BUS606: Operations and Supply Chain Management

Task precedence and dependency models as discussed above view DDPs as essentially similar in nature to other business processes, albeit with a high level of uncertainty and with the expectation of iteration. One criticism that might be levelled at such models is that they attempt to represent design processes but do not explicitly integrate an important insight gained from research into the nature of design activity - its situatedness (see Sect. 3.3). Rule-based models offer a possible route to address this limitation. They aim to model how process outcomes emerge through the interaction between the rules that define task properties and the design situation which changes as tasks are executed.

Some meso-level work in this area built on the Signposting approach of Clarkson and Hamilton, which was discussed in Sect. 3.2. This model was extended through a series of Ph.D. projects to study the multitude of routes that might be possible in a complex, concurrent design process. Features added to the model to do this included: a probability density function defining the duration of each task; multiple outcomes from each task with a probability of each occurring; and resources required by each task along with their limited availability. Among other insights this model, called Extended Signposting, was used to show how both the probability and desirability of each route should be considered when planning a design process. The adaptive test process (ATP) takes a similar approach, viewing a DDP as a complex adaptive system that emerges from a "primordial soup" of activities together with rules governing their selection. In comparison to Signposting, ATP offers more concrete criteria for selecting tasks, considering their roles in driving technical performance measures (TPMs) closer to specified targets. Lévárdy and Browning argue that at each step, the next task should be selected to maximise expected project value in terms of the TPMs, time, and cost. The ATP incorporates a simulation model that can be used to examine the value generated by different tasks and activity modes at different points in a project, among other contributions. More recently, Wynn et al. describe a process model in which key properties of tasks are defined according to rules that consider evolving uncertainty levels relating to design information. To illustrate, the time spent on an FEA task would be influenced by the expected accuracy of boundary conditions, which would propagate through the task to influence the expected accuracy of its outputs. In this model, a design is progressed through iterative cycles which continue until uncertainty levels converge to acceptable values. Wynn et al. suggest that this approach can be used to explore how different facets of design uncertainty may contribute to project delays.

Apart from the possibility of capturing a process' interdependency with the evolving situation, a noteworthy feature of Signposting and ATP in particular is that they in principle allow models to be constructed from knowledge of individual tasks or process fragments, because an information flow network does not need to be explicitly represented. This bypasses the requirement for an integrated overview of the process, which can be difficult to develop in practice. On the other hand, when compared to the approaches discussed in the previous two subsections, rule-based models are difficult to visualise and it is not clear how to validate all possible routes they allow. Research towards addressing these limitations is reported by Clarkson et al. For the moment though, such models remain mainly of academic interest.