Business Process Engine Simulator (original) (raw)

A test-bed for the evaluation of business process prediction techniques

2011

Business process prediction technologies are being increasingly used by organisations to provide timely feedback to their customers and improve their overall productivity. In order to provide valuable information to both customers and system managers, the timings of business processes need to be forecast with high accuracy and efficiency. In particular, organisations require to predict the process and event flows, recognize their patterns, and forecast the total time it would take for a workflow to complete, in order to meet the Service Level Agreements (SLAs) signed with the customers. In this paper, we focus on the prediction models that could be used for forecasting time to completion of business processes by analysing historical event logs. First, we propose a service oriented architecture that provides a test-bed for carrying out predictions on business processes. Second, we propose a Hidden Markov Model (HMM) based prediction technique that produces a model based on event logs, and compare it against existing prediction models. Finally, we describe an implementation of the system, where we simulate the execution of a business process and obtain predictions using both the proposed and existing prediction techniques.

Prediction of Business Process Execution Time

Lecture notes in networks and systems 715, 2022

The use of immense amounts of data on the execution of applications based on business processes can make it possible, thanks to Process Mining, to detect trends. Indeed, human intelligence in decisionmaking is enriched by Machine Learning in order to avoid bottlenecks, improve efficiency and highlight potential process improvements. In this research article, we present a method (BPETPM) for predictive monitoring of business processes. This method allows to predict the execution time of a business process according to the path followed by the process instance. It predicts whether a process instance will run in time or late. We follow the CRISP-DM approach, known in Data Science, to carry out our method. The input data for learning is extracted from the event logs saving the execution traces of the workflow engine of a BPMS. We start by cleaning data, adding additional attributes, and encoding categorical variables. Then, at the modelling level, we test six classification algorithms : KNN, SVM(kernel=linear), SVM(kernel=rbf), Decision Tree, Random Forest and Logestic Regression. Then, using the BPETPM method, we create an intelligent process management system (iBPMS4PET). This system is applied to a process for managing incoming mail in the mutual health sector.

A Cloud-based Service-oriented Architecture For Business Process Modeling And Simulation

2017

The adoption of Modeling & Simulation (M&S) approaches is widely recognized as a valuable solution for enacting a timely analysis of business processes (BPs). Despite their relevance, the effective introduction of such approaches in the BP lifecycle is still limited, due to the know-how and skills for building and implementing a simulation model and to the cost and effort for setting up and maintaining the execution platform. In this respect, this paper proposes a cloud-based architecture that exploits the M&S as a Service (MSaaS) paradigm and containerization technology for the flexible and dynamic composition of M&S services, so to allow business analysts to carry out an effortless and effective M&Sbased BP analysis. An example case study dealing with an e-commerce scenario is also presented in order to show the actual application of the proposed approach. • a containerization method, which allows to wrap services in a self-contained runtime environment, in order to ease their cloud-based deployment and execution. Specifically, this paper adopts Docker [5],

Business Process Simulation: An Overview

Recent times have seen an increasing use of discrete simulation to model and analyse business processes under the banner of Business Process Simulation. However, most of the literature merely justifies the use of simulation or describes case studies. This paper provides an overview of Business Process Simulation. It outlines the differences between the modelling of business processes and manufacturing processes, and discusses the issues arising when developing a business process simulation model. Different approaches that can be adopted when simulating business processes are presented. It then concludes by suggesting a number of requirements for business process simulators as well as avenues for further research.

Comparative Analysis of Different Tools Business Process Simulation

Business process modelling is an increasingly popular research area for both organisations and enterprises due to its usefulness in facilitating better planning of resources, business reengineering and optimized business performance. The modelling and simulation of Business Processes has been able to show Business Analysts, and Managers where bottleneck exists in the system, how to optimize the Business Process to reduce cost of running the Organization, and the required resources needed for an Organization An important part of the evaluation of designed and redesigned business processes is Business Process Simulation (BPS). Although an abundance of simulation tools exist, the applicability of these tools is diverse. In this paper we thrash out a number of simulation tools that are applicable for the BPM field, we estimate their applicability for BPS and formulate recommendations for further research. This paper is limited to analysis three tools that is IBM WebSphere, FLOWer and FileNet (process management); and Arena and CPN Tools (discrete event simulation)) are compared based on the capabilities of modelling, support of simulation and output analysis.

Business Process Simulation Revisited

Computer simulation attempts to "mimic" real-life or hypothetical behavior on a computer to see how processes or systems can be improved and to predict their performance under different circumstances. Simulation has been successfully applied in many disciplines and is considered to be a relevant and highly applicable tool in Business Process Management (BPM). Unfortunately, in reality the use of simulation is limited. Few organizations actively use simulation. Even organizations that purchase simulation software (stand-alone or embedded in some BPM suite), typically fail to use it continuously over an extended period. This keynote paper highlights some of the problems causing the limited adoption of simulation. For example, simulation models tend to oversimplify the modeling of people working part-time on a process. Also simulation studies typically focus on the steady-state behavior of business processes while managers are more interested in short-term results (a "fast forward button" into the future) for operational decision making. This paper will point out innovative simulation approaches leveraging on recent breakthroughs in process mining.

Business Process Simulation for Operational Decision Support

Contemporary business process simulation environments are geared towards design-time analysis, rather than operational decision support over already deployed and running processes. In particular, simulation experiments in existing process simulation environments start from an empty execution state. We investigate the requirements for a process simulation environment that allows simulation experiments to start from an intermediate execution state. We propose an architecture addressing these requirements and demonstrate it through a case study conducted using the YAWL workflow engine and CPN simulation tools.

Advanced Business Simulations -Incorporating business and process execution data

Lecture Notes in Business Information Processing

Key Performance Indicators (KPIs) and their predictions are widely used by the enterprises for informed decision making. Nevertheless , a very important factor, which is generally overlooked, is that the top level strategic KPIs are actually driven by the operational level business processes. These two domains are, however, mostly segregated and analysed in silos with different Business Intelligence solutions. In this paper, we are proposing an approach for advanced Business Simulations, which converges the two domains by utilising process execution & business data, and concepts from Business Dynamics (BD) and Business Ontologies, to promote better system understanding and detailed KPI predictions. Our approach incorporates the automated creation of Causal Loop Diagrams, thus empowering the analyst to critically examine the complex dependencies hidden in the massive amounts of available enterprise data. We have further evaluated our proposed approach in the context of a retail use-c...

Systemic Business Process Simulation using Agent-based Simulation and BPMN

2020

The current paradigm of a business process model is that it is a representation of a sequence of tasks that act upon some data input, to produce an output, aiming the production of a new service or product to be delivered from a producer to a customer. Although this is a valid way of thinking, it neglects to consider in enough detail the influence of some phenomenon on inputs, e.g. human behaviour, communication, social interactions, the organisational culture which can have a significant effect on the output delivered by a business process. As the dynamics of these phenomena are non-linear, they can be interpreted as a complex system. This holistic way of thinking about business processes opens the doors to the possibility of combining different simulation methods to model different aspects that influence a process. A BPMN engine and an agent-based simulation (ABS) engine are chosen to serve the basis of our framework. In its conception, we not only consider the technical aspects of the framework but also delve into exploring its management and organizational dimensions, with the intent of facilitating its adoption in enterprises, as a tool to support decision support systems. We analyse how accurate the simulation results can be when using these two tools as well as what considerations need to be considered within organizations.

Survey of business process simulation tools: a comparative approach

Proceedings of SPIE, 2011

Simulation in general is the operation of imitating a real thing such as a process. Simulation is known for its importance in the fields of research and operation management. It is used in many fields such as engineering, training etc. This survey is interested in the field of scientific simulation of business systems. Business process simulation is used to give the business owners a real view of their systems in certain aspects such as system's behavior, cost of resources, return on investment, and the time each process will take. By trying different input alternatives, business process simulation is usually used to save the costs of having a real system with potential problems. This paper emphasizes on business process simulation tools, comparing them according to certain evaluation criteria and presenting the recommended applications for each tool.