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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.
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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.
M. Zapf / Computers in Industry 55 (2004) 5371 71