International Business Machines Corporation provides information technology (IT) products and services worldwide. ~380,000 employees

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3 International Business Machines Corporation provides information technology (IT) products and services worldwide Cognitive Solutions Global Business Services Business Consulting Systems Integration Application Management, Maintenance, and Support Services Business Process Outsoucing Technology Services & Cloud Platforms ~380,000 employees Slide 3

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6 Reimagining computing Developing core AI Transforming industries through science and AI Defining and optimizing blockchain

7 Over 40 years of significant contributions to the field of mathematical sciences Over 100 top scientists solving the most challenging problems for IBM and its customers Parallel Computing Fast Fourier Transform Business Services Modeling and Simulation Pharmaceutical Supply Chain Risk Integer Programming Fast Matrix Multiplication Fractals Air Mail Optimizer Traffic Prediction Tool Production Planning and Operations Scheduling Foundations of Complexity Intelligent Inventory Management for Retail Combinatorial Matrix Theory Complexity of Reals Lattice Based Cryptography Algebraic Complexity Adversarial Queueing Debt/Tax Collection Optimization Workforce Capacity Planning Passenger-Based Airline No-show Prediction Revenue Forecasting Demand Conditioning Customer Targeting and Sales Force Productivity 7

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9 Workforce management transformation at IBM has been in place for over a decade. Optimizing Workforce Decisions Demand Forecasting, Capacity Planning, Workforce & Talent Management, People/Job matching, Scheduling View of Supply Expertise Taxonomy Job families, Job roles, Skill sets, skills View of Demand Bills of Materials Staffing templates Demand Capture, PMP, Siebel, PDTool, Professional Marketplace, CV Wizard 9

10 At Research, cross-lab collaborative efforts in workforce management research Active player in the internal IBM workforce transformation initiatives, projects and tooling Several years of work in developing and internally deploying workforce analytics, solutions and tools Pioneering research in developing a number of unique methodologies Machine Learning, Statistical Sciences, and Mathematical Optimization Under Uncertainty Collaborative technologies Software engineering 10

11 WEO Workforce Evolution & Optimization is a unique modeling and analysis framework for studying workforce dynamics and optimizing workforce decisions, such as how the workforce will evolve in future and what policies/strategies will lead to optimal workforce composition. Used in a number of internal IBM Workforce and Talent Planning applications OnTheMark is an integrated workforce management framework and a suite of tools to: compute demand forecasts, determine optimal capacity plans, determine optimal resource allocations, compute skill gaps/gluts, and support higher level strategic planning. Deployed in several IBM business units for demand forecasting, capacity planning, resource management OPTIMATCH Optimatch and Optimanage are decision-support tools to help organizations make better informed resource planning and allocation decisions. Provide companies with workforce management engine to optimize assignments to open positions A number of IBM deployments SWOPS is a tool for optimizing shift work, ensuring that the right employees are in place to handle forecasted workload, while attending to business needs, employee constraints, and preferences Several external customer engagements and internal IBM pilots/deployments Growth and Performance is a decision support tool to analyze/improve resource productivity and revenue performance by modeling relationship between resource capacity and revenue. Enables testing of hiring strategies and business plan viability Deployed as a web-tool and key input to fall-plan across all IBM brands and regions 11

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16 A statistical forecasting methodology to analyze the opportunity pipeline data and predict how many of these opportunities will turn into reality How likely are these opportunities to be won When will these opportunities become wins and when will these engagements start How much revenue will these engagements generate Better accuracy in predicting win probability and more reliable prediction of decision date Deal odds adjusted based on statistical models

17 Available Population Groups of Individuals with Skills of Interest Optimal Selection of Individuals to Hire Prediction and Optimization models take into account: Demand for skills Quality and strength of individual Propensity of individual to accept offer Details of offer (cost, salary, etc.) Likelihood of success and high productivity of individual And so on Complex trade-offs among demand, availability, quality, propensities, financials, productivity, lead times, and uncertainty associated with all

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19 Relevant Population Of particular interest are people with cold skills and their ability to reskill to (nearby) hot skills, people with ability to grow within current hot skills, positive career paths for individuals Groups of Individuals with Skills of Interest Optimal Selection of Individuals to Reskill Prediction and Optimization models take into account: Demand for skills Quality and strength of individual s skills Propensity of individual to be trained/reskilled Impact to costs, revenues, Lead times, likelihood of success, impact to productivity And so on Complex trade-offs among skill demand, quality, propensities, financials, success, productivity, lead times, and uncertainty associated with all

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21 Relevant Population Of particular interest are people with high propensity to attrite, especially in certain skill areas Groups of Individuals with High-Attrition Probability Optimal Selection of Individuals for Incentivizing Retention Prediction and Optimization models take into account: Demand for skills Quality and strength of individual s skills Propensity of individual to attrite Financial impact of incentives, attrition Likelihood of success, impact to productivity, impact to total population And so on Complex trade-offs among skill demand, quality, propensities, financials, success, productivity, lead times, and uncertainty associated with all

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23 Prediction and Optimization models take into account: Demand over time Quality, strength, productivity of individuals Hiring, reskilling, attrition and associated policies Propensities of responses Cost, revenue, etc. Likelihood of success of policies and workforce decisions And so on Complex trade-offs over time among demand, quality, productivity, policies, lead times, propensities, financials, and uncertainty with all

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25 Workforce Evolution and Optimization empowers strategic workforce decisions with long-term implications to business success Analysis of historical workforce trends and dynamics for prior years (e.g., hires, attrition, promotions, transfers) Predictive modeling of future workforce dynamics based on past activities and sophisticated probability calculations Ability to model the future based on what if adjustments and scenario modeling Ability to optimize strategic decisions relative to business goals (e.g., hiring, reskilling, retention, promoting) at any level of views Workforce intelligence beyond pie and bar charts, into advanced predictive, analytical and optimization capabilities, delivered in real time through one integrated platform How will the workforce look 3 years from now if we keep our current hiring, training and attrition policies (so-called current course and speed)? What is the best way to reduce labor costs by 15%- 20% in the next year while optimizing strategic business objectives? Does the current composition guarantee that the business is running optimally -- if not, what actions do we need to take, and what policies need to be changed in order to meet business objectives? Will we have enough of critical skills A, B and C to meet product demand?

26 $45 $40 $35 $30 $25 $20 $15 $10 $5 $- $35.4 $35.8 $26.0 $26.5 $37.1 $28.0 $9.4 $9.4 $9.1 $8.3 $5.56M (optimal profit) Expected Profit Expected Revenue Expected Labor Cost Revenue Curve $5.2M (all risks < 20%) $3.9M (all risks < 10%) $39.0 $30.7 $2.0M (all risks < 5%) $4.4 $40.8 $36.4 $0.2M (all risks < 0.5%) Labor Cost Curve Gross Profit Curve Impact to Business and Science: Deployed as part of Cognitive Human Capital Management process within an IBM business unit Relative revenue-cost benefits over previous approach commensurate with 2-4% of revenue targets Highlighted aspect of INFORMS Wagner Prize (excellence in Operations Research) Each solution on x-axis comprises optimal hiring, reskilling, retention policies over time to maximize expected profit subject to risk tolerance while addressing complex trade-offs that include uncertainty and risks associated with all aspects of problem

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28 Representative Example of Optimal Workforce Decisions Over Time Evolution based on existing policies Hired in Oct (arrive after lead time) Software Architect OTHER SKILLS Significant recent trend of baby-boomer retirements in Nov and Dec t = Sept t = Oct t = Nov t = Dec t = Jan t = Feb t = Mar 28

29 Representative Example of Optimal Workforce Decisions Over Time Evolution based on existing policies Demand Forecast Software Architect OTHER SKILLS Significant recent trend of baby-boomer retirements in Nov and Dec t = Sept t = Oct t = Nov t = Dec t = Jan t = Feb t = Mar 29

30 Representative Example of Optimal Workforce Decisions Over Time Evolution based on existing policies Demand Forecast Software Architect OTHER SKILLS Incentives for people to stay 30 Significant recent trend of baby-boomer retirements in Nov and Dec t = Sept t = Oct t = Nov t = Dec t = Jan t = Feb t = Mar

31 Representative Example of Optimal Workforce Decisions Over Time Evolution based on existing policies Demand Forecast Software Architect OTHER SKILLS Reduced shortage through optimal retention incentive Significant recent trend of baby-boomer retirements in Nov and Dec t = Sept t = Oct t = Nov t = Dec t = Jan t = Feb t = Mar 31

32 $45 $40 $35 $30 $25 $20 $15 $10 $5 $- $35.4 $35.8 $26.0 $26.5 $37.1 $28.0 $9.4 $9.4 $9.1 $8.3 $5.56M (optimal profit) Expected Profit Expected Revenue Expected Labor Cost Revenue Curve $5.2M (all risks < 20%) $3.9M (all risks < 10%) $39.0 $30.7 $2.0M (all risks < 5%) $4.4 $40.8 $36.4 $0.2M (all risks < 0.5%) Labor Cost Curve Gross Profit Curve Selection of Optimal Solution from Frontier: Examine optimal solution for each risk tolerance constraint Each optimal solution comprises optimal policies for workforce decisions over time Based on efficient frontier, select best solution that balances trade-offs among expected rewards and measure of risks Each solution on x-axis comprises optimal hiring, reskilling, retention policies over time to maximize expected profit subject to risk tolerance while addressing complex trade-offs that include uncertainty and risks associated with all aspects of problem

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