iBusiness, 2013, 5, 36-40
http: //dx.doi.org/10.4236/ib.201 3.53B008 Published Online September 2013 (http://www.scirp.org/journal/ib)
Simulation Based Prospective Productivity Assessment of
Complex Services
Andreas Petz, Sönke Duckwitz, Susanne Mütze-Niewöhner, Christopher M. Schlick
Work Organization Department, Chair and Institute of Industrial Engineering and Ergonomics at the RWTH Aachen University,
Aachen, Germany.
Email: a.petz@iaw.rwth-aachen.de
Received May, 2013
Although services play a crucial role in developed countries, service productivity management is not as much re-
searched as productivity management in production systems. Especially complex, knowledge inten sive service systems
have specific characteristics that have to be considered, in order to comprehensively evaluate their performance. A
novel productivity model considering quantitative as well as qualitative aspects from both customer and provider side
lays the foundation for a simulation based prospective productivity assessment of complex services. The paper con-
cludes by presenting a use case scenario from the chemical industry.
Keywords: Complex Service Productivity; Key Performance Indicators; Simulation
1. Introduction
Services, especially complex, knowledge intensive services
are gaining more and more importance not only because
of the high workforce rate in this sector but also due to
their cont ribut ion on grow th and w elf are in emerg ing an d
developed countries. In Germany, the gross added value
in this sector is growing slower than the workforce rate
and offers an evidence for slower productivity gains in
comparison to the production sector [13]. Although ser-
vices play an important role, service productivity man-
agement is not as much investigated as productivity
management in the production industry mostly due to the
specific characteristics of services and due to a meas-
urement mismatch [4,13].
1.1. Complex, Knowledge Intensive Services
Defining services is a challenging task. The most suitable
approach is given by Donabedian and Hilke [1,16]. Ac-
cording to them, services can be specified from three
dimensions: the potential, the process and the outcome
dimension. From the potential perspective a service is
defined as the availability of resources for service provi-
sion or the willingness to perform a specific service. In
the process dimension, services are defined as joint or
standalone activities during the service provision. Finally,
from the outcome perspective, services are defined as the
material or immaterial result of the service prov ision.
The traditional definition is supp lemented by generally
accepted service specific characteristics known as the
IHIP characteristics: Intangibility, Heterogeneity, In-
separability and Perishab ility [9].
Following this three dimensional differentiation, com-
plex services such as research and development or con-
sultancy are characterized by a high degree of these spe-
cific attributes and are usually organized in projects. In
the potential dimension the amount of the composing
elements (input factors), their heterogeneity as well as
their interrelations are highly time and space dependent
resulting in an increase of complexity [10,11]. In the
process dimension an intensive customer to service pro-
vider interaction and thereby the induced uncertainty and
changeability of the process over time (dynamics) in-
creases complexity. Finally, the outcome is also hetero-
geneous, vague beforehand and the service perception is
highly immaterial as an evidence of high complexity.
1.2. Service Productivity
The traditional productivity definition as a ratio of vol-
umes of output and input has its origins in the assessment
of isolated production systems [6]. Thus, service inherent
characteristics like customer integration and the simulta-
neity of production and consumption (service perishabil-
ity) are not considered at all. In complex service systems
the simultaneity is considered especially when exchang-
ing information in terms of communication or collabora-
tion. Some scholars tried to close this gap by developing
service specific productivity models with focus on some
Copyright © 2013 SciRes. IB
Simulation Based Prospective Productivity Assessment of Complex Services 37
specific aspects[2,5,12].
In order to enhance these concepts, the authors of this
paper conducted empirical studies and identified success
factors and phase specific value drivers for a comprehen-
sive definition of service productiv ity [3]. Based on these
findings, a novel p roductivity model was develope d. The
novel productivity model considers the influence of ser-
vice provider as well as customer from an objective,
quantitative (directly quantifiable measures e.g. costs) as
well as subjective, qualitative (indirectly quantifiable
measures e.g. communication) point of view. Further-
more it defines partial productivities with regard to ser-
vice effectiveness and efficiency in every single service
The conceptual model is static in its nature and does
not completely fulfill the need for a prospective assess-
ment particularly due to the dynamic characteristic of
services. Simulation is considered to be a suitable solu-
tion for a dynamical analysis and assessment of a service
2. Methodology
The service system modeling and simulation approach
aims to bridge the gap b etween traditional manufacturing
productivity management and knowledge worker pro-
ductivity management. Therefore, based on the novel
service productivity model, a service process simulation
tool as decision support for service planners and manag-
ers is developed.
2.1. Service Process Modelling
In order to prospectively evaluate a service system or a
single service provision it is prerequisite to represent the
underlying system in a formal manner as a partial repre-
sentation of the real system [7,8]. Consequently the ser-
vice system model should build up upon the service defi-
nition in all relevant dimensions considering also a role
specific differentiation (at least customer and service
Furthermore, it is important to consider only relevant
attributes of the modeled en tities. In the potential dimen-
sion we focus on human resources as input factors due to
their high degree of influence within knowledge inten-
sive services. Human resources are characterized by their
availability, suitability for given roles as well as costs per
hour. In the process dimension the service process is
mapped by tasks and their parameters in terms of tempo-
ral effort, interrelations (communications effort) and ad-
ditional effort due to an overlapping processing of the
tasks. Finally, the outcome dimension is represented by
the simulation results presenting the overall process
schedules and costs, as well as resource allocation tables.
The actual representation is done by using the Design
Structure Matrixes (DSM) method for modeling complex
systems, their elements and their interdependencies [14].
2.2. Service Process Simulation
A Monte-Carlo simulation environment was developed
and implemented in MATLAB. It is an adaptation of the
simulation approach developed by Gärtner for new de-
velopment projects in the automotive industry [15].
After the parameterization of the DSM-model, simula-
tion runs can be performed. The simulation algorithm
starts with the determination of the normal temporal ef-
fort of the service tasks and the availability of the work-
ing persons by random value generation through the
latin-hypercube sampling method. The normal temporal
effort and the availability are inpu t variables modeled by
statistical triangular distribution s.
A working person is associated to a working task
based on his availability and his suitability for the re-
quired role. There are three different assignment strate-
gies implemented so far: random, best fit (in terms of the
worker’s competence to the required role) and in a given
order. Based on the assignment of the working persons to
a specific task, the final effort for processing the given
task is computed. This final task effort is also dependent
on the head count of the assigned persons to the task as
well as their interactions in terms of communication and
collaboration effort. The task flow is determined by the
allowable degree of task overlapping as well as their in-
terrelations according to the DSM. If the starting crite-
rion is fulfilled, the activated task is executed and avail-
able working persons are associated for processing the
given task. Furthermore, iterations are also considered by
their probability of occu rren ce. The cu stomer influ ence is
considered by a customer specific degree of task accu-
racy. Finally, the durations of the tasks and the resulting
service schedule are computed.
2.3. Prospective Service Assessment
Table 1 presents an excerpt of the implemented key per-
formance indicators in accordance to the novel produc-
tivity model. These indicators refer to partial productiv-
ities of the underlying service system.
Table 1. Key performance indic a tor s.
Service Dimensions
Partial service
productivities Potential Process Outcome
Efficiency Capacity
utilization Task effort Overall costs
Effectiveness Degree of task
incompleteness Task duration/
planned duration Adherence to
Copyright © 2013 SciRes. IB
Simulation Based Prospective Productivity Assessment of Complex Services
Copyright © 2013 SciRes. IB
3. Case Study probability after each iteration loop.
In this case study the ten tasks are assigned to four
different worker roles (T1: project leader, T2: project
controller/financial officer, T3 and T8: specialist process
engineering, T4 – T7 and T9 – T10: chemical process
engineer) and 30 workers from the company are consid-
ered to be suitable for working on this project, each of
them covering different roles with different levels of
competence and availability. Thereby, the availability of
the workers is limited due to commitments in other pro-
jects or holidays.
3.1. Study Design
The case study presented in this paper refers to the early
planning phases of a chemical facility. This section of the
project consists of ten tasks representing the preparatory
work on information gathering and the generation of ba-
sic data for the two following phases “basic engineering”
and “detail engineering”.
The structure of the project’s tasks is given in the
DSM in Figure 1. In this DSM, entries on the principle
diagonal describe the normal temporal effort (effort to be
performed by one average skilled worker) that has to be
provided to fulfill the respective task in form of a trian-
gular distribution (e.g. T4 best case=6 hours, most likely
case=13 hours, and worst case=15 hours), while entries
below the diagonal of the matrix describe the minimum
degree of completion of predecessor tasks that is neces-
sary to start the task (e.g. T5 can be started once T4
reaches a degree of completion of 20%). Entries above
the diagonal represent possible iterations in the process.
In this case study after each of the other nine tasks addi-
tional effort has to be spen t on the task T1 “Project man-
agement” to consolidate the information for the follow-
ing project pha ses.
3.2. Simulation Results
Based on the previously defined data, a simulation study
is carried out. The study aims to assess the duration and
costs of the proposed project and defines a set of valid
project structures as decision support for the project
manager. The results of 1000 simulation runs have been
analyzed and are presented in the following.
In the first step, a most likely assignment of the ten
project tasks to workers according to their suitability is
given in Figure 2. According to their engagement the
simulated capacity utilizatio n can be computed.
The corresponding Gantt-diagram presenting task du-
rations of the project is depicted in Figure 3, showing the
overlapping of the tasks and the iterations of T1 “Proj ect
In addition to the parameters given in Figure 1, further
variables are considered in form of a multidimensional
input-DSM not visualized here. The amount of rework or
additional effort resulting from an overlapping of tasks
has to be defined as well as the decrease of the iteration The cloud plot in Figure 4 presents the frequency of
occurrence of different cost-d urat i o n c ombinations.
T 1T 2T 3T 4T 5T 6T 7T 8T 9T 10
T 1Projec t management3/8/151.0 1.0 1.0 1.0 1.0 1.01.0 1.0 1.0
T 2Pro jec t assignment / cust omer order0.5 1/2/15
T 3Kick- Off0.1 1/3/5
T 4Gathering/ pro cessing Info rmation0.1 0.16/13/15
T 5Defining streams0.2 6/13/15
T 6Calculating0.20.2 6/13/15
T 7Meetings with the customer/
job-site inspection0.2 0.2 0.210/30/35
T 8Reporting0.2 0.2 0.2 0.2 0.210/13/17
T 9Traveling1.0 1.0 1.0 1.0 1.0 0.21/5/7
T 1 0Do cumentation1.0 1.0 1.0 1.0 1.0 1.01/2/3
Figure 1. DSM representing the case study’s project tasks.
Simulation Based Prospective Productivity Assessment of Complex Services 39
Figure 2. Most likely assignment of tasks to workers (WPxxx) based on the working persons suitability and availability for
the given task.
Figure 3. Gantt-chart of the project plan.
Figure 4. Duration and costs of different project settings.
Copyright © 2013 SciRes. IB
Simulation Based Prospective Productivity Assessment of Complex Services
Based on this information, the project duration is ap-
proximately between 33 and 48 days, the most likely
duration is 41 days. The costs of the project vary between
17 and 25 thousand Euros and are most likely 21 thou-
sand Euros. These results represent valuable information
for the project planner. He is able to assess the risk of the
project and select a project organization that will with a
high probability lead to a successful project outco me.
4. Outlook
In order to verify and validate the conceptual productiv-
ity model and the simulation system broader studies will
be conducted. Furthermore, more detailed use cases from
our project partners will be gathered and will assist to
prove the usefulness of a dynamical assessment of ser-
vice productivity. This will enable service planners and
managers to take reliable decisions based on a system-
atical support.
As a further enhancement an optimization algorithm
will be developed and connected to the simulation tool.
This will close the loop by identifying optimal organiza-
tional settings under given circumstances of uncertainty.
5. Acknowledgements
The research is funded by the German Federal Ministry
of Education and Research (BMBF), Project: ProLoDi,
according to Grant No. 01FL10050, Research Program
“Productivity of Services” supervised by the Project
Management Agency of the German Aerospace Center
(PT-DLR). The authors would like to express their grati-
tude for the support given.
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