Understanding performance across the whole training process

3.14 In 2015, we recommended that the Department establish a robust baseline to measure, monitor and evaluate performance across the whole training process. This followed our finding that the Department did not use available information effectively to understand performance and would struggle to measure the impact of changes.

3.15 Since our report, the Department has started to collect more information on Phase 2 training, such as the number of aircrew who are between courses. However, it still does not systematically collect student data across the whole training process, from Phase 1 to Phase 3. Each front-line command collects and manages its own data. Consequently, it remains difficult to easily determine the time taken to train aircrew and for the Department to make decisions on how the system should operate. As part of our work, we identified inconsistencies between data sources and were unable to validate some of the information used by the Department for management purposes.

3.16 Given training costs sit across different areas, the Department does not know how much flying training costs. It does not have data on: the total cost from Phase 1 to Phase 3; how much it costs for it to meet its MFTS responsibilities, such as military instructors; or the full cost of training aircrew outside the MFTS. It told us the financial structures covered by MFTS mean these figures are not easily accessible.

3.17 The Department's current analytical approach relies on manual input of data and an understanding of the training pipeline to interpret data. It does not have bespoke software or a dedicated data analytics team to analyse aircrew in training. In 2018, it recognised the need to change how it held student information to monitor student throughput more efficiently. The Department told us it has commissioned consultants to help it improve its data-tracking. In June 2019, it also formalised a broader transformation project with Ascent aiming to improve end-to-end data, the ability to predict and analyse student throughput, and understanding the root cause of problems.