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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. Introduction to Workforce Planning Multi-Level Workforce Planning in Call Centers
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Multi-Level Workforce Planningin Call Centers Arik Senderovich Examination on an MSc thesis Supervised by Prof. Avishai Mandelbaum Industrial Engineering & Management Technion January 2013
Outline • Introduction to Workforce Planning • 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 • Insights into Workforce Planning
Workforce Planning Life-cycle Literature Review: Robbins (2007)
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)
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)
Multi-Level Planning • A singledynamic 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+1 t t+2 Meet Demand Literature Review: Abernathy et al., 1973; Bordoloi and Matsuo, 2001; Gans and Zhou, 2002
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
The Theoretical Framework • 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 definitionsand optimal control Learning Learning ? 1 2 m … Turnover Turnover Turnover
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
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
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
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 tasksare 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
Newly hired agent Agent 227, Whole day October 4th, 2010
Old timer Agent 513, Whole day October 4th, 2010
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: • Solve the unconstrained LPP and get b* (target workforce vector, over the entire planning horizon). • Calculate the optimal hiring policy by applying Theorem 1. • Hire by the optimal policy for periods t = 1,…,T-1
Model2: Full Model • Model 1 is extended to include 3 skill-levels • Stationary turnover and learning • Inner recruitment “solves” unattainability
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
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
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…
In reality – growth is gradual • 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: • Plan a single quarter (or any planning horizon where assumptions hold) using data • Towards the end of planning period update models using new data (demand modeling, staffing function, turnover, learning, absenteeism…)