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Using simulation-based forecasting, workforce man...
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  1. 1. RESEARCH POSTER PRESENTATION DESIGN © 2011 Using simulation-based forecasting, workforce managers are able to more accurately determine the staffing requirements. Mixed integer programming can then be used to provide optimal staffing plans based on a service performance goal. Finally, the simulation results can be used to develop sensitivity analysis charts to help workforce managers analyze staffing trade-offs that influence service quality, profit and cost. Traditional planning for contact centers involves both long term strategic planning and short term tactical staff scheduling. Long term strategic planning is particularly challenging as the lead time to hire and train agents must be taken into account. In addition, contact centers typically experience high attrition rates among their agents. INTRODUCTION OBJECTIVES Traditional long-term workforce planning methods use: A.Erlang-C equations using a set of demand constraints to estimate staffing requirements B.Historical average approaches to determine inter-day and intra-day forecast C.Ad-hoc hiring plans based on subjective reasoning and prior planning experience CHALLENGES We describe a solution that allows for simulation-based forecasting, optimal staffing plans and staffing trade-off analyses 1. Discrete Event Simulations 2. Forecast A.Inter-day and intra-day forecast (see figure below) B.Performance forecast C.Monthly/Weekly forecast using time-series methods 3. Mixed Integer Programming Mixed integer programming models are formulated based on business rules of the contact center network. This allows us to develop mathematically optimal staffing plans which include: A.Hiring plans that account for lead time to hire and train each new class of agents B.Extra-time and under-time plans C.Outsourcing plans to determine the optimal mix of call volumes allocated to outsourced services METHODOLOGIES 4. Sensitivity Analysis Sensitivity analysis are derived from the discrete event simulation results and represent the range of feasibility options varied against an output parameter. This can then be used to determine staffing trade-offs and analyze the effect on service quality, profit and cost. 200 300 400 500 600 10 11 12 13 14 15 16 Contact Volume Hour Of Day Hourly Interval Contact Arrival Volume (Monday) Week A Week B Week C Week D Figure B: Conceptual model of contact center simulation RESULTS 4. Sensitivity analysis charts determine staffing tradeoffs 60.0 62.0 64.0 66.0 68.0 70.0 72.0 74.0 12/7/2011 12/14/2011 12/21/2011 Required Staff Weeks Comparison of Erlang-C and Simulation Staffing Requirements Erlang Simulation Actual 1.Discrete event simulations provide increased accuracy over Erlang- C Erlang-C overstates staffing requirements by between 2% and 7%. 2. Historical inter-day and intra-day distributions applied to weekly forecast yield high accuracy 3. Mixed integer programming provides optimal staffing plan 30 50 70 90 110 March April May June July August September Service Level Months Comparison of Historical and Forecasted Performance Historical Forecasted $223 $225 $227 $229 $231 60 65 70 75 80 85 90 95 100 Profit (in 000s) Service Level (%) Profit Analysis: Total Profit Vs. Service Level Current CONCLUSION Figure A: Contact center network of a telecommunications organization R² = 0.912 METHODOLOGIES RESULTS Using our mixed-integer programming approach, we are able to determine a balanced approach using a mixture of extra- time and under-time agents to meet the staffing requirements. Optimal 35 40 45 50 55 60 65 70 March May July September November January Agents (FTE) Months Extra/Under Time Plans for Service level of 80% Under Time Extra Time Effective Staff Required Staff David Woo, Amit Garg, Bayu Wicaksono Long Term Strategic Workforce Planning For Contact Centers Using Mixed-Integer Programming and Simulation-Based Forecasting