1. 1 ANALYSIS OF EMAIL PROCESSING STRATEGIES TO ENHANCE EFFICIENCY AND EFFECTIVENESS Robert Greve Oklahoma State University

2. 2 AGENDA ► INTRODUCTION ► OVERVIEW OF RESEARCH  MISSION, GOALS, STRATEGY, & OBJECTIVES ► MODELING STRATEGIES  QUEUING THEORY  STOCHASTIC PROGRAMMING  SIMULATTION ► SIMULATION STUDY ► FUTURE RESEARCH ► QUESTIONS & COMMENTS

3. 3 INTRODUCTION ► ► "To make knowledge work productive will be the great management task of this century just as to make manual work productive was the great management task of the last century. The gap between knowledge work that is left unmanaged is probably a great deal wider than was the tremendous difference between manual work before and after the introduction of scientific management.“ (Peter Drucker, 1998)

4. 4 INTRODUCTION ► “And then there’s your work flow during the day. An information worker gets lots of e-mails as people want you to bid on something or respond to a problem. All these ‘events’ are coming in on your PC. Does the software help you know which of those you should ignore or pass along to somebody else, and how to prioritize them? No. We don’t do that yet.” (Bill Gates, 2003)

5. 5 INTRODUCTION ► KNOWLEDGE WORKER  “True, knowledge workers are still a minority, but they are fast becoming the largest single group. And they have already become the major creator of wealth.” (Drucker, 2002) ► EMAIL OVERLOAD  “More than 1 million messages pass through the Internet every hour. An estimated 2.7 trillion e-mail messages were sent in 1997.” And it was projected that nearly 7 trillion messages would be sent in 2000 (Overly, Foley & Lardner, 1999).  Intel (1999 Intel Employee Email Use Survey) ► 200: average number of emails waiting in an employee’s inbox ► 2.5: average number of hours of each day employees spend managing email ► 30: percentage of email that is unnecessary

6. 6 RESEARCH STREAMS ► MISSION  IMPROVEMENT OF KNOWLEDGE WORK ► GOALS  DECISION SUPPORT FOR KNOWLEDGE WORKERS ► STRATEGY  MODELING AND MANIPULATION OF EMAIL PROCESSING SCHEMES ► OBJECTIVES  DISCOVERY OF HEURISTICS & CONTINGENCIES  VALIDATION OF HEURISTICS & CONTINGENCIES  IMPLEMENTATION ► DSS ► ES ► INTELLIGENT AGENTS

7. 7 SCENARIO/POLICY TABLE EXAMPLE FREQUENCY OF EMAIL UTILIZATION OF KNOWLEDGE WORKER NATURE OF OUTSIDE WORK... PERFORMANCE CRITERIA OPTIMAL POLICY INFREQUENTHIGHINFREQUENT RESPONSE TIME PRIORITIZE BY TYPE... RESOLUTION TIME PRIORITIZE BY ITERATION MINIMIZE DISTRACTIONS EMAIL HOURS

8. 8 QUEUING THEORY EMAIL ANALOGIES ► SERVER → KNOWLEDGE WORKER ► CUSTOMER → EMAIL ► QUEUE → INBOX ► WAIT IN THE SYSTEM → RESPONSE TIME ► QUEUING DISCIPLINE → PROCESSING SCHEME

9. 9 QUEUING THEORY

10. 10 SINGLE SERVER QUEUE EXAMPLE A FACULTY MEMBER’S WEEKLY EMAIL

11. 11 SINGLE SERVER QUEUE EXAMPLE A FACULTY MEMBER’S EMAIL ► ASSUMPTIONS  FIFO  EXPONENTIAL INTERARRIVAL AND PROCESSING TIMES ► RAQS (Kamath, et. al., 1999) ► UTILIZATION: 0.952  PERCEIVED INFORMATION OVERLOAD???

12. 12 SINGLE SERVER QUEUE EXAMPLE A FACULTY MEMBER’S EMAIL

13. 13 SINGLE SERVER QUEUE EXAMPLE A FACULTY MEMBER’S EMAIL

14. 14 MULTI-SERVER QUEUES EXAMPLE A KNOWLEDGE NETWORK

15. 15 MULTI-SERVER QUEUES EXAMPLE A KNOWLEDGE NETWORK ► ASSUMPTIONS  FIFO  POISON ARRIVALS  EXPONENTIAL PROCESSING TIME DISTRIBUTIONS ► UTILIZATIONS  REP 1: 0.80  REP 2: 0.86  REP 3: 0.81 ► AVERAGE TIME IN THE SYSTEM  0.4356 DAYS

16. 16 STOCHASTIC PROGRAMMING ► Objective: Maximizing the utility of processed email  Utility of a processed email may decrease with time.  Potential arrival of different types of email in the future. ► Decision Variables - whether or not to process an email in a given stage ► The stochastic parameters – arriving email messages & processing time.

17. 17 SIMULATION ► CONSIDERATIONS  Utilization  Categorization/Prioritization  Prioritization of Ongoing Email Message  Frequency & Duration of Interruptions  Frequency & Duration of Email Processing Requirements  Email Hours

18. 18 MODELED SCENARIO ► PARAMETERS  ENVIRONMENT ► NATURE OF EMAIL  FREQUENT, SHORT  INFREQUENT, LONG ► UTILIZATION  LOW (60%)  HIGH (80%)  EXTREME (90%) ► NATURE OF OUTSIDE WORK (INTERRUPTIONS)  FREQUENT, SHORT  INFREQUENT, LONG

19. 19 MODELED SCENARIO ► PARAMETERS  POLICIES ► EMAIL HOURS  NONE (CONTINUOUS)  MORNING  SPLIT ► PRIORITY SCHEME  1111, 1122, 1212, 2121, 1234  (PRIORITY GIVEN TO NEW TYPE 1 EMAIL, ONGOING TYPE 1 EMAIL, NEW TYPE 2 EMAIL, AND ONGOING TYPE 2 EMAIL, RESPECTIVELY)

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21. 21 GENERAL HYPOTHESES ► Higher utilization will cause slower response and resolution times. ► Priority given to type one email messages will significantly reduce type one email response and resolution times. ► Priority given to type one email messages will significantly increase type two email response and resolution times. ► Priority given to ongoing email messages will significantly reduce resolution times.

22. 22 GENERAL HYPOTHESES ► Infrequent, long duration interruptions will correlate with slower response times, compared to frequent, short duration interruptions. ► Infrequent, long duration email processing requirements will cause slower response times, compared to frequent, short duration processing requirements. ► Morning email hours will significantly increase response and resolution times, but to a lesser extent.

23. 23 RESULTS OF INTEREST

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26. 26 RESULTS OF INTEREST

27. 27 “EMAIL HOURS”

28. 28 “SPLIT EMAIL HOURS”

29. 29 MANOVA RESULTS ► Utilization was a significant predictor of response and resolution times (.01 level). ► Priority schemes favoring type one email messages significantly reduced type one email response and resolution times (.01 level). ► Priority schemes favoring type one email messages did significantly increase type two email response and resolution times (.01 level). ► Priority given to ongoing email messages did NOT significantly reduce resolution times(.01 level).

30. 30 MANOVA RESULTS ► The frequency and duration of email was a significant factor (.01 level). ► The frequency and duration of outside work interruptions was a significant factor (.01 level). ► Morning email hours did significantly increase response and resolution times (.01 level). ► Split email hours did significantly increase response and resolution times, but significantly less than morning email hours (.01 level).

31. 31 IMPLICATIONS OF RESULTS ► Strategy matters. ► Strategy will depend on timeliness of email, and tolerance for interruptions. ► Analysis can provide a concrete basis for informed decisions.

32. 32 FUTURE RESEARCH ► CONTINUED MODELING ► VALIDATION  CASE STUDY ► IMPLEMENTATION  DSS  ES  INTELLIGENT AGENTS ► BEHAVIORIAL ASPECTS  Perceived Information Overload

33. 33 QUESTIONS & COMMENTS???