It is in a 2002 paper that I first developed an organisational context model driven by ambiguity and uncertainty (Thiry, 2002b). Based on a number of theoretical concepts (., Earl & Hopwood, 1980; Mintzberg, 1990; Weick, 1995; Siggelkow & Rivkin, 2005), one could argue that ambiguity is driven by complexity and uncertainty by turbulence. Complexity is a situation where many interactions between elements of a system create high number of possible options for each decision, therefore increasing ambiguity: a state of confusion where a situation is subject to more than one interpretation (Weick, 1995). Turbulence, on the other hand, is characterised by the pace of change and therefore prevents the collection of accurate knowledge before a decision has to be made; this fosters uncertainty, which can be described as a state where knowledge is limited (Galbraith, 1977, p. 38; Weick, 1995). Both affect predictability, but in different ways. 2 In my 2002 paper, I argued that ongoing organisational activities were typically a low uncertainty area, whereas change in general, and programs and projects in particular, were subjected to high uncertainty. I also argued that high levels of decisions, supported by the need to learn, bred ambiguity whereas execution was typically an area of low ambiguity.
For each project, the cost in billions of dollars, the engineering manpower requirement for basic design in thousands of hours, the engineering manpower requirement for detailed engineering in millions of hours, the skilled labor requirement for construction in millions of hours and the material requirement in billions of dollars are shown in Table 5-1. To build several projects of such an order of magnitude concurrently could drive up the costs and strain the availability of all resources required to complete the projects. Consequently, cost estimation often represents an exercise in professional judgment instead of merely compiling a bill of quantities and collecting cost data to reach a total estimate mechanically.