Provision of External Support to Project Data Analytics

Tender description

• Advise and peer review the Department’s optimism bias policy. Advise and peer review the Department’s approach to cost growth and cost escalation. • Introduction of a reference class forecast for Defence projects, to prevent approval of overly optimistic projects. • Conduct Reference Class Forecasting (RCF) on a minimum of 100 projects/programmes to provide benchmarking and forecasting on live and future MOD projects, enriched with RCF data from projects from around the world (e.g., US DOD). • Develop detailed RCF from comparing individual project work breakdown structures. • Identify solutions to blocks in the Department’s current data landscape that prevent end-to-end oversight of projects from Head Office to suppliers. • Run two experiments on selected and agreed MOD programmes to better understand cost and schedule gaps by mapping granular project-level data from the various fragmented systems for domains such as cost and time etc and applying pre-built unique Artificial Intelligence / Machine Learning (AI/Ml) algorithms. • Propose solutions that can help the Department move from descriptive analytics to predictive analytics with portfolio data. The above will provide the MOD with better Project Delivery Data Analytics such as: a. Enhanced LFE to support forecasting at project initiation and through life. b. Reduced cost of projects – delivering more for less. c. Reduced duplication of work across common projects. d. Improved delivery performance and confidence. e. Continuous improvement. f. Improved pan-MOD and MOD-Industry collaboration. g. Increased transparency. Lot 1: • Advise and peer review the Department’s optimism bias policy. Advise and peer review the Department’s approach to cost growth and cost escalation. • Introduction of a reference class forecast for Defence projects, to prevent approval of overly optimistic projects. • Conduct Reference Class Forecasting (RCF) on a minimum of 100 projects/programmes to provide benchmarking and forecasting on live and future MOD projects, enriched with RCF data from projects from around the world (e.g., US DOD). • Develop detailed RCF from comparing individual project work breakdown structures. • Identify solutions to blocks in the Department’s current data landscape that prevent end-to-end oversight of projects from Head Office to suppliers. • Run two experiments on selected and agreed MOD programmes to better understand cost and schedule gaps by mapping granular project-level data from the various fragmented systems for domains such as cost and time etc and applying pre-built unique Artificial Intelligence / Machine Learning (AI/Ml) algorithms. • Propose solutions that can help the Department move from descriptive analytics to predictive analytics with portfolio data. The above will provide the MOD with better Project Delivery Data Analytics such as: a. Enhanced LFE to support forecasting at project initiation and through life. b. Reduced cost of projects – delivering more for less. c. Reduced duplication of work across common projects. d. Improved delivery performance and confidence. e. Continuous improvement. f. Improved pan-MOD and MOD-Industry collaboration. g. Increased transparency.