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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