MultiLevel Workforce Planning in Call Centers Arik Senderovich

Multi-Level Workforce Planning in Call Centers Arik Senderovich Examination on an MSc thesis Supervised by Prof. Avishai Mandelbaum Industrial Engineering & Management Technion January 2013

Outline 1. Introduction to Workforce Planning 2. 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 2

Workforce Planning Life-cycle 3 Literature Review: Robbins (2007)

Workforce Planning Levels 4

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 5 Literature Review: Bartholomew (1991)

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) 6

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: Recruit/Promote t t+1 t+2 Meet Demand Literature Review: Abernathy et al. , 1973; Bordoloi and Matsuo, 2001; Gans and Zhou, 2002 7

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 8

The Theoretical Framework • Modeling Workforce Planning in Call Centers via Markov Decision Process (MDP) in the spirit of Gans and Zhou, 2002: ? Learning 1 Learning … 2 Turnover • Control: Recruitment into skill 1 • Uncontrolled: Learning and Turnover • Formal definitions and optimal control m Turnover 9

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 17

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 18

Model 1: 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 19

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 recognized • Outgoing calls and other back-office important, but assumed to be postponed hours • Factor of 11% compensation at Top-Level (uniform over all shift-types, daytimes etc. ) tasks are work are to “slow” workforce • We use Server Networks to analyze agent’s utilization profile 39

Newly hired agent Agent 227, Whole day October 4 th, 2010 40

Old timer 41 Agent 513, Whole day October 4 th, 2010

Model 1: 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 42

Model 1: Top-Down vs. Bottom-Up 43

Model 2: Full Model • Model 1 is extended to include 3 skill-levels • Stationary turnover and learning • Inner recruitment “solves” unattainability 44

Model 2: 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 47

Model 3: 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 48

Numerical Results: Models and Reality 49

Total Workforce 50

Comparing Total Costs 51

Models vs. Reality… 52

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… 53

In reality – growth is gradual 54 Recruitment in large numbers is usually impossible

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 data 2. Towards the end of planning period update models using new data (demand modeling, staffing function, turnover, learning, absenteeism…) 55

The End 56