Knowledge management-centric help desk: specification and

www.elsevier.com/locate/dsw

Decision Support Systems 40 (2005) 389–405

Knowledge management-centric help desk: specification

and performance evaluation

Luz Minerva Gonzalez, Ronald E. Giachetti*, Guillermo Ramirez

Department of Industrial and Systems Engineering, Florida International University, 10555 W. Flagler Street, Miami, FL 33199, USA

Received 21 February 2003; received in revised form 20 April 2004; accepted 21 April 2004

Available online 1 June 2004

Abstract

The technology help desk function has grown in importance as information technology has proliferated throughout the

organization. The primary objective of the help desk is to resolve problems related to IT in the organization. As such, the agents

in the help desk must be very knowledgeable of the information systems, applications, and technologies supported. Most efforts

at improving help desk performance have been to make the current system more efficient through application of information

technologies. In this paper we propose a new approach, called a knowledge management-centric help desk. The proposed

knowledge management system draws upon diverse knowledge sources in the organization including databases, files, experts,

knowledge bases, and group chats. The knowledge management system is designed to be incorporated into the daily operation

of the help desk in order to ensure high utilization and maintenance of the knowledge stores. The benefits of the knowledge

management-centric help desk are evaluated using a simulation study with actual data from a help desk. The experimental

results indicate the knowledge management-centric approach would significantly reduce the time to resolve problems and

improve the throughput of the help desk.

D 2004 Elsevier B.V. All rights reserved.

Keywords: Help desk system; Knowledge management system; Knowledge-based system; Expert systems; Simulation evaluation

1. Introduction

Help desks serve an important role of the infor-

mation technology department by providing the pri-

mary point of contact for clients to contact analysts to

help them resolve problems with information tech-

nology including hardware, software, and networks.

To resolve the information technology problems

reported by callers, the help desk analysts must

0167-9236/$ - see front matter D 2004 Elsevier B.V. All rights reserved.

doi:10.1016/j.dss.2004.04.013

* Corresponding author. Tel.: +1-305-348-2980; fax: +1-305-

348-3721.

E-mail address: [email protected] (R.E. Giachetti).

possess knowledge of the information technologies

supported by the help desk. Knowledge has been

defined as, ‘‘a justified personal belief that increases

an individual’s capacity to take effective action’’

[2,30]. The product of the help desk is this knowl-

edge. Acquiring and maintaining the knowledge to

support these information technologies is becoming

increasingly difficult. According to a study conducted

by the Gartner Group, the average number of infor-

mation technologies supported by help desks has

increased from 25 to 2000 in the past 5 years [34].

One reason for the increase is the proliferation and

distribution of information technology such as differ-

L.M. Gonzalez et al. / Decision Support Systems 40 (2005) 389–405390

ent personal computers, software applications, print-

ers, and servers throughout the organization. More-

over, Sharer [24,36] finds that the more distributed

the information technology, the more support the end

users require. As a result, help desks have experi-

enced both an increase in the number of information

technologies they must support and an increase in

workload.

There are two types of help desks depending on

whether the clients served are internal or external to

the organization [17,40]. Internal help desks are

usually organized as part of the IT Department. It

has been observed that the internal help desk has a

great impact on the productivity of the organization

since the help desk is resolving problems that may

stop, delay, or otherwise impact the completion of

daily business activities [18]. As an example, in the

company we studied a problem with a network

router prevented employees from accessing an im-

portant server. Such a problem has significant dele-

terious effect on the productivity of the affected

employees since they could not perform their prima-

ry job function. The faster the help desk can trou-

bleshoot and resolve the problem the better [20,24].

External help desks are for paying clients of the

company who have service agreements for technical

support. In the case of the external help desk, it is an

important value-added service provided to the client.

The speed and quality of the solutions provided

influence customer satisfaction and therefore the

business’s image [12,17].

In the traditional help desk, the agent is responsible

for handling a call and solving the problem by

resorting to various information and knowledge sour-

ces [24]. We call this an agent-centric help desk.

There are at least two problems with the agent-centric

approach. The first problem is of recognizing repeat

problems as such. Help desk personnel report about

60–70% of their time is spent on solving repeat

problems [34,37]. However, when the help desk

receives a problem call, it may be assigned to an

agent who has not previously resolved that type of

problem. The agent-centric help desk does not capture

an agent’s knowledge about resolving a particular

situation in a way that it can be searched, reviewed,

disseminated, and updated by others. Consequently,

the benefits of learning are not fully realized because

the structure of the agent-centric help desk does not

facilitate sharing knowledge. The second problem is

that in today’s business environment employee turn-

over is high, especially for technical employees [11].

In the help desk this is a problem because the help

desk performance is heavily dependent on the knowl-

edge, skills, and ability of the help desk agents to

quickly resolve problems. Help desk agents are stores

of significant knowledge concerning the systems,

business processes, and technologies and if they leave

their knowledge often goes with them [27,32]. These

two problems reduce both the efficiency and effec-

tiveness of the help desk.

The two problems identified with an agent-centric

help desk are both related to the ability of the help desk

to acquire, maintain, and disseminate the knowledge

of all the agents. In this paper we present a knowledge-

centric help desk system that addresses these two

problems by improving how knowledge is managed

by the help desk. Knowledge management is a disci-

pline that provides strategy, process, and technology to

share and leverage information and expertise that will

increase our level of understanding to more effectively

solve problems and make decisions [35].

In the next section we review help desk operations

and trends. Then we examine knowledge manage-

ment practices and technologies. A knowledge man-

agement-centric help desk system is defined. To

evaluate the benefits of the proposed system we

perform experiments to compare the agent-centric

to the knowledge-centric system. Actual data from

an internal IT help desk was collected and used to

create a simulation model. A three factor two level

experiment was conducted. The results are presented

and conclusions are drawn in the last section. Our

contributions are first the specification of a knowl-

edge-based centric help desk and second the perfor-

mance evaluation of the system using actual industry

data.

2. Help desk operations and technologies

An agent resolves a problem by accessing many

different information and knowledge sources as shown

in Fig. 1. These sources range from files on the agent’s

computer, access to the database, communication with

other agents, and access to the Internet. We call this the

agent-centric approach since the onus of finding and

Fig. 1. Typical help desk with agent-centric collection of data, information, and knowledge.

L.M. Gonzalez et al. / Decision Support Systems 40 (2005) 389–405 391

collecting the requisite information and knowledge to

solve a problem is the responsibility of the agent.

In automating the agent-centric help desk, many

have focused on computer– telephony integration

(CTI). The basis of CTI is to integrate computers

and telephones so they can work together seamlessly

and intelligently [10]. The major hardware technolo-

gies are as follows: Automatic call distributor (ACD);

voice response unit (VRU), Interactive voice response

unit (IVR), predictive dialing, headsets, and reader

bounds [3,4]. These technologies are used to make the

existing process more efficient by minimizing the

agent’s idle time and evenly loading the agents in

the help desk. These technologies do not address the

problem of knowledge loss when agents leave nor do

they provide information to the agent in helping to

resolve problems.

Several authors have investigated the application

of case-based reasoning systems to improve the

performance of help desks [7,8,14,37]. Case-based

reasoning captures, stores, and adapts solutions to

old problems to use them to solve ether recurrence

of the old problem or a new problem [21]. The

storage of knowledge is in the form of cases in

which each case describes a problem that may occur

and a solution to that problem. The cases are

organized according to a taxonomy. For example,

Goker and Roth-Berghofer [14] use a failure de-

scription that comprises the topic, subject, and

behavior of the failure. Using the classifications,

the help desk agents can search for cases that match

the current problem they are handling. To develop a

case-based system the knowledge must be captured

and represented in the form of these cases. The

knowledge acquisition process reported by Chan et

al. [7] was through interviewing the more experi-

enced help desk agents. An issue for case-based

systems is continuing the acquisition of knowledge

in the form of new cases after the initial develop-

ment of the system. Goker and Roth-Berghofer [14]

found that the acquisition process and the mainte-

nance process are as important as the technology

installed. In their approach they recommend a sep-

arate case author who is in charge of system

maintenance and incorporating new cases into the

system.

A related approach is instead of finding related

cases the system can store information on experts and


their expertise so that the agent can be guided to the

appropriate expert for solving a problem. These types

of systems are called people finders or expert finders

and several exist such as the one at HP and the SAGE

system developed for the Florida State University

Systems [6].

The research shows that case-based reasoning is an

appropriate technology for help desk applications.

However, there are four outstanding issues concerning

the application of case-based systems for help desks

that should be addressed. The first issue is the cases

are the only source of knowledge in the system and

often ignore available information and knowledge

sources outside of the case-based system. Taylor et

al. [39] found that help desk agents access a wide

variety of knowledge sources. One of the more

important knowledge sources is not embedded in

physical systems but in the employees themselves

[23,39]. Secondly, cases are intended for helping

resolve recurring problem types and provide little

support for resolving new problem types. Consequent-

ly, a case-based system alone is insufficient for the

help desk environment. A third issue is updating and

maintaining the knowledge in the system is perceived

as difficult [22]. The provisions for continued knowl-

edge acquisition are weak since new knowledge must

be formulated in the structure of a case. Often, a

systems expert knowledgeable in the support system

and programming language is responsible for system

maintenance and generation of new cases. These

systems run the risk of becoming outdated since

generation of cases is often not a continuous process.

IT help desks that support dynamic and rapidly

changing technical products need continuous knowl-

edge acquisition; otherwise, the knowledge base

would quickly become obsolete. The fourth issue is

overemphasis on the technology solution without

reengineering the supported business process often

fails. Nissen et al. [29] assert that information tech-

nology must be integrated with the design of the

process it supports. In the domain of knowledge

systems they find the literature provides little discus-

sion of incorporating knowledge-based systems into

the process. Likewise, Weber et al. [41] found that

many knowledge management systems are not incor-

porated into the processes the systems support. The

repercussion is the systems are underutilized and as a

result do not achieve their goal of knowledge-sharing.

While case-based reasoning systems enable help

desks to store and share knowledge in the form of

cases, there is room in improvement by addressing the

aforementioned issues. The strategy taken in this

article is that instead of relying on a single technology

such as case-based reasoning, the coordination of

several technologies and their integration into the

business process could improve the productivity and

effectiveness of the help desk. Knowledge manage-

ment is used as the framework for integrating the

technologies, people, and process for improved help

desk performance.

3. Knowledge management

Knowledge management is about acquisition and

storage of employees’ knowledge and making the

knowledge accessible to other employees within the

organization [1,26,27,35]. Nonaka and Takeuchi [31]

have extensively studied knowledge in the organiza-

tion and developed a model that describes knowledge

as existing in two forms. Tacit knowledge is defined

as personal, context-specific knowledge that is diffi-

cult to formalize and communicate. Explicit knowl-

edge is factual and easily codified so that it can be

formally documented and transmitted. Through

knowledge management a company changes individ-

ual’s knowledge into organizational knowledge [38].

Organizational knowledge is knowledge held by the

organization. The organization maintains the organi-

zational knowledge in organizational knowledge

resources which are operated on by human or com-

puter processes that manipulate the knowledge to

create value for the organization [19].

Nonaka and Takeuchi [31] define organizational

learning as, ‘‘a process that amplifies the knowledge

created by individuals and crystallizes it as part of the

knowledge network of the organization.’’ In a help

desk environment, much of the knowledge is from

experiential learning [24,39]. A challenge is how to

transfer the knowledge gained by individuals into

organizational knowledge.

Many authors have described processes for knowl-

edge management [13,26,33,35]. Nissen et al. [29]

review several knowledge management process mod-

els and propose an amalgamated process that involves

the following steps: (1) collecting knowledge, (2) or-


ganize knowledge, (3) storing knowledge, (4) making

knowledge available, (5) using the knowledge, and (6)

knowledge evolution. Technology is available to sup-

port each one of these knowledge management pro-

cess steps [29]. Knowledge management systems

(KMS) are systems that gather, organize, and dissem-

inate an organization’s knowledge as opposed to

information or data [1].

For the help desk, the relevant knowledge man-

agement approach is of problem solving. Gray [16]

presents a framework that categorizes knowledge

management according to a problem-solving perspec-

tive. The framework defines four cells according to

the type of problem and the process supported. Along

the horizontal axis they define two classes of prob-

lems as new problems and previously solved prob-

lems. Along the vertical axis they define two

processes of problem recognition and problem solv-

ing. The primary function of the help desk is problem

solving of both new and previously solved problems.

When solving new problems, Gray [16] calls this

knowledge creation. Solving previously solved prob-

lems is called knowledge acquisition.

Several characteristics can be defined that would

make a KMS successful in the help desk. The KMS

must be able to gather knowledge from humans and

other sources. In a help desk environment, the infor-

Fig. 2. Knowledge managem

mation and knowledge resides in many disparate

forms such as databases, files, people, electronic

documents, and procedures. Part of the knowledge

management task is the coding and classification of

the stored information and knowledge so that it can be

put to use by help desk agents in resolving problems.

4. A knowledge management system for a help

desk

The knowledge management-centric approach to a

help desk is shown in Fig. 2. In this approach the

knowledge management system (KMS) serves as an

intermediary between the help desk agent and all data,

information, and knowledge sources. The strength of

this approach is twofold; first by becoming the inter-

mediary all information passes through the system and

thus should facilitate the knowledge acquisition func-

tion. Knowledge acquisition is often an obstacle [22],

since busy knowledge-workers may overlook the

capturing of knowledge into the system and thus the

KMS would stagnate. A second advantage of the

knowledge management-centric system is it specifies

a single uniform interface for the help desk agent to

access various knowledge sources. It is recognized

that the help desk agent must access a multitude of

ent-centric help desk.


knowledge sources with different file formats, at

remote locations, and often organized differently.

Except for the knowledge base, the knowledge from

the other sources are not organized since the knowl-

edge sources are external to the system. Rather the

interface for searching for the knowledge is organized.

The knowledge management system points to the

location where the knowledge can be found. For

example, if the knowledge resides in a document on

a file server, the knowledge management system

contains an entry for the knowledge source and a

pointer to link the location to the entry.

The knowledge management system is designed to

support both tacit and explicit knowledge as classified

by Nonaka and Takeuchi [31]. To accomplish this

goal the proposed knowledge management system

integrates several technologies including group-ware,

information retrieval, and document management.

The group-ware element is the ability to collaborate

Fig. 3. Prototype input

on a problem with other help desk agents and to

access them through the system. The group-ware

aspect addresses tacit knowledge, which is personal

and context-specific making it difficult to formalize.

The information retrieval element is evident in the

ability to access remote information whether in a

database, on the Internet (such as a FAQ from a

vendor), or document files. Document management

is evident in the storage and indexing of documents on

file servers. The later two technologies address ex-

plicit knowledge, which can be codified.

An important element of the knowledge manage-

ment system is organizing access to the knowledge so

that it can be retrieved as needed. Knowledge is

organized according to a taxonomy of problem scope,

product, and feature. The taxonomy is context-specific

to the help desk and how the help desk agents perceive

the problem domain. Problem scope describes the

general type of problem such as software, hardware,

screen for search.


or network. The product is the specific product the

problem is being experienced with. The feature is an

identification of the feature in that product causing the

problem. The knowledge management system inter-

face is shown in Fig. 3 to illustrate how the taxonomy

is used to access various knowledge sources. On the

left-hand side are the search criteria for the problem.

On the right-hand side there are knowledge sources

including experts for the identified problem, docu-

ments that match the problem, associated files, and

knowledge bases. Experts in the system are self-

Fig. 4. Knowledge management-centric

classified according to the taxonomy described above.

Some of the identified sources such as the documents

that match the problem may or may not help the agent

in resolving the call. The knowledge bases are cases as

used in the case-based reasoning approach. These

cases would be directly relevant to the problem and

can be adapted to solve the current problem.

Implementation of the knowledge management

system changes the problem resolution process fol-

lowed by the help desk and the new process is shown in

Fig. 4. A short examination of the process flow shows

help desk resolution process flow.


several potential performance enhancers. First, it is

possible that the help desk clerk, usually a lower skill

job classification than a support agent, can with the aid

of the KMS resolve the problem. This is possible when

the client’s problem matches a case in the system. Then

both the time to resolve a problem will be improved

and at a lower wage rate than if utilizing a support

agent. The second potential performance improvement

is that through the knowledge management system the

help desk agent can leverage the organization’s knowl-

edge and solve the problem faster than if working

without the knowledge management system.

The knowledge management-centric system helps

achieve organizational learning. When a problem is

resolved by any agent then the solution becomes part

of the organizational memory and is available to all

other agents. The knowledge management system is

incorporated into the processes of the help desk.

Acquisition of new knowledge and maintenance of

the knowledge is not a separate process. Consequent-

ly, we address the concerns raised by Weber et al. [41]

that show low system utilization when the system is

not incorporated into the business process.

5. Performance evaluation of the knowledge

management-centric help desk

The research objective is to analyze the perfor-

mance of the knowledge management-centric help

desk system. The research hypothesis is the knowl-

edge management-centric system will have a shorter

problem resolution time. A shorter problem resolution

time will occur because the knowledge management

system will facilitate organizational learning and will

enable agents to access knowledge sources acquired

by the entire group which will enable them to resolve

the problem faster. A second reason the knowledge

management-centric system will reduce the problem

resolution time is many calls that would have been

elevated can be solved at a lower level, which would

greatly reduce the time to resolve that problem. A

consequence of a shorter resolution time should be a

higher throughput and a decrease in the average queue

size for problems. Formally, the hypothesis is:

Hypothesis 1. The time in system for all problem

calls except for critical severity calls will be lower in

the knowledge management-centric system than the

agent-centric system.

The hypothesis excludes a class of calls termed

critical severity because these are typically handled

specially. More on the call classification is discussed

in a later section. To test the hypothesis a simulation

model is developed that describes the current agent-

centric help desk and the knowledge management-

centric help desk. Several authors have studied help

desks and/or help desks using simulation techniques.

Simulation enables help desks to perform analysis

that captures the entire interrelationship between

callers, agents, skills, and technology [5,9,28]. For

example, Chin and Sprecher [9] analyzed the impact

of staffing levels on a goal of meeting a service level

agreement of 95% calls answer rate. In this case, the

simulation model research approach is adopted so that

we can conduct experiments to evaluate the knowl-

edge management system without disrupting the help

desk’s daily operations. The simulation enables an

evaluation of the performance of the knowledge

management system prior to full-scale implementa-

tion in the help desk. The simulation model will help

to analyze the benefits or advantages that can be

obtained with the implementation of the knowledge

management system.

6. Description of current agent-centric help desk

Here we describe a particular information tech-

nology (IT) help desk of a fortune 500 company in

the hospitality industry. IT is a component of the

firm’s strategy. The mission of IT is to assist the

business units in achieving their strategies and to

recommend technology applications to accomplish

greater operating efficiency, improved client experi-

ence and increased revenues. One of the main areas

of interested of IT is the Problem Management

Process owned by the help desk that is the process

of detecting, correcting, and reporting problems

impacting services committed by the business and

supported by IT. Incidents such as hardware, soft-

ware, applications, operations, and facilities failures

cause these problems. The goal of problem man-

agement is to provide a process to resolve problems

caused by these failures in the most expeditious and


cost-effective manner, and to ensure that the analy-

sis is done on a regular basis to fix recurring

problems.

When the help desk was first formed, it was

composed of a single person that attended to the

phone calls and wrote down on a paper form the

problems to be solved. Oftentimes, to resolve a simple

problem, like connections to printers, took as much

time as a week. This process was very inefficient;

many calls were abandoned due to the phone line

being busy. The problem reporter had to leave a

message in the voice mail and if this was full, the

problem reporter did not have another way to com-

municate with the help desk agent. The calls waited in

the voice mail queue until the single agent had time to

check it and either resolve or assign the problem to

someone else.

Two years ago, the company has changed to a

multi-person and multi-tier help desk. Now, the help

desk is composed of four support levels. The first

level includes the agents who answer the telephone

calls. The second level is called senior support and

consists of the senior help desk agents. The third

level includes specialists who do not directly work

for the help desk but are called when a problem

occurs in their specialty. The fourth level includes

the technicians who will travel to the business unit

to make any necessary repairs and resolve the

problem. Also, now a computer telephony integrat-

Fig. 5. Conceptual model of

ed software package, called Remedyk, is used to

track calls and their resolution. Remedyk features

ensure that a case is entered quickly and tracked

through its life cycle and thus provide a better

service.

According to Marcella [24], many help desks are

organized in a similar fashion to the one described

above. Marcella [24] found that most help desks

have several support levels, they utilize technology

for tracking calls and performance, their organiza-

tional focus is limited to problem resolution, the use

of AI/knowledge bases is limited, and they rely

primarily on staff expertise. The majority of help

desks came into being by ‘‘evolutionary’’ means, i.e.

developed in reaction to demand. Consequently, the

one described here is not unlike many other help

desks.

Fig. 5 shows the possible flow of problems through

the current help desk as modeled in the simulation. A

calling population of calls arrives to the agents at the

first level. When an agent of the help desk answers a

call, they check if the problem has been previously

reported in order to update it and inform the problem

reporter about the status of the ticket or generate a

new ticket, where the ticket is a mechanism for

tracking problems. If the problem has never been

reported the agent (first level) attempts to solve it. If

the problem is solved in the first level and there are no

other problems, the call is finished and the agent

help desk operations.

Table 1

Frequency of each category

Problem category Percentage of frequency

Phone Call 25.40

Network 22.98

Software 22.88

AS400 12.79

Hardware 5.32


completes the ticket form and closes it. If the problem

cannot be solved at the first level, the operator

appends additional information and assigns a priority

to the problem. The priority is assigned according the

following criteria:

Critical severity: A system or a major system

component is down or unavailable to a substantial

portion of the user community, or the user cannot

conduct critical business operations that will result

in a significant loss of revenue, profit, or

productivity.

High severity: A problem that causes a partial or

potential system or application outage.

Medium severity: A problem that must be resolved

but does not impact the service level commitments

of the information technology organization. The

problem does not severely impede the user’s ability

to conduct business and/or it can be circumvented.

Low severity: A low impact problem that does not

require immediately resolution, as it does not

directly affect the user’s productivity or system or

application availability.

Similar prioritization is implemented in most help

desks. According to the priority, the problem is

assigned to an agent or technician who is responsible

for resolving the problem.

The system represents an agent-centric help desk as

previously described. This means that an agent deter-

mines a solution for the problem and this information

is stored in personal files or database that the agent in

the future can use to resolve similar problems. How-

ever, this information is not shared among the rest of

the agents. Then, if a similar problem arrives to a

second agent, that agent has to start researching the

problem without any base and will spend approxi-

mately the same amount of time that was spent by the

first agent.
Software Ship 3.95
Telecom 1.35

Remote Access-DSM 1.03

Database System 1.01

Procurement 0.95

Remote Access-General 0.77

Remedy 0.62

Support SVC Calls 0.32

Communications 0.28

Data Transmission 0.28

Backups Ships 0.08

7. Data collection

Prior to data collection unstructured interviews

were held with the management and help desk agents.

The purpose of the interviews was to learn the help

desk operating procedures, the key performance indi-

cators (KPI), demand levels, and to obtain insight

from the agents working in the help desk. The KPIs

are management performance tools to help determine

the help desk performance in meeting objectives and

established service level agreements. Among the KPIs

identified the relevant ones to our study were: (1)

number of calls received versus number of calls

abandoned; (2) number of calls resolved at first

contact; and (3) average time to resolve a problem

at each level. Based on these KPIs, the data require-

ments were identified in order to build a simulation

model.

Data was collected from the Remedyk CTI system

for four separate weeks randomly selected from a 6-

month period starting in January to June. The data

collected was for a total of 4965 calls and consisted of

the time between arrivals, number of resources, types

of calls, and service times. Sample data is provided in

Appendix A. It is noted that some problems do not

have a recorded resolve time. The interviews with the

help desk agents indicate that these calls are handled

at first contact and resolved within less than 2 min.

The Remedy system cannot provide data of aban-

doned calls. The interviews with the help desk agents

suggest that this was generally not a problem for the

help desk. An analysis of staffing levels and arrival

patterns confirm that abandoned calls were not an

issue.

The arrival rate determines the demand load of the

help desk. The arrival rate depends on the day of the

week. For each day of the week a statistical analysis

Table 2

Classification of calls by priority

Priority No. of calls %

Low 4488 90.39

Medium 256 5.16

High 200 4.03

Critical 21 0.42

Totals 4965 100.00


was performed on the data to fit a probability distri-

bution to the data. It was observed that Mondays have

the highest average arrival rate of 27 calls/h and

Sunday has the lowest average arrival rate at 2

calls/h.

The problems are classified into 16 categories as

shown in Table 1 with their frequency of occurrence.

A Pareto phenomenon is observed whereby the top

seven problem categories account for 94.67% of the

total types of calls received. The frequency of each

priority is shown in Table 2.

The service times for each problem category were

analyzed and determined. The service time is the time

between logging a problem call and the time the

problem is resolved. The service time is correlated

to the problem category and assigned level. For each

problem category a probability distribution was fitted

to the data collected to arrive at a function for service

time to be used in the simulation model.

8. The simulation model

The agent-centric and knowledge management-

centric help desks were modeled in the simulation

package Arenak. Arenak is a commercial discrete-

event simulation package. A full exposition of the

simulation model is available in [15]. The simulation

model was verified to make sure that it works

properly in terms of Arenak functionalities and the

entities (problem calls) follow the same path as

described in the conceptual model. The verification

was done using the Trace function. The Trace was

run for one replication for 4 weeks of operation time.

The Trace output allows following the sequence of

an entity as it flows through the system, from entity

creation until entity disposal. The knowledge man-

agement-centric help desk simulation model was also

verified using the Trace function. The logic and

entity process flow was determined to agree with

the intended design. In addition to verifying the

Trace output, the model was run with different

replication numbers to verify that it works under

different conditions.

After verifying operation of the simulation model

it was validated. Four replications were conducted

with different random number streams on the simu-

lation model. A t-test with a 95% confidence level

was conducted to compare the results of the simula-

tion model with the results recorded for the actual

system based on the data collected from Remedyk.

For each variable the null hypothesis of no differ-

ence between the systems was rejected with a 95%

confidence level which indicates the simulation

model adequately represents the actual system’s

behavior.

9. Experimental design and analysis

The purpose of the experimental design is to

identify the effects of three different factors on five

dependent variables. The factors are:

Factor A: Time to type problem information and

search the knowledge management system for

relevant knowledge sources (minutes).

Factor B: Time to resolve a problem using the

knowledge management system (minutes).

Factor C: Time to add new information into the

knowledge management system (minutes).

The dependent output variables are:

O1: Throughput (Total number of calls resolved in

time period)

O2: Time in the system of critical priority problems

(minutes)

O3: Time in the system of high priority problems

(minutes).

O4: Time in the system of medium priority problems

(minutes).

O5: Time in the system of low priority problems

(minutes).

O6: Number of problem calls in technicians’ queue.

O7: Number of problem calls in second level queue.

O8: Number of problem calls in third level queue.

Table 5

Summary output for the agent-centric versus knowledge manage-

ment-centric help desk

Variables Agent-centric

system (average)

Knowledge

management-centric

system (average)

O1: Throughput

(calls/time period)

2733 3371

O2: Time in system

critical calls (minutes)

416.73 329.74

O3: Time in system high

priority calls (minutes)

503.07 240.54

O4: Time in system

medium priority calls

(minutes)

547.07 193.99

Table 4

Eight-treatment combinations, 23 factorial experiments

Factor C Factor A

FA Level 1 FA Level 2

Factor B Factor B

FB Level 1 FB Level 2 FB Level 1 FB Level 2

FC Level 1 A1B1C1 A1B2C1 A2B1C1 A2B2C1

FC Level 2 A1B1C2 A1B2C2 A2B1C2 A2B2C2


The dependent variables are performance variables

tracked by the help desk and according to Anton and

Gusting [4] these are common performance measures.

A different output variable is needed for each problem

priority since they follow different paths through the

help desk.

The factors are analyzed with two levels (low and

high). The values for low and high were determined

by expert opinion obtained during the interviews and

by observing the help desk operations. Table 3 shows

the factors and their respective levels.

The experimental design is a full factorial of two

levels and three factors 23, giving a total of eight-

treatment combination. Table 4 shows the combina-

tion of these factors and their levels (1 = low and

2 = high) for each experiment, which is a cell in the

table. Six replications of each of the eight experi-

ments were run in a random order and the results

were recorded for further statistical analysis. Each

simulation experiment was for 1 week (17,640 min).

The same random number seed was used for the

agent-centric and the knowledge management-cen-

tric models. The summarized results are shown in

Table 5.

The analysis of variance (ANOVA) for full facto-

rial design is done to test that the main effects or

interaction parameters are equal to zero. In statistical

analysis, the factors with a P value lower than 0.05

(P < 0.05) are considered as important factors that

significantly influence the results. The ANOVA anal-

ysis shows that only the dependent variable through-

put (O1) is significantly influenced by Factor A, time

to type and search the knowledge-base, and Factor B,

time to resolve a problem using the knowledge

management system. Time to add new information

into the knowledge management system is marginally

significant because the P value is equal to 0.05. The

other dependent variables do not have any factors that

affect them significantly (i.e. in all cases P>0.05).

Table 3

Factors and their levels

Factor Low (best case) High (worst case)

A 3 min 6 min

B Triangular (2, 5, 7 min)a Triangular (4, 7, 10 min)

C 2 min 5 min

a Indicates a triangular distribution with these endpoints for min,

mid, and max.

Table 6 shows the values of the t-statistic and the

value of the t-critical two-tail (t-table) for each

dependent variable. From Table 6, it can be noticed

that in almost all the cases the p-value is lower than

the t-statistic; this means that H0 is rejected. In other

words, the means are not equal. This is the case for

Throughput, Time in the System High Priority Calls,

Time in the System Medium Priority Calls, Time in

the System Low Priority Calls, Number of Problem

in Technicians’ queue, and Number of Problem in

Second Level’ queue. On the other hand, for Time

in the System Critical Calls, and Number of Prob-

lem in Third Level’ queue can be seen that the p-

value is higher than the t-statistic, then H0 is not

O5: Time in system low

priority calls (minutes)

360.94 152.06

O6: Number of problem

calls in technicians’

queue

88 6


calls in second level’

queue

103 25


calls in third level’

queue

83 88

Table 6

t-test for comparison of agent-centric system versus knowledge-

centric system

Variables t-statistics t-critical

two-tail

Throughput 29.488 2.571

Time System Critical Calls 0.653 2.571

Time System High Priority Calls 3.661 2.571

Time System Medium Priority Calls 3.737 2.571

Time System Low Priority Calls 6.939 2.571

Number of Problem in Technicians’ queue 25.599 2.571

Number of Problem in Second Level’ queue 13.354 2.571

Number of Problem in Third Level’ queue � 1.527 2.571


rejected, then it is concluded that its means are

equal.

10. Discussion of results

The intention of the hypothesis was to prove that

applying a knowledge management system would

decrease the time in the system of high, medium,

and low priority calls. Table 5 shows the results for

each priority level. At the low, medium, and high

priorities, the knowledge management-centric system

outperforms the agent-centric system significantly.

The time in system for low priority calls was

improved by 57.9%, for medium priority by 64.5%,

and for high priority by 52.2%. At the critical

priority level the t-test failed and no statistically

significant difference can be concluded with confi-

dence for critical priority problems. However, it was

expected that there would be no significant improve-

ment in resolving critical calls. Critical calls are non-

recurring problems that stop a system or have a

significant detrimental impact on a business process.

Critical calls are few in number (0.42%) and often

require a specialist to make modifications to the

effected application. The knowledge management

system is not designed to support these types of

calls.

The simulation output shows the knowledge man-

agement-centric system will have almost 19% higher

throughput than the agent-centric system. This is a

significant improvement. The knowledge manage-

ment-centric system could lower the load for a stable

level of calls thus releasing agents to perform other

tasks. Or, the knowledge management-centric system

could accommodate greater increases in calls from

company growth without requiring additional support

staff.

The knowledge management-centric system had

92.5% fewer calls in queue at the technician level.

The reason for the large decrease can be attributed to

more problems being resolved at the first level due to

the knowledge provided by the system. Likewise, a

75.3% decrease in the number in queue at the second

level was observed for the same reason.

The experiments show that the number in queue

at the Third Level is the same for both the agent-

centric and knowledge management-centric systems.

The reason is the knowledge management system

does not typically include this specialized knowledge

for infrequent problems. The problems that are

elevated to the Third Level often require the special-

ist to make modifications to the application in

question in order to resolve the problem. The knowl-

edge management system is not designed to support

this activity.

The experiments indicate the potential cost bene-

fits of the knowledge management-centric approach.

Cost savings can be realized for several reasons. First,

the knowledge management-centric approach enables

the resolution of problems at lower levels. Typically,

the agents at lower levels are also at lower salary

levels. Second, the knowledge management-centric

system resolves problems in a shorter time. If the

problem was causing downtime to a business unit,

this means the unit can resume normal operations

faster. The decreased downtime is a cost savings.

Furthermore, since the problems are resolved in a

shorter amount of time, reductions in staffing require-

ments may occur. This staff can be used to improve

the knowledge base or be assigned to other tasks

within the organization.

The experiments were based on a comparison of

two models, the agent-centric help desk and the

proposed knowledge management-centric help desk.

Extensive data for the former is available and was

used to validate the model. The knowledge manage-

ment system is in the prototype stage and has not

been implemented in the help desk. Consequently,

there is no actual data. The validity of the simulation

model of the knowledge management system

depends on the accuracy of the data used in Table

3 for the three input factors. If following implemen-


tation it was found that the values deviate far from

the values used, then this would invalidate the results

of the experiments.

Other issues in the knowledge management system

still require further investigation. First, the identifica-

tion of experts is currently by self-identification but a

potential enhancement is to classify experts based on

the problems they solve. This can be accomplished so

that an agent who resolves a high number of problems

associated with technology X would become an

expert in technology X. The system could also incor-

porate keystroke logging and similar technologies to

facilitate the creation of new cases. A further enhance-

ment would be an indication of the usefulness or

relevance of the source based on how many times it

has been previously used such as done with Internet

search engines.

11. Conclusion

The article makes two contributions. The first

contribution is the specification of a knowledge-cen-

tric system for a help desk. The knowledge-centric

system incorporates aspects of case-based reasoning

systems, expert people finders, and group-ware sys-

tems, and indexes them for easy retrieval by help desk

agents. The integration of several disparate knowl-

edge sources enables the knowledge-centric system to

support resolution of both repeat problems as well as

new problems. The knowledge-centric system is cen-

tralized and integrated into the help desk process to

better ensure its use while making maintenance and

evolution a part of the everyday business activities.

Thus, the knowledge management-centric system

avoids the problems associated with systems that

require specialized personnel to periodically update

the knowledge contained in the system. Because all

problems and problem solutions pass through the

knowledge management system this information and

knowledge becomes available to all help desk agents.

Thus, the knowledge is captured by the organization

as well as by the individual and promotes organiza-

tional learning.

The research hypothesis was that the use of

several knowledge sources and the incorporation of

the knowledge management system as the central-

ized component of the help desk would lead to

performance improvements. The second contribution

was to conduct a discrete-event computer simulation

to quantitatively compare the agent-centric and

knowledge management-centric help desk. The sim-

ulation study showed a greater than 50% decrease in

average time to resolve a problem and a 19%

increase in throughput. These improvements are

significant and provide justification for implementing

the knowledge management system. The advantage

of simulation is to conduct a study without disrupt-

ing the operations of the actual help desk. Moreover,

we are able to evaluate the proposed system prior to

installation.

There are several issues related to the adoption of a

knowledge management system into the help desk

organization that are not addressed. The experiments

were conducted with the assumption that cases existed

for the top 20% of the problems, which account for

almost 80% of the calls. Consequently, the experi-

mental results are only valid with the preexistence of a

knowledge base. As Ref. [14] recommend, new

installations of knowledge management systems

should have sufficient cases to cover at least some

of the problems likely to be encountered. If the

knowledge management system were installed with

no cases in its knowledge base, then there would

probably be no performance improvement. However,

it is noted the system also is an expert-finder and

group-ware system, so these components of the sys-

tem could aid problem resolution. The centralized

architecture of the system was designed so that it

would not hinder the problem resolution process even

when no cases are found. The power of simulation is

that different assumptions, such as no knowledge

base, could be quickly evaluated. A second issue

not addressed is the cultural barriers to acceptance

and adoption of the system. Adoption of technology

and unwillingness to share knowledge are well-docu-

mented [25]. Computer simulation experiments are

not the best way to examine cultural issues or human

acceptance of a system.

Acknowledgements

Luz Minerva Gonzalez would like to acknowledge

the financial support of Royal Caribbean Cruise Lines

during the completion of this project.


Appendix A. Sample data collected from Remedy CTI System

Ticket number Arrived

date

Arrival

time

Resolved

date

Resolved

timeaPriority Group + Category Description

MIA-000506-0001 05/06/00 7:58 Low Customer

Care

NETWORK NETWORK SECURITY

PASSWORD

MIA-000506-0002 05/06/00 8:41 5/6/00 8:41 Low Customer

Care

SOFTWARE Error performance operation when

updating the virus definitions.

MIA-000506-0003 05/06/00 8:43 Low Customer

Care


LOCKED OUT

SVS-000506-0001 05/06/00 9:01 5/12/00 16:58 Medium Customer

Care Level-2

SOFTWARE

(SHIP)

There appears to be some problem

with the cross mounted drive

permissions. Users are having

trouble running programs which

previously worked; these programs

work under the administrative

logins (ss1/ss2) but not under

ttyxxx logins. This appears to be

independent of the Encore login.

The programs known to be affected

so far are: Prepaid Gratuities

Crew APIS reports Crew

Resolution Reports We need to find

a solution to these issues. They can

be run the Systems Manager at this

time, but this is only a temporary

solution.

MIA-000506-0004 05/06/00 9:13 Low Customer

Care

NETWORK NETWORK SECURITY LOCKED

OUT

MIA-000506-0005 05/06/00 9:16 3.56 Low Customer

Care


PASSWORD

MIA-000506-0006 05/06/00 9:49 2.42 Low Customer

Care

AS400 AS400 COLONIAL PASSWORD

ENABLE

MIA-000506-0007 05/06/00 10:16 2.20 Low Customer

Care

AS400 AS400 COLONIAL PASSWORD

ENABLE

MIA-000506-0008 05/06/00 10:23 5/8/00 15:38 Low Technicians HARDWARE Printer jam \\mia-fps-03\mia-prn-ic-

01 giving error printer jam after user

as open an check unit hp iisi

(13.1 internal jam)

a When resolved time is missing it is assumed to be under 5 min per interview with help desk agents.

References

[1] M. Alavi, D. Leidner, Knowledge management systems:

emerging views and practices from the field, Proceedings

of the 32nd Hawaii Conference on System Sciences, Los

Altimos, CA, IEEE Computer Society, Maui, HI, USA,

1999, pp. 239.

[2] M. Alavi, D. Leidner, Knowledge management systems:

issues, challenges, and benefits, Communications of the As-

sociation for Information Systems 1 (7) 1–37.

[3] J. Anton, The past, present, and future of customer access

centers, International Journal of Service Industry Management

11 (2) (2000) 120–130.

[4] J. Anton, D. Gusting, Call Center Benchmarking: How Good

Is Good Enough, Purdue Univ. Press, Indiana, 2000.

[5] V. Bapat, E. Pruitte, Using simulation in call centers, Winter

Simulation Conference Proceedings, IEEE, Washington, DC,

1998, pp. 1390–1395.

[6] I. Becerra-Fernandez, The role of artificial intelligence tech-

nologies in the implementation of people-finder knowledge

management systems, Knowledge-Based Systems 13 (2000)

315–320.

Luz Minerva Gonzalez was born in Nicaragua where she completed

her BS in Industrial Engineering. She earned an MS in Industrial

Engineering at Florida International University in Miami, FL. While

earning her degree, she worked at Royal Caribbean Cruise Lines.

She is now in the Management of Information Systems Department

at Americatel in Miami, FL.


[7] C.W. Chan, L.L. Chen, L. Geng, Knowledge engineer-

ing for an intelligent case-based system for help desk

operations, Expert Systems with Applications 18 (2000)

125–132.

[8] K.H. Chang, P. Raman, W.H. Carlisle, J.H. Cross, A self-

improving helpdesk service system using case-based rea-

soning techniques, Computers in Industry 30 (2) (1996)

113–125.

[9] V. Chin, S.C. Sprecher, Using a manufacturing based simula-

tion package to model a customer service center, Winter Sim-

ulation Conference Proceedings, IEEE, New Orleans, LA,

USA, 1990, pp. 904–907.

[10] B. Cleveland, J. Mayben, Call Center Management on Fast

Forward, Call Center Press, Maryland, 1997.

[11] K. Dawson, The Complete Guide to Starting, Running, and

Improving Your Call Center, CMP Books, New York,

1999.

[12] R.A. Feinberg, I.S. Kim, L. Hokama, K. de Ruyter, C. Keen,

Operational determinants of caller satisfaction in the call cen-

ter, International Journal of Service Industry Management 11

(2) (2000) 131–141.

[13] G. Fischer, J. Ostwald, Knowledge management: problems,

promises, realities, and challenges, IEEE Intelligent Systems,

(2001) 60–72.

[14] M.H. Goker, T. Roth-Berghofer, The development and utili-

zation of the case-based help-desk support system homer, En-

gineering Applications of Artificial Intelligence 12 (1999)

665–680.

[15] L.M. Gonzalez, Analysis of applying knowledge manage-

ment to an information technology help desk, Thesis, In-

dustrial and Systems Engineering, FIU (2002).

[16] P.H. Gray, A problem-solving perspective on knowledge

management processes, Decision Support Systems 31 2001,

pp. 87–102.

[17] R. Heckman, A. Guskey, Sources of customer satisfaction

and dissatisfaction with information technology help

desks, Journal of Market Focused Management 3 (1998)

59–89.

[18] G. Held, Network Management: Techniques, Tools, and Sys-

tems, Wiley, Chichester, UK, 1992.

[19] C.W. Holsapple, K.D. Joshi, Organizational knowledge

resources, Decision Support Systems 31 (2001) 39–54.

[20] A. Lazarov, P. Shoval, A rule-based system for automatic

assignment of technicians to service faults, Decision Support

Systems 32 (2002) 343–360.

[21] D. Leake, Case-Based Reasoning: Experiences, Lessons, and

Future Directions, AAAI Press, Menlo Park, CA, 1996.

[22] S. Lee, R.M.O. Keefe, The effect of knowledge representation

schemes on maintainability of knowledge-based systems,

IEEE Transactions on Knowledge Data Engineering 8 (1996)

173–178.

[23] D. Leonard-Barton, Wellsprings of Knowledge, Harvard Busi-

ness School Press, Boston, 1995.

[24] R. Marcella, I. Middleton, The role of the help desk in the

strategic management of information systems, OCLC Systems

and Services 12 (4) (1996) 4–19.

[25] R. McDermott, C. O’Dell, Overcoming cultural barriers to

sharing knowledge, Journal of Knowledge Management 5

(1) (2001) 76–85.

[26] K. Mertins, P. Heisig, J. Vorbeck, Knowledge Management:

Best Practices in Europe, Springer-Verlag, Berlin, 2001.

[27] P. Meso, R. Smith, A resource-based view of organizational

knowledge management systems, Journal of Knowledge Man-

agement 4 (3) (2000) 224–234.

[28] K. Miller, V. Bapat, Case study: simulation of the call center

environment for comparing competing call routing technolo-

gies for business case Roi projection, Winter Simulation

Conference Proceedings, IEEE, Washington DC, USA,

1999, pp. 1694–1700.

[29] M. Nissen, M. Kamel, K. Sengupta, Integrated analysis and

design of knowledge systems and processes, Information

Resources Management Journal, 2000, pp. 24–43.

[30] I. Nonaka, A dynamic theory or organizational knowledge

creation, Organization Science 5 (1) (1994) 14–37.

[31] I. Nonaka, H. Takeuchi, The Knowledge-Creating Company,

Oxford Press, New York, 1995.

[32] S.E.A. Piggott, Internet commerce and knowledge manage-

ment—the next megatrends, Business Information Review

14 (4) (1997) 169–172.

[33] B. Rubenstein-Montano, J. Liebowitz, J. Buchwalter, D.

McCaw, B. Newman, K. Rebeck, A systems thinking frame-

work for knowledge management, Decision Support Systems

31 (2001) 5–16.

[34] S. Sandborn, Structuring the service desk, Information World

23 (52) (2001) 28.

[35] A. Satyadas, U. Harigopal, Knowledge management tutorial:

an editorial overview, IEEE Transactions on Systems, Man,

and Cybernetics—Part C: Applications and Reviews 31 (4)

(2001) 429–437.

[36] R.J. Sharer, Applying policy management to reduce support

costs for remote and mobile computing, International Journal

of Network Management 8 (1998) 211–218.

[37] E. Simoudis, Using case-based retrieval for customer technical

support, IEEE Expert 7 (5).

[38] K.E. Sveiby, The New Organizational Wealth. Managing and

Measuring Knowledge-Based Assets, Berrett Koehler Publish-

er, San Francisco, 1997.

[39] M.J. Taylor, D. Gresty, R. Askwith, Knowledge for network

support, Information and Software Technology 43 (2001)

469–475.

[40] A.H. Thomas, The Virtual Help Desk, Thomson Computer

Press, New York, 1996.

[41] R. Weber, D.W. Aha, I. Becerra-Fernandez, Intelligent lessons

learned systems, Expert Systems with Applications 17 (2001)

17–34.

Ronald E. Giachetti is Associate Professor

of Industrial & Systems Engineering at

Florida International University. He is also

the director of the Masters Program, Infor-

mation Systems Track. Dr. Giachetti con-

ducts research in enterprise systems,

systems integration, design methodologies,

and application of operations research. He

has managed research projects which total

over $1 million with funding from NSF,

NASA Ames Research Center, US Army,

and industry. He has published over 25 refereed articles in journals,

including International Journal of Production Research, Interna-

tional Journal of Production Economics, European Journal of

Operations Research, and the Journal of Robotics and Computer

Integrated Manufacturing. He received his Ph.D. in Industrial

Engineering from North Carolina State University.

Guillermo Ramirez earned his MS in Engineering Management

from Florida International University in Miami, FL. While earning

his degree, he worked in the technical call center for Vodophone.


Documents

Knowledge management-centric help desk: specification and