BENEFITS OF USING FINANCIAL MODELS IN DECISION SUPPORT

Providing Numerical Information

A model calculates the possible values of variables that are considered important in the context of the decision at hand. Of course, this information is often of paramount importance, especially when committing resources, budgeting and so on.

Nevertheless, the calculation of the numerical values of key variables is not the only reason to build models; the modelling process often has an important exploratory and insight-generating aspect (see later in this section). In fact, many insights can often be generated early in the overall process, whereas numerical values tend to be of most use later on.

Capturing Influencing Factors and Relationships

The process of building a model should force a consideration of which factors influence the situation, including which are most important. Whilst such reflections may be of an  intuitive or qualitative nature (at the early stages), much insight can be gained through the use of a quantitative process. The quantification of the relationships requires one to consider the nature of the relationships in a very precise way (e.g. whether a change in one would impact another and by how much, whether such a change is linear or non-linear, whether other variables are also affected, or whether there are (partially) common causal factors between variables, and so on).

Generating Insight and Forming Hypotheses

The modelling process should highlight areas where one’s knowledge is incomplete, what further actions could be taken to improve this, as well as what data is needed. This can be valuable in its own right. In fact, a model is effectively an explicit record of the assumptions and of the (hypothesised) relationships between items (which may change as further knowledge is developed). The process therefore provides a structured approach to develop a better understanding. It often uncovers many assumptions that are being made implicitly (and which may be imprecisely understood or incorrect), as well as identifying the assumptions that are required and appropriate. As such, both the qualitative and the quantitative aspects of the process should provide new insights and identify issues for further exploration.

The overlooking or underestimation of these exploratory aspects is one of the main inefficiencies in many modelling processes, which are often delegated to junior staff who are competent in “doing the numbers”, but who may not have the experience, or lack sufficient project exposure, authority, or the credibility to identify and report many of the key insights, especially those that may challenge current assumptions. Thus, many possible insights are either lost or are simply never generated in the first place. Where a model produces results that are not readily explained intuitively, there are two generic cases:

◾ It is over-simplified, highly inaccurate or wrong in some important way. For example, key variables may have been left out, dependencies not correctly captured, or the assumptions used for the values of variables may be wrong or poorly estimated.

◾ It is essentially correct, but provides results which are not intuitive. In such situations, the modelling process can be used to adapt, explore and generate new insights, so that ultimately both the intuition and the model’s outputs become aligned. This can be a value-added process, particularly if it highlights areas where one’s initial intuition may be lacking.

In this context, the following well-known quotes come to mind:

◾ “Plans are useless, but planning is everything” (Eisenhower).

◾ “Every model is wrong, some are useful” (Box).

◾ “Perfection is the enemy of the good” (Voltaire).

Decision Levers, Scenarios, Uncertainties, Optimisation, Risk Mitigation and Project Design

When conducted rigorously, the modelling process distinguishes factors which are controllable from those which are not. It may also highlight that some items are partially  controllable, but require further actions that may not (currently) be reflected in the planning nor in the model (e.g. the introduction of risk mitigation actions). Ultimately, controllable items correspond to potential decisions that should be taken in an optimal way, and non-controllable items are those which are risky or subject to uncertainty.

The use of sensitivity, scenario and risk techniques can also provide insight into the extent of possible exposure if a decision were to proceed as planned, lead to modifications to the project or decision design, and allow one to find an optimal decision or project structure.

Improving Working Processes, Enhanced Communications and Precise Data Requirements 

A model provides a structured framework to take information from subject matter specialists or experts. It can help to define precisely the information requirements, which improves the effectiveness of the research and collection process to obtain such information. The overall process and results should also help to improve communications, due to the insights and transparency generated, as well as creating a clear structure for common working and co-ordination.