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From the customer to the firm: evaluating generic serviceprocess designs for incoming customer requests

Michael Zapf*

Department of Information Systems 1, University of Mannheim, Schloss, D-68131 Mannheim, Germany

Received 12 March 2003; received in revised form 25 October 2003; accepted 25 October 2003

Available online 4 June 2004

Abstract

In this paper, generic service process designs are presented for handling customer requests within a communication center.

The process characteristics which are relevant in this domain are the level of difficulty (standard versus special requests) and the

communication channel (synchronous versus a-synchronous requests). The design dimensions are task allocation to generalists

and specialists, front-office and back-office role and degree of integration of synchronous and a-synchronous requests. The

process designs are evaluated within an experimental simulation study with empirical data from a car rental company. The

analysis shows strengths and weaknesses of the process designs under different conditions. Based on these results a model of

competing effects is developed which helps to understand the complex dependencies within service process designs.

# 2004 Elsevier B.V. All rights reserved.

Keywords: Business process design; Service processes; Customer interaction; Inbound communication center; Business process simulation

1. Introduction

The direct contact between customer and firm is

more and more critical for business success. A suc-

cessful interaction helps to establish and strengthen a

close relationship to the customer whereas poor per-

formance often leads to the loss of customers (e.g.

[1,2]). Therefore it is important to design appropriateinteraction processes which allow an effective and

efficient handling of customer requests.

The objective of this paper is to derive and evaluate

generic service process designs within communication

centers. This analysis helps to understand the relevant

design dimensions and the characteristics of generic

process designs which are used in practice.

First relevant process characteristics for handling

incoming customer requests are identified. The char-

acteristics which are relevant in this domain are the

level of difficulty (standard versus special requests)

and the communication channel (synchronous versus

a-synchronous requests). The design dimensions aretask allocation to generalists and specialists, front-

office and back-office role and degree of integration of

synchronous and a-synchronous requests. Based on

these dimensions generic service process designs are

derived and evaluated within a simulation study. For

the experiments empirical data from a car rental

company is used and strengths and weaknesses of

the process designs are shown under different condi-

tions. Based on these results a model of competing

effects is finally developed which helps to understand

Computers in Industry 55 (2004) 5371

* Present address:Ottmannsreuth 23, D-95473 Creussen, Germany.

Tel.: 49-929-918718; fax: 49-929-918719.E-mail addresses: [email protected],

[email protected] (M. Zapf).

0166-3615/$ see front matter # 2004 Elsevier B.V. All rights reserved.

doi:10.1016/j.compind.2003.10.009

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the complex dependencies within service process

designs.

2. A brief literature survey

The following section gives a brief survey of the

relevant literature. Especially those sources are

included which deal with the evaluation of generic

business processes designs but also other contributions

which give normative statements about how business

processes should be designed.

Many approaches which deal with the design of

business processes trace back to Hammer et al. [3],

who presented some general business process designs

and describe design guidelines but did not give evi-

dence for their recommendations. Refs. [46] evaluate

some of these designs with queuing theory respec-

tively linear programming. Because of the limitation

of these methods concerning the modeling complex-

ity, only simple designs have been analyzed under

strong restrictions. Our approach uses discrete event

simulation to overcome these restrictions and allows

therefore the evaluation of process designs close to

reality.

In this context also some interesting contributions

should be mentioned which present methods to re-engineer business processes on an analytical base like

[710] or [11]. But these approaches do not address

the specific requirements for customer interaction

processes within communication centers.

The coordination theory from Thompson [12] is an

important source for the deduction of design guide-

lines for business processes. Thompson differentiates

between (1) reciprocal, (2) sequential and (3) pooled

interdependencies within an organization and recom-

mends to build organizational units according to inter-

dependencies (1) and (2). Refs. [1315] usecoordination theory as basis for deriving business

process typologies and design methods. In this paper,

coordination theory is used to interpret some of the

experimental results.

Basic knowledge to understand the efficiency of

process designs comes also from queueing theory.

Here especially the pooling effect has to be mentioned

which states that bigger resource groups lead to smal-

ler waiting times and therefore to a better process

performance (e.g. [1618]). This general statement

reflects the enhancement of resource groups which are

assigned to one task each. Within service processes

multiple resource groups are assigned to multiple

tasks. This complex dependencies will be analyzedfor generic service process designs in the following.

3. The conceptual framework: generic service

process designs

Within a communication center two basic activities

have to be performed in order to handle incoming

customer requests successfully.

Classify incoming request: accept request and if

necessary forward it to a suitable qualifiedemployee.

Handle request: give required information or makethe necessary arrangements dependent on the cus-

tomer needs.

The classifying activity is used to partition the

overall request volume and provide separate process

versions and resources for each partition (e.g.,

[19,20]). In practice the request volume is often

partitioned according to specialization and commu-

nication reasons. Therefore we distinguish between

the design dimensions qualification-mixture and com-munication-mixture [21].1

Since incoming customer requests deal with vary-

ing topics and have different levels of difficulty they

can be divided up into different request types. Each

request type has its own qualification requirements.

Our generic designs for the qualification-mixture

dimension distinguish between two types: standard

and special requests [20,22]. Standard requests deal

for example with the processing of simple transac-

tions, the modification of customer data or general

enterprise or product information. They are normallyhandled by employees (agents) with basic knowledge.

These agents will be called generalists. Some other

requests refer to difficult technical problems, exten-

sive consultations or complaints. They are called

special requests and can only be handled by specialists

who have specific, in-depth knowledge or special

1 The dimensions and generic designs have been discussed in

interviews with communication center professionals who have

several years of experience in this domain.

54 M. Zapf / Computers in Industry 55 (2004) 5371

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skills. The main design question for the qualification-

mixture is: what is the appropriate mixture of general-

ists and specialists for handling a given volume of

standard and special requests?

The deployment of different communication chan-

nels like phone, e-mail, fax or chat lead to another type

of process partitioning according to the channel which

is used by the customer. In this area we distinguish

between synchronous and a-synchronous channels

which can both be used for standard and special

requests. Synchronous communication takes place if

all parties are communicating with each other at the

same time (e.g., phone or chat). E-mail and fax are

examples for a-synchronous communication channels,where the communication partners do not need to get

in contact at the same time and longer time intervals

pass by between single communication steps. The

assignment of requests from a particular channel to

the suitable agent is the main design question in the

communication-mixture dimension where an (a) inte-

gration or (b) separation of different communication

channels is possible. Integration means that agents

handle both synchronous and a-synchronous requests.

Within the separation of channels different agent

groups will be established for synchronous and a-synchronous media.

3.1. Qualification-mixtures with integrated

communication channels

The generic service designs with integrated commu-

nication channels are characterized by the assignment

of process activities (classify, handle) to agent groups

(generalists, specialists) for each request type. Three

different assignments are presented in Table 1 and

named as two-level, back-office and one-level design.

The details of each design is presented below. In every

design agents handle both synchronous and a-synchro-

nous requests according to their arrival time. If more

requests are in the queue synchronous request will be

prioritized but incoming synchronous requests do

not interrupt the processing of a-synchronous requests.

Within the classical two-level design which is

often used in practice the internal communication

structure is divided up into a first and a second level

(Fig. 1).2 Generalists accept all incoming requests.

Standard requests are directly handled by the accept-

ing generalist in the first level. Special requests are

forwarded to a specialist agent in the second level. Inthe case of complex tasks this design will be expanded

with further levels in practice.

The back-office design contrasts with a stronger

distinction between synchronous and a-synchronous

communication. Generalists handle only synchronous

requests in the front office whereas specialists classify

and handle all a-synchronous requests additional to

their normal workload of special requests in the back-

office (Fig. 2).

The strongest integration of qualification groups is

realized within the one-level design. In this design firstand second level (or front office and back-office) will

Table 1

Activities and agent groups

Group Generalist group Specialist group

Activity Classify Handle Classify Handle

Two-level All standard All standard

All special All special

Back-office Syn. Standard Syn. standard Asyn. standard Asyn. Standard

Syn. Special Asyn. special All special

One-level All standard All standard All standard All standard

All special All special All special

syn.: Synchronous; asyn.: a-synchronous.

2 The service process designs have been modeled as Petri nets

[30] with additional communication center specific symbols. The

telephone symbol next to a place means that this place may contain

synchronous requests. The letter symbol next to a place means that

this place may contain a-synchronous requests. Some places may

contain bot synchronous and a-synchronous requests. In this

section we focus on the process structure and do not present

specific markings. The markings for our case study are discussed in

Section 5.

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notbedistinguished.Generalistsandspecialistsareboth

able to classify and handle all types of requests (Fig. 3).

Since most specialists are more expensive than

generalists, requests are primarily assigned to a free

generalist (priority 1). Only if no generalist is available

the request will be assigned to a specialist (priority 2).

3.2. Qualification-mixtures with separated

communication channels

In the preceding section the process designs two-

level, back-office and one-level have been presented

with integrated communication channels. In order to

achieve separated channels additional agent groups

have to be built.

In the two-level design two groups generalists 1

and specialists 1 will be established for synchro-

nous requests and two additional groups generalists

2 and specialists 2 will be entrusted with a-

synchronous requests (Fig. 4). The routing remains

the same as shown in Fig. 1.

Since in the back-office design generalists handle

only synchronous requests there is no difference be-

tween integrated and separated channels in the frontoffice (Fig.2).Intheback-office an additional group has

tobedefined for separated communication channels, so

that synchronous requests are handled by the group

specialists 1 and a-synchronous requests by the

group specialists 2.3

Fig. 1. Two-level design.

Fig. 2. Back-office design.

3 The back-office design and the 1-level design can be easily

derived from Figs. 2 and 3. These designs are not presented as

figures for reasons of space.


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Fig. 3. One-level design.

Fig. 4. Two-level design with separated communication channels.


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For the separated version of the one-level design

four groups (generalists 1, generalists 2, specialists 1,

specialists 2) have to be built similar to the two-level

pattern. The basic routing strategy remains the same asshown in Fig. 3.

4. The research site: Car Rental Inc.

Our research site was an organization which we call

Car Rental Inc. in order to protect its anonymity. The

business of Car Rental Inc. is renting automobiles and

trucks to both business and private customers. For

incoming customer requests the company has estab-

lished a communication center which offers different

types of services for the phases pre-sales, sales and

after-sales. We identified the relevant tasks and clas-

sified them regarding the level of difficulty into stan-

dard and special requests (Table 2).

Standard requests comprise the tasks information,

consulting and reservation during the pre-sales and

sales phase. These tasks require general knowledge

about the car fleet and the current price list and can be

performed by generalists.

During the after-sales phase customers need contact

persons for service tasks, emergency help and com-

plaint handling. These requests represent pressingproblems and do require special professional and

social skills on the agent side. Therefore these tasks

are classified as special requests which have to be

handled by specialists.

Incoming requests arrive from the customer through

different communication channels. Most of the

requests are phone calls (synchronous communica-

tion), but also a-synchronous channels like fax, e-mail

or letter are used by customers. It is expected that

especially e-mail communication gets even more

important for Car Rental Inc. in the future.

5. Constructing the simulation models

Since the communication center domain is extre-

mely dynamic most of the parameters are non-deter-

ministic with random distributions. Therefore for

every generic process design from Section 3 a sto-

chastic discrete event simulation model has been

created with the high-level simulator ARENA which

is based on the SIMAN simulation language [23].

Some model parts have been created with the call

center specific extension Call$im [24], other parts

have been implemented through individual routines.

With these tools it was possible (a) to build the

computer models in relative short time and (b) to

obtain a good simulation performance by using a fast

simulation language.

Since the basic process logic has already been

discussed in Section 3 we describe in the following

specific details of the implemented service process

designs. The single subjects of this section are mod-

eling incoming requests, request classification and

handling, performance measures and the assignmentof agent resources to agent groups.

5.1. Incoming requests and waiting tolerance

Customer requests arrive with non-deterministic

intervals at Car Rental Inc., so the arrival rate is

modeled as stochastic Poisson-distribution which is

often suggested in literature for comparable processes

(e.g. [4,6,23,25,26]).

The average volume of synchronous requests,

which is required as input parameter for this distribu-tion, has been derived empirically from the output of

the ACD (automatic call distribution) system from Car

Rental Inc. Based on one typical week without

extreme work loads an average request volume of

3023 requests per day has been determined. This

volume contains 2579 standard calls and 444 special

calls. The request volume of a-synchronous requests

has been derived from the overall request volume per

month which leads to 100 standard and 90 special

requests per day.

Table 2Task classification for ABC (Car Rental Inc.)

Phases Tasks Level of

difficulty

Task

classification

Pre-sales Information Low Standard request

Pre-sales Consulting Low Standard request

Sales Reservation Low Standard request

After-sales Service High Special request

After-sales Emergency help High Special request

After-sales Handle complaints High Special request


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In the case of synchronous requests customers do

not wait for an agent as long as you like but only for a

particular time period. If calls are in the waiting queue

for a longer time the customer hangs up (Fig. 5). We

call this period the waiting tolerance which is deter-

mined by the customers preferences and his current

situation. Since the ACD system does not log the

waiting tolerance we use an estimate from Car Rental

Inc. experts which results in an average waiting tol-

erance of 1:00 min per customer request. The variation

of the waiting tolerance between single requests is

reflected by using the exponential distribution for this

parameter.After hanging up some customers re-dial in order

to get a free agent. The part of re-dialers is repre-

sented by the percentage of re-dialing. The para-

meter time between dial attempts defines the time

interval between hanging up and re-dialing and is

also supposed as exponential distributed. Commu-

nication center experts estimated 75% percentage of

re-dialing and an average time between dial attempts

of 0:06 min.

The waiting tolerance and re-dial process has also

modeled for calls which have been classified by a

generalist and have been transferred in a waiting queue

of a specialist agent.

5.2. Request classification and handling

The handling of a synchronous request is not fin-

ished with the completed call. Additional after-call

work has to be done afterwards by the agent (Fig. 6).This work comprises administrative tasks like data

entry and necessary internal communication. In our

model only this part of the after-call work is included

which is done immediately by the agent who has

handled the call. After finishing this work the agent

is available for accepting further requests.

Fig. 5. Call abandonment process for synchronous requests.

Fig. 6. Request classification and handling for synchronous requests without forwarding.


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Since the handling times are not constant in reality

they are modeled with stochastic distributions. In

order to determine the appropriate stochastic distribu-

tion and average values one typical week of Car Rental

Inc. has been statistically analyzed based on empirical

data from the ACD system. A sample of real call times

has been compared with data from Exponential dis-

tributions. The w2-test delivered a P-value of 0.467

and the test of Kolmogorov/Smimov resulted in a

lower bound for the P-value of 0.15. A P-value of

more than 0.10 stands for a good correspondence be-

tween the distribution and the sample data [23].

Based on these results the Exponential distribution

has been used for modeling the relevant handling

times: classification time, call time and after-call time.

The average values for the call time and after-call timehave been derived from the ACD system, the average

classification time has been estimated by experts from

Car Rental Inc. (Table 3).

For a-synchronous requests no distinction between

call handling and after-call work is necessary, there-

fore only one handling activity has been modeled for

each process design. The average values for the cor-

responding handling time have been estimated by

experts (Table 3).

5.3. Performance measures

In the communication center domain many mea-

sures are used to evaluate the process performance

[20]. Since the goal of a communication center is to

handle customer requests efficiently a customer has to

get in contact with an agents first. Therefore the

accessibility of the agents is used as one important

performance criterion for synchronous requests. It is

often measured as percentage of lost calls which is

defined as quotient of hung up calls and total calls. A

high percentage stands for many dissatisfied custo-

mers who have not reached an agent in time and with

that for a poor process performance.

A-synchronous requests reach the communication

center anyway but are processed with different speed.

From the customer point of view a short throughput

time is desirable. So the overall throughput and wait-

ing time is used as measure for a-synchronous requests

where a long throughput and waiting time indicates a

poor performance.

5.4. Validation and verification

The suitability of the simulation model has been

checked according to [27], in the following validation

and verification steps.

Conceptual model validation: determine that theconceptual model is reasonable and correct for the

intended application.

Computerized model verification: ensure that thecomputer programming and implementation of the

model is correct.

Data validity: ensure that data is appropriate, accu-rate and sufficient.

Operational validity: determine that the results aresufficient accurate for the intended purpose over the

application domain.

For the conceptual model validation the face valid-

ity technique has been used. The generic service

process designs (see Section 3) and the model details

(see Sections 5.1 and 5.2) have been developed and

discussed with communication center experts in order

to ensure that the models are reasonable. For building

the conceptual model also different real communica-

tion centers of the domains bank, book trade, car rental

and energy industry have been analyzed [21]. Note

Table 3

Classification and handling times

Request type Classify, average

classification time (min)

Handle

Average call time/handling

time (min)

Average after-call

time (min)

Standard synchronous 0:45 3:10 0:12

Special synchronous 0:45 2:12 0:21

Standard a-synchronous 1:00 3:00

Special a-synchronous 1:00 15:00


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that the main objective of our simulation study is to

identify the differences between general service pro-

cess designs based on empirical data and not to

improve the specific process designs of one company.Therefore specific details of Car Rental Inc. like

forwarding requests to an outsourcing partner have

been left out of the model and the simulation model

has not been validated against real performance values

of Car Rental Inc. After face validation which was

mainly based on graphical process models and verbal

process descriptions the process logic has been

checked through trace technique. The different

request types have been tracked through every sub-

model to determine whether the logic is correct and the

necessary accuracy is maintained.

Different dynamic testing techniques have been

applied for computerized model verification and the

simulation models have been executed under various

conditions (Sargent, 1988).

1. Fixed values: fixed values (constant factors) have

been defined for selected input variables (e.g.

classification time, handling times, arrival rates)

and the performance values have been checked

against hand calculated values.

2. Comparison to other models: sub-models have

been compared to analytical M/M/l and M/M/nqueueing models (Kleinrock, 1976).

3. Sensitivity analysis: selected input parameters

(e.g. after-call time, percentage of re-dialing) have

been modified and the effect upon the results has

been determined.

To ensure data validity we used real data which has

been collected automatically by the ACD system

(Automatic call distribution) of Car Rental Inc. for

the average request volume and average handlingtimes of synchronous request (Table 4). The stochastic

distribution for the handling times has been derived

from the empirical data and statistically validated with

the w2-test and the test of Kolmogorov/Smirnov (see

Section 5.2.). The other data has not been logged by

the ACD system and was therefore provided by

experts from Car Rental Inc. Within the computerized

model verification we used sensitivity analysis to

ensure that small changes of these parameters are

not critically for the performance measurement.

Regarding the operational validity 95% confidence

intervals have been calculated for every performance

measure. In order to reflect the nature of a commu-

nication center the single experiments have been

performed in the form of multiple terminating simula-

tion runs. For every experiment 30 independent repli-

cations have been made according to the general rule

of [28]. At the end of each replication it has been

checked that sufficient data has been collected and that

the output data is not correlated [23].

5.5. Resource assignment to agent groups

Before performing the simulation experiments, the

different models have to be initialized with the number

of agents per agent group. Hereby it has to be kept in

mind that even the worst service design is able to

obtain a given efficiency if enough agents are available

Table 4

Simulation parameters and data gathering

Parameter group Parameter Data gathering

Request volume Average volume of synchronous

standard and special requests

Real data of Car Rental Inc., based on a typical week,

collected by ACD systemAverage volume of a-synchronous

standard and special requests

Real data of Car Rental Inc., based on monthly

values, collected by experts

Waiting tolerance Average waiting tolerance Estimated values, provided by experts

Percentage of re-dialing

Average time between dial attempts

Request classification and handling Average classification time Estimated values, provided by experts

Average handling times for

a-synchronous requests

Average call time and after-call

time for synchronous requests

Real data of Car Rental Inc., based on a typical week,

collected by ACD system


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and in the same way the best design obtains a poor

performance if not enough agents are available. In

order to make a fair initialization we performed the

following initialization steps.

Step 1: define performance targetsFor all designs the same performance targets

have been defined based on interviews with com-

munication center specialists: For synchronous

requests the average lost call rate has to be lower

than 10%, for a-synchronous requests the average

waiting time has to be lower than 15 min and as an

additional constraint the agent utilization has to be

less than 80% within all agent groups.

Step 2: determine the minimal amount of agents forevery process design

Some initialization experiments have been per-

formed in order to determine the minimum amount

of agents per group which is necessary to achieve

the performance targets for every design. The

following algorithm describes this procedure using

the interval bisection method (see [29]) for every

service process design Pi with integrated commu-

nication channels (see Section 3.1).

1. Set aGen 1, bGen 99.2. Set mGen dbGen aGen=2e.3. Perform initialization experiment for design Pi

with mGen generalists and 0 specialists and obtain

performance values.

4. If performance targets have been met set bGen mGen else set aGen mGen.

5. If bGen aGen 1 set kGen,i bGen and go tostep 6; else continue with step 2.

6. Set aSpec 1, bSpec 99.7. Set mSpec dbSpec aSpec=2e.8. Perform initialization experiment for design Pi

with mSpec specialists and kGen generalists and

obtain performance values.

9. If performance targets have been met set bSpec mSpec; else set aSpec mSpec.

10. IfbSpec aSpec 1 set kSpec,i bSpec and stop;else continue with step 7.

Step 3: determine the agent group capacitiesThe agent group capacities kGen and kSpec

can finally be calculated as maximum over all

process designs: kGen maxi (kGen,i) and kSpec maxi(kSpec,i). Designs with a minimum group

capacity which is lower than kGen or kSpec get

additional resources. The resulting group capaci-

ties are summarized in Table 5. The listed capa-

cities for separated channels have been obtained

after enhancing steps 2 and 3 by additional agent

groups exclusive for a-synchronous requests (see

Section 3.2).

6. Numerical analysis

6.1. Different qualification-mixtures with

integrated communication channels

In the first series of experiments we use integrated

communication channels and compare the qualifica-

tion-mixtures two-level design, one-level-design and

back-office design.

Herewith the characteristics of the process

designs will be evaluated under different worst case

Table 5

Resource capacities by service process design and agent group

Service process design Agent group Resource capacity for

integrated channels

Resource capacity for separated channels

Synchronous A-synchronous

Two-level Generalists 16 15 1

Specialists 7 4 3

Back-office Generalists 15 15

Specialists 8 5 3

One-level Generalists 15 14 1

Specialists 8 5 3


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scenarios. The scenarios represent (S1) overload

situations with an increasing request volume, (S2)

the extension of call and handling times, (S3) the

reduction of agent availability e.g. on account ofsudden illness, (S4) the reduction of waiting toler-

ance by the customer, (S5) the increase of recall

percentage and (S6) the reduction of time between

dial attempts. Table 6 lists all scenarios together

with the modified parameters and worst case

values.

6.1.1. Results for synchronous standard requests

For synchronous standard requests the one-level

design dominates the other designs in all scenarios

(Fig. 7). The two-level design takes the second placeand the back-office design is the last one with the

highest lost call percentage. The results may be

explained by two effects.

1. The coordination effect: consolidation of sequen-

tial dependencies leads to a lower coordina-

tion effort and to a better performance (e.g.

[46,12,13]).

In the one-level design the activities classify

and handle are not only consolidated for

standard requests but also for all special requests

which are directly accepted by specialists

(Table 7). In the back-office design this consolida-

tion has not been realized for synchronous specialrequests but for all a-synchronous special request.

Within the two-level design consolidation is

realized for none of the special requests. With

that the advantages of the one-level design

compared to the two-level design may be

explained but not the differences between two-

level and back-office design and also not the clear

differences between one-level and back-office

design.

2. The pooling effect: bigger resource groups lead to

smaller waiting times and to a better performance(e.g. [1618]).

In the one-level design all 23 agents are able to

accept synchronous requests whereas in the two-

level design 16 generalists and in the back-office

design only 15 generalists may accept synchro-

nous requests. The connection between agent

capacity and lost call rate are directly reflected

in the experimental results: the one-level design

has the lowest and the back-office design the

highest lost call rate.

Table 6

Scenarios for evaluating qualification-mixtures with integrated communication channels

Scenario Modified parameters Original value(s) New value(s)

S0 normal None See Section 5

S1 overload Average volume of standard syn. requests per day 2579 5158

Average volume of special syn. requests per day 444 888

Average volume of standard asyn. requests per day 100 200

Average volume of standard asyn. requests per day 90 180

S2 long handling Call time for standard synchronous requests (min) Exp(3:10) Const(6:20)

After-call time for standard syn. requests (min) Exp(0:12) Const(0:24)

Call time for special syn. Requests (min) Exp(2:12) Const(4:24)

After-call time for special syn. requests (min) Exp(0:21) Const(0:42)

Handling time for standard asyn. requests (min) Exp(3:00) Const(6:00)

Handling time for special asyn. requests (min) Exp(15:00) Const(30:00)

S3 agent absence Number of generalists (two-level/back-office/l-level) 16/15/15 13/12/12

Number of specialists (two-level/back-office/l-level) 7/8/8 6/7/7

S4 low tolerance Average waiting tolerance (min) 1:00 0:06

S5 increase recalling Percentage of re-dialing 75 85

S6 short recall period Average time between dial attempts (s) 6 0,06

syn.: Synchronous; asyn.: a-synchronous; min: minutes; s: seconds, Const(x): constant x, Exp(x): exponential distribution with average

value x.


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6.1.2. Results for synchronous special requestsFor synchronous special requests the results are not

so homogeneous as for synchronous standard requests.

The one-level design dominates the other designs in

scenario S1 and S4S6 but it does not cope with

the extreme high workloads in scenario S1 and S2

(Fig. 8). Between the two-level and back-office only

little performance differences can be stated for all

scenarios.

In order to explain these results we have to bear in

mind that synchronous special requests may get lost

at two different places in the process: (1) in the firstwaiting queue before any contact with an agent has

taken place and (2) in the forwarding queue afterhaving classified and forwarded by the accepting

generalist. The isolated pooling effect for the first

waiting queue would lead to a similar performance

sequence as described above for synchronous stan-

dard requests: one-level before two-level and two-

level before back-office design. But these sequence

can not be observed in our experimental results

because of a specialist occupation with standard

requests in the one-level design. Specialists help

out if not enough generalists are available to

accept incoming calls which leads to a lower agentcapacity for the second waiting queue and affects

Fig. 7. Percentage of lost standard calls with integrated channels for one-level, two-level and back-office design (indicator shows 95%

confidence interval).

Table 7

Activity consolidation

Process design Syn. standard Asyn. standard Syn. special Asyn. special

One-level Consolidated Consolidated Partial consolidated Partial consolidated

Back-office Consolidated Consolidated Consolidated

Two-level Consolidated Consolidated

syn.: synchronous; asyn.: a-synchronous.


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the performance especially in high work loadsituations.

6.1.3. Results for a-synchronous standard requests

The results for a-synchronous standard requests

show only little differences between the one-level

and back-office design (Fig. 9). The two-level design

leads to longer average throughput times in scenarios

S1 (overload), S2 (long handling) and S3 (agent

absence).

The drawback of the two-level design results from

the fact that in high load situations accepting agentsare busy with high priority synchronous requests and

put aside a-synchronous requests for later handling.

Compared with this in the back-office design a-syn-

chronous requests are handled by specialists who are

not involved in accepting calls and therefore can focus

themselves on handling a-synchronous requests. The

high agent flexibility in the one-level design where all

agents are able to accept all request types results also

in a higher agent capacity for a-synchronous standard

requests than in the two-level design.

6.1.4. Results for a-synchronous special requestsIn the two-level and back-office design the results

for a-synchronous special requests are similar to the

previous results for a-synchronous standard requests

(Fig. 10). Only the one-level design has longer average

throughput times than for a-synchronous standard

requests which can be explained by the specialist

occupation with standard requests as described in

the previous paragraph for synchronous special

requests.

6.2. Integrated versus separated communicationchannels

The second series of experiments compares inte-

grated and separated communication channels for the

process designs one-level, two-level and back-office.

The input data for these experiments comes from

scenario S0 which is described in Section 5. Before

performing the experiments it has been expected that

integrated channels lead always to less lost calls than

separated channels, because bigger agent groups

Fig. 8. Percentage of lost special calls with integrated channels for one-level, two-level and back-office design (indicator shows 95%

confidence interval).


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reduce the average waiting time according to the

pooling effect described above. But this presumptiondid not hold in true in every case.

6.2.1. Results for synchronous requests

The comparison of separated and integrated com-

munication channels in Fig. 11 is based on the dif-

ference between the accompanying lost call rates

which is presented on the primary axis. A positive

value means that separated channels lead to more lost

calls than integrated channels. On the secondary axis

the difference between agent capacities is represented.

For standard synchronous requests the results areintuitive accessible. The reduction of one generalist in

the one and two-levels design leads to more lost calls.

The capacity reduction can be compensated better in

the one-level design, because in this design also

specialists are available for accepting incoming calls.

Since no reduction of generalists takes place in the

back-office design the lost call rates hardly differ from

each other.

In the case of special synchronous requests the

agent capacity is reduced by three specialists in every

design. This reduction leads to more lost calls both in

the one- and two-levels design. Surprising is the factthat the lost call difference is bigger in the one-level

design than in the two-level design. This observation

may be explained by the additional specialist occupa-

tion with standard requests which has already been

discussed in Section 6.1.

Unexpected is also the result for the back-office

design where separated channels result in less lost

calls than integrated channels. This may be explained

with a competition effect which operates contrarily to

the pooling effect. In the case of integrated commu-

nication channels an agent handles a-synchronousrequests as soon as no synchronous request exists in

the queue. During the processing time the agent is

occupied and cannot accept a new synchronous

request. Therefore synchronous and a-synchronous

requests compete for the same agents and disadvan-

tages arise for accepting synchronous requests.

6.2.2. Results for a-synchronous requests

Fig. 12 shows the differences between average

waiting times of separated and integrated communi-

Fig. 9. Average throughput time for a-synchronous standard requests with integrated channels for one-level, two-level and back-office design

(indicator shows 95% confidence interval).


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cation channels on the primary axis. Differences in

agent capacities are represented on the secondary axis.For handling a-synchronous standard requests with

separated channels only one generalist is available in

the one- and two-levels design. This fact is reflected by

a higher average waiting time in the two-level design

whereas in the one-level design the capacity reduction

can be compensated by specialists.

In the back-office design the reduction of specialists

leads to longer average waiting times for standard and

special requests. The differences are similar to the

values for special requests in the two-level design since

theagentcapacitiesarealsosimilar.Onlyintheone-leveldesign the drawbacks of separated channels can be

better reduced by flexible utilization of specialists.

7. Discussion

7.1. Competing effects

Within our experiments we identified three compet-

ing effects: (1) agent pooling, (2) task consolidation

and (3) task competition. Fig. 13 shows one example

where these effects are presented.Through agent pooling (1) the number of resources

for one task is increased. In our case specialists are

used for classifying and handling standard requests

(dotted line) additional to the already available gen-

eralists. Agent pooling leads to bigger resource groups

and therefore to a better performance for standard

requests. Since the total number of agents remains the

same, less resources are available for special requests.

The tasks classify handle standard requests andhandle special requests compete for the same

resource specialists. This task competition (3) leadsto a worse performance for special requests.

Task consolidation (2) means that two tasks which

have been performed by different agent groups before

are consolidated and handled by one agent group

afterwards. In our example the tasks classify and

handle are consolidated for special requests and

handled by specialist agents (dotted lines). This con-

solidation leads to less handling time and therefore to a

better performance for special requests. Contrary to

this acceleration the specialists have an additional

Fig. 10. Average throughput time for a-synchronous special requests with integrated channels for one-level, two-level and back-office design

(indicator shows 95% confidence interval).


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work load through the classification and have less

capacity for handling special requests. The tasks

classify and handle compete for the same

resources which reduces the number of handled tasks

and therefore the overall performance for special

requests.

7.2. Strengths and weaknesses per

qualification-mixture

Table 8 gives a rough summary of the identifiedstrengths and weaknesses per qualification-mixture

(see Section 6.1). The one-level design is very good

for handling standard requests because of pooling

generalists and specialists. The design has weaknesses

for synchronous special requests in overload situations

and for special a-synchronous requests since specia-

lists are additional occupied with standard requests.

The back-office design has strengths in handling a-

synchronous requests since specialists are reserved for

these requests. Classify synchronous requests is the

weakness of the back-office design because of a small

generalist group. The two-level design does badly for

synchronous and a-synchronous requests.

7.3. Strengths and weaknesses per

communication-mixture

The integration of communication channels is in

most cases more efficient than the separation (Table 9).

Only in the back-office design the integration of

communication channels leads to worse performancefor special synchronous requests which can be

explained through task competition between handling

synchronous and a-synchronous requests.

7.4. Feedback from practice

The simulation study has been presented and dis-

cussed with communication center professionals in

order to get feedback from practice concerning the

applied method and the obtained results.

Fig. 11. Differences between separated and integrated communication channels for different qualification-mixtures and synchronous request

types.


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Regarding the methodology the simulationapproach has been seen as very useful for this domain

which gets more and more complex. So far process

design and capacity planning is mainly done by

computer tools based on simple queueing formulas

in combination with rough hand calculations and thetry-and-error approach which often leads to wrong

results and is not longer suitable for analyzing and

controlling the complexity of modern communication

centers. As substantial drawbacks of simulation have

Fig. 12. Differences between separated and integrated communication channels for different qualification-mixtures and a-synchronous request

types.

Fig. 13. Competing effects.


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been mentioned the high effort (model building, data

gathering and performing the experiments), the lack of

automatic optimization and the focus on quantitative

measures.

The discussion of the results showed that the per-

formance relations between multiple request typessharing the same resources have been underrated by

most of the experts. Therefore the competition effect

has not been expected before. Also the clear disad-

vantages of the two-level design regarding standard

requests have been surprising. Altogether a strong

need for more knowledge about generic process

designs in communication centers came to light and

the request for the extension regarding skill based

routing concepts and overflow strategies.

8. Summary and conclusions

In this paper, we presented generic service process

designs for handling customer requests within a com-

munication center. The process characteristics which

are relevant in this domain are the level of difficulty

(standard versus special requests) and the communi-

cation channel (synchronous versus a-synchronous).

The design dimensions are task allocation to general-

ists and specialists, front-office and back-office role

and degree of integration of synchronous and a-syn-

chronous requests.

The generic process designs have been evaluated in

a case study with empirical data from a car rental

company. Simulation models have been built for every

design and scenarios have been defined for a experi-

mental performance analysis. The analysis showed

strengths and weaknesses of the process designs under

different conditions. Based on these results we devel-

oped a model of competing effects which helps us to

understand the complex dependencies within the dif-

ferent service process designs.

Our experimental approach is a starting point for a

systematic analysis of service processes within the

communication center. We identified important design

dimensions for this domain and derived assumptions

about the cause and effect relationship for genericprocess designs. These findings show the way for

further research activities.

1. Since our simulation results reflect the character-

istics of generic designs for one company under

specific conditions it is necessary to analyze

other domains and other scenarios in the future

in order to examine and enhance the results. The

described evaluation procedure can be used for

this purpose.

2. The presented analysis is focused on quantitativeperformance measures and disregards qualitative

measures. Qualitative measures like conversation

quality or customer satisfaction are very import

for the overall performance but if no agent is

accessible no conversation takes place and the

customer could not be satisfied at all. So quanti-

tative measures are the basis but not sufficient for

an overall evaluation of organizational designs.

Qualitative measures have to be included in

further extensions of the approach.

Table 8

Strengths and weaknesses per qualification-mixture

Qualification-mixture Standard synchronous Standard a-synchronous Special synchronous Special a-synchronous

One-level Pooling Pooling Pooling consolidationcompetition

Competition

Back-office Pooling Pooling Pooling PoolingTwo-level Pooling Competition Pooling Competition

() Strength; () weakness.

Table 9

Strengths and weaknesses per communication-mixture

Communication-

mixture

One-level Back-office Two-level

Integrated Special synchronous A-synchronous

Separated Special synchronousA-synchronous

() Strength; () weakness.


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3. The evaluated generic process designs reflects

basic characteristics of communication center

processes. In the future the analysis should be

enhanced by further important design character-istics, like sophisticated skill based routing con-

cepts, overflow strategies, call routing between

different locations, integration of outbound activ-

ities and integration of outsourcing provider.

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Michael Zapf studied business adminis-

tration and mathematics. After his

diploma he worked as a research and

teaching assistant in information systemsat the Universities of Bayreuth and

Mannheim.He focused his research work

on the analysis of business processes

especially for communication centers

and conducted simulation studies in

cooperation with different German com-

panies. In 2001 he received his PhD in

information systems from the University of Bayreuth. As senior

consultant he continued his career path with national and interna-

tional projects in the field of business process design and CRM

software. At present Michael Zapf is responsible for business

process management at a world-wide operating industrial enterprise.


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