Multi-Level Workforce Planning in Call Centers Arik Senderovich Examination on an MSc thesis Supervised by Prof. Avishai Mandelbaum Industrial Engineering.

Multi-Level Workforce Planningin Call Centers

Arik Senderovich

Examination on an MSc thesis Supervised by Prof. Avishai Mandelbaum

Industrial Engineering & Management Technion

January 2013

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Outline

1. Introduction to Workforce Planning2. Multi-Level Workforce Planning in Call

Centers• Our theoretical framework – MDP • Three Multi-Level Models: Main Results• The Role of Service Networks• Summary of Numerical Results and

comparison to reality

3. Insights into Workforce Planning

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Workforce Planning Life-cycle

Literature Review: Robbins (2007)

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Workforce Planning Levels

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Top-Level Planning• Planning Horizon: Quarters, Years,…• Planning periods: Weeks, Months,…• Control: Recruitment and/or promotions• Parameters:• Turnover rates (assumed uncontrolled)• Demand/Workload/Number of Jobs on an aggregate level• Promotions are sometimes uncontrolled as well (learning)• Costs: Hiring, Wages, Bonuses etc.

• Operational regime is often ignored

Literature Review: Bartholomew (1991)

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Low-Level Planning• Planning horizon: Months • Planning periods: Events, Hours, Days,….• Control: • Daily staffing (shifts, 9:00-17:00,…) • Operational regime (work scheduling and routing, managing

absenteeism,…)

• Parameters:• Staffing constraints (shift lengths, work regulations,…)• Operational Costs (shifts, extra-hours, outsourcing,…)• Absenteeism (On-job, shift)• Detailed level demand

Literature Review: Dantzig (1954); Miller et al. (1974); Pinedo (2010)

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Multi-Level Planning• A single dynamic model that accounts for both

planning levels:• Low-Level staffing levels do not exceed aggregate

constraints • Top-Level employed numbers adjusted to meet

demand at low-level time resolution

• Dynamic Evolution:

Literature Review: Abernathy et al., 1973; Bordoloi and Matsuo, 2001; Gans and Zhou, 2002

t t+1

t+2Meet

Demand

Recruit/Promote

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Workforce Planning in Call Centers

• High varying demand (minutes-hours resolution)• Tradeoff between efficiency and service level• High operational flexibility - dynamic shifts• Low employment flexibility - agents learn several

weeks• Multiple skills (Skills-Based Routing)

Models were validated against real Call Center data

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• Modeling Workforce Planning in Call Centers via Markov Decision Process (MDP) in the spirit of Gans and Zhou, 2002:

• Control: Recruitment into skill 1 • Uncontrolled: Learning and Turnover• Formal definitions and optimal control

The Theoretical Framework

1 2 m…?

Turnover

Learning

Learning

Turnover

Turnover

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Applying Three Multi-Level Planning Models

• Validating assumptions and estimating parameters using real Call Center data

• The role of Service Networks in Workforce Planning

• Numerical results – Models vs. Reality

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Test Case Call Center: An Israeli Bank• Inbound Call Center (80% Inbound calls)•Operates six days a week• Weekdays - 7:00-24:00, 5900 calls/day• Fridays – 7:00-14:00, 1800 calls/day

• Top-Level planning horizon – a quarter• Low-Level planning horizon– a week • Three skill-levels:• Level 1: General Banking• Level 2: Investments• Level 3: Consulting

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Model1: Base Case Model

Assumptions:• Single agent skill (no learning/promotion)• Deterministic and stationary turnover rate• Recruitment lead-time of one month – Reality• Formulation and Statistical Validation

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Service Networks in Workforce Planning• During shifts: agents go on breaks, make

outgoing calls (sales, callbacks) and perform miscellaneous tasks• More (half-hour) staffing is required• Israeli bank policy: • Only breaks and some miscellaneous tasks are

recognized • Outgoing calls and other back-office work are

important, but assumed to be postponed to “slow” hours

• Factor of 11% compensation at Top-Level workforce (uniform over all shift-types, daytimes etc.)

• We use Server Networks to analyze agent’s utilization profile

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Newly hired agentAgent 227, Whole day

October 4th, 2010

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Old timer

Agent 513, Whole dayOctober 4th, 2010

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Model1: Theoretical Results

• Theorem 1:• There exists an optimal solution for the equivalent

LPP • The “hire-up-to” target workforce is provided

explicitly (recursive calculation)• The LPP solution minimizes the DPP as well

• Algorithm 1:1. Solve the unconstrained LPP and get b* (target

workforce vector, over the entire planning horizon).

2. Calculate the optimal hiring policy by applying Theorem 1.

3. Hire by the optimal policy for periods t = 1,…,T-1

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Model1: Top-Down vs. Bottom-Up

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Model2: Full Model

• Model 1 is extended to include 3 skill-levels• Stationary turnover and learning• Inner recruitment “solves” unattainability

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Model2: Theoretical Results

• Theorem 2:• There exists an optimal solution for the

equivalent LPP • The “hire-up-to” target workforce is not

explicitly provided (LPP solution is the target workforce)• LPP Solution minimizes the DPP as well

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Model3: Controlled Promotions

• Both hiring and promotions are controlled (between the three Levels)• The LPP is not necessarily solvable• If the LPP is solvable then its solution is optimal for the DPP as well

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Numerical Results: Models and Reality

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Total Workforce

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Comparing Total Costs

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Models vs. Reality…

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Models vs. Reality• Uniformly high service levels (5%-15%

aban. rate)• Absenteeism is accurately estimated

(influences peak-hours with high absenteeism rate)• No overtime assumed – in reality each

person is equivalent to more than one full-time employee• Having all that said – let us observe reality…

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In reality – growth is gradual

Recruitment in large numbers is usually impossible

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Insights on Workforce Planning

• A simple model can be of value, so if possible solve it first• Planning Horizons are to be selected: • Long enough to accommodate Top-Level

constraints (hiring, turnover,…) • Short enough for statistical models to be up to

date • Improve estimates through newly updated data

• Workforce planning is a cyclical process: 1. Plan a single quarter (or any planning horizon

where assumptions hold) using data2. Towards the end of planning period update

models using new data (demand modeling, staffing function, turnover, learning, absenteeism…)

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The End