, the answer is distorted by the perception of risk of the respondent.

Having the three values for the constraints as defined above, LP calculations can be made for each set of values:

1. The ideal values. The solution space will, in general, be zero (no solution possible);

2. The acceptable values. In many cases, the solution space will be positive;

3. The walk-out values. In general, the solution space will be positive.

A sensitively analysis can be made to establish which constraints are crucial. In this way, a lot more insight can be obtained before going into negotiations to change crucial constraints than in the case of an analysis based on single values.

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