=Paper=
{{Paper
|id=Vol-2403/paper1
|storemode=property
|title=A Method for Business Process Model Analysis and
Improvement
|pdfUrl=https://ceur-ws.org/Vol-2403/paper1.pdf
|volume=Vol-2403
|authors=Andrii Kopp,Dmytro Orlovskyi
|dblpUrl=https://dblp.org/rec/conf/icteri/KoppO19
}}
==A Method for Business Process Model Analysis and
Improvement==
https://ceur-ws.org/Vol-2403/paper1.pdf
A Method for Business Process Model Analysis and
Improvement
Andrii Kopp[0000-0002-3189-5623] and Dmytro Orlovskyi[0000-0002-8261-2988]
National Technical University “KhPI”, Kyrpychova str. 2, 61002 Kharkiv, Ukraine
{kopp93, orlovskyi.dm}@gmail.com
Abstract. Since business process modeling is considered as the foundation of
Business Process Management, it is required to design understandable and mod-
ifiable process models used to analyze and improve depicted business process-
es. Therefore, this article proposes a method for business process model analy-
sis and improvement. The lifecycle of Business Process Management from
business process modeling to applying the Business Intelligence and process
mining techniques is considered. Existing approaches to business process model
analysis are reviewed. Proposed method is based on best practices in business
process modeling, process model metrics, and corresponding thresholds. The
usage of business process model metrics and thresholds to formalize process
modeling guidelines is outlined, as well as the procedure of business process
model analysis and improvement is shown. The application of Business Intelli-
gence techniques to support the proposed method is demonstrated.
Keywords: Business Process Management, Business Process Modeling, Pro-
cess Model Analysis, Process Model Improvement.
1 Introduction
Today Business Process Management (BPM) is one of the most popular management
concepts. It is based on the set of methods and tools used to design, analyze, improve,
and automate organizational business processes. In its turn, business process is a
structured set of activities that takes one or more kinds of input and produces a prod-
uct or service valuable for a particular customer [1].
According to professor van der Aalst [2], BPM combines knowledge from infor-
mation technology and knowledge from management sciences and applies this to
operational business processes. It has received considerable attention over the last
decade due to its potential for significantly increasing productivity, saving costs, and
reducing flow-time. Business Process Intelligence (BPI) is a concept that can be de-
scribed as the application of Business Intelligence (BI) techniques in BPM in order to
understand organization’s business processes [3]. BI tools are used to integrate trans-
actional data generated by business processes in a Data Warehouse (DWH) and con-
solidate this raw data into Key Performance Indicators (KPIs) that serve as basis for
business process improvement decisions [4].
The fundamental technique of BPM is business process (process) modeling. It is
used to understand, document (e.g., for instructing people), analyze (e.g. to find errors
and measure performance), and improve the business processes they describe [2].
Therefore, it is required to design such business process models that can be easily
understood and modified both during business process execution and the transfor-
mation from “as-is” to “to-be” according to improvement decisions obtained by appli-
cation of BPI.
The object of this research is the procedure of business process structure design
and analysis using various modeling notations. The subject of this research is devel-
opment of the method for business process model analysis and improvement. The aim
of this research is to eliminate violations of business process model correctness that
affects its understandability and modifiability.
2 Related Work
2.1 Business Process Management Lifecycle
According to [2], the BPM lifecycle includes steps related to business process model-
ing, implementation, and monitoring (Fig. 1).
Fig. 1. BPM lifecycle
Business process modeling. Business process models are widely used in documenta-
tion of business operations. There are various business process modeling notations of
different perspectives for this purpose [5]. Latest survey demonstrates that Business
Process Model and Notation (BPMN) models are used by 64% of organizations that
support BPM initiative. Event-driven Process Chain (EPC) models are used by 18%
of respondents, while IDEF-based techniques IDEF0 and Data Flow Diagram (DFD)
are used by 4% of survey participants [6].
Analysis and improvement of business process models. The main goal of business
process modeling is to provide high quality diagrams that show understandable and
modifiable structure of described business process. This goal might be achieved by
applying Plan-Do-Check-Act (PDCA) method for the control and continuous im-
provement of business process models designed during BPM projects (Fig. 2) [7].
Fig. 2. Control and continuous improvement of business process models
Storage for business process models. Reuse of business process models is a way to
reduce the cost of modeling business processes from scratch by using existing process
models. A process model repository offers a central location for collecting and shar-
ing process knowledge for future reuse [8]. Repository is a specialized, extensible
database application that adds value to a database system by being tailored to a specif-
ic domain. A business process model repository enables stakeholders to retrieve pro-
cess models for understanding business operations; updating, simulating and analyz-
ing business process models; and reusing process models [9].
Interchange of business process models. Although there is no standardized formats
to interchange process models described using IDEF0 and DFD notations, there was
an attempt by Mendling and Nuttgens [10] to provide XML-based interchange format
for EPC models. The ARIS Markup Language (AML) is the proprietary file format
the ARIS Toolset uses when a model is exported to a file. However, the AML is non-
EPC specific format [10]. The XML Process Definition Language (XPDL) is the for-
mat proposed by the Workflow Management Coalition (WfMC) to interchange pro-
cess definitions between different modeling tools and management systems [11]. The
XPDL is widely used as the file format for exchange of BPMN diagrams. However,
BPMN 2.0 notation introduced its own XML-based interchange format.
Business process intelligence and process mining. As the application of BI tech-
niques to BPM, BPI refers to methods that use event data to support decision making
in the field of business processes, e.g., Business Activity Monitoring (BAM) and
Complex Event Processing (CEP). Nevertheless, even mature data mining capabilities
offered by BI tools are not process-centric, i.e., the focus is on data and local decision
making, rather than end-to-end processes [12]. Thus, the process mining is proposed
to bridge the gap between BI and BPM. The goal of process mining is to automatical-
ly generate a business process model using process-related event data [2, 12]. Howev-
er, business process models discovered from event data still should be understandable
and modifiable for its further use in BPM lifecycle, e.g., to check the conformance of
a given model by comparing it with reality.
2.2 Business Process Model Analysis
Authors of research [13] have analyzed several approaches to make a business pro-
cess model understandable, reliable, and reusable. They classified them based on their
main research topic (Fig. 3):
Approaches focused on improving business process design through the suggestion
of modeling guidelines.
Approaches which identify business process model metrics to evaluate model cor-
rectness.
Approaches which establish thresholds for the identified metrics.
Fig. 3. Current state-of-the-art of business process models analysis
Process modeling guidelines. Process modeling guidelines (7PMG) by Mendling et.
al. are supposed to guide the modeler in designing understandable models that are less
prone to errors. Guidelines recommend to use as few elements as possible (G1), min-
imize the degree of elements (G2), use one start and one end event (G3), make sure
that every split connector matches a respective join connector of the same type (G4),
avoid OR split and join connectors (G5), use verb-object activity labels (G6), and
decompose the model if it has too many elements (G7) [13, 14].
Process model metrics. Rolon et. al. [15] have proposed metrics (e.g., total number
of sequence flows, events, gateways etc.) based on software metrics to evaluate the
complexity of BPMN models. Cardoso [16] has proposed metric to measure the com-
plexity of BPMN-based process models from a control flow perspective. Mendling et.
al. [17] have proposed complexity metrics for EPC models used to predict errors by
applying a logistic regression model. As for IDEF0 and DFD models, a balance coef-
ficient is used to measure unevenness of arcs distribution in process diagrams [18].
Thresholds for process model metrics. Sanchez-Gonzalez et. al. [19] and Mendling
et. al. [20] have identified thresholds for structural metrics (e.g., number of elements,
gateway mismatch, density, connectivity, control flow complexity etc.) by analyzing
their impact on the complexity and error probability of business process models.
As the result of the review, we can conclude that existing approaches were designed
separately by different authors with various visions. Therefore, it is quite difficult to
find correspondence between certain guidelines, metrics, and thresholds. Also there is
lack of approaches used to provide recommendations on business process model im-
provement, e.g., which nodes to add, remove or replace, or how to interconnect them,
etc. Thus, a method outlined in this paper is intended to fill this gap.
3 Business Process Model Analysis and Improvement Method
3.1 Formalization of Business Process Modeling Best Practices
According to Mendling et. al. [17] a business process model might be formalized as a
coherent, directed graph:
(1)
Where:
─ is the set of nodes which represent various elements of the
business process model described using pairwise disjoint and finite subsets: func-
tions , events , connectors , and other notation-specific elements (e.g., data
stores and external entities for DFD models, and interfaces for
IDEF0 models);
─ is the subset of connectors which, in its turn, consists of the subsets of
split and join connectors;
─ is the mapping that defines types of connectors;
─ is the binary relation A that represents arcs of the process model.
Use as few elements as possible or decompose the model if it has too many ele-
ments. It is not recommended to use more than 31 elements in EPC and BPMN, 7
elements in DFD, and 6 elements in IDEF0 diagrams. At the same time, IDEF0 dia-
grams must consist of at least 3 functions, while other process models (EPC, BPMN,
and DFD) – of at least 1 function [14, 18]:
(2)
(3)
Minimize the degree of an element in the business process model. The higher the
degree of elements in the process model the harder it becomes to understand the mod-
el [14]. The following equations based on the balance coefficient [18] might be used
to measure compliance with this guideline:
(4)
(5)
Where:
─ is the balance coefficient of connectors (for EPC and BPMN models);
─ is the balance coefficient of functions;
─ is the -th connector of the business process model;
─ is the -th function of the business process model;
─ is the number of arcs connected to the -th connector;
─ is the number of arcs of -th type connected to the -th function, :
(6)
─ is the recommended number of arcs per connector [13], ;
─ is the recommended number of arcs of -th type connected to the -th func-
tion (one arc of each type for EPC and BPMN models [13], not more than 3 arcs of
each type for IDEF0 and DFD models [18]):
(7)
─ is the required number of arcs of -th type, (this equation defines that it
is possible that functions on IDEF0 diagrams may not have any input arcs [18]):
(8)
Use one start and one end event. According to [13, 14], the number of start and end
events (in EPC and BPMN models) is positively connected with the increase in error
probability, and models that satisfy this requirement are easier to understand:
(9)
Where:
─ is the subset of start events;
─ is the subset of end events.
Make sure that every split connector matches a respective join connector of the
same type. It is required to model business processes as structured as possible. Un-
structured models are more likely to have errors, as well as less understandable [20]:
(10)
Where:
─ is the coefficient of connectors mismatch (for EPC and BPMN models);
─ is the number of split connectors of the -th type;
─ is the number of join connectors of the -th type;
─ is the subset of split connectors;
─ is the subset of join connectors.
It is recommended to avoid OR routing elements. Models that do not have OR split
or join connectors are less error-prone [14, 20]:
(11)
Where is the subset of OR routing elements (for EPC and
BPMN models), both splits and joins.
3.2 Business Process Model Analysis and Improvement Procedure
The main steps of the proposed method for business process model analysis and im-
provement are shown in Fig. 4. The method is based on process modeling best prac-
tices, metrics, and corresponding thresholds described in the previous section. Math-
ematical models are intended to represent optimization problems used
to elaborate recommendations in order to eliminate found violations of business pro-
cess modeling guidelines. Therefore, the following models should be formulated:
Optimization problem used to define structural changes of a process model in
order to obtain desired values for the following metrics .
Optimization problem used to define structural changes of a process model in
order to obtain a desired value for the metric.
Optimization problem used to define structural changes of a process model in
order to obtain a desired value for the metric.
Optimization problem used to define structural changes of a process model in
order to obtain a desired value for the metric.
The underlying idea of the described models refers to parametric optimization. Thus,
they will be used to find best values of , ,
, and respectively.
Fig. 4. Procedure of business process model analysis and improvement
3.3 Applying Business Intelligence Techniques
Implementation of the proposed method requires processing the considerable amounts
of business process models. Hence, we propose the usage of the well-known BI tech-
niques such as integration and consolidation of raw data into metrics which serve as
the basis for management decisions [4]. The proposed data flow (Fig. 5) includes the
following elements:
Data Sources. Some BPM tools use databases to store process models (e.g., Bizagi
Studio uses Microsoft SQL Server Express to store business process data). Besides,
business process models might be stored using XPDL or BPMN 2.0 formats.
Business Process Model Analysis and Improvement. It is required to develop the
software used to extract data from various data sources, calculate metrics, and plan
changes of a business process model structure if it is necessary.
Data Storage. Calculated metrics and planned changes should be stored in a DWH
for analytic purposes. Relational databases (e.g., SQL Server or MySQL) might be
used to build such DWH.
Data Visualization. Visualization (e.g., using Microsoft Power BI) of the DWH
content is necessary to support decisions on business process model correctness.
Fig. 5. Applying BI techniques to support the proposed method
4 Conclusion and Future Work
In this paper we have proposed a method for business process model analysis and
improvement. This method is based on formalization of process modeling best prac-
tices using metrics and corresponding thresholds in order to find and eliminate viola-
tions of business process model correctness that affects its understandability and mod-
ifiability. Existing guidelines and metrics designed mostly for EPC and BPMN mod-
els were extended also for IDEF0 and DFD process diagrams. Future work includes
formulation of the optimization problems used to elaborate recommendations for
business process model improvement, and implementation of the proposed method
(Fig. 4) by using BI techniques and tools as it is shown in Fig. 5. It is also planned to
elaborate an evaluation criteria for the proposed method and apply it to a set of busi-
ness process models described using various modeling notations.
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