BUS606: Operations and Supply Chain Management

Another important contextual issue that is not emphasised in meso-level analytical models is the impact of influences and pressures on a process. System dynamics (SD) models address this issue by representing project governance structures and other influences, showing how these are coupled with the process and affect how it unfolds. Most such models draw on the work of Cooper, who developed a canonical development project model which can be adapted and calibrated for a particular situation. In Cooper’s model, tasks or work packages are represented as interchangeable units which flow between four pools, as shown in Fig. 15. A project begins with all tasks in the original backlog of work and is considered complete once they have flowed through the system into work actually accomplished. The crux of the model as indicated in Fig. 15 is that quality problems in task execution cause a backlog of flawed work that needs to be redone, and some of this backlog may remain undiscovered for some time. Thus, the model shows why perceived progress tends to fall behind schedule, and why actual progress lags even further behind. This model structure became known as the rework cycle and has been adapted and extended to form the basis of many later SD models. For instance, Ford and Sterman show how a sequence of rework cycles, each chained onto the next, can be used to represent overlapping stages of a development project. In these models, project influences are often dependent on the states of the activity pools and influence the rates at which tasks flow between pools. For example, one influence cycle may indicate that a rate of completing work (e.g., productivity, in Fig. 15) is influenced by schedule pressure, determined by whether the perceived amount of work remaining to be done is slipping behind a predetermined schedule. At the same time, increased work completion rate might reduce work quality causing progress to lag further behind perceptions. SD models can be useful to identify tipping points at which certain influences begin to dominate a situation. The equations that govern feedback effects are of great importance in determining a model’s behaviour.

A related macro-level analytical modelling approach is the use of qualitative causal networks to study project influences. This approach can be used to analyse factors that influence a DDP by modelling how they interact to exacerbate or suppress each other, and ultimately how these interactions might impact aspects of process performance. For example, Browning and Le both apply causal network modelling to analyse causes and effects of iteration in product development. They model the structure of influences relating to iteration by integrating individual factors and relationships revealed in case studies and prior research. Although the strengths of interactions might vary from one situation to the next, generic causal networks such as Fig. 16 may provide useful templates to guide the modelling and analysis of a specific situation.

For more information on models in this category, the reader is referred to Lyneis and Ford who provide a focused review of SD models applied to project management - many of which are either applicable or specific to the development project domain.