Far too many executives are dependent on conventional economic calculation tools when they have to make a decision. For example, if they should develop a new product or expand into a new market. It works perfectly well in situations where the company has a clear view of their possible choices and the consequences that follows.
But in many situations you do not know everything – for example, if it is the first time you expand into a new market or launch a new product. Here you obviously do not have a complete view on how it will affect the company. What will the turnover be, how big is the demand, will the new product claim market shares from an existing product, etc. In situations like these there has to be other tools for mapping possibilities and risk, so that the company is able to make the best possible choice – or at least make the choice based on as much enlightenment as possible.
And this is what this tool does. It helps you find out when to apply which type of tool. Based on three questions you identify which of the 7 categories the decision you are considering belongs to, and what tools you can use that reduces uncertainty regarding the decision.
We collected the tool from a HBR toolkit called: Deciding how to decide (2013) by Hugh Courtney, Dan Lovallo and Carmina Clarke. This is just a description, if you want to dive further into the toolkit, we recommend reading the whole hbr-article.
The model consists of three questions:
Is the entire causality known? This means, do you know, what it takes for a decision to become a success?
Are the results of the decision known? In which degree are you able to predict the possible results?
Should I gather more knowledge? How centralized is the relevant information (in- or outside the organization)?
To answer the questions the authors list a range of aid-questions you can consider in order to get a clearer image of the diagnosis of the decision. We will go through these now:
The power of the knowledge you have about causality between critical and economic conditions, what the authors calls your causality model, is determent for how good of a decision you are able to make. A way to test the strength of your causality model is to consider, if you with certainty are able to articulate a range of “then-if” statements regarding the decision. An example from the text: “If our recommended new process technology slows the costs with X % and we are able to obtain Y % of the market shares, then we ought to invest in this technology.”
Besides you should consider, if you are able to describe an economic model in which you can add different assumptions. For example, how much the technology lowers the costs and how big a part of the market shares you can achieve.
As the authors state, it applies for far most of the strategic decisions that a specific causality model does not exist. In some cases you will be able to have a somewhat clear overview of critical risk factors, and in others you could be confused about which frame the situation should be placed within.
Do you understand which combination of critical factors will be determent for your decisions ability to succeed?
Do you know which goals you have to reach for it to succeed?
Do you have an exact understanding of – almost a recipe for – how to reach success?
Sometimes it is possible to predict a result with a certain accuracy, for example when the company has made a decision many times before. At other times you can predict a series of results. In case of great uncertainty it often applies that executives cannot describe which results a decision can lead to.
Can you define a range of results that has the potential to be the result of your decision?
Can you measure/assess the probability of each result?
By answering the following questions you can gain a general view of which type of information you need, and which tools you can use for greater results.
Is the information you need centralized or decentralized? This means, is it accessible inside the organization or do you have to seek outside?
If decentralized: Do you have access to those experts that possess the knowledge you need?
Is it possible and does it makes sense to use crowdsourcing to collect parts of this knowledge?
Is it possible to collect knowledge from the crowd without releasing classified information?
With this knowledge in hand you follow the information charter below to find out which tools that matches your decision.