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A recent trend in powertrain development organisations has been to apply processes historically associated with manufacturing. The aim is to capitalise on the resulting productivity gains to contain the increasing test demand necessary to develop current and future product. Significant obstacles to the implementation of manufacturing derived methods include the lack of clarity of the engineering test requirements and existing working practices in the product development environment. The System Optimisation Approach has been presented in previous work as a potential solution [1]. As an extension, this paper introduces a new concept closely related to the established manufacturing principle of Process Capability (Cp). Application of the resulting method quantifies the test facility’s capability to provide a test result subject to a specified statistical confidence within a certain number of test repeats.

This paper documents some of the findings from a joint JLR and AVL project which was conducted at the JLR Gaydon test facility in the UK. A testing and development efficiency concept is presented and test data quality is identified as a key factor. In support of this methods are proposed to correctly measure and set targets for data quality with high confidence. An illustrative example is presented involving a Diesel passenger car calibration process which requires response surface models (RSMs) of key engine measured quantities e.g. engine-out emissions and fuel consumption. Methods are proposed that attempt to quantify the relationships between RSM statistical model quality metrics, test data variability measures and design of experiment (DOE) formulation. The methods are tested using simulated and real test data.