Organizations make multiple types of decisions on a daily basis, such as: hiring people, selecting technologies, designing operations, etc. It is logical to think that different types of decisions will require different types of decision-making methods. Roy (1974) made a classification of decisions types. These can be summarized as follows.
Describing: Description of each alternative and its main consequences.
Sorting: Sorting all the alternatives into classes or categories.
Ranking: Constructing a ranking of all admissible alternatives.
Choosing: Select one and only one alternative (or a combination), the best of all.
There are also other types of decisions described later by Belton and Stewart
(2002).
Selecting a Portfolio: To choose a subset of alternatives from a larger set of possibilities.
Designing: To research for, identify or create new decision alternatives to meet the goals and aspirations revealed though the decision process.
Different types of methods can support these types of decisions. Belton and Stewart
made a useful classification of multiple-criteria decision-making (MCDM) methods.
In addition, Arroyo (2014) also added a new category, which is CBA (developed by
Jim Suhr). We can categorize MCDM methods under four categories:
1. Goal-programming and multi-objective optimization methods (linear
optimization)
2. Value-based methods (e.g., AHP and WRC)
3. Outranking methods (e.g., ELECTRE)
4. Choosing by advantages (e.g., CBA Tabular Method)
The first three methods are found in the literature about MCDM methods. The fourth
method is mainly found in the lean community literature and is not part of the
decision-making literature related to operation-research or MCDM methods
publications.
A clear preference for using value-based methods in the AEC industry exists, especially
the AHP method, which is often used and documented in the literature for alternatives.
Goal-programming and outranking methods are found less in the literature compared
with AHP. Applications of CBA are mostly found within the lean community.
CBA is different from other methods because it focuses in differentiating
alternatives from one another by highlighting advantages.
These methods can yield different decisions, even using the same raw data. So,
which one should you use? Recent research has found that CBA is a superior method
than utility based methods (AHP and WRC) in order to provide transparency, and
reaching consensus. More over, experiments done with AEC practitioners suggest
that reaching consensus is faster with CBA. Although it requires an adaptation
period in which one needs to unlearn utility base methods, which have been around
in the engineering toolbox for more than 50 years.
