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Intelligent Procedures for Intra-Day Updating of Call Center Agent Schedules University of Montreal Call Center Work

Intelligent Procedures for Intra-Day Updating of Call Center Agent Schedules University of Montreal Call Center Workshop, May 2006. Vijay Mehrotra and Ozgur Ozluk Department of Decision Sciences, College of Business San Francisco State University. Presentation Roadmap. “Who is this Guy?”

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Intelligent Procedures for Intra-Day Updating of Call Center Agent Schedules University of Montreal Call Center Work

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  1. Intelligent Procedures for Intra-Day Updating of Call Center Agent Schedules University of Montreal Call Center Workshop, May 2006 Vijay Mehrotra and Ozgur OzlukDepartment of Decision Sciences, College of BusinessSan Francisco State University

  2. Presentation Roadmap • “Who is this Guy?” • Customer Conversations / Embedded Problems • Intra-Day Re-Scheduling Framework • Literature • Components • Numerical Experiment and Results • Questions and Extensions

  3. About Vijay • PhD in OR, Stanford University, 1992 • 1993 – 1994: Consultant, DFI • 1994 - 2002: Co-Founder and CEO, Onward Inc. • 2002 - 2004: Vice President of Solutions, Blue Pumpkin Software

  4. More Than 1200 Companies Depend on Blue Pumpkin/Witness For Workforce Management Software Insurance & Lending Banking & Brokerage Outsourcers Retail & Catalog Communications Technology Travel & Transportation Healthcare Consumer Goods Manufacturing

  5. About Vijay • Fall 2003: “Radical Portfolio Adjustment” • Return to Academic World • SFSU Dept of Decision Sciences, College of Business • Full-Time Tenure Track Position • Teach Courses in Statistics, Operations, Quality, and Project Management to Undergraduates and MBAs • Still in “Real World” • Regular Stream of Consulting Projects • Focus on Call Center Operations, Enterprise Software, and Revenue Management • Thrust into Brave New World – Spring 2004 • Became First-Time Father • Moved to East Bay from SF

  6. Presentation Roadmap • “Who is this Guy?” • Customer Conversations / Embedded Problems • Intra-Day Re-Scheduling Framework • Literature • Components • Numerical Experiment and Results • Questions and Extensions

  7. Call Center WFM: The Right Number of Agents Working at the Right Times to Deliver the Right Queues – Not So Hard, Right? • Several Hundred Papers in the Academic Literature on Different Aspects of the Call Center WFM problem • Gans, Koole, and Mandelbaum (MSOM 2003) is an excellent literature survey • But We Still Have Many Managers and Executives with Real, Unsolved Call Center WFM Problems

  8. So Many “Improvements” to Consider:The Exploding Head of the CC Manager • MORE Routing Complexity • Skill-Based Routing • Multiple Customer Channels • Inbound/Outbound Blended • MultiSite / Outsourcing • MORE Demand Uncertainty • New Policies/Processes for Existing Businesses • New Businesses/Services • M&A Activity • New Operating Hours • Increased Service Level Goals • Cross-Channel Dynamics • MORE Pressure / Urgency • Tighter Budgets • “Solve the Problem Now”

  9. Uncertain Demand (Forecasted) Recommended Deployment of Resources System Definition, Available Resources, And Restrictions Estimated System Performance Costs and Objectives Vijay’s Grand Theory of EvORything Optimization/ Performance Model

  10. The Focus of This Paper: Short-Term Decision Making Based on Newly Available Information Strategic Cycle Hiring and Training Plan  Available Staff Tactical Cycle Schedules & New Call FCs  Plan for Future Week(s) Real Time Cycle Adjustments to Schedules Adjustments to Forecasts Historical Data

  11. Conversation 1: Customer=VP of Operations for Huge Division of Massive FinSvcs Conglomerate • Vijay: “So where else do you guys need help?” • Customer (upbeat): “We do our forecasts and schedules about a month ahead of time.” • C: “But things are changing all the time, so we are monitoring and updating our forecasts all the time, every single day.” • C: “Then, we react by trying to commit and de-commit resources as best we can – ratchet outsourcers up and down, offer our employees OT or VTO, offer more hours to our PT staff…” • C: “Last year, we estimate that we saved about $8mm doing this.” • V (nervously): “So what’s the problem with that?” • C: “First of all, we have no idea if we’re doing well or not, and we think we might be leaving a lot of money on the table.” • C: “Secondly, it’s all one big email nightmare, and it drives our ops staff nuts trying to keep all of it straight.” • V: “Hmmm….Thanks…”

  12. Conversation 2: Customer=VP of Finance for Big Division of Large Financial Svcs Conglomerate • Customer (abruptly): “How does your system quantify the risk?” • Vijay: “What do you mean by ‘risk’?” • C: “From what you’ve said, you take my forecasts and my service goals and come up with a number of agents for each 15-minute interval. Then, your scheduling algorithm tries to match that target.” • V (excited – customers never get this!): “That’s right! You’ve got it!!” • C: “So what percentage of the time will we actually meet our goals with that staffing plan?” • V: “Well, what you’d need to do is to do a Monte Carlo simulation on your forecasts and do a bunch of replications…And the answer depends on how you respond to different levels of demand, and on how accurate your forecasts are…” • C: “Your product doesn’t do that for us?” • V: “Uh, no. But I’ll put it on the list…”

  13. Presentation Roadmap • “Who is this Guy?” • Customer Conversations / Embedded Problems • Intra-Day Re-Scheduling Framework • Literature • Components • Numerical Experiment and Results • Questions and Extensions

  14. Initial Call And AHT Forecasts M/M/s Queueing Equations Initial Agent Requirements Per Period Individual Agents’ Availability Information Initial Schedule Assignments (Typically 1-4 Weeks Prior) Actual Call Volumes (1,2,..u-1)  Updated FCs Info on Actual Agent Attendance as of u-1 Updated Agent Schedules for u, u+1,…T M/M/s Queueing Equations Incremental Agent Reqs (u, u+1, …T) Framework for Intra-Day Schedule Updating

  15. Key Relevant Literature: Workload FC and Update • Identifying and Modeling Arrival Rates Per Period as Random Variables… • Thompson (1999), Chen & Henderson (2001) • Ross (2001), Jongblooed & Koole (2001) • Whitt (2004) • …Which Are Correlated with One Another • Brown et al (2002) • Avramidis et al (2004) • Steckley et al (2004)

  16. Key Relevant Literature: RT Schedule Adjustments • Models for “Real Time Schedule Adjustments” for Service Systems • Thompson (1999) • Hur, Mabert, and Bretthauer (2004) • Easton and Goodale (2005) • Surprisingly Small List • Absent from the Literature: RT Schedule Updating Papers in the Context of Call Centers

  17. Initial Call And AHT Forecasts M/M/s Queueing Equations Initial Agent Requirements Per Period Individual Agents’ Availability Information Initial Schedule Assignments (Typically 1-4 Weeks Prior) Actual Call Volumes (1,2,..u-1)  Updated FCs Info on Actual Agent Attendance as of u-1 Updated Agent Schedules for u, u+1,…T M/M/s Queueing Equations Incremental Agent Reqs (u, u+1, …T) Framework for Intra-Day Schedule Updating

  18. Step 0: Operating Parameters & Initial Schedules Initial Schedule Assignments (Typically 1-4 Weeks Prior)

  19. Step 1: Update Workload Forecast and Demand for Agents Actual Call Volumes (1,2,..u-1)

  20. Actual Call Volumes (1,2,..u-1) Updated Call Forecasts for (u, u+1, u+2,…T) Step 1: Update Workload Forecast As in (Whitt 99) and (Avramidis et al 2005), we model arrival process as NHPP with Random Arrival Rate L(s) =H * ((s): s >=0), where  is piecewise constant on intervals 1,2,…T and H is a (scalar) Random Variable with E[H] = 1  E[L] = 

  21. Actual Call Volumes (1,2,..u-1)  Updated FCs Info on Actual Agent Attendance as of u-1 M/M/s Queueing Equations Incremental Agent Reqs (u, u+1, …T) Step 2: Update Demand for Agents • Use Standard Queueing Equation for Translation (minimum s to satisfy SL goals for M/M/s queue) based on updated forecasts to determine incremental agent needs dt for t=u,u+1, …T

  22. Step 3: Update Agent Schedules for periods u…T Initial Schedule Assignments Individual Agents’ Availability Information Updated Agent Schedules for u, u+1,…T Incremental Agent Reqs (u, u+1, …T)

  23. Step 3: Update Agent Schedules for periods u…T

  24. Step 3: Update Agent Schedules for periods u…T Dimensionality of IP is Quite Small [ TxN Integer Variables, Tx(T+N) Constraints ]

  25. Step 3: Update Agent Schedules for periods u…T • When Arrival Rate Variability Dominates Attendance: • Special Cases • Strictly overstaffed • H<1  dt <=0 for all t=u,u+1, …T • Address with Voluntary Time Off and/or Release of Contracted Agents • Strictly understaffed • H>1  dt >=0 for all t=u,u+1, …T • Address with “Holdover OT” and “Call-In OT”

  26. Presentation Roadmap • “Who is this Guy?” • Customer Conversations / Embedded Problems • Intra-Day Re-Scheduling Framework • Literature • Components • Numerical Experiment and Results • Questions and Extensions

  27. Goal: Test Methodology on Real Call Center Data to Understand Dynamics Model From Saltzman 2005 Sales-and-Service Call Center in Travel Industry Relatively Small Call Center Roughly 360 agent hours/day Mixture of FT and PT Agents Experimental Framework

  28. Experimental Framework Build initial schedules Based on l values (expected arrival rates) Choose a value for H, And simulate arrivals for Periods 1,2,..u-1

  29. Results for Overstaffed Cases (0.5 <= H < 1) Lesson: After recognizing that original FCs are too high, our Update Methodology delivers desired SLs with less staff/lower cost

  30. Key Lessons Not responding to new information is very damaging to service quality When H is large, Schedule Updating cannot fully make up for initial poor performance during first four hours Results for Understaffed Cases (1 < H <= 1.5)

  31. When staffing based on expected l, cannot meet goals easily without “Call-in” OT ? Given plans to update, where should initial staffing be set? ? What structure for contingent resource contracts makes sense given different arrival rate uncertainties? Results for Understaffed Cases (1 < H <= 1.5): The Rest of the Story

  32. Presentation Roadmap • “Who is this Guy?” • Customer Conversations / Embedded Problems • Intra-Day Re-Scheduling Framework • Literature • Components • Numerical Experiment and Results • Questions and Extensions

  33. Questions and Ideas? Please Call or Email! Vijay Mehrotra & Ozgur Ozluk Department of Decision Sciences San Francisco State University vjm@sfsu.edu / 650-465-8443 ozgur@sfsu.edu / 415-338-1007

  34. Extension to this Research [Currently in Progress] • Almost all Call Center Research to date assumes that # arrivals in a period is Poisson distributed • Data often strongly refutes this • e.g., mean = 2000, std dev = 500 or more • Model arrival process as B ((t): t ¸ 0), (Whitt 99), where  is piecewise constant

  35. Random Arrival Rates: A Graphical View (t) t

  36. Individual Agents’ Availability Information Initial Schedule Assignments (Typically 1-4 Weeks Prior) SHM Performance Measure Approximations (Higher) Initial Agent Requirements Per Period Extension to this Research [Currently in Progress] • Where to set initial staffing? • Hypothesis: Performance (and Cost-Effectiveness) can be improved by accounting for Arrival Rate Variability in setting initial staffing levels • Method: Use Analytic Approximations from (Steckley, Henderson, and Mehrotra 2005) to determine # of agents needed to achieve particular SL when creating initial weekly schedule Initial Call And AHT Forecasts M/M/s Queueing Equations Initial Agent Requirements Per Period

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