CONNECT 13 - WFM Scheduling and Forecasting
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Transcript of CONNECT 13 - WFM Scheduling and Forecasting
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Forecasting and Scheduling
Problems and Solutions
Aaron CashBeki NowlanMark MooreDillon Vincent
Forecasting
• Base foundation for your schedules• Accuracy is important• Automated collection of data• Spotting trends• Tagging events
Scheduling
• Automated process• Powerful behind the scenes computation• Ability to see over/under staffing numbers• Metrics by agents – not generic averages• KPI metrics – OT, Service Level, AHT
Forecasting - The Problem
• Predict how many contacts (calls, chats, emails) will arrive in a given timeframe
• What information can we use?• How do we know our algorithm is any
good?
Standard Solutions
• Naïve seasonal factors• Holt-Winters – exponential smoothing• Box-Jenkins – auto-regressive moving
average
Problems with Standard Solutions
• Reliance on magic parameters – difficult to automate
• Lack of flexibility for events, time zones, and external metrics
• Difficulty with non-standard seasonality – e.g. increased contacts on 1st of month
Our Solution - Neural Networks
• Highly extensible• Can use standard machine learning techniques• Not sensitive to magic parameters• Incremental solutions for quick re-forecasting• Parallelizable – suitable for expansion to large
contact centers
Scheduling - The problem
• Balance agent limitations with service goals
• Decompose into two sub-problems– Estimate service outcomes for an existing
schedule– Generate new candidate schedules
Estimating Service Outcomes
• Traditional call center – single-skill, single-channel agents– Queuing theory– Erlang formulas
• Modern call center – multi-skill, multi-channel agents– No equivalent mathematical formulation: It is too hard!– Do as physicists do: Run a simulation and measure it
Simulation
• Model arbitrarily complex contact center workflows
• Easily incorporate additional situations• Calculate multiple optimization metrics
simultaneously –e.g., service level and outbound volume
Generating Schedules - Linear Programming
• Pre-existing software components – well-developed technique
• Mathematically guaranteed to find optimal solutions – if they exist
• Requires linear constraints up front, inconsistent with simulation
Our Solution – Black Box Optimization
• Change your schedule slightly: See if it is better than before
• Works remarkably well with a few heuristics• Model automatically obeys labor rule restrictions• Parallelizable: Suitable for expansion to large
contact centers
Questions and Answers