Beyond Spam: OR/MS Modeling Opportunities for Email Response Management

1. Beyond Spam: OR/MS Modeling Opportunities for Email Response Management Ramesh Sharda Robert A. Greve, Ashish Gupta, Manjunath Kamath, Mohan R. Chinnaswamy Oklahoma State University Ramesh.Sharda@okstate.edu

2. Managing Email • Pull the plug! • Spam control • Email filtering and organization • Effective management policies and strategies • Organizational and Individual level • Modeling opportunities APMOD2004 Sharda/OSU

3. Our Projects • Routing and priority decisions in an email contact center • Simulation and queuing theory • How often should we process our emails • Simulation • Which email messages to process • Stochastic Programming with Recourse APMOD2004 Sharda/OSU

4. Queuing and Simulation Models For Analyzing Customer Contact Center Operations APMOD2004 Sharda/OSU

5. Inbound E-mail Contact Center Issues • Operational planning • Number of agents • Agents’ schedule • Routing policies and priorities • Routing to agents • Processing order • Performance measures • Response/Resolution time • Agent utilization • Organizational behavior • Human factors APMOD2004 Sharda/OSU

6. Call/Contact Center Literature • Koole and Talim [2000] • Exponential approximation in the design of call centers • Koole and Mandelbaum [2002] • Queueing models of call centers • Koole, Pot and Talim [2003] • Performance of call center with skill-based routing • Armony and Maglaras [2002, 2003] • Optimal staffing policy • Estimation scheme for the response time • Whitt [2002] • Challenges and research directions in the design of customer contact centers APMOD2004 Sharda/OSU

7. Contact Center Description START RECEIVE E-MAIL IDENTIFY E-MAIL TYPE USING SOFTWARE DIRECT E- MAIL TO AN AGENT YES DELAY PROCESS E-MAIL FORWARD E-MAIL ? PRE-PROCESS E-MAIL NO RESOLVED ? END NO YES E-MAIL HANDLING LOGIC APMOD2004 Sharda/OSU

8. Model Details • Poisson arrivals • Agents process e-mails according to a FCFS discipline • For an unresolved problem, the e-mail enters the system with a delay independent of the prior processing • The pre-processing time follows a uniform distribution • The processing time follows a general distribution • Erlang, Exponential, and Hyperexponential • 2 types of e-mail and 3 agents APMOD2004 Sharda/OSU

9. Open Queuing Network Model • Nodes represent agents • Customers represent e-mails • Model parameters • Number of nodes and the number of servers at each node • Markovian routing probability matrix • Mean and SCV (=Variance/Mean2) service time at each node • Arrival rate and SCV for new e-mails APMOD2004 Sharda/OSU

10. Numerical Experiments • Queueing model was solved using the Rapid Analysis of Queueing Systems (RAQS) package • RAQS is a software package for analyzing general queueing network models based on a two-moment framework [http://www.okstate.edu/cocim/raqs/] • Simulation results obtained using a model in Arena 7.0 • Replications – 10 • Run length – 9,240 hours • Warm up – 840 hours APMOD2004 Sharda/OSU

11. EXPERIMENTAL DESIGN * Numbers refer to priorities assigned to new email, previously processed (by the same agent) email, and previously processed (by the a different agent) email, respectively. First Come First Served (FCFS) gives priority only based on arrival time. APMOD2004 Sharda/OSU

12. EXPERIMENTAL DESIGN • PERFORMANCE MEASURES • Purchase Inquiry Response Time • Purchase Inquiry Resolution Time • Problem Resolution Request Response Time • Problem Resolution Request Resolution Time APMOD2004 Sharda/OSU

13. RESULTS (high utilization) • Prioritization schemes that gave last priority to new email messages result in longer response and resolution times. • By routing incoming messages to the agent with the fewest messages waiting for processing, the load is balanced across the agents. • Routing messages to the agent who previously processed the message may result in disparity in individual agent utilizations, causing a gap between the best and worst performance. APMOD2004 Sharda/OSU

14. CONCLUSIONS • Simulation, which has been used for modeling customer call centers, can also be used to model the unique characteristics of customer contact centers • Management decisions regarding routing and priority schemes can impact performance • The queuing model consistently underestimates the system performance measures. APMOD2004 Sharda/OSU

15. Scheduling Email Processing to Reduce Information Overload and Interruptions APMOD2004 Sharda/OSU

16. Prior relevant research on Email Overload & interruptions research • First reportedby Peter Denning (1982),Later by Hiltz, et al. (1985), Whittaker, et al.(1996) and many… • According to distraction theory, interruption is “an externally generated, randomly occurring, discrete event that breaks continuity of cognitive focus on a primary task“ (Corragio 1990; Tétard F. 2000). • Research done in HCI is rich but in MS/OR??? • Research that looks at the problem of information overload and interruptions simultaneously is scarce. (Speier et al.1999, Jackson, et al., 2003, 2002, 2001), Venolia et al. (2003) APMOD2004 Sharda/OSU

17. Resource utilization Resource utilization change Email Policy Number of Interruptions per task Task Complexity mix Interrupt arrival pattern Task Completion time Research Model *Utilization: Probability of a knowledge worker being busy (λ/µ) APMOD2004 Sharda/OSU

18. Recall time- RL IL + Interrupt processing Pre-processing Post-processing Interrupt arrives Interrupt departs Our approach- SIMULATION • Phases of task processing • (Miyata & Norman, 1986):- • Planning • Execution • Evaluation • Policies that we are comparing :- • Triage:(C1-morning, C1-Afternoon) • Scheduled: (C2, C4, C8(Jackson et al. 2003)) • Flow (continuous): C APMOD2004 Sharda/OSU

19. Notations used i Task types- simple (S), complex (C) , email(E) thus, i = {S, C, E} Prim Primary task, which is either a simple or a complex task. Prim ={S, C} ρMinimum utilization of knowledge worker i arrival rate for task of type i μi Service rates for task of type i P Planning phase of a task Exe Execution phase of task Eval Evaluation phase of task Stage Current stage of task processing. Thus Stage= {P, Exe, Eval} IPrim-Stage Interruption lag for a primary task at a particular processing stage. RPrim-Stage Resumption lag for a primary task at a particular processing stage. RPrim, ĬPrim Mean Resumption lag & Mean Interruption lag for a primary task APMOD2004 Sharda/OSU

20. Mathematical conditions and equations Following conditions were implemented in the simulation model: APMOD2004 Sharda/OSU

21. 0 ≤ ≤ 1 Mathematical conditions & equations APMOD2004 Sharda/OSU

22. Parameters chosen for Beta distribution are k=2 & l =1 For positive linear relationship between ε and . Mathematical conditions & equations APMOD2004 Sharda/OSU

23. Model Implementation Sn, Cn- new simple & complex task Si, Ci – interrupted simple & complex task E – Email (Interrupt) APMOD2004 Sharda/OSU

24. Results Profile plots • Policy C4 resulted in • minimum percentage increase in utilization • minimum # of interruptions per simple task • minimum # of interruptions per complex tasks • Result holds under: • The work environment requires high, medium or low utilization of knowledge worker, or • The work environment requires processing of • either more simple or more complex tasks, or • For both arrival patterns (Pattern I: when all email arrived during office hrs, Pattern II: when 80% emails arrived during office hrs). RU RU # of interruptions per simple or complex task % increase in utilization 9

25. Practical implications • If other tasks are more important and email communication is secondary ! • Process emails 4 times a day with each processing block not exceeding 45 min. • Is timely email processing a survival issue for your kind of organization? • Use flow (continuous) policy APMOD2004 Sharda/OSU

26. A Stochastic Programming Approach to Managing Email Overload APMOD2004 Sharda/OSU

27. Email Overload • Inability to respond to all email in a timely manner • The knowledge worker must not only take into consideration the current email that is in need of processing and the timeliness of this email, but he or she must also consider what future email demands may be on the horizon. • Stochastic Programming takes possible FUTURE scenarios into consideration • All other efforts consider only the present state APMOD2004 Sharda/OSU

28. An Illustrative Example: Optimizing Email Processing • With respect to email processing, the optimization involves maximizing the utility or value of the emails that are processed. • The optimal solution must take into consideration that the utility of a processed email may decrease with time. • The optimal solution must also consider the potential arrival of different types of email in the future. • The decision variables correspond to whether or not to process an email in a given stage (time frame). • The stochastic parameters include the potential arrival of various types of emails. APMOD2004 Sharda/OSU

29. An Illustrative Example: Optimizing Email Processing • Beginning Inbox (i = type, j = age) APMOD2004 Sharda/OSU

30. An Illustrative Example: Optimizing Email Processing • Utility of email processed (i = type, j = age) APMOD2004 Sharda/OSU

31. An Illustrative Example: Optimizing Email Processing • Arrival scenarios (number of type i email arriving) APMOD2004 Sharda/OSU

32. An Illustrative Example: Optimizing Email Processing • Time needed to process email (days) APMOD2004 Sharda/OSU

33. Formulations • LP – Single-period • LP – Multi-period • SP – Perfect Information • SP – Here and Now APMOD2004 Sharda/OSU

34. Formulation Sets and Indices • T is the set of the different days under consideration • I is the set of possible types of email messages • J is the set of possible ages of an email message in days • Q is the set of possible arrival scenarios • t = 1..4 denotes the day under consideration • i = 1..2 denotes the type of email message • j = 1..5 denotes the age of an email message • q = 1..64 denotes the arrival scenario APMOD2004 Sharda/OSU

35. SP Formulation(Here and Now) Parameters • Nt= 1,i,j,qThis represents the number of email of type i that are j days old on day one. This represents the beginning inbox. • At,i,qThis represents the number of arriving email of type i, given scenario q. • Ui,jThis represents the utility or value of an email of type i, having an age of j. • PqThis represents the probability of scenario q. • DiThis represents the time needed, in days, to process an email of type i. APMOD2004 Sharda/OSU

36. SP Formulation (cont.)(Here and Now) Variables • Xt,i,j,q This represents the number of email that are processed on day t, that are of type i and have an age of j, given scenario q. • Nt,i,j,qThis represents the number of email of type i that are j days old on day t, given scenario q. Objective Function • Max ΣqΣiΣj Pq Xt,i,j,q Ui,j APMOD2004 Sharda/OSU

37. SP Formulation (cont.)(Here and Now) Constraints • Nt,i,j,q = Nt-1,i,j-1,q – Xt-1,i,j-1,q t > 1, i = 1..2, j > 1, q = 1..64 • Nt,i,j,q = At,i,q t > 1, I – 1..2, j = 1, q = 1..64 • Xt,i,j,q <= Nt,i,j,q t = 1..4, i = 1..2, j < 5, q = 1..64 • Xt,i,j,q = Nt,i,j,q t = 1..4, i = 1..2, j = 5, q = 1..64 • Σi Σj Xt,i,j,q Di <= 1 t = 1..4, i = 1..2, j = 1..5, q = 1..64 • Xt,i,j,q = Xt,i,j,q+1 t = 1, i = 1..2, j = 1..5, q < 63 • Xt,i,j,q = Xt,i,j,q+1 t = 2, i = 1..2, j = 1..5, q < 63 • Xt,i,j,q = Xt,i,j,q+1 t = 3, i = 1..2, j = 1..5, q < 63 • Xt,i,j,q = Xt,i,j,q+1 t = 4, i = 1..2, j = 1..5, q < 63 APMOD2004 Sharda/OSU

38. Sample Results APMOD2004 Sharda/OSU

39. Extensions • More realistic modeling of the problem needed: • Differences in service times for different email classes • Identification of utilities • Automatic identification of email categories • Real time solution of the SPR problem before the Inbox is shown to the user • Another optimization challenge APMOD2004 Sharda/OSU

40. Future research • Perform these studies in experimental or field settings. • Use measures of Perceived Information Overload (NASA-TLX, SWAT) • More realistic modeling by incorporating email characteristics as well as knowledge worker differences • Single vs. multi-user settings/Network modeling • Nonlinear formulations • Stochastic knapsack A really rich domain for OR/MS modeling!!! APMOD2004 Sharda/OSU