The ever increasing complexity in engine control systems and diagnostics functionality, which are vital to meet current and future emissions legislation, presents the industry with the problem of increased programme development and labour costs. As the degree of subsystem complexity intensifies, so too does the requirement for anticipatory driving behaviour knowledge; thereby introducing predictive operating strategies into drivetrain management systems. For example, it is possible to improve the accuracy of gear pre-selection calibrations in dual clutch transmissions by including GPS and gradient sensor data as input parameters; thus improving the off-cycle fuel efficiency of the vehicle. Similarly, benefits can be found by avoiding frequent changes in the combustion engine's operating point or taking the engine's cold start behaviour into account, as well as the effectiveness of the exhaust after-treatment systems, etc. The calibration of conventional powertrain systems are often only solvable with significant effort and the calibration of hybrid management systems are becoming just as complex. Therefore, a future that will require a deeper integration of more complex technologies without the support of structured methodologies, the trade-offs for CO2 and other regulated emissions, performance, comfort, cost and effort cannot be efficiently found. This paper intends to present a standardised approach with which the immense effort in the measurement and simulation of these variables is significantly supported by methods of statistical based test planning, model generation and optimisation, which can be implemented across a wide variety of increasingly flexible development environments. A vision is presented of how the methodologies and tools that are being used today can be adapted in the future to enable engineers to continue to deliver industrialised calibrations that meet the growing legal requirements and fulfil customers’ expectations.