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ANALYSIS OF EMAIL ANALYSIS OF EMAIL PROCESSING STRATEGIES PROCESSING STRATEGIES TO ENHANCE EFFICIENCY TO ENHANCE EFFICIENCY

AND EFFECTIVENESSAND EFFECTIVENESS

Robert GreveRobert Greve

Oklahoma State UniversityOklahoma State University

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AGENDAAGENDA

► INTRODUCTIONINTRODUCTION► OVERVIEW OF RESEARCH OVERVIEW OF RESEARCH

MISSION, GOALS, STRATEGY, & OBJECTIVESMISSION, GOALS, STRATEGY, & OBJECTIVES► MODELING STRATEGIESMODELING STRATEGIES

QUEUING THEORYQUEUING THEORY STOCHASTIC PROGRAMMINGSTOCHASTIC PROGRAMMING SIMULATTIONSIMULATTION

► SIMULATION STUDYSIMULATION STUDY► FUTURE RESEARCHFUTURE RESEARCH► QUESTIONS & COMMENTSQUESTIONS & COMMENTS

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INTRODUCTIONINTRODUCTION

► "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)

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INTRODUCTIONINTRODUCTION

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

(Bill Gates, 2003)(Bill Gates, 2003)

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INTRODUCTIONINTRODUCTION► KNOWLEDGE WORKERKNOWLEDGE WORKER

““True, knowledge workers are still a minority, but they are fast True, knowledge workers are still a minority, but they are fast becoming the largest single group. And they have already becoming the largest single group. And they have already become the major creator of wealth.” (Drucker, 2002)become the major creator of wealth.” (Drucker, 2002)

► EMAIL OVERLOADEMAIL OVERLOAD ““More than 1 million messages pass through the Internet More than 1 million messages pass through the Internet

every hour. An estimated 2.7 trillion e-mail messages were every hour. An estimated 2.7 trillion e-mail messages were sent in 1997.” And it was projected that nearly 7 trillion sent in 1997.” And it was projected that nearly 7 trillion messages would be sent in 2000 (Overly, Foley & Lardner, messages would be sent in 2000 (Overly, Foley & Lardner, 1999).1999).

Intel (1999 Intel Employee Email Use Survey)Intel (1999 Intel Employee Email Use Survey)► 200: average number of emails waiting in an employee’s 200: average number of emails waiting in an employee’s

inboxinbox► 2.5: average number of hours of each day employees 2.5: average number of hours of each day employees

spend managing emailspend managing email► 30: percentage of email that is unnecessary30: percentage of email that is unnecessary

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RESEARCH STREAMSRESEARCH STREAMS► MISSION MISSION

IMPROVEMENT OF KNOWLEDGE WORKIMPROVEMENT OF KNOWLEDGE WORK

► GOALSGOALS DECISION SUPPORT FOR KNOWLEDGE WORKERSDECISION SUPPORT FOR KNOWLEDGE WORKERS

► STRATEGYSTRATEGY MODELING AND MANIPULATION OF EMAIL PROCESSING MODELING AND MANIPULATION OF EMAIL PROCESSING

SCHEMESSCHEMES► OBJECTIVES OBJECTIVES

DISCOVERY OF HEURISTICS & CONTINGENCIESDISCOVERY OF HEURISTICS & CONTINGENCIES VALIDATION OF HEURISTICS & CONTINGENCIESVALIDATION OF HEURISTICS & CONTINGENCIES IMPLEMENTATION IMPLEMENTATION

► DSSDSS► ESES► INTELLIGENT AGENTSINTELLIGENT AGENTS

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SCENARIO/POLICY TABLESCENARIO/POLICY TABLEEXAMPLEEXAMPLE

FREQUENCFREQUENCY OF Y OF EMAILEMAIL

UTILIZATIOUTILIZATION OF N OF KNOWLEDGKNOWLEDGE WORKERE WORKER

NATURE OF NATURE OF OUTSIDE OUTSIDE WORKWORK . . . . . .

PERFORMANCE PERFORMANCE CRITERIACRITERIA

OPTIMAL OPTIMAL POLICYPOLICY

INFREQUENINFREQUENTT HIGHHIGH INFREQUENTINFREQUENT RESPONSE TIMERESPONSE TIME

PRIORITIZE PRIORITIZE BY TYPEBY TYPE

..

..

.. RESOLUTION TIMERESOLUTION TIME

PRIORITIZE PRIORITIZE BY BY ITERATIONITERATION

MINIMIZE MINIMIZE DISTRACTIONSDISTRACTIONS

EMAIL EMAIL HOURSHOURS

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QUEUING THEORYQUEUING THEORYEMAIL ANALOGIESEMAIL ANALOGIES

►SERVER → KNOWLEDGE WORKERSERVER → KNOWLEDGE WORKER►CUSTOMER → EMAILCUSTOMER → EMAIL►QUEUE → INBOXQUEUE → INBOX►WAIT IN THE SYSTEM → RESPONSE WAIT IN THE SYSTEM → RESPONSE

TIMETIME►QUEUING DISCIPLINE → PROCESSING QUEUING DISCIPLINE → PROCESSING

SCHEMESCHEME

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QUEUING THEORYQUEUING THEORY

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SINGLE SERVER QUEUE EXAMPLESINGLE SERVER QUEUE EXAMPLEA FACULTY MEMBER’S WEEKLY A FACULTY MEMBER’S WEEKLY

EMAILEMAIL

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SINGLE SERVER QUEUE SINGLE SERVER QUEUE EXAMPLEEXAMPLE

A FACULTY MEMBER’S EMAILA FACULTY MEMBER’S EMAIL►ASSUMPTIONSASSUMPTIONS

FIFOFIFO EXPONENTIAL INTERARRIVAL AND EXPONENTIAL INTERARRIVAL AND

PROCESSING TIMESPROCESSING TIMES

►RAQS (Kamath, et. al., 1999)RAQS (Kamath, et. al., 1999)►UTILIZATION: 0.952UTILIZATION: 0.952

PERCEIVED INFORMATION OVERLOAD???PERCEIVED INFORMATION OVERLOAD???

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SINGLE SERVER QUEUE SINGLE SERVER QUEUE EXAMPLEEXAMPLE

A FACULTY MEMBER’S EMAILA FACULTY MEMBER’S EMAIL

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SINGLE SERVER QUEUE SINGLE SERVER QUEUE EXAMPLEEXAMPLE

A FACULTY MEMBER’S EMAILA FACULTY MEMBER’S EMAIL

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MULTI-SERVER QUEUES MULTI-SERVER QUEUES EXAMPLEEXAMPLE

A KNOWLEDGE NETWORKA KNOWLEDGE NETWORK

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MULTI-SERVER QUEUES MULTI-SERVER QUEUES EXAMPLEEXAMPLE

A KNOWLEDGE NETWORKA KNOWLEDGE NETWORK► ASSUMPTIONSASSUMPTIONS

FIFOFIFO POISON ARRIVALS POISON ARRIVALS EXPONENTIAL PROCESSING TIME DISTRIBUTIONSEXPONENTIAL PROCESSING TIME DISTRIBUTIONS

► UTILIZATIONSUTILIZATIONS REP 1: 0.80REP 1: 0.80 REP 2: 0.86REP 2: 0.86 REP 3: 0.81REP 3: 0.81

► AVERAGE TIME IN THE SYSTEMAVERAGE TIME IN THE SYSTEM 0.4356 DAYS0.4356 DAYS

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STOCHASTIC PROGRAMMINGSTOCHASTIC PROGRAMMING

► Objective: Maximizing the utility of processed email Objective: Maximizing the utility of processed email Utility of a processed email may decrease with time. Utility of a processed email may decrease with time. Potential arrival of different types of email in the future. Potential arrival of different types of email in the future.

► Decision Variables - whether or not to process an Decision Variables - whether or not to process an email in a given stage email in a given stage

► The stochastic parameters – arriving email The stochastic parameters – arriving email messages & processing time.messages & processing time.

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SIMULATIONSIMULATION

►CONSIDERATIONSCONSIDERATIONS Utilization Utilization Categorization/PrioritizationCategorization/Prioritization Prioritization of Ongoing Email MessagePrioritization of Ongoing Email Message Frequency & Duration of InterruptionsFrequency & Duration of Interruptions Frequency & Duration of Email Processing Frequency & Duration of Email Processing

RequirementsRequirements Email HoursEmail Hours

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MODELED SCENARIOMODELED SCENARIO

► PARAMETERSPARAMETERS ENVIRONMENTENVIRONMENT

► NATURE OF EMAILNATURE OF EMAIL FREQUENT, SHORTFREQUENT, SHORT INFREQUENT, LONGINFREQUENT, LONG

► UTILIZATIONUTILIZATION LOW (60%)LOW (60%) HIGH (80%)HIGH (80%) EXTREME (90%)EXTREME (90%)

► NATURE OF OUTSIDE WORK (INTERRUPTIONS)NATURE OF OUTSIDE WORK (INTERRUPTIONS) FREQUENT, SHORTFREQUENT, SHORT INFREQUENT, LONGINFREQUENT, LONG

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MODELED SCENARIOMODELED SCENARIO

►PARAMETERSPARAMETERS POLICIESPOLICIES

►EMAIL HOURSEMAIL HOURS NONE (CONTINUOUS)NONE (CONTINUOUS) MORNINGMORNING SPLITSPLIT

►PRIORITY SCHEMEPRIORITY SCHEME 1111, 1122, 1212, 2121, 12341111, 1122, 1212, 2121, 1234 (PRIORITY GIVEN TO NEW TYPE 1 EMAIL, ONGOING (PRIORITY GIVEN TO NEW TYPE 1 EMAIL, ONGOING

TYPE 1 EMAIL, NEW TYPE 2 EMAIL, AND ONGOING TYPE 1 EMAIL, NEW TYPE 2 EMAIL, AND ONGOING TYPE 2 EMAIL, RESPECTIVELY)TYPE 2 EMAIL, RESPECTIVELY)

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GENERAL HYPOTHESESGENERAL HYPOTHESES

► Higher utilization will cause slower response Higher utilization will cause slower response and resolution times.and resolution times.

► Priority given to type one email messages Priority given to type one email messages will significantly reduce type one email will significantly reduce type one email response and resolution times.response and resolution times.

► Priority given to type one email messages Priority given to type one email messages will significantly increase type two email will significantly increase type two email response and resolution times.response and resolution times.

► Priority given to ongoing email messages Priority given to ongoing email messages will significantly reduce resolution times.will significantly reduce resolution times.

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GENERAL HYPOTHESESGENERAL HYPOTHESES

► Infrequent, long duration interruptions will Infrequent, long duration interruptions will correlate with slower response times, correlate with slower response times, compared to frequent, short duration compared to frequent, short duration interruptions.interruptions.

► Infrequent, long duration email processing Infrequent, long duration email processing requirements will cause slower response requirements will cause slower response times, compared to frequent, short duration times, compared to frequent, short duration processing requirements.processing requirements.

► Morning email hours will significantly Morning email hours will significantly increase response and resolution times, but increase response and resolution times, but to a lesser extent. to a lesser extent.

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RESULTS OF INTERESTRESULTS OF INTERESTScenario Policy Performance

Nature of

Email

Utilization Nature of Outside Work

Email

Hours

Priority

Scheme

Type 1

Response

Type 1

Resolution

Type 2

Response

Type 2

ResolutionInfrequent 80% Infrequent No 1111 6.258 7.094 6.74 8.858

1122 5.664 6.423 7.165 9.5281212 6.445 7.064 6.72 8.9082121 6.364 7.053 6.696 8.8481234 5.694 6.423 7.194 9.528

Yes 1111 13.187 14.751 14.147 18.7791122 10.585 11.818 15.753 21.1791212 12.967 14.305 14.205 18.9222121 12.895 14.441 14.169 18.8891234 10.593 11.819 15.738 21.181

Infrequent 60% Infrequent No 1111 5.632 6.204 5.807 7.7161122 5.406 5.97 6.017 8.1341212 5.529 6.2 5.793 7.682121 5.566 6.208 5.727 7.6691234 5.437 5.97 5.952 8.134

Yes 1111 11.332 12.471 12.047 16.1321122 10.578 11.629 12.835 16.8721212 11.194 12.386 11.9 16.2632121 11.427 12.432 12.161 16.1891234 10.472 11.63 12.893 16.871

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Infrequent Email, Infrequent Outside Work, 80% Utilization

0

5

10

15

20

N 111

1

N 1122

N 1212

N 2121

N 1234

Y 111

1

Y 112

2

Y 121

2

Y 212

1

Y 123

4

Policy

Ave

rag

e R

esp

on

se

Tim

e Type 1

Type 2

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Infrequent Email, Infrequent Outside Work, 60% Utilization

02468

101214

N 111

1

N 1122

N 1212

N 2121

N 1234

Y 111

1

Y 112

2

Y 121

2

Y 212

1

Y 123

4

Policy

Ave

rag

e R

esp

on

se

Tim

e Type 1

Type 2

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RESULTS OF INTERESTRESULTS OF INTEREST

Nature of

Email

Utilization Nature of Outside Work

Email

Hours

Priority

Scheme

Type 1

Response

Type 1

Resolution

Type 2

Response

Type 2

ResolutionInfrequent 90% Infrequent No 1111 7.01 7.717 7.275 9.653

1122 6.112 6.771 7.982 10.7751212 6.978 7.669 7.315 9.8282121 6.868 7.697 7.188 9.571234 6.122 6.771 8.026 10.779

Yes 1111 13.714 15.377 15.014 19.961122 10.754 11.843 16.778 22.3151212 13.103 14.715 14.94 19.932121 13.231 14.814 14.295 18.9981234 10.713 11.843 16.892 22.319

Split 1111 8.898 9.826 9.609 12.8181122 6.371 7.119 10.721 14.2571212 8.746 9.502 9.484 12.5762121 8.884 9.788 9.52 12.7921234 6.338 7.119 10.642 14.253

Scenario Policy Performance

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““EMAIL HOURS”EMAIL HOURS”

Infrequent Email, Infrequent Outside Work, 90% Utilization

0

5

10

15

20

N 111

1

N 1122

N 1212

N 2121

N 1234

Y 111

1

Y 112

2

Y 121

2

Y 212

1

Y 123

4

Policy

Ave

rag

e R

esp

on

se

Tim

e Type 1

Type 2

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““SPLIT EMAIL HOURS”SPLIT EMAIL HOURS”

Infrequent Email, Infrequent Outside Work, 90% Utilization

02468

1012

N 111

1

N 1122

N 1212

N 2121

N 1234

S 111

1

S 112

2

S 121

2

S 212

1

S 123

4

Policy

Ave

rag

e R

esp

on

se

Tim

e Type 1

Type 2

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MANOVA RESULTSMANOVA RESULTS

► Utilization was a significant predictor of Utilization was a significant predictor of response and resolution times (.01 level).response and resolution times (.01 level).

► Priority schemes favoring type one email Priority schemes favoring type one email messages significantly reduced type one email messages significantly reduced type one email response and resolution times (.01 level).response and resolution times (.01 level).

► Priority schemes favoring type one email Priority schemes favoring type one email messages did significantly increase type two messages did significantly increase type two email response and resolution times (.01 level).email response and resolution times (.01 level).

► Priority given to ongoing email messages did Priority given to ongoing email messages did NOT significantly reduce resolution times(.01 NOT significantly reduce resolution times(.01 level).level).

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MANOVA RESULTSMANOVA RESULTS► The frequency and duration of email was a The frequency and duration of email was a

significant factor (.01 level).significant factor (.01 level).► The frequency and duration of outside work The frequency and duration of outside work

interruptions was a significant factor (.01 level).interruptions was a significant factor (.01 level).► Morning email hours did significantly increase Morning email hours did significantly increase

response and resolution times (.01 level). response and resolution times (.01 level). ► Split email hours did significantly increase Split email hours did significantly increase

response and resolution times, but significantly response and resolution times, but significantly less than morning email hours (.01 level). less than morning email hours (.01 level).

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IMPLICATIONS OF RESULTSIMPLICATIONS OF RESULTS

►Strategy matters.Strategy matters.

►Strategy will depend on timeliness of Strategy will depend on timeliness of email, and tolerance for interruptions.email, and tolerance for interruptions.

►Analysis can provide a concrete basis Analysis can provide a concrete basis for informed decisions.for informed decisions.

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FUTURE RESEARCHFUTURE RESEARCH►CONTINUED MODELINGCONTINUED MODELING►VALIDATIONVALIDATION

CASE STUDYCASE STUDY► IMPLEMENTATIONIMPLEMENTATION

DSSDSS ESES INTELLIGENT AGENTSINTELLIGENT AGENTS

►BEHAVIORIAL ASPECTSBEHAVIORIAL ASPECTS Perceived Information OverloadPerceived Information Overload

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QUESTIONS & COMMENTS???QUESTIONS & COMMENTS???