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Manpower Forecasting in MOD

Manpower Forecasting in MOD. Anthony Carter Ministry of Defence GSS Conference 2016. Outline. Forecasting personnel MOD structure and collaboration Processes and topics State of play and impact The future. Why forecast people?. £9 Billion annual expenditure on Armed Forces personnel

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Manpower Forecasting in MOD

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  1. Manpower Forecasting in MOD Anthony Carter Ministry of Defence GSS Conference 2016

  2. Outline • Forecasting personnel • MOD structure and collaboration • Processes and topics • State of play and impact • The future

  3. Why forecast people? • £9 Billion annual expenditure on Armed Forces personnel • Is this money spent well to ensure the planned structure is maintained? • Are we going to have the right deployable force in 10 years time?

  4. A brief history of MOD Forecasting • 2008: beginnings • Project begun • Customers engaged • Software chosen • 2011-13 • Military forecasting tool signed off • Initial forecasts developed • Pre-2008 • Deterministic, Excel-based models • No consistent development • 2014-present • Tools refined • Demand increased • Pay modelling begins • 2008-11 • Tools developed • Teams established 2008 2010 2012 2014 2016

  5. Forecasting programmeStochastic modelling of personnel Grade 6 Programme Oversight Development/QA Bristol Civilian London/Bristol Army Andover Naval Service Portsmouth Air High Wycombe Pay Forecasting London

  6. MOD StructureThe need for collaboration • Collaboration across 4 budgetary areas • Head Office • Naval Service • Army • Air • Distinct budgets can lead to conflicting demands

  7. The overall process Iterate requirements Collaborators Customers Plans Rules Structures Iterate parameters Future Requirements Parameters Excel Bespoke Primer tool Arena Modelling environment Forecasting results Rates Historic trends Administrative Data Initial populations Known events HR Systems

  8. Software – ArenaDiscrete Event Simulation “Arena discrete event simulation software provides supply chain simulation, manufacturing simulation and healthcare simulation software solutions” • Entities tested for success/fail at individual events

  9. Software – ArenaDiscrete Event Simulation “Arena discrete event simulation software provides supply chain simulation, manufacturing simulation and healthcare simulation software solutions” • Entities tested for success/fail at individual events • Heavily adapted for our purposes • Controlled by model primer in Excel • Repeated tests indicate variability of results and centre on expected outcomes • Individuals can be split into various levels

  10. Annual Start population Outflows Transfers ID Age Length of Service Time in Rank Sex Ethnicity Nationality Contract Pension Qualifications … Promotions Inflows Training Annual End Population Contract changes

  11. We don’t predict reality! • Weather forecasts tell us whether to take an umbrella • We take action, we don’t get wet! • We observe; policy changes accordingly; forecast is already wrong! • Baseline to understand what needs to be done to meet requirements • Typically based on meeting certain targets (e.g. recruitment)… not always met in reality

  12. Our models • Quarterly forecasts • Officers • Other Ranks • Future – Reserves • Ad hoc forecasts for specific questions • “What If” investigations perturbing main models • Training models (Air only) • Pay analyses – what if we change contract terms?

  13. Areas of impact Pinch points • Pinch points identified across services • Shows future demographic issues • Informs Single Service decisions • Informs department-wide policy

  14. Areas of impact “What Ifs” • What if Voluntary Outflow increased? • What if we fail to reach recruitment targets? • Services given warning to mitigate these challenges • Head Office gets better warning of risks

  15. Areas of impactPay • Pay forecasting informed new Armed Forces pay structures • Responsive work modelled impact of proposals • Direct work to policy teams in Head Office • Now informing future pay developments Regular Forecasts Army Pay Forecasting Policy Naval Service Air

  16. **Note: Extreme and fictional results** ExampleReducing Outflow Holding recruitment steady, how long to meet requirements? A long time in this case!

  17. **Note: Extreme and fictional results** ExampleRecruitment What happens if we try to increase recruitment? Shows lag in policy effect

  18. The future Opportunities • Forecasting recruitment levels by looking at training pipelines • Integrating in-service training to inform promotions • Modelling Reserves populations • Informing policy for current projects • Greater harmonisation of models • Trade-level forecasts in Army

  19. The future Challenges • Changes of definitions – what will be counted?! • Administrative data quality • Reserves hold interesting questions • Some challenging customers and data providers • MacPherson compliance and model ownership

  20. Summary • Stochastic forecasting in MOD has come a long way • We’re making a big impact in a high value area • There are more opportunities to grow • We need to embrace challenges and continue collaborating Any questions?

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