BUS606: Operations and Supply Chain Management

Meso-level models of the fourth and final type, MS/OR, are similar in many respects to the meso-level analytical models discussed in Sect. 4.2. The key distinction is that models in this category are created as mathematical or computational tools for research in which representative or synthetic cases are analysed to extract general insights - whereas the analytical models discussed earlier provide approaches that practitioners might in principle use to model, analyse, and improve their specific situations.

One stream of work in this category focuses on developing mathematical models to study how concurrency may help to reduce lead time by bringing more resource to bear, at the cost of increased rework. For example, AitSahlia et al. develop algebraic models that show how the number of tasks that have to be redone if iteration occurs increases as their concurrency increases. Their models demonstrate how the tipping point at which further increases in concurrency start to yield increases instead of reductions in process duration is determined by the probability of each task creating rework for others. Hoedemaker et al. consider a similar situation, developing models to explore how the increased need for communication and the need to reintegrate tasks cause additional efficiency losses as concurrency is increased. Other authors consider design reviews. For example, Ha and Porteus develop a mathematical model to study the optimal timing of such reviews during concurrent product and process design. In this model, the desirable effects of frequent design reviews are to find flaws before they are incorporated into the design, and to validate interim product design work so that it can be released to process design, enabling concurrency. This is set against the time required to set up and execute the reviews. Ha and Porteus show that the optimal frequency of reviews depends on whether the concurrency or quality issues dominate. Their model is extended by Ahmadi and Wang to also consider how resource is allocated to different design stages. In this case, the model is used to consider how the reviews should be scheduled with a view to minimising the risk of missing targets. A number of other MS/OR models focus on managerial decisions relating to stage overlapping, without explicitly representing the interactions among numerous discrete tasks - these are accordingly categorised as macro-level and discussed in Sect. 5.4.

Another group of models emphasise how the task decomposition influences convergence of a concurrent, iterative design process - as explained by Browning, "tightly coupled, highly iterative processes can expect greater difficulty converging to an acceptable design under a given schedule and budget". Considering this issue, Yassine et al. develop an MS/OR model to study the causes of oscillatory situations in which progress is repeatedly thought to be on schedule before falling behind, arguing that this causes several knock-on problems such as short-termism in resource allocation. They use their model to show that this situation arises because teams that work concurrently on interdependent problems only coordinate periodically and thus often make design decisions based on outdated information. Braha and Bar-Yam focus on structural characteristics of the information flow network among tasks being worked concurrently. They develop a model considering that when any task is solved, it is possible that this will cause any interdependent tasks to require iteration. They analyse task networks from several domains and find there are common characteristics. In particular, most tasks are not strongly connected, but those that are strongly connected are shown to be especially susceptible to such iterations. Other researchers have studied convergence problems using spectral analysis, developing MS/OR models based on the Work Transformation Model (WTM) developed by Smith and Eppinger. In one such model, Loch et al. apply an eigenstructure analysis to show that convergence of an iterative process becomes less probable and more time-consuming as the number of coupled tasks increases. In two others, Huberman and Wilkinson and Schlick et al. create spectral models incorporating fluctuations in task performance, both showing that variance in overall process time can increase dramatically if the fluctuations exceed a certain threshold.

Concurrent engineering wheels emphasise integrating the ‘total job' in a CE project. The coupled wheel structure indicates that issues are simultaneously addressed and capabilities are developed in unison.

The above models consider a process in terms of tasks only, without reference to characteristics of the emerging design. In contrast, Mihm et al. describe a model of design convergence in which decision-making considering design trade-offs is explicitly represented. Their model represents a design situation as a network of interconnected components, each defined by a single design parameter. Every design parameter should be chosen to minimise a performance parameter for the corresponding component. However, a component's performance depends not only on its own design, but also on the designs of all components connected to it. The model simulates how iteration can be used to converge on a solution, through a series of steps in which all parameters are updated simultaneously. Running simulations based on randomly generated data sets, Mihm et al. show that convergence takes longer with larger problem sizes and eventually becomes impossible. They develop recommendations to improve the speed of iterative convergence: ensuring designers aim for the global performance function instead of optimising locally; accepting a slightly lower level of performance overall; minimising information transfer delays so that decisions are based on up-to-date information; converging step-by-step towards the desired outcome, e.g., by exchanging preliminary information through a series of iteration cycles; and structuring the design into relatively independent modules.

Overall, the models discussed his subsection, and others in the same category, are rather general in nature and do not offer guidance tailored to specific situations. However, researchers' conclusions from the models can provide useful insight into the drivers of (desirable or undesirable) development project behaviours.