Contact Center Template User's Guide

7/31/2019 Contact Center Template User's Guide

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USERS GUIDE

PUBLICATION ARENCC-UM001H-EN-PJanuary2012Supersedes Publication ARENCC-UM001G-EN-P

PN-111657

ArenaContact Center


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Contact Rockwell Automation Customer Support Telephone 1-440-646-3434Online Support http://www.rockwellautomation.com/support

Copyright Notice 2012 Rockwell Automation, Inc. All rights reserved. Printed in USA.

This document and any accompanying Rockwell Software products are copyrighted by Rockwell Automation, Inc. Anyreproduction and/or distribution without prior written consent from Rockwell Automation, Inc. is strictly prohibited.Please see the license agreement for details.

Trademark Notices Arena, Rockwell Automation, and SIMAN are registered trademarks of Rockwell Automation, Inc.

Other Trademarks ActiveX, Microsoft, Microsoft Access, SQL Server, Visual Basic, Visual C++, Visual SourceSafe, Windows, WindowsME, Windows NT, Windows 2000, Windows Server 2003, and Windows XP are either registered trademarks ortrademarks of Microsoft Corporation in the United States and/or other countries.

Adobe, Acrobat, and Reader are either registered trademarks or trademarks of Adobe Systems Incorporated in theUnited States and/or other countries.

ControlNet is a registered trademark of ControlNet International.

DeviceNet is a trademark of the Open DeviceNet Vendor Association, Inc. (ODVA)

Ethernet is a registered trademark of Digital Equipment Corporation, Intel, and Xerox Corporation

OLE for Process Control (OPC) is a registered trademark of the OPC Foundation.

Oracle, SQL*Net, and SQL*Plus are registered trademarks of Oracle Corporation.

All other trademarks are the property of their respective holders and are hereby acknowledged.

Warranty This product is warranted in accordance with the product license. The products performance may be affected by system

configuration, the application being performed, operator control, maintenance, and other related factors. RockwellAutomation is not responsible for these intervening factors. The instructions in this document do not cover all thedetails or variations in the equipment, procedure, or process described, nor do they provide directions for meeting every

possible contingency during installation, operation, or maintenance. This products implementation may vary amongusers.

This document is current as of the time of release of the product; however, the accompanying software may havechanged since the release. Rockwell Automation, Inc. reserves the right to change any information contained in thisdocument or the software at anytime without prior notice. It is your responsibility to obtain the most current informationavailable from Rockwell when installing or using this product.

Version: 14.00.00

Modified: January 18, 2012 11:02:10 AM
http://www.rockwellautomation.com/http://www.rockwellautomation.com/http://www.rockwellautomation.com/http://www.rockwellautomation.com/


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1 Welcome to Arena Contact Center Template 1

What is the Arena Contact Center template? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Intended audience. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Simulation of contact centers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

The Arena Contact Center template: A custom-designed simulation systemfor contact centers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Where can I go for help? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Refer to the users guides . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Explore our examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Get help. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Use the Smarts library . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Get phone support. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Get Web support . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6Refer to the Arena Web site . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Get training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Get consulting services. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Contact us . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2 Introduction to Simulation 9

Simulation defined . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Systems and models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Advantages of simulation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

The simulation process. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Problem definition and project planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Style definition and model formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Experimental design. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Input data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Verification and validation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Documentation and implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

3 General Concepts 23

Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Planning horizon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Timeslots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Contact types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25Data sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Contents


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Arrival pattern . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25Data sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Trunk Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Data sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Routing Scripts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Data sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Agent Skill Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Data sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Schedules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27Data sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Agent Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Data sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Parent Groups. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Data sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Queues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Animation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Performance measures/reporting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

4 Features 31

Different stages in the contact life span . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Contact arrival (required) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Blocked contacts (required) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Offered contacts (required). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Abandoned contacts (optional). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Disconnected contacts (optional) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34Contacts leaving messages (optional). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

Handled contacts (required) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

Talk time (required) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Conference (optional) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Transfer (optional) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

After-contact work (optional) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Contact back (optional). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Queue behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38Queue construction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

Queue ranking. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Agent selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Skill-based routing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

Routing script construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

Begin Script . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

Queue for Agent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

Remove from Queue. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41


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Wait. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41Priority . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

Message . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Disconnect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Overflow. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Transfer to Script . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Transfer to Agent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Conference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Branch. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42Assignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

End Script . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

Costing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

Agent costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

Trunk costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

Miscellaneous features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

Pattern entry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

Agent states. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44Individual agents. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

Advanced configuration agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

5 Getting Started 47

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

Loading and running an existing example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

General modeling skills and concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

Panels and modules. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48Names . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

Lists. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

Module copy and paste . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

Repeat group duplication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

Disable animation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

Building an Arena Contact Center template model . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

Defining the business application. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

Model overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50Model construction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Running the model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

6 The Contact Data Panel 65

Configuration module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

Schedule module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70Pattern module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74


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Agent module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77Contact module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

Animate module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

Report module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

7 The Script Panel 107

Begin Script module. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107

Queue for Agent module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109

Remove from Queue module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

Wait module. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112

Priority module. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113

Message module. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

Disconnect module. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116

Overflow module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117

Transfer to Script module. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118

Transfer to Agent module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120

Conference module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122Branch module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124

Assignment module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

End Script module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130

Script restrictions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130

Arena Contact Center template script examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131

8 Reports 133

Agents and Trunks report. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134

Trunk Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134

Agent Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134

Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135

Contact Times and Counts report. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135

Contact Times. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136

Contact Counts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136

Other Contact Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137Contact Count Statistics report. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138

Contact Time Statistics report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139

Agent Group Utilization report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141

Parent Group Utilization report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142

Trunk Group Utilization report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144

Overflow Count Statistics report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145


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CONTENTS

vii

9 Case Studies 147

Purposes of cases and examples. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

Example 1Bilingual Contact Center model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

Overview and business objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

Key modeling techniques illustrated in this example . . . . . . . . . . . . . . . . . . . . . 147

The data detail for the Bilingual Contact Center example . . . . . . . . . . . . . . . . . 149

Example 2Bank model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155



The data detail for the Bank example. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157

Example 3Skill-based Routing model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164


The data detail for the Skill-based Routing example . . . . . . . . . . . . . . . . . . . . . 165

Example 4Premium Service model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170



The data detail for the Premium Service example . . . . . . . . . . . . . . . . . . . . . . . 172Example 5Teamwork model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179

The data detail for the Teamwork example . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180

Example 6Multi-site model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189



The data detail for the Multi-site example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190

Other examples. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198

Outbound/blend examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198

A Reserved Words 199

B Reports 201

Index 205


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1 Welcome to Arena Contact CenterTemplateWhat is the Arena Contact Center template?

The Arena Contact Center template is a simulation system developed by RockwellAutomation, Inc., for the performance analysis of contact centers. It is built on

Rockwell Automations Arena simulation system and has been customized to allow

its users to build and run simulation models of contact center operations quickly and

easily and to analyze the results that these models produce.

Intended audience

The Arena Contact Center template is designed for contact center managers and

analysts and for industrial or systems engineers. It is typically deployed as an

enterprise business analysis and productivity tool.

You are interested in improving business productivity and are responsible for

evaluating and predicting the impact of proposed strategic and tactical changes. A

familiarity with the basic concepts and terms used in these types of system is assumed

as is a familiarity with computers and the Microsoft

Windows

operating system. Afamiliarity with the concepts and terms used in simulation is also helpful.

Simulation of contact centers

The planning problems of contact center managers and analysts are far easier to

describe than to model or to solve:

Ive got my staffing budget for the next fiscal year, but I dont know how manypeople I need to make service levels, what shifts to hire for, or what skills to train

my workers on.

Service levels look pretty good right now, but our peak season is coming up.

What I dont know is how badly our service levels and abandonment rates will

suffer if our forecasts turn out to be too low.


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Our service levels are in bad shape. We are considering either hiring anoutsourcer to help share the handling load or extending our hours. I wish I knew

where to get the most bang for the buck.

My telecomm guy has a new set of routing scripts to make use of some of our

advanced phone switch capabilities. I wonder how this is going to impact our

average speed of answer and our staff utilization.

Marketing has come up with a new program giving our preferred customers a

special priority when they contact us with questions. What Im worried about ishow this new program will affect the waiting times that the rest of our customers

experience.

Weve been asked to provide telephone service and support for another business

unit. Theyre asking us how much staff we need to hire or cross-train in order to

handle this increased load.

Contact center managers have traditionally approached such problems using various

methods, including gut feel estimates, back-of-the-envelope calculations, elaboratespreadsheets, and analytical queueing formulas such as Erlang C. Each of these

approaches, however, has significant limitations when applied to contact centers and

contact center networks.

Simulation can effectively and accurately model a contact center (or a network of

contact centers). Such a model can be used to study the performance of the system.

The simulation method is based on creating a computerized copy of the actual

contact center system and running this system for a period of time representing a day,a week, or a month.

In particular, simulation explicitly models the interaction between contacts (for

example, calls or e-mail), routes, and agents, as well as the randomness of individual

contact arrivals and handle times.

By using simulation, managers and analysts can translate contact center data (such as

forecasts, contact-routing vectors, contact-handle time distributions, agent schedules,

or agent skills) into actionable information about service levels, customerabandonment, agent utilization, first-contact resolution, and other important contact

center performance measures. These results are used to support key management

decisions that drive contact center operations and expenditures.


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1 WELCOME TOARENA CONTACT CENTER TEMPLATE

3

The Arena Contact Center template: Acustom-designed simulation system forcontact centers

The successful use of simulation in many contact center environments led to the

development of the Arena Contact Center template. It was developed by Rockwell

Automation in partnership with Onward, a management consulting firm based in

Mountain View, California, who specialize in contact center operations.

In conjunction with a team of contact center managers and analysts from many

different business environments, Rockwell Automation and Onward designed the

Arena Contact Center template to: Make it easy for analysts to build accurate and detailed simulation models of

contact centers, ranging from fairly simple to very complex, without extensive

simulation or management science training.

Support a process of managing input data for these contact center simulation

models that is as easy and sensible as possible.

Have the capacity to deliver real-time statistics, animation, and output statistics

that provide insight into key contact center performance measures.


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Use standard contact center terminology wherever possible to make the modelbuilding and usage process as intuitive as possible for contact center

professionals.

The Arena Contact Center template is a Microsoft Windows operating system-

based simulation system. It is one of a family of application solution templates

(ASTs) built on top of the Arena simulation system, leveraging Arenas development

environment to create a focused and easy-to-use tool for contact center managers and

analysts.

Where can I go for help?

Our commitment to your success starts with the suite of learning aids and assistance

we provide for Arena. Whether youre new to simulation or a seasoned veteran using

a new tool, youll quickly feel at home with the Arena Contact Center template.

Refer to the users guides

The documentation set includes this manual,Arena Contact Center Template Users

Guide, which cover the product basics; theArena Users Guide, which covers the

general product modules and offers an easy, click-by-click tutorial; and the

Variables Guide, a separate reference booklet providing complete descriptions of

Arena variables found in the Arena product templates.

DOCUMENT CONVENTIONSThroughout the guides, a number of style conventions are used to help identify

material. New terms and concepts may be emphasized by use of italics or bold text;

file menu paths are in bold with a (>) separating the entries (for example, go to Help

> Arena Help); text you are asked to type is shown in Courier Bold (for example, in

this field, type Work Week), and dialog box and window button names are shown in

bold (for example, clickOK).

Explore our examples

Arena provides a number of sample models that illustrate many of the commonly

used approaches for capturing the essence of a variety of processes for both job shop

and flow shop environments. For a list of Arenas examples, go to Help > Arena

Help. On the Contents tab, choose Model Building Basics, and then select Viewing

Arena Example Models.


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Get helpOnline help is always at your fingertips! Arena incorporates the latest in help

features, including Whats This? help that displays a brief description of fields in

dialog boxes, context-sensitive help on menu and toolbar buttons, and a help button

on each of Arenas modules. See the Arena Help table of contents and index for a list

of all help topics.

Use the Smarts libraryAs you craft models of your own systems processes, use our Smarts library to

explore how to best use Arena. This suite of tutorial models covers topics ranging

from modeling resources to animation techniques. The library is organized into

categories to help you find the right model with ease. When youre wondering how to

take the next step in your model, browse the Smarts library for a ready-made solution.

For a list of categories and their related Smarts, go to Help > Arena Help. On the

Contents tab, first clickModel Building Basics, and then Learning Arena with

Smart Files.

Get phone support

Rockwell Automation provides full support for the entire Arena family of products.

Questions concerning installation, how to use the software, how modules work, and

the use of the model editor are handled by technical support.

ARENA TECHNICAL SUPPORT INCLUDES: (for users on active maintenance) a technical support hotline and e-mail address

staffed by full-time, experienced professionals

help with installation problems or questions related to the softwares requirements

troubleshooting

limited support regarding the interaction of Arena with other programs

support for the Arena Object Model, which is used in Microsoft Visual Basic for

Applications.

If you call the support line (1.440.646.3434, option 3 & 7), you should be at your

computer and be prepared to give the following information:

the product serial number

the product version number

the operating system you are using

the exact wording of any messages that appeared on your screen

a description of what happened and what you were doing when the problem

occurred

a description of how you tried to solve the problem.


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International technical support is provided by the global representatives. For contactinformation on the representative nearest you, visit the Global Partners page on

www.ArenaSimulation.com.

Get Web support

In addition to phone support, the Rockwell Automation Customer Support Center

offers extensive online knowledgebases of technical notes and frequently asked

questions for support of non-urgent issues. These databases are updated daily by oursupport specialists. Go to http://www.rockwellautomation.com/support/ to sign up for

online support.

Once you have signed up for online support you can elect to receive regular e-mail

messages with links to the latest technical notes, software updates, and firmware

updates for the products that interest you. You can also submit online support

requests.

If you cant find the answer you need, contact your local representative or Arenatechnical support.

Refer to the Arena Web site

The Arena Web site provides a collection of brief how-to videos and FAQ topics

that may be of assistance to you. This material and more is available through the

Tools and Resources tab of www.ArenaSimulation.com.

Get training

Do you need training? Rockwell Automation offers a standard training course

consisting of lectures and hands-on workshops designed to introduce you to the

fundamental concepts of modeling with Arena.

We also offer customized training courses designed to meet your specific needs.

These courses can be held in our offices or yours, and we can accommodate one

person or twenty. You design the course thats right for you! Simply contact ourconsulting services group to discuss how we can help you achieve success in your

simulation efforts.

Get consulting services

Rockwell Automation provides expert consulting and turnkey implementation of the

entire Arena product suite. Please contact your local representative for more

information.


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Contact usWe strive to help all of our customers become successful in their manufacturing

improvement efforts. To help us achieve this objective, we invite you to contact your

local representative or Rockwell Automation at any time that we may be of service to

you. Be sure to connect with us on Facebook and join the Arena Users Group on

LinkedIn.

Support E-mail: [email protected] Phone: 1.440.646.3434 (options 3 & 7)

General E-mail: [email protected]

U. S. Sales Phone: 1.724.741.4000

URL: www.ArenaSimulation.com

URL: www.rockwellautomation.com


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2 Introduction to SimulationThis chapter contains excerpts from the simulation textbook entitledIntroduction to

Simulation Using SIMAN, Second Edition (McGraw-Hill, 1995) written by C. Dennis

Pegden, Randall P. Sadowski, and Robert E. Shannon.

Simulation definedSimulation is one of the most powerful analysis tools available to those responsible

for the design, analysis, and operation of complex processes or systems. In an

increasingly competitive world, simulation has become a very powerful tool for the

planning, design, and control of systems. It is viewed today as an indispensable

problem-solving methodology for engineers, designers, and managers.

To simulate, according to Websters Collegiate Dictionary, is to feign, to obtain the

essence of, without the reality. According to Schriber [1987], Simulation involves

the modeling of a process or system in such a way that the model mimics the response

of the actual system to events that take place over time. We will define simulation as

the process of designing a model of a real system and conducting experiments with

this model for the purpose of understanding the behavior of the system,or evaluating

various strategies for the operation of the system, or both. We consider simulation to

include both the construction of the model and the experimental use of the model for

studying a problem. Thus, you can think of simulation modeling as an experimentaland applied methodology that seeks to:

describe the behavior of systems,

construct theories or hypotheses that account for the observed behavior, and

use the model to predict future behavior; that is, the effects produced by changes

in the system or in its method of operation.

The terms model and system are key components of our definition of simulation.By model, we mean a representation of a group of objects or ideas in some form other

than that of the entity itself. Bysystem, we mean a group or collection of interrelated

elements that cooperate to accomplish some stated objective. We can simulate

systems that already exist and those that can be brought into existence; that is, those

in the preliminary or planning stage of development.


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Systems and modelsThe conceptualization and development of models have played a vital part in our

intellectual activity ever since we began to try to understand and manipulate our

environment. People have always used the idea of models to attempt to represent and

express ideas and objects. Historically, modeling has taken many forms, from

communicating through wall paintings to writing complex systems of mathematical

equations for the flight of a rocket through outer space. As a matter of fact, the

progress and history of science and engineering are reflected most accurately in theprogress of our ability to develop and use models.

One of the major elements required in attacking any problem is the construction and

use of a model. We use models because we want to learn something about some real

system that we cannot observe or experiment with directlyeither because the

system does not yet exist, or because it is too difficult to manipulate. A carefully

conceived model can strip away the complexity, leaving only that which the analyst

finds important. Such a model can take many forms, but one of the most usefulandcertainly the most often usedis simulation.

The concept of systems also plays a critical role in our modern view of the world. The

fundamental idea of thinking about the world in terms of systems and trying to take

the systems approach to attacking problems has become so ingrained in contempo-

rary practice that we tend to take it for granted. The systems approach tries to con-

sider total system performance rather than simply concentrating on the parts

[Weinberg, 1975]; it is based on our recognition that, even if each element or subsys-tem is optimized from a design or operational viewpoint, overall performance of the

system may be suboptimal because of interactions among the parts. The increasing

complexity of modern systems and the need to cope with this complexity underscore

the need for engineers and managers to adopt a systems approach to thinking.

Although complex systems and their environments are objective (that is, they exist),

they are also subjective (that is, the particular selection of included (and excluded)

elements and their configuration is dictated by the problem solver). Different analyses

of the same objective process or phenomenon can conceptualize it into very different

systems and environments. For example, a telecommunications engineer may think of

a contact center system as a collection of trunk lines and routing scripts. The contact

center director, however, is more likely to view the system as the combination of

phone lines, scripts, contacts, agents, and schedules. The vice president in charge of

contact center operations may see the system as the collection of all the centers her

company runs along with all outsourcers under contract. Hence, several different


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2 INTRODUCTION TO SIMULATION

11

conceptualizations of any particular real-world systemand thereby several differentmodelscan exist simultaneously.

System elements are the components, parts, and subsystems that perform a function

or process. The relationships among these elements and the manner in which they

interact determine how the overall system behaves and how well it fulfills its overall

purpose. Therefore, the first step in creating any model is to specify its purpose.

There is no such thing as the model of a system: we can model any system in

numerous ways, depending on what we wish to accomplish. Both the elements and

the relationships included must be chosen to achieve a specific purpose. The model

developed should be as simple as the stated purpose will allow.

The types of simulations of interest here are those used to develop an understanding

of the performance of a system over time. We typically use simulation models to help

us explain, understand, or improve a system. To be effective, simulation must

concentrate on some previously defined problem (otherwise, we do not know what

elements to include in the model or what information to generate and collect). We

typically use models to predict and comparethat is, to provide a logical way offorecasting the outcomes that follow alternative actions or decisions and (we hope) to

indicate a preference among them. Although this use of models is important, it is by

no means its only purpose. Model building also provides a systematic, explicit, and

efficient way to focus judgment and intuition. Furthermore, by introducing a precise

framework, a simulation model can effectively communicate system configuration

and assist the thought process.

Advantages of simulation

Because its basic concept is easy to comprehend, a simulation model is often easier to

justify to management or customers than some of the analytical models. In addition,

simulation might have more credibility because its behavior has been compared to

that of the real system, or because it has required fewer simplifying assumptions, and

thereby has captured more of the true characteristics of the real system.

Virtually all simulation models are so-called input-output models; that is, they yield

the output of the system for a given input. Simulation models are therefore run

rather than solved. They cannot generate an optimal solution on their own as

analytical models can; they can only serve as tools for the analysis of system behavior

under specified conditions. (The exception is a simulation model used to find the

optimum values for a set of control variables under a given set of inputs.)

We have defined simulation as experimentation with a model of the real system. An

experimental problem arises when a need develops for specific system information


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that isnt available from known sources. The following list describes some of thebenefits associated with simulation.

In a contact center, the impact of new types of contacts, new agent schedules,

modified contact priorities, contact volumes, and other key inputs can be explored

without disrupting ongoing operations.

New routing scripts or transfer logic can be tested before committing resources to

implementation.

Hypotheses about how or why certain phenomena occur can be tested for

feasibility.

Time can be controlled: it can be compressed, expanded, and so on, allowing us to

speed up or slow down a phenomenon for study.

Insight can be gained about which variables are most important to performance

and how these variables interact.

A simulation study can prove invaluable to understanding how the system reallyoperates as opposed to how everyone thinks it operates.

New situations, about which we have limited knowledge and experience, can be

manipulated in order to prepare for theoretical future events. Simulations great

strength lies in its ability to let us explore what if questions.

The simulation processThe essence or purpose of simulation modeling is to help the ultimate decision maker

solve a problem. Therefore, to learn to be a good simulation modeler, you must merge

good problem-solving techniques with good software engineering practice. The

following steps should be taken in every simulation study.

1. Problem Definition. Defining the goals of the study clearly so that we know the

purpose; that is, why are we studying this problem and what questions do we hope

to answer? What is the business impact of these answers?

2. Project Planning. Being sure that we have sufficient personnel, management

support, computer hardware, and software resources to do the job with a relevant

timetable.

3. System Definition. Determining the boundaries and restrictions to be used in

defining the system (or process) and investigating how the system works.


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4. Conceptual Model Formulation. Developing a preliminary model eithergraphically (for example, block diagrams) or in pseudo-code to define the

components, descriptive variables, and interactions (logic) that constitute the

system.

5. Preliminary Experimental Design. Selecting the measures of effectiveness to be

used, the factors to be varied, and the levels of those factors to be investigated;

that is, what data need to be gathered from the model, in what form, and to what

extent.

6. Input Data Preparation. Identifying and collecting the input data needed by the

model.

7. Model Translation. Formulating the model in an appropriate simulation

language or software package such as the Arena Contact Center template.

8. Verification and Validation. Confirming that the model operates the way the

analyst intended (debugging) and that the output of the model is believable and

representative of the output of the real system.

9. Final Experimental Design. Designing an experiment that will yield the desired

information and determining how each of the test runs specified in the

experimental design is to be executed.

10. Experimentation. Executing the simulation to generate the desired data and to

perform a sensitivity analysis.

11. Analysis and Interpretation. Drawing inferences from the data generated by thesimulation.

12. Implementation and Documentation. Putting the results to use, recording the

findings, and documenting the model and its use.

Problem definition and project planning

It should be obvious that before you can solve a problem you must know what theproblem is. (This is sometimes easier said than done.) Experience indicates that

beginning a simulation project properly may well make the difference between suc-

cess and failure. Simulation studies are initiated because a decision maker or group of

decision makers face a problem and need a solution. Often the project is initiated by

someone who cant necessarily make the final decision, but who is responsible for

making recommendations. In such a case, the results of the study may have to serve

two purposes simultaneously: helping the sponsor to formulate the recommendations;

and justifying, supporting, and helping to sell those recommendations.


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We begin our analysis by collecting enough information and data to provide anadequate understanding of both the problem and the system to be studied. A typical

project begins with the description of the situation to be modeled in a general and

imprecise way, in terms such as service levels, agent utilization, abandonment rates,

or other key system performance measures. We must view the problem description as

a set of symptoms requiring diagnosis. We begin, therefore, by diagnosing the

symptoms; then we define the problem; and, finally, we formulate a model.

To make that diagnosis, we must become thoroughly familiar with all relevant aspects

of the organizations operations, including influential forces (or factors) outside theorganization and the subjective and objective aspects of the problem. Minimally, we

should perform the following steps.

1. Identify the primary decision maker(s) and the decision-making process relative

to the system being studied.

2. Determine the relevant objectives of each of those responsible for some aspect of

the decision.

3. Identify other participants in the final decision (especially those likely to oppose

changes in the system) and determine their objectives and vested interests.

4. Determine which aspects of the situation are subject to the control of the decision

maker(s) and the range of control that can be exercised.

5. Identify those aspects of the environment or problem context that can affect the

outcome of possible solutions but that are beyond the control of the decision

maker(s).

An important aspect of the planning phase involves ensuring that we have considered

certain factors critical to project success:

Clearly defined goals. Do we know the purpose of the studythat is, why are we

doing it and what do we expect to find?

Sufficient resource allocation. Are we sure that there is sufficient time,

personnel, and computer hardware and software available to do the job?

Management support. Has management made its support for the project known

to all concerned parties?

Project plans and schedules. Are there detailed plans for carrying out the

project? What are the key dates?


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Competent project manager and team members. Are we assured of having thenecessary skills and knowledge available for successful completion of the

project?

Responsiveness to the clients. Have all potential users of the results been

consulted and regularly apprised of the projects progress?

Adequate communication channels. Are we continually concerned that

sufficient information is available on project objectives, status, changes, user or

client needs, and so on, to keep everyone (team members, management, andclients) fully informed as the project progresses?

The major thrust of the planning and orientation period is the determination of the

explicit goals or purpose of the simulation project. Simulation experiments are

conducted for a wide variety of purposes, including the following:

Evaluation: determining how well a proposed system design performs in an

absolute sense when evaluated against specific criteria.

Comparison: comparing several proposed operating policies or procedures or

other input scenarios.

Prediction: estimating the performance of the system under some projected set of

conditions.

Sensitivity analysis: determining which of many factors affect overall system

performance the most.

Optimization: determining exactly which combination of factor levels producesthe best overall system response.

Functional relations: establishing the nature of the relationships among one or

more significant factors and the systems response.

Although not exhaustive, this list identifies the most common simulation goals or

purposes. The explicit purpose of the model has significant implications for the entire

model-building and experimentation process. For example, if a models goal is to

evaluate a proposed (or existing) system in an absolute sense, then the model must be

accurate; and there must be a high degree of correspondence between the model and

the real system. On the other hand, if the goal for a model is the relative comparison

of two or more systems or operating procedures, the model can be valid in a relative

sense even though the absolute magnitude of responses varies widely from that which

would be encountered in the real system. The entire process of designing the model,

validating it, designing experiments, and drawing conclusions from the resulting

experimentation must be closely tied to the specific purpose of the model. No one


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should build a model without having an explicit experimental goal in mind.

Unfortunately, the analyst does not always understand the real-world problem well

enough at first to ask the right questions. Therefore, the model should have an easily

modified structure so that additional questions arising from early experimentation can

be answered later.

Style definition and model formulation

The essence of the modeling art is abstraction and simplification. We try to identify

that small subset of characteristics or features of the system that is sufficient to serve

the specific objectives of the study. So, after we have specified the goal or purpose for

which the model is to be constructed, we then begin to identify the pertinent compo-

nents. This process entails itemizing all system components that contribute to the

effectiveness or ineffectiveness of its operation. After we have specified a complete

list, we determine whether each component should be included in our model; this

determination may be difficult because, at this stage of model development, a compo-nents significance to the overall goal is not always clear. One of the key questions to

be answered is whether a particular component should be considered part of the

model or part of the outside environment, which is represented as inputs to the model.

In general, we have little difficulty deciding on the output variables. If we have done

a good job specifying the goals or purposes of the study, the required output variables

become apparent. The real difficulty arises when we try to determine which input and

status variables produce the effects observed and which can be manipulated to

produce the effects desired.

We also face conflicting objectives. On the one hand, we try to make the model as

simple as possible for ease of understanding, ease of formulation, and computational

efficiency. On the other hand, we try to make the model as accurate as possible.

Consequently, we must simplify realitybut only to the point where there is no

significant loss of accuracy of outputs with respect to the studys objectives.

We want to design a model of the real system that neither oversimplifies the system to

the point where the model becomes trivial (or worse, misleading) nor carries so much

detail that it becomes clumsy and prohibitively expensive. The most significant danger

lies in having the models become too detailed and including elements that contribute

little or nothing to understanding the problem. Frequently, the analyst includes too

much detail, rather than too little. The inexperienced tend to try to transfer all the

detailed difficulties in the real situation into the model, hoping that the computer will

somehow solve the problem.

This approach is unsatisfactory: it increases programming complexity (and theassociated costs for longer experimental runs), and it dilutes the truly significant


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aspects and relationships with trivial details. The definition of the model boundary is

usually a trade-off between accuracy and cost. The greater the degree of detail to be

modeled, the more precise and expensive the required input data. Therefore, the

model must include only those aspects of the system relevant to the study objectives.

One should always design the model to answer the relevant questions and not to

imitate the real system precisely. According to Paretos law, in every group or

collection of entities there exist a vital few and a trivial many. In fact, 80% of system

behavior can be explained by the action of 20% of its components. Nothing really

significant happens unless it happens to the significant few. Our problem in designingthe simulation model is to ensure that we correctly identify those few vital

components and include them in our model.

Once we have tentatively decided which components and variables to include in our

model, we must then determine the functional relationships among them. At this

point, we are trying to show the logic of the model; that is, what happens. Usually we

use a flowchart or pseudo-code to describe the system as a logical flow diagram.

Experimental design

We have defined simulation as being experimentation via a model to gain information

about a real-world process or system. It then follows that we must concern ourselves

with the strategic planning of how to design an experiment (or experiments) that will

yield the desired information for the lowest cost. The next step, therefore, is to design

an experiment that will yield the information needed to fulfill the studys goal orpurpose.

The design of experiments comes into play at two different stages of a simulation

study. It first comes into play very early in the study, before the model design has

been finalized. As early as possible, we want to select which measures of

effectiveness we will use in the study, which factors we will vary, and how many

levels of each of those factors we will investigate. By having this fairly detailed idea

of the experimental plan at this early stage, we have a better basis for planning the

model to generate the desired data efficiently.

Later, after we have developed the model, verified its correctness, and validated its

adequacy, we again need to consider the final strategic and tactical plans for the

execution of the experiment(s). We must update project constraints on time

(schedule) and costs to reflect current conditions, and we must impose these

constraints on the design. Even though we have exercised careful planning and

budget control from the beginning of the study, we must now take a hard, realistic

look at what resources remain and how best to use them. At this point, we adjust the


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is quite another to assume that the idiosyncrasies of a particular year will always be

repeated.

Second, it is much easier to change certain aspects of the input if theoretical random

variate generation is being used; that is, there is greater flexibility. For example, if we

want to determine what happens if inputs increase by 10% per week, we need only

increase the mean arrival rate of the theoretical distribution by the required 10%. On

the other hand, if we are sampling directly from the empirical data, it is not clear how

we increase the contact arrival rate by the required amount.

Third, it is highly desirable to test the sensitivity of the system to changes in the

parameters. For example, we may want to know how much the contact arrival rate

can increase before system performance deteriorates to an unacceptable degree.

Again, sensitivity analysis is easier with theoretical distributions than with sampling

directly from empirical data.

The problem is exacerbated when no historical behavioral data exist (either because

the system has not yet been built or because the data cannot be gathered). In these

cases, we must estimate both the distribution and the parameters based on theoretical

considerations.

Verification and validation

After the development of the model is functionally complete, we should ask

ourselves a question: Does it work? There are two aspects to this question. First, does

it do what the analyst expects it to do? Second, does it do what the user expects it todo? We find the answers to these questions through model verification and validation.

Verification seeks to show that the computer program performs as expected and

intended, thus providing a correct logical representation of the model. Validation, on

the other hand, establishes that model behavior validly represents that of the real-

world system being simulated. Both processes involve system testing that

demonstrates different aspects of model accuracy.

Verification can be viewed as rigorous debugging with one eye on the model and theother eye on the model requirements. In addition to simply debugging any model

development errors, it also examines whether the code reflects the description found in

the conceptual model. One of the goals of verification is to show that all parts of the

model work, both independently and together, and use the right data at the right time.

The greatest aid to program verification is correct program design, followed by

clarity, style, and ease of understanding. Very often, simulation models are poorly

documented, especially at the model statement level. Verification becomes much


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easier if the analyst comments the model liberally. This includes comments wherever

Arena Contact Center allows the modeler to enter them, as well as separate

documentation of model assumptions, model inputs, and logical relationships.

Validation is the process of raising to an acceptable level the users confidence that

any simulation-derived inference about the system is correct. Validation is concerned

with three basic questions:

Does the model adequately represent the real-world system?

Are the model-generated behavioral data characteristic of the real systemsbehavioral data?

Does the simulation model user have confidence in the models results?

Consequently, we are concerned with tests that fall into three groups: tests of model

structure, tests of model behavior, and tests of the policy implications of the model.

Because a model is constructed for a specific purpose, its adequacy or validity can

only be evaluated in terms of that purpose. We try to build a model that creates thesame problems and behavioral characteristics as the process or system being studied.

Validation occurs throughout model development, beginning with the start of the

study and continuing as the model builder accumulates confidence that the model

behaves plausibly and generates symptoms or modes of behavior seen in the real

system. Validation then expands to include persons not directly involved in

constructing the model.

Validation is a communication process requiring the model builder to communicatethe basis for confidence in a model to a target audience. Unless that confidence can be

transferred, the models usefulness will never be realized. Thus, through verification

testing, we develop personal confidence in the model and, through validation

measures, transfer that confidence to others.

We must realize that there are degrees of validation; it is not merely an either-or

notion. Validation is not a binary decision variable indicating whether the model is

valid or invalid. No one or two tests can validate a simulation model. Rather,

confidence in the usefulness of a model must gradually accumulate as the model

passes more tests and as new points of correspondence between model and reality are

found. Validation testing occurs continually in the process of designing, constructing,

and using the model.

We should also remember that verification and validation are never really finished. If the

model is to be used for any period of time, the data and the model itself will need periodic

review to ensure validity. Verification and validation are intertwined and proceed

throughout the study. They are not tacked on toward the end of the study; rather, they are



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an integral process that starts at the beginning of the study and continues through model

building and model use. It should also be pointed out that involving the ultimate user in

the entire simulation process makes validation much easier.

Documentation and implementation

At this point, we have completed all the steps for the design, development, and run-

ning of the model and for analyzing the results; the final elements in the simulation

effort are implementation and documentation. No simulation project can be consid-ered successfully completed until its results have been understood, accepted, and

used. Although documentation and implementation are obviously very important,

many studies fall short in the reporting and explaining of study results.

Documentation and reporting are closely linked to implementation. Careful and

complete documentation of model development and operation can lengthen the

models useful life and greatly increase the chances that recommendations based on

the model will be accepted. Good documentation facilitates modification and ensuresthat the model can be usedeven if the services of the original developers are no

longer available. In addition, careful documentation can help us to learn from

previous mistakes; it may even provide a source of submodels that can be used again

in future projects.

Amazingly, modelers often spend a great deal of time trying to find the most elegant

and efficient ways to model a system, and then they throw together a report for the

sponsor or user at the last minute. If the results are not clearly, concisely, andconvincingly presented, they will not be used. If the results are not used, the project is

a failure. Presenting results is as critical a part of the study as any other part, and it

merits the same careful planning and design.

Several issues should be addressed in model and study documentation: appropriate

vocabulary (that is, suitable for the intended audience and devoid of jargon), concise

written reports, and timely delivery. We must also ensure that all reports (both oral

and written) are pertinent and address the issues that the sponsor or user considers

important.

References

McKay, K. N., J. A. Buzacott, and C. J. Strang (1986), Software Engineering

Applied to Discrete Event Simulation, inProceedings of the 1986 Winter

Simulation Conference, Washington, D.C., pp. 485-493.



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Schriber, T. J.(1987), The Nature and Role of Simulation in the Design of

Manufacturing Systems, in Simulation in CIM and Artificial Intelligence

Techniques, J. Retti and K. E. Wichmann (eds.), Society for Computer

Simulation, pp. 5-18.

Sheppard, S. (1983), Applying Software Engineering to Simulation, Simulation,

vol. 10, no. 1, pp. 13-19.

Weinburg, G. M. (1975),An Introduction to General Systems Thinking, John Wiley &

Sons, Inc., New York, NY.


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3 General ConceptsThis chapter provides a high-level overview of the components of a model built using

the Arena Contact Center template. In particular, this chapter explains the

terminology used within the software and the type of information that is needed to

represent the Contact Center Core Process, that is, the way in which contacts arrive

and are processed in a contact center system. The major modeling elements are also

described in some detail.Once you have read this chapter, we hope you will have a better understanding of the

process of creating a model with the Arena Contact Center template.

Overview

The basic process of contact center simulation is to generate a stream of arriving

contacts, assign them to trunk lines, and route them through the center to an agent. Tocreate a simulation model of a contact center or network of contact centers, you will

describe the sequence of events that occur as contacts move through the system, from

the arrival of the contacts at the contact center to successful resolution. You will also

need to specify information about the contact center itself (trunk-line capacity, agent

skills, agent schedules, and so on).

As you build your contact center models, it may be helpful to keep in mind the

Contact Center Core Process, as illustrated below.

The basic components of this process are:

Contacts

Arrival Patterns

Trunk Groups

Routing Scripts

Schedules

Agents



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The relationships between these components are illustrated below.

In addition, the length of the simulation runnd granularity of data specification and

collection (see Planning horizon and Timeslots) need to be specified. Animation

and performance measure reporting are also important components of models.

Planning horizon

Theplanning horizon is defined as the time period that is being examined by a

particular simulation model. The planning horizon is typically one day, one week, or

one month.

Timeslots

The planning horizon is broken into specific timeslots for data specification and

collection. These intervals are typically 30 minutes or one hour long.

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With the Contact Center template, the basic unit of time is the minute. With the

exception of the planning horizon, trunk costs, agent costs, and contact service level,

all inputs are in terms of minutes or fractions of minutes.

Contact types

Describing the different types of contact is generally the starting point for contact

center modeling and analysis. Each contact name represents a particular customer

request for agent services. It is characterized by the expected talk time, as well as theassociated arrival pattern and the trunk group on which the contacts enter the center.

The following more advanced aspects of contact behavior may also be modeled using

the Contact Center template:

Abandonment

After-Contact Work

Prioritization

Contact Back

Data sources

Information about contact volumes is typically taken from forecasts. Expected talk

time is available either from contact center ACD databases or from a contact centers

contact-tracking system.

Arrival pattern

Contact patterns describe the arrival of contacts across the planning horizon by

specifying the distribution of contacts across each timeslot. Within the Pattern

module, this distribution is specified in terms of expected contact counts for each

timeslot.

The arrival times of contacts within the timeslot are randomly generated according to

a Poisson process with the defined rate. Therefore, the actual number of contacts

arriving within the timeslot may differ from the expected number.

EXAMPLE

Suppose that the planning horizon is one day (24 hours), the timeslots are 60 minutes

long. If the arrival pattern specifies that 240 contacts are handled during the

10:00 AM-11:00 AM timeslot, the simulation model would assume 240 expected



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contacts during the 10:00 AM-11:00 AM timeslot. The Poisson arrival rate for the

timeslot is 0.25 (60/240) or, on average, one contact every 15 seconds.

Data sources

Arrival pattern data is available either from contact center ACD databases or from a

contact centers tracking system.

Trunk GroupsTrunk Groups represent groups of phone lines that are dedicated to a particular set of

contact types. A single trunk group can serve multiple contact types and names, but

each contact name can only be served by one trunk group.. Trunk groups have an

associated capacity (# of lines), cost, and a default routing script and contact priority.

Any incoming contact assumes the default priority and follows the default routing

script unless these attributes are overridden at the contact level.

Trunk-line capacity determines the maximum number of contacts that the contact

center can accommodate at any one time. If a trunk line is not available when a

contact attempts to enter the center, the contact is blocked and does not gain entry.

Otherwise, the contact is attached to a trunk line and remains with that particular line

until it exits the center or until it transfers to another trunk line.

Data sources

Fundamental components of the contact center infrastructure, trunk-line organization,and capacity are typically specified in the phone-switching hardware.

Routing Scripts

Routing Scripts are sequences of actions that control the flow of contacts through the

centers system. They result in contacts being connected with agents, leaving

messages, being disconnected, or abandoning the center.

From a simulation modeling perspective, scripts allow contact flow logic to be

categorized into six general areas:

1. Time delays (playing announcements, music, doing nothingwaiting)

2. Conditional route branching (caller-entered information, center dynamics)

3. Allocation of contacts into queues (single or simultaneous) or message ports

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4. Contact prioritization within queues (ranking)

5. Contact flow between queues (movement of contacts out of and into queues,

overflow from one queue into another)

6. Contact flow between scripts

Data sources

These action sequences are generally referred to as scripts, although each switch

vendor has a different name for their particular variety (that is, Vector, Telescript, CallControl Table). These scripts specify the actions, activities, and states that each

contact undergoes as it attempts to reach an agent.

The process of creating routing scripts that match the behavior of your ACD switch

and assigning these scripts to specific contact names is described in more detail in

Chapter 6.

Agent Skill Sets

Agent Skill Sets are composed of three elements that define how particular contacts

are processed. The agents repertoire of handling skills specifies what contacts the

agent is sufficiently skilled to handle, the priority (or order) in which the agent will

perform available work, and the agents proficiency in each contact name, expressed

as a multiplier of average talk time for the contact name.

Data sources

Estimates of handling proficiency may be obtained by a careful study of handle time

statistics collected from the ACD database or tracking system, or may be based on the

expertise of group managers. For example, a group of experienced agents may have a

very high proficiency level, while a group of newly hired agents may experience

significantly higher handle times.

Schedules

Schedules dictate when agents are available to handle contacts. Each schedule

specifies on-duty shifts for each day in the planning horizon. In addition to phone

time, these schedules can include lunches, breaks, meetings, or other off-duty time

spent away from the phones.



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Data sources

Agent schedules can usually be obtained from a human resources department or a

planning and analysis group.

Agent Groups

Agents are the primary resource of the contact center. AnAgent Group represents a

group of agents within the contact center who have the same skill sets and follow thesame schedule. From a modeling perspective, an agent group is a set of identical

agents. In building a model, the key questions to answer regarding agent groups are:

How many agents are in this group?

What hours do these agents work?

What types of contacts can an agent of this type handle, and in what priority

order?

How long does it take for these agents to handle each contact name?

Data sources

The definition of agent groups may depend on the purpose of the simulation study

and will not necessarily correspond to the group definition within the organization.

However, the agent lists and skill sets maintained by the human resources or planning

and analysis group are a good starting point.

Parent Groups

AParent Group is a collection of agent groups. Parent groups are used to:

implement simultaneous queueing

simplify routing scripts by masking the underlying complexity of agent group

definitions (multiple schedules, sites, groups, and so on)

collect statistics across a set of agent groups

Data sources

Parent group definition typically supports contact routing and may depend on the

purpose of the simulation study. However, if a model being made is of current contact

center operations, insight into parent groupings may be obtained from examination of

existing routing scripts.

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QueuesQueues are the mechanism by which contacts and agents interact in the contact

center. Each agent group has a queue associated with it to hold its contacts while they

wait to be handled. Contacts may move from one queue (that is, one agent group) to

another before being serviced, based upon the routing script that is assigned to that

contact name.

While queues are an important concept to understand, the data and logic associated

with queues are specified in the Agent and Script modules and related moduleslocated on the Script panel (such as Queue for Agent module or Transfer to Agent

module).

Animation

Simulation animation is intended to provide dynamic graphical insight into contact

center conditions. A variety of plots, graphs, and counters are available to animatespecific contact center elements. These animations are often useful for validation and

verification of the contact center model.

Performance measures/reporting

In addition to a default report covering the entire planning horizon, there are focused

reports that collect and report data by user-defined timeslot. These results quantifythe impact of various changes on contact center operations. Contact center reports are

available for:

contact counts

contact times

agent utilization

trunk utilization

overflowThe output of these reports is discussed in detail in Chapter 7.


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4 FeaturesThis chapter is intended to provide a description of all Arena Contact Center templatefeatures. Once you have read this chapter, you will have a better understanding of the

capabilities of the software and the simulation process.

The features described in this chapter are organized as follows:

Different stages in the contact life span Queue behavior

Routing script construction

Costing

Miscellaneous features

Different stages in the contact life span

This section describes the potential avenues that a contact may travel as it moves

through the contact center, as shown in Figures 4.1 and 4.2. Each stage is described

and identified as either optional or required to the model. Particular attention is given

to the module(s) involved in each stage.

Figure 4.1 The path of a contact before processing begins



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Figure 4.2 The path of a contact after processing begins

Contact arrival (required)For each timeslot, contacts of a particular name arrive according to a Poisson process

with an arrival rate based on the expected contact volumes per timeslot, as defined in

the associated pattern module. Upon arrival at the contact center, a contact is assigned

to a trunk line from the trunk group associated with that contact name.

Arrivals may also be generated bya contact returning to the contact center (contact

backs) after being blocked, abandoned, or disconnected, as well as contact backs due

to messages or previously served but unresolved contacts.

RELEVANT MODULES AND RELATED CONCEPTS

Patterns are defined in the Pattern module and associated with a contact name in

the contact module.

Trunk groups are defined in the Configuration module and associated with a

contact name in the Contact module.

Blocked contacts (required)

When there are no available trunk lines in the relevant trunk group to accommodate

an arriving contact, the contact is blocked. Depending on the model, blocked contacts

may attempt to contact backfollowing a specified delay.

4 FEATURES


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Contact back is defined in the Contact Back section of the Contact module. It is

described in greater detail later in this chapter.

Offered contacts (required)

When an arriving contact is able to secure a trunk line, it is considered to be offeredtothe contact center for service. The newly offered contact then begins to follow the

routing logic specified in its associated script.




Scripts are defined by connecting a series of modules located on the Script paneland are associated with trunk groups in the Configuration module. Contacts either

inherit their routing scripts by default through their associated trunk group or

specifically identify a routing script by overriding the trunk default in the

Advanced section of the Contact module.

Abandoned contacts (optional)

Abandonmentoccurs when the contactor terminates the contact before reaching an

agent. For each contact name, abandonment may be modeled by specifying a

distribution for the amount of time a contactor will wait prior to abandoning the

center. For each contact, a value is generated from this distribution to determine at

what time the contactor will abandon if not yet connected with an agent.

Once a contact abandons the contact center, it may contact back, depending on the

model.

RELEVANT MODULES AND RELATED CONCEPTS Abandonment is defined in the Abandonment section of the Contact module.

Once defined for a contact, abandonment logic is initiated during the Contact

Arrival and Transfer to Script stages of the contact life span that are described in

this section.

Contact back is defined in the Contact Back section of the Contact module. It is

described in greater detail later in this chapter.



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Disconnected contacts (optional)

Contacts may be disconnected(that is, dispatched from the contact center) by their

controlling routing script.

Once a contact has been disconnected, it may contact back, depending

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