How Structural Measurement is done?

This article will introduce the concept of inductive, or structural, measurement device, which displays a radical departure from the idea of classical measurement device. The concept of structural measurement process is a far-reaching generalization of the classical, numeric, measurement process. The major steps are, first, to replace the numeric representation space by the recently introduced structural representation space, and second, to add the dynamic component into the measurement process by determining that process with the inductive learning process. The second feature grants the process with the capability to record much more accurately the interaction of the agents with the environment. Such interactions can be recorded, as all inductive processes suggest, only on the basis of the agent’s past history and the present context. It is interesting to note that the necessity of this second, interactive, feature of the measurement process can be explained by the simple fact that the variety of inductive learning constructions, and therefore of the object/class structural representation is incomparably richer than that under the numeric representation.

Industry and scientific research both demand methods to support the management of complex engineering development processes in such a way that identifies and highlight the characteristics of their structural complexity. This property leads to the development of a systematic and scientific rigorous approach to modeling and analysis process, exhibited by its application to two case studies of automotive design. This process is typically known as structural measurement.

A research proposes a structural measurement system that makes use of complexity metrics to exhibit various patterns of the interplay of process entities in the spirit of a Balanced Scorecard, all the while adapted to the needs of process improvement. The metrics are used to draw inferences about the process’s behavior. This way, knowledge about a process can be extracted from existing process models, or new process models can be structured systematically by addressing desirable patterns.

The metrics are lead by a meta-model for process modeling. The meta-model uses multiple-domain matrices, integrating existing process models across common domains and relationships. The modeling method is enhanced with additional constructs of modeling that act as a bridging between existing dependency models and established process models.

Moreover, the analysis approach is operationalized by a framework to select the metrics in accordance with the goals of the process analysis. To this end, the metrics are classified and allocated to the common goals of process analysis with regard to the structure of a process, producing eight different guidelines. To enable a flexible application, a modular set-up consisting of three steps is chosen: As a starting point, the strategic level is addressed using common goals of process analysis. Then, these goals are concretized by typical questions that can be posed in their context. Finally, these questions are answered using the metrics and parts of the meta-model.

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