Why is decision analysis a good idea in the first place? Why should we focus on making some decisions supported by careful modelling, data gathering and analysis? Here, I provide some arguments as to why decision analysis is beneficial. Of course, not all decisions benefit from it: some considerations are too unimportant to warrant much analysis, and some might be simple enough to not need it. But then again, many problems are important, or complex, or politically hot. For these problems, decision analysis can be especially beneficial.
Identification of the best alternative
The main point of decision analysis (DA) is of course to arrive at the best possible alternative, or even a “good enough” one. This is essentially the focus on most discussions of DA, and therefore I won’t dwell on it more. How to determine the best feasible option is a very hard problem in its own right, deserving a book of its own.
Identification of objectives
Book examples of decision analysis start from a defined problem, and the point is to somehow satisfactorily solve it. Reality, however, starts from a different point. The first problem in reality is defining the problem itself. In fact, as a few classic books in DA emphasize, formulating the problem is one of the hardest and most important steps of DA. Much of the benefit of DA comes from forcing us to formulate the problem carefully, and preventing us from pretending to solve complex dynamic issues by intuition alone.
Identification of the best alternative
The main point of decision analysis (DA) is of course to arrive at the best possible alternative, or even a “good enough” one. This is essentially the focus on most discussions of DA, and therefore I won’t dwell on it more. How to determine the best feasible option is a very hard problem in its own right, deserving a book of its own.
Identification of objectives
Book examples of decision analysis start from a defined problem, and the point is to somehow satisfactorily solve it. Reality, however, starts from a different point. The first problem in reality is defining the problem itself. In fact, as a few classic books in DA emphasize, formulating the problem is one of the hardest and most important steps of DA. Much of the benefit of DA comes from forcing us to formulate the problem carefully, and preventing us from pretending to solve complex dynamic issues by intuition alone.
The first step of decision analysis!
Creation of new alternatives
Many descriptions of DA also assume that the alternatives are already there, and that the tricky part is comparing them. Unfortunately, in actual circumstances the decision maker or his supporters are commonly responsible for coming up with alternatives, too. This is likewise critical for success, because alternatives that won’t be chosen include the ones you didn’t think of – no matter how good they would be. Duke University’s DA guru Professor Keeney has emphasized this heavily.
Analysis of complex causal relationships
It goes without saying that many issues are complex and difficult to solve – that’s why decision analysis is used, after all. A benefit of thinking the model properly through is that it can reveal some of our unvetted assumptions, even radically changing our perception of an issue. For example, I was once involved in a project setting up a new logistics center for a company. Their goal was to increase customer satisfaction by shorter delivery times. After careful analysis it turned out that the new center wouldn’t reduce delivery time by very much. So someone thought that “wow, delivery time must be really important for the customers to warrant this” and looked up the satisfaction survey data. Well, it turned out it wasn’t very important: current deliveries were well within the limit defined by customers as satisfactory. In fact, it was clear from the surveys that to increase satisfaction they ought to be doing something else entirely, like improving customer service or product quality! It sure was an interesting finding, but it took some time to convince the directors that logistics really wasn’t their problem.
Many descriptions of DA also assume that the alternatives are already there, and that the tricky part is comparing them. Unfortunately, in actual circumstances the decision maker or his supporters are commonly responsible for coming up with alternatives, too. This is likewise critical for success, because alternatives that won’t be chosen include the ones you didn’t think of – no matter how good they would be. Duke University’s DA guru Professor Keeney has emphasized this heavily.
Analysis of complex causal relationships
It goes without saying that many issues are complex and difficult to solve – that’s why decision analysis is used, after all. A benefit of thinking the model properly through is that it can reveal some of our unvetted assumptions, even radically changing our perception of an issue. For example, I was once involved in a project setting up a new logistics center for a company. Their goal was to increase customer satisfaction by shorter delivery times. After careful analysis it turned out that the new center wouldn’t reduce delivery time by very much. So someone thought that “wow, delivery time must be really important for the customers to warrant this” and looked up the satisfaction survey data. Well, it turned out it wasn’t very important: current deliveries were well within the limit defined by customers as satisfactory. In fact, it was clear from the surveys that to increase satisfaction they ought to be doing something else entirely, like improving customer service or product quality! It sure was an interesting finding, but it took some time to convince the directors that logistics really wasn’t their problem.
A little analysis needed.
Quantification of subjective knowledge
Somewhat related to the previous example, many analyses come up against the problem of uncertain or vague knowledge. Especially organizations have a habit of being full of people very knowledgeable about the business and its environment, but this knowledge isn’t really anywhere for the development department to use. It seems to go something like this. First, the analyst finds out he needs some data on, say, failure rate of delivery cars. The analyst asks a Business Development Manager, who doesn’t know anything, and tells the analyst to use some estimate. The analyst doesn’t know anything either, so ends up interviewing some delivery people, uncovering subjective, unquantified knowledge about the actual failure rate. There’s nothing wrong with subjective knowledge, it’s just that it’s of no use if the DM isn’t aware of it! By uncovering and quantifying subjective knowledge in the organization, the analysis can actually benefit the company very much also in the long term, since now they have even more knowledge to base their future decisions on.
Creation of a common decision framework
Speaking of the future, one final benefit of DA is that it provides the decision maker with a decision framework, a model to replicate next time when faced with a similar decision. This is especially beneficial in organizations, since they get stuck in meta-level issues: arguing about how to make decisions in the first place.
Somewhat related to the previous example, many analyses come up against the problem of uncertain or vague knowledge. Especially organizations have a habit of being full of people very knowledgeable about the business and its environment, but this knowledge isn’t really anywhere for the development department to use. It seems to go something like this. First, the analyst finds out he needs some data on, say, failure rate of delivery cars. The analyst asks a Business Development Manager, who doesn’t know anything, and tells the analyst to use some estimate. The analyst doesn’t know anything either, so ends up interviewing some delivery people, uncovering subjective, unquantified knowledge about the actual failure rate. There’s nothing wrong with subjective knowledge, it’s just that it’s of no use if the DM isn’t aware of it! By uncovering and quantifying subjective knowledge in the organization, the analysis can actually benefit the company very much also in the long term, since now they have even more knowledge to base their future decisions on.
Creation of a common decision framework
Speaking of the future, one final benefit of DA is that it provides the decision maker with a decision framework, a model to replicate next time when faced with a similar decision. This is especially beneficial in organizations, since they get stuck in meta-level issues: arguing about how to make decisions in the first place.
In the best case, DA can provide an almost ready-made framework to follow, so that the managers can focus on actually making the decision. However, it’s important to recognize that different decisions have different stakeholders and take that into account. For example, a new logistics center may be an issue mostly about operational efficiency, but a new factory demands the inclusion of environmental and labour organizations. Just taking a previously used DA framework does not ensure it’s a good fit with the new problem. But the DA frame can be something to start from, which can help in reducing political conflicts between stakeholders. In fact, there’s nothing to prevent using DA from different perspectives. For example, DA has been used successfully in problems such as oil industry regulation, or moving from a segregated schooling system to a racially integrated one. Both politically hot examples can be found in Edwards’ and von Winterfeldt’s classic.
I guess if you wanted to summarize the benefits of DA in a sentence, it could look something like this: creating structure and helping to use it. So, in fact what it does is it helps us to think better as we are forced to consider things more thoroughly and explicitly. It’s a methods that helps us to deal with uncertainty and still make a decision.
I guess if you wanted to summarize the benefits of DA in a sentence, it could look something like this: creating structure and helping to use it. So, in fact what it does is it helps us to think better as we are forced to consider things more thoroughly and explicitly. It’s a methods that helps us to deal with uncertainty and still make a decision.
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