=Paper=
{{Paper
|id=Vol-365/paper-17
|storemode=property
|title=Exploring the Dynamic Costs of Process-aware Information Systems through Simulation
|pdfUrl=https://ceur-ws.org/Vol-365/paper17.pdf
|volume=Vol-365
|dblpUrl=https://dblp.org/rec/conf/emmsad/MutschlerR07
}}
==Exploring the Dynamic Costs of Process-aware Information Systems through Simulation==
https://ceur-ws.org/Vol-365/paper17.pdf
Exploring the Dynamic Costs of Process-aware
Information Systems through Simulation
Bela Mutschler and Manfred Reichert
Information Systems Group, University of Twente, The Netherlands
{b.b.mutschler;m.u.reichert}@utwente.nl
Abstract. Introducing process-aware information systems (PAIS) in enterprises
(e.g., workflow management systems, case handling systems) is associated with
high costs. Though cost evaluation has received considerable attention in software
engineering for many years, it is difficult to apply existing evaluation approaches
to PAIS. This difficulty particularly stems from the inability of these techniques
to deal with the complex interplay of the many technological, organizational and
project-driven factors which emerge in the context of PAIS engineering projects.
In response to this problem this paper proposes an approach which utilizes si-
mulation models for investigating costs related to PAIS engineering projects. We
motivate the need for simulation, discuss the design and execution of simulation
models, and give an illustrating example.
Keywords: Cost Modeling, Simulation Models, Method Engineering.
1 Introduction
Process-aware information systems (PAIS) separate process logic from application code
and orchestrate business processes according to their defined logic during run-time
[1]. To enable the realization of PAIS, a variety of process support paradigms (e.g.,
workflow management, service flows, case handling), process modeling standards (e.g.,
BPEL4WS, BPML), and process management tools (e.g., ARIS Toolset, Staffware)
have been introduced [2].
While the benefits of PAIS are typically justified by improved business process per-
formance [3–5] and cheaper process implementation [6], there exist no approaches for
systematically analyzing related costs. In particular, existing cost evaluation techniques
are unable to cope with the numerous technological, organizational and project-driven
factors to be considered in the context of a PAIS (and which do only partly exist in
projects developing data- or function-centered information systems) [7]. As an exam-
ple, consider costs for analyzing and redesigning business processes. Another challenge
results from the many causal dependencies between evaluation factors. Activities re-
lated to business process redesign, for example, can be influenced by impact factors like
available process knowledge or end user fears. These dependencies result in dynamic
economic effects which can influence the overall costs of a PAIS engineering project
significantly. Existing approaches are typically not able to deal with such effects as they
rely on static models based upon snapshots of the analyzed software system.
What is needed is a comprehensive approach that enables system engineers to model
the complex interplay between the cost and impact factors that arise in the context of
PAIS, and to investigate resulting effects. In response to this need we have introduced
the notion of evaluation models in [8, 9]. This paper deals with the simulation of the dy-
namic costs of PAIS engineering projects. Section 2 describes background information
necessary for understanding the paper. Section 3 deals with simulation as envisioned in
our approach. Section 4 concludes with a summary.
2 The EcoPOST Evaluation Framework
In [8, 9] we have introduced a model-based approach for systematically investigating
the complex cost structures of PAIS engineering projects. This approach distinguishes
between different kinds of evaluation factors to be considered when dealing with the
costs of PAIS engineering projects.
Terminology. A Static Cost Factors (SCF) represent costs whose value does not
change during a PAIS engineering project (except for its time value, which is not fur-
ther considered in this paper). As typical examples of SCF consider software license
costs, hardware costs, or costs for external consultants. Dynamic Cost Factors (DCF),
in turn, represent costs that are determined by activities related to a PAIS engineering
project. These activities cause measurable efforts, which, in turn, vary due to the influ-
ence of impact factors. The (re)design of business processes prior to the introduction of
PAIS, for example, constitutes such an activity. The DCF ”Costs for Business Process
Redesign”, for instance, may be influenced by an intangible factor ”Willingness of Staff
Members to support Redesign Activities”. Obviously, if staff members do not contribute
to a redesign project by providing needed information (e.g., about process details), any
redesign effort will be ineffective and will increase costs. If staff willingness is addi-
tionally varying during the redesign activity (e.g., due to a changing communication
policy), the DCF will be subject to more complex effects.
In the EcoPOST framework, intangible factors like ”Willingness of Staff Members
to support Redesign Activities” are represented by Impact Factors (ImF). They are in-
tangible evaluation factors that influence DCF (or more precisely, the activities under-
lying a DCF). ImF cause the value of a DCF to change, making the evaluation of DCF
a difficult task to accomplish. As examples consider factors such as ”End User Fears”,
”Availability of Process Knowledge”, or ”Ability to redesign Business Processes”. Op-
posed to SCF and DCF, the values of ImF are not quantified in monetary terms, but
are based on qualitative scales describing the degree of an ImF (ranging from ”low” to
”high”). ImF can be further classified into static and dynamic ImF. The value of a static
ImF does not change. The value of a dynamic ImF, by contrast, may change (due to the
influence of other ImF).
Evaluation Models. To better understand the evolution of DCF as well as DCF
interference through ImF, we use evaluation models. In particular, each DCF is repre-
sented and analyzed by exactly one evaluation model. These models are specified using
the System Dynamics (SD) [10–12] notation (cf. Fig. 1A) [13]. SCF, DCF, and ImF are
represented by different types of variables. State variables, for example, are used to
represent dynamic factors, i.e., to capture changing values of DCF (e.g., the ”Costs for
Business Process Redesign”; cf. Fig. 1B) and dynamic ImF (e.g., degree of ”Process
Knowledge”). A state variable is graphically denoted as rectangle (cf. Fig. 1B), and its
value at time t is determined by the accumulated changes of this variable from starting
point t0 to present moment t (t > t0 ); similar to a bathtub which accumulates – at a
defined moment t – the amount of water which has been poured into it in the past. Each
state variable needs to be connected to at least one source or sink. Both sources and
sinks are graphically denoted as cloud-like symbols.
A) Notation B) State Variables & Flows C) Using Auxiliary Variables as Intermediate Variables
Dynamic Cost Factors
Cost Costs for Cost Process
Process
Dynamic Impact Factors Increase Business Decrease Analysis SCF1
Knowledge
Process - + Costs +
Static Cost Factor [Text] Process
Redesign
Process Modeling Definition SCF2
+ Auxiliary
Static Impact Factor [Text] Ability to redesign Variable
DCF Costs Costs
Sources and Sinks
Controls Controls Business -
the Inflow the Outflow - Processes ImFS
Rate Variables -
Auxiliary Variables [Text] Water Water +
Tap Drain Business Process
Links [+|-]
Cost Increase Redesign Costs Cost Decrease
Flows
Fig. 1. Evaluation Model Notation and initial Examples.
Values of state variables change through inflows and outflows. Graphically, both flow
types are depicted by twin-arrows which either point to (in the case of an inflow) or
out of (in the case of an outflow) the state variable (cf. Fig. 1B). Picking up the bathtub
image, an inflow is a pipe that adds water to the bathtub, i.e., inflows increase the value
of a state variable. An outflow, by contrast, is a pipe that purges water from the bath-
tub, i.e., outflows decrease the value of a state variable. The DCF ”Costs for Business
Process Redesign” as shown in Fig. 1C, for example, increases through its inflow ”Cost
Increase” and decreases through its outflow ”Cost Decrease”. Returning to the bathtub
image, we further need ”water taps” to control the amount of water flowing into the
bathtub, and ”drains” to specify the amount of water flowing out. For this purpose, a
rate variable is assigned to each flow (graphically depicted by a valve; cf. Fig. 1B).
In addition to state variables representing DCF and dynamic ImF, evaluation models
comprise constants and auxiliary variables (which are both graphically represented by
their name). Constants are used to represent static evaluation factors, i.e., SCF and static
ImF in our context. As an example for a SCF consider license costs. As an example for
a static ImF consider a given degree of ”Process Complexity”. Auxiliary variables, in
turn, represent intermediate variables. As an example consider the auxiliary variable
”Process Definition Costs” in Fig. 1C. Both constants and auxiliary variables are em-
bedded in an evaluation model with links (not flows), i.e., labeled arrows. A positive
link (labeled with ”+”) between x and y (with y as dependent variable) indicates that
y will tend in the same direction if a change occurs in x. A negative link (labeled with
”-”) expresses that the dependent variable y will tend in the opposite direction.
Illustrating Example. Fig. 2 shows a model which describes the influence of the
dynamic ImF ”End User Fears” on the DCF ”Costs for Business Process Redesign”.
More specifically, this model reflects the assumption that the introduction of a PAIS
may cause end user fears, e.g., due to a high degree of job redesign and due to changed
social clues. Such end user fears can lead to emotional user resistance. This, in turn,
results in a decreasing ability to acquire process knowledge. Reason is that an increas-
ing emotional resistance makes profound process analysis (e.g., based on interviews
with process participants) a difficult task to accomplish. A decreasing ability to acquire
process knowledge results in a decreasing ability to redesign business processes.
Illustrating Example: The Impact of „End User Fears“ on „Costs for Business Process Redesign“ Notation
Dynamic Cost Factors
Impact due to Ability to redesign Ability to acquire
Degree of Job Job Redesign Business Processes Process Dynamic Impact Factors
Redesign + Knowledge Static Cost Factor [Text]
+ + Resistance
Decreasing Ability to Static Impact Factor [Text]
Impact due to Changes Fear + Growth Rate redesign Business Increasing Ability
Sources and Sinks
concerning Social Clue + Growth
+
Processes to acquire
and Interactions
Rate - Process Rate Variables
Knowledge
Auxiliary Variables [Text]
+ End User Emotional
Links [+|-]
Fears Resistance
Flows
Communication
Change of Fear Reduction Growth Rate
Social Clue and Rate + Costs for Business
Interactions + Process Redesign
Communication
Cost Rate
Fig. 2. Dealing with the Impact of End User Fears.
3 Simulating EcoPOST Evaluation Models
Evaluation models, like the one depicted in Fig. 2, are very useful for PAIS engineers.
However, the evolution of DCF and dynamic ImF is difficult to comprehend. For this
reason, we add components for analyzing this evolution to our overall evaluation frame-
work. More precisely, this section describes how evaluation models can be simulated in
order to unfold their dynamic effects.
3.1 Understanding PAIS Engineering Projects as Feedback Systems
As mentioned, we use System Dynamics (SD) for defining evaluation models. SD is a
formalism for studying and modeling complex feedback systems, as they can be found,
for example, in biological, environmental, industrial, business, and social systems [10,
11]. Its underlying assumption is that human mind is excellent in observing the elemen-
tary forces and actions out of which a system is composed (e.g., fears, delays, resistance
to change), but unable to understand dynamic implications resulting from these forces
and actions. In PAIS engineering projects we have the same situation. Such projects
are characterized by a strong nexus of organizational, technological, and project-driven
factors. Thereby, the identification of these factors constitutes one main problem. Far
more difficult is to understand causal dependencies between factors and resulting ef-
fects. Only by considering PAIS engineering projects as feedback system we are able
to unfold the dynamic effects caused by these dependencies and the different organiza-
tional, technological, and project-driven system parts.
”Feedback” refers to situations in which a factor X (e.g., user fears) affects another
factor Y (e.g., emotional resistance of end users), and factor Y, in turn, affects X (either
directly or indirectly). SD denotes such causal structures (or cyclic chains of causes and
effects) as ”feedback loops” (see below). It assumes that it is not possible to study the
causal dependency between X and Y without considering the entire system.
There are other formalisms that can be used to model complex systems of interact-
ing factors. Causal Bayesian Networks (BN) [14], for example, promise to be a useful
approach in this context as well. BN deal with (un)certainty and focus on determin-
ing probabilities of events. A BN is a directed acyclic graph which represents inde-
pendencies embodied in a given joint probability distribution over a set of variables.
Variables can be measurable or intangible parameters or random variables (which form
the ”Bayesian” aspect of a BN). In our context, we are interested in the interplay of
the parts (components) of a system and the effects resulting from this interplay. BN do
not allow to model feedback loops as cycles in BN would allow infinite feedbacks and
oscillations that would prevent stable parameters of the probability distribution.
Agent-based modeling provides another promising approach. Resulting models com-
prise a set of reactive, intentional, or social agents encapsulating the behavior of the
various variables that make up a system [15]. During simulation, the behavior of these
agents is emulated according to defined rules [16]. System-level information (e.g., about
intangible factors being effective in a PAIS engineering project) is thereby not further
considered. However, as system-level information is an important aspect in our ap-
proach, we have not further considered the use of agent-based modeling.
3.2 Feedback Loops
Changes of DCF and dynamic ImF are caused by the interplay of the different elements
of an evaluation model, i.e., the complex interdependencies between dynamic and static
evaluation factors, flows and links. In this context, feedback loops are of particular im-
portance. A feedback loop is a closed cycle of causes and effects. Within this cycle, past
events (like the change of a DCF or dynamic ImF) are utilized to control future actions
(like another change of the same evaluation factor). In other words, if a change occurs
in a model variable, which is part of a feedback loop, this change will be propagated
around the loop [12].
As an example consider the feedback loop depicted in Fig. 2. Basic to this model
is a cyclic structure connecting the four dynamic ImF ”End User Fears”, ”Emotional
Resistance”, ”Ability to acquire Process Knowledge”, and ”Ability to redesign Business
Processes”. As aforementioned, it reflects the assumption that the introduction of a
PAIS may cause end user fears, e.g., due to a high degree of job redesign. Such end
user fears lead to increased emotional resistance. This, in turn, decreases the ability
to get support from end users during process redesign and thus decreases the ability
to effectively redesign business processes. Finally, a lower ability to redesign business
processes results in decreased end user fears. Reason is that the end users will be less
afraid of change if the ability to redesign processes decreases.
We distinguish between two kinds of loop polarities. Positive loops generate growth
of DCF and dynamic ImF (cf. Fig. 3A). Negative loops, in turn, counteract and oppose
growth (cf. Fig. 3B). If evaluation models contain both positive and negative feedback
loops, more complex effects will result (cf. Fig. 3 C-E).
The polarity of a feedback loop is equivalent to the sign of the open loop gain.
”Gain” refers to the strength of the change returned by a loop and ”open loop” means
A) Exponential Growth B) Goal-seeking Behavior C) Oscillation D) S-shaped Growth
x1I
Maximum Degree Evolution
Evolution
Degree of ImF
of a DCF
of a DCF
Costs
Costs
Costs Evolution
Evolution of of a DCF
a dynamic ImF
time time time time
E) Overshoot and Collapse F) Calculating the „Open Loop Gain“ of a Feedback Loop
1) Break the loop an any point x1 x 1I x 1O
2) Trace the effect of a change around the loop
Evolution
Costs
of a DCF
x2 x4 x2 x4
Polarity = SGN (∂x1 / ∂x1 )
O I
∂x1 / ∂x1 = (∂x1 / ∂x4 )(∂x4 / ∂x3 )(∂x3 / ∂x2 )(∂x2 / ∂x1 )
O I O I
time
x3 x3
Fig. 3. Feedback in Evaluation Models: Overview of potential dynamic Effects.
that the gain is calculated for just one feedback cycle by opening the closed loop at
some point [12]. Consider Fig. 3F which shows a closed feedback loop consisting of
four variables x1 , ..., x4 . Assume that we open the loop at x1 . This splits x1 into an input
variable (x1I ) and an output variable (x1O ). The open loop gain is then defined as the
(partial) derivative of x1O with respect to x1I ; i.e., the feedback effect of a change in
a variable as it is propagated around a loop. Thus, loop polarity can be calculated as
SGN(δx1O /δx1I ), where SGN() is the sign function, returning +1 in case of positive loop
polarity, and -1 otherwise (if the open loop gain is zero, there will be no loop).
It is important to mention that dynamic effects caused by feedback loops are not
easy to understand [11, 17]. In order to systematically investigate their effects in detail,
we simulate our evaluation models.
3.3 Computing a Simulation
In the EcoPOST framework, simulation is based on a step-by-step numerical solution
of algebraic equations, which specify how to perform a simulation from an initial con-
dition and how to compute succeeding conditions [11]. In other words, the equations
define how the variables of an evaluation model change over time.
Illustrating Example. Consider Fig. 4 which depicts the simulation of two dynamic
evaluation factors: a DCF and a dynamic ImF. The condition at time t0 has been calcu-
lated and the condition at time t1 is now being evaluated. DT stands for ”Difference in
Time” and denotes the length of the time interval between two conditions. DCF.t0 and
ImF.t0 designate the two values of DCF and ImF at time t0 (cf. Fig. 4A). R1.[t0 ,t1 [ is a
rate variable specifying the inflow of DCF within the time interval [t0 ,t1 [. Similarly, the
rate variables R2.[t0 ,t1 [ and R3.[t0 ,t1 [ specify the inflow respectively outflow of ImF
within the time interval [t0 ,t1 [. Therewith, all information needed to compute the new
values of DCF and ImF is available.
Within the time interval [t0 ,t1 [, the rate variables act on DCF and ImF and cause
them to change. The new values of DCF and ImF at time t1 are calculated by adding
and subtracting the changes represented by these rates (cf. Fig. 4B). Finishing the com-
A) At time t0... B) At time t1... C) Next Rates taking Effect...
R2.[t0,t1[
R2.[t1,t2[
ImF.t0 ImF.t0
R3.[t0,t1[
ImF.t1 ImF.t1
R1.[t0,t1[
DCF.t1 DCF.t1
R1.[t1,t2[
DCF.t0 DCF.t0
DT DT DT
time time time
t0 t1 t2 t0 t1 t2 t0 t1 t2
Fig. 4. Computing a Simulation Model.
putation creates the situation shown in Fig. 4B. In the following, only these values are
needed to compute the forthcoming rates for the [t1 ,t2 [ interval (cf. Fig. 4C).
Sensitivity Analysis. Note that the numerical solution of equations does not allow
to determine an arbitrary future condition during a simulation without first computing
through all previous conditions. Each step-by-step numerical solution represents one
simulation run with one final condition. In order to determine another condition, an ad-
ditional step-by-step computation has to be conducted. Therewith, it becomes possible
to conduct behavioral ”experiments” based on a series of simulation runs. During these
simulation runs equations are manipulated in a controlled manner to systematically in-
vestigate the effects of changed simulation parameters. Therewith, it becomes possible
to accomplish sensitivity analysis, i.e., to investigate how the output of a simulation will
vary if the initial condition of a simulation is changed.
3.4 Specifying a Simulation Model
In the EcoPOST framework, a simulation model consists of a number of algebraic equa-
tions – one for each model variable (i.e., dynamic and static evaluation factors as well
as rate variables and auxiliary variables). We use different types of algebraic equations
for the different variables of an evaluation model (cf. Fig. 5A).
Elements of a Simulation Model. Static evaluation factors (i.e., SCF and static ImF)
are specified based on numerical values in constant equations (e.g., ”Process Redesign
Costs = 1000 $/Week”). Dynamic evaluation factors (i.e., DCF and dynamic ImF), in
turn, are specified by integral equations [11]. Such equations specify the accumulation
of a dynamic evaluation factor from a starting point t0 to the present moment t (cf. Fig.
5B). More specifically, DCF and dynamic ImF integrate their net flow. The net flow
during any interval [t1 ,t2 ] is the area bounded by the graph of the net rate between the
start and the end of the interval (cf. Fig. 5C). Thus, the value of a dynamic evaluation
factor at t2 can be calculated as the sum of its value at t1 and the area under the net rate
curve between t1 and t2 . In Fig. 5C, the value at t1 is S1 . Adding the area under the net
rate curve between t1 and t2 increases the value to S2 .
Rate variables are specified by rate equations. A rate equation specifies the net
change caused by a particular flow (influencing either a DCF or a dynamic ImF) be-
tween two computed conditions (cf. Section 3.3). Rate equations for DCF-related flows
A) Elements of a Simulation Model B) Specifying Dynamic Evaluation Factors C) Graphical Integration (DCF & dyn. ImF)
Change of the
Constant Integral DCF = Grey Area
Net Rate
DCF*
Equations Equations
Set of
Inflow Outflow
Equations t
Rate Auxiliary DCF (t ) = ∫ [ Inflow( s ) − Outflow( s )]ds + DCF (t0 ) 0 time
Equations Equations t0
where
Value of a DCF
- Inflow(s) represents the value of the inflow at any time s S2
between between the initial time t0 and the current time t.
Equation-based Simulation Model - Outflow(s) represents the value of the outflow at any time s
Change
between between the initial time t0 and the current time t. of the DCF
- DCF(t0) represents the initial value of DCF at t0. S1
Step-by-Step Numerical Solution * also valid for dynamic ImF t1 t2 time
Fig. 5. Integration of Flows for Dynamic Evaluation Factors.
specify the ”amount of costs” flowing to, from, or between DCF. Rate equations for
ImF-related flows specify the ”impact” flowing to, from, or between dynamic ImF. A
rate equation comprises those model variables which influence the flow it controls. This
can be SCF, DCF, dynamic ImF, and auxiliary variables.
Finally, auxiliary variables are specified by auxiliary equations. Their constituting
elements may be static and dynamic evaluation factors as well as other auxiliary vari-
ables. Though the value of auxiliary variables changes during simulation, they do not
represent a model state. Instead, they are used for intermediate calculations.
Nonlinear Relationships. An important part of our evaluation models are ImF. If an
(either static or dynamic) ImF has a nonlinear impact on DCF, such nonlinearities will
have to be represented in our simulation models as well. In our simulation models,
nonlinearities are represented by an additional auxiliary variable between the ImF and
the DCF. This auxiliary variable is specified by a table1 function f transferring an input
value X (e.g., a certain level of process knowledge) into a corresponding output value
Y (e.g., expressing a specific effect on a DCF).
1 2 2
A B C
Impact Impact
Impact Rating Rating
(IR) > 1 (IR) > 1
ImpactDuetoImpactFactor
ImpactonDCFduetoImpactFactor
ImpactonDCFduetoImpactFactor
Impact
Rating
Impact Impact
(IR) < 1
0 1 1
0 Degree of Impact Factor (normalized) 1 0 Degree of Impact Factor (normalized) 1 0 Degree of Impact Factor (normalized) 1
IR = f(x) with x, IRin [0,1] IR = f(x) with x in [0,1] and IR in [1,2] IR = f(x) with x in [0,1] and IR in [1,2]
Fig. 6. Table Functions for quantifying Nonlinear Relationships.
Fig. 6 shows typical table functions. Dependent on the degree of an ImF (represented
by X) a specific impact rating is derived (represented by Y ). An impact rating less than
1 Linear interpolation is used for values lying between the specified table values.
1 results in decreasing costs (cf. Fig. 6A). A rating equal to 1 does neither increase nor
decrease costs. A rating larger than 1 results in increasing costs (cf. Fig. 6B and Fig.
6C). Quantifications based on such impact ratings are also known from software cost
models like COCOMO [18]. Generally, there exists no standard way of building robust
table functions (a ”best practice” guideline is given in [12]).
Empirical and Experimental Research. The expressiveness of simulation results al-
ways depends on the plausibility and resilience of the underlying simulation model.
In particular, the specification of nonlinear dependencies is a difficult task to accom-
plish. In order to be able to build simulation models, it is often inevitable to rely on
hypotheses, sometimes even arguable assumptions.
In response to this problem (i.e., to generate needed data), we have accomplished
various empirical and experimental research activities in the EcoPOST project (e.g.,
software experiments, online surveys, case studies) in order to put our simulation mod-
els on a more reliable basis (see [19] for examples).
3.5 Illustrating Example
Fig. 7A shows a simple2 evaluation model. Assume that the evolution of a DCF ”Busi-
ness Process Redesign Costs” caused by the dynamic ImF ”End User Fears” shall be
analyzed. Such end user fears can lead to emotional resistance of users, and, in turn, to
a lack of support from the users while redesigning business processes, e.g., during an
interview-based process analysis.
Notation A) Evaluation Model B) Simulation Model
Dynamic Cost Factors
BPR Costs + Equations:
Dynamic Impact Factors Impact due to A) BPR Costs per Week[$] = 1000$
per Week End User Fears B) Cost Rate[$] =
Static Cost Factor [Text]
CONSTANT TABLE FUNCTION BPR Costs per Week[$] * Impact due to End User Fears[Dimensionless]
Static Impact Factor [Text] C) Business Process Redesign Costs[$] = Cost Rate[$]
D) Fear Growth = 2[%]
Fear Growth
Sources and Sinks EQUATION E) Fear Growth Rate[%] = Fear Growth[%]
CONSTANT F) End User Fears[%] = Fear Growth Rate[%]
Rate Variables + + G) Impact due to End User Fears = LOOKUP(End User Fears/100)
Auxiliary Variables [Text] Business Process + End User
Cost Rate Redesign Costs Fears Initial Values: Normalization
[+|-] EQUATION
Fear Growth A) Business Process Redesign Costs[$] = 0$
Links Rate EQUATION
EQUATION B) End User Fears[%] = 30%
Flows
C) Computing a Simulation Run D) Graphical Diagramm illustrating Simulation Outcome
TIME Change ($) BPR Costs ($) Cost Rate ($) Change (%) User Fears (%) Costs Business Process Redesign Costs
00 - 0 1000 - 30 60,000
01 1000 1000 1010 2 32
02 1010 2010 1020 2 34 45,000
03 1020 3030 1030 2 36
30,000
04 1030 4060 1040 2 38
05 1040 5100 1050 2 40
15,000
06 1050 6150 1060 2 42
... ... ... ... ... ...
0
30 1840 38300 1900 2 90 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32
31 1900 40200 2020 2 92 Time (Weeks)
Business Process Redesign Costs : without User Fears
32 2020 42220 2140 2 94 Business Process Redesign Costs : with User Fears
Fig. 7. Dealing with the Impact of End User Fears.
Assume that the business process redesign activities are scheduled for 32 weeks. In
order to simulate the evolution of the resulting costs along this time frame, we use the
2 Note that it is the basic goal of this example to illustrate the simulation of our evaluation models. Usually, evaluation
models are more complex. However, due to lack of space we cannot give a more extensive example.
simulation model depicted in Fig. 7B. The nonlinear impact of end user fears on the
DCF is represented through a table function. Fig. 7C shows the values of the evaluation
model’s dynamic evaluation factors over time when executing the simulation model.
Fig. 7D shows the outcome of the simulation. As can be seen, there is a significant
negative impact of end user fears on the costs of business process redesign.
4 Summary
Our paper has illustrated the use of simulation to investigate the dynamic implications
described by EcoPOST evaluation models. We have motivated the use of simulation as a
means to analyze the dynamic effects caused by feedback loops. We have described the
constituting elements of EcoPOST simulation models and have discussed the execution
of simulation models. Finally, we have given an illustrating example.
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