The nature of complexity varies as construction progresses. This paper presents
concepts and practices with which project (knowledge) management must foster
complexity when it is necessary and dampen complexity when it is unnecessary in
order to generate value and control time and costs. Complexity management has to
be adjusted to the current state of the project.
Before and during programming the building as a solid object can not be predicted;
the user activities, extent, mass and materials are unknown. We might renovate,
build a new building or we might not invest at all. The problem is inductive since
there are several correct answers, not right or wrong but good or poor. After design
and before on-site construction we know the object and its performances, the single
“right answer” for construction. The system is deductive. The building process is
initially inductive and becomes predominantly deductive, being complex all the time.
The destruction of an inductive system can be avoided only if there is enough variety
in the controller. Only a management system which contains variation can produce
alternatives in a creative way to keep to goals in spite of disturbance. It is called
necessary or requisite variety. If a problem “do we need an activity?” is dealt with
simultaneously as the question “where would it be located in a plan?”, there are
limitless possible alternatives. If we first answer “no” to the first question, there are
no alternatives left. Does the “Where it will be” answer create more valuable
information to the question “do we need it”? If not, the variables are orthogonal.
Combining orthogonal variables causes more iterations and can be called
unnecessary complexity.
In the beginning of construction the building as an object can be predicted. However,
due to the peculiarities of construction, there is a lot of complexity confronting the
production phase. The issue is to consider whether any peculiarity could be
eliminated or at least reduced. In operations management, three different
conceptualizations should be simultaneously used: production as transformation, flow
and value generation. From these, the transformation model is in an auxiliary
position, whereas the flow model addresses the time-dependent complexity and value
generation addresses the time-independent complexity. In the framework of these
conceptualizations, the insights and principles of complexity thinking should be
applied as appropriate.
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