Creating Internal Transparency to Forecast Workforce Needs Robert D. Motion Director, Workforce Planning & Strategy Intelligence, Information & Services November 17, 201 Copyright 201 Raytheon Company. All rights reserved.
Initiative Background Evolving Portfolio Aggressive Growth Targets Cost Challenges Hyper-Competitive Talent Market BURNING PLATFORM Talent is the foundation of the software and services business for a major defense contractor: People directly drive revenue and margins, and every open seat is potential lost revenue; Business growth and successful execution are inhibited by not having the right people in the right place at the right time and at the right cost. CURRENT STATE Lots of smart people doing the best they can with limited resources Matrixed organization leads to siloes, limited communication, and no accountability Volatility accepted as the norm without asking why Resource intensive with 250+ employees involved in staffing planning High variability in process no one does this the same Inefficiencies increase costs and impact competitiveness Creates constant strain and stress on the organization How to increase the predictability and alignment of workforce forecasts and plans to minimize risk? 2
Workforce Planning Stakeholder Perspectives Program Management Engineering Responsible for strategy and capturing new business Key metrics:bookings, capabilities Manages program execution Key metrics:ftes bid/in baseline Provides technical staff to programs Key metrics: Staffing of engineers by skill Facilitie s Site planning Key metrics:capacity utilization, heads on-site/ft 2 Forecasting and managing margins, revenue, rates Key metrics:$$$, Direct charging heads Hiring, development, retention (etc) for all employees Key metrics: hires, voluntary turnover, time to fill, headcount Multiple stakeholders with different care-abouts involved in planning 3
Where We Were: A Picture Is Worth 1,000 Words Engineering (Upper, Mid, & Lower Limits) Headcount Projections, Oct 201-Dec 2017 Program Management # of Employees Differences based on: Risk tolerance Assumptions Calculation method Estimation method Timing latencies Lack of modeling Finance Putting different perspectives on paper crystalized the issue 4
Where We Went: Exchange of Information 1. Align on Assumptions 2. Monte Carlo Simulation & Trending Analytics Target Upper Limit # of Employees 3. Stakeholders agree on forecast with tolerances Lower Limit Transparency and communication led to alignment 5
The Case For HR Data Transparency: Increase Plan Fidelity and Actionability Number of Hires 800 700 00 500 400 300 200 100 Application of cyclical models to identify and validate outliers Model Accuracy Annual: 94.2% Absolute: 91.0% Pinpointing and forecasting retirement based on timing and environment Magic 75 contributory plan tends to retire on time more often (53%) Salaried Plan Employees most often retire late (75%) Number of Voluntary Attrits 0 300 250 200 150 100 50 0 Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec Model Accuracy Annual: 92.1% Absolute: 92.1% Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec More employees on PBGC-Impacted Plans Tend to retire when rates drop Historical ceiling is 92 retirements in 2013 PBGC-related retirements have ranged between 5%-24% as a % of PBGC-Related employees eligible to retire Trends and data created checks and balances in forecasting Number of Retirements Employees on PBGC-Impacted Plans tend to retire closer to eligibility; Others tend to work longer and retire later PBGC-Impacted Plan Retirements vs. PBGC Rate 400 350 300 250 200 150 100 50 0 90 3.25% 2.25% 27 1.75% 132 144 12 1.50% 0.75% 1.75% 57 345 Historical high+25%: 180 1.00% 150 1.25% 0 24% of Eligible: 0.50% 2009 2010 2011 2012 2013 2014 2015 201 2017 # Retirements PBGC Rate 3.5% 3.0% 2.5% 2.0% 1.5% 1.0% 0.5% 0.0% PBGC Rate
Lessons Learned Stakeholders have different perspectives for a reason seek to understand them to bridge gaps Decision science with visualization enables and accelerates alignment Leverage everyone s individual expertise to benefit the collective whole Analytics in isolation has limited impact Data Transparency Drives Alignment and Action 7