technology services & cloud...

Technology Services & Cloud Platforms

Global Business Services

Business Consulting

Systems Integration

Business Process

Outsoucing

Application Management,

Maintenance, and Support Services

Cognitive Solutions

International Business Machines Corporation provides information

technology (IT) products and services worldwide

~380,000 employees

Defining andoptimizing blockchain

Transforming industries through science and AI

Developing core AIReimagining computing

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Over 40 years of significant contributions to the field of mathematical sciences

Debt/Tax Collection Optimization

Passenger-Based Airline No-show Prediction

Customer Targeting and Sales Force Productivity

Revenue Forecasting

Traffic Prediction

Tool

Intelligent

Inventory

Management for

Retail

Production Planning

and Operations

Scheduling

Business Services Modeling and Simulation

Workforce Capacity Planning

Air Mail Optimizer

Demand

Conditioning

Pharmaceutical

Supply Chain

Risk

Integer

Programming

Fast Matrix

Multiplication

Fast Fourier TransformParallel

Computing

Fractals

Foundations of

Complexity

Combinatorial

Matrix Theory

Complexity of Reals

Lattice Based CryptographyAdversarial

Queueing

Algebraic Complexity

Over 100 top scientists solving the most challenging problems for IBM and its customers

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Workforce management transformation at IBM has been in place for over a decade.

Expertise Taxonomy

View of Supply

“Bills of Materials”

View of Demand

Optimizing Workforce Decisions

Job families, Job roles, Skill sets, skills…

PDTool, Professional Marketplace, CV Wizard…

Staffing templates

Demand Capture, PMP, Siebel, …

Demand Forecasting, Capacity Planning, Workforce & Talent Management, People/Job matching, Scheduling

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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

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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

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 deploymentsOPTIMATCH

WEO

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

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

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

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Deal odds adjusted based on

statistical models

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

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

Relevant Population Groups of Individuals

with Skills of Interest


Individuals to Reskill



• 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

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 High-Attrition

Probability


Individuals for

Incentivizing Retention



• 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…

Relevant Population

Of particular interest are people with high propensity to attrite, especially in certain skill areas

Complex trade-offs among skill demand, quality, propensities, financials, success, productivity, lead times, and uncertainty associated with all


• 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

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 thebusiness 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?”

Impact to Business and Science:

• Deployed as part of Cognitive Human Capital Management process within an IBM business unit

• Relative revenue-cost benefits overprevious approach commensurate with 2-4% of revenue targets

• Highlighted aspect of INFORMS Wagner Prize (excellence inOperations 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

$9.4 $9.4 $9.1 $8.3

$4.4

$35.4 $35.8 $37.1$39.0

$40.8

$26.0 $26.5$28.0

$30.7

$36.4

$-

$5

$10

$15

$20

$25

$30

$35

$40

$45

$5.56M

(optimal profit)

$5.2M

(all risks < 20%)

$3.9M

(all risks < 10%)

$2.0M

(all risks < 5%)

$0.2M

(all risks < 0.5%)

Expected Profit Expected Revenue Expected Labor Cost Revenue Curve

Labor Cost Curve

Gross Profit Curve

t = Sept t = Oct t = Nov t = Dec t = Jan t = Feb t = Mar

150.98

.01

.03

.01

149.91

.05

.03

.04136

.86

.04

.03

.10

129.86

.04

.03

.10 .03

125.93

.04

.03

127.93

.04

.03

.03

Software

Architect

OTHER SKILLS

Evolution based on existing policies

Hired in Oct (arrive after lead time)

151

.01 .04 .04 .04.02

.02

Significant recent trend of baby-boomer

retirements in Nov and Dec

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Representative Example of Optimal Workforce Decisions Over Time


150.98

.01

.03

.01

149.91

.05

.03

.04136

.86

.04

.03

.10

129.86

.04

.03

.10 .03

125.93

.04

.03

127.93

.04

.03

.03

Software

Architect

OTHER SKILLS


151

150 151 150 148 146148151Demand Forecast

.01 .04 .04 .04.02

.02



29



150.98

.01

.03

.01

149.91

.05

.03

.04136

.86

.04

.03

.10

129.86

.04

.03

.10 .03

125.93

.04

.03

127.93

.04

.03

.03

Software

Architect

OTHER SKILLS


151

150 151 150 148 146148151Demand Forecast

.01 .04 .04 .04.02

.02



Incentives for people to stay

30


Reduced shortage through

optimal retention incentive


150.98

.01

.03

.01

149.91

.05

.03

.04136

.86

.04

.03

.10

129.86

.04

.03

.10 .03

125.93

.04

.03

127.93

.04

.03

.03

Software

Architect

OTHER SKILLS


151

150 151 150 148 146148151Demand Forecast

.01 .04 .04 .04.02

.02



31


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

$9.4 $9.4 $9.1 $8.3

$4.4

$35.4 $35.8 $37.1$39.0

$40.8

$26.0 $26.5$28.0

$30.7

$36.4

$-

$5

$10

$15

$20

$25

$30

$35

$40

$45

$5.56M

(optimal profit)

$5.2M

(all risks < 20%)

$3.9M

(all risks < 10%)

$2.0M

(all risks < 5%)

$0.2M

(all risks < 0.5%)

Expected Profit Expected Revenue Expected Labor Cost Revenue Curve

Labor Cost Curve

Gross Profit Curve

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

http://research.ibm.com/

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