Business Process Simulation Model Research Articles

Enhancing business process simulation models with extraneous activity delays

Business Process Simulation (BPS) is a common approach to estimate the impact of changes to a business process on its performance measures. For example, it allows us to estimate what would be the cycle time of a process if we automated one of its activities, or if some resources become unavailable. The starting point of BPS is a business process model annotated with simulation parameters (a BPS model). In traditional approaches, BPS models are manually designed by modeling specialists. This approach is time-consuming and error-prone. To address this shortcoming, several studies have proposed methods to automatically discover BPS models from event logs via process mining techniques. However, current techniques in this space discover BPS models that only capture waiting times caused by resource contention or resource unavailability. Oftentimes, a considerable portion of the waiting time in a business process corresponds to extraneous delays, e.g., a resource waits for the customer to return a phone call. This article proposes a method that discovers extraneous delays from event logs of business process executions. The proposed approach computes, for each pair of causally consecutive activity instances in the event log, the time when the target activity instance should theoretically have started, given the availability of the relevant resource. Based on the difference between the theoretical and the actual start times, the approach estimates the distribution of extraneous delays, and it enhances the BPS model with timer events to capture these delays. An empirical evaluation involving synthetic and real-life logs shows that the approach produces BPS models that better reflect the temporal dynamics of the process, relative to BPS models that do not capture extraneous delays.

Learning business process simulation models: A Hybrid process mining and deep learning approach

Business process simulation is a well-known approach to estimate the impact of changes to a process with respect to time and cost measures – a practice known as what-if process analysis. The usefulness of such estimations hinges on the accuracy of the underlying simulation model. Data-Driven Simulation (DDS) methods leverage process mining techniques to learn business process simulation models from event logs. Empirical studies have shown that, while DDS models adequately capture the observed sequences of activities and their frequencies, they fail to accurately capture the temporal dynamics of real-life processes. In contrast, generative Deep Learning (DL) models are better able to capture such temporal dynamics. The drawback of DL models is that users cannot alter them for what-if analysis due to their black-box nature. This paper presents a hybrid approach to learn process simulation models from event logs wherein a (stochastic) process model is extracted via DDS techniques, and then combined with a DL model to generate timestamped event sequences. The proposed approach allows us to simulate different types of changes, including the addition of new activity types to a process. This latter capability is achieved by encoding the activities by means of embeddings, rather than representing them as one-hot-encoded categories. An experimental evaluation shows that the resulting hybrid simulation models match the temporal accuracy of pure DL models, while partially retaining the what-if analysis capability of DDS approaches. The evaluation also sheds light into the relative performance of multiple embedding approaches to represent the activities.

РАЗРАБОТКА ПОДХОДА К СОЗДАНИЮ ИМИТАЦИОННЫХ МОДЕЛЕЙ ПОТОКОВ ДАННЫХ НА ОСНОВЕ МЕТОДОЛОГИИ DFD С ПРИМЕНЕНИЕМ ТЕОРИИ СЕТЕЙ ПЕТРИ

This article is devoted to the development of approaches aimed at creating simulation models of data flows for modeling business processes using the Data Flow Diagram (DFD) methodology. The use of the mathematical apparatus of the theory of Petri nets is proposed as the main tool in the development of simulation models. The article discusses the main components of the DFD notation and the modeling rules laid down by the developers of this methodology and evaluates the possibility of using the theory of Petri nets to solve the problem of developing simulation models that work in accordance with these rules and notation objects. The theory of Petri nets allows you to simulate both discrete and continuous processes, which is relevant when working with data streams. Taking into account the specifics of the subject area, the mathematical apparatus used has all the functional properties that will allow the implementation of simulation modeling of DFD diagrams. As a data stream, this study proposes the use of labels; if there is a need to introduce control labels, processing of process functionality; it is possible to use color networks that allow you to separate data streams and control signals using color characteristics: labels, arcs and transitions. When implementing decomposition, it is proposed to use hierarchical Petri nets, which allow you to use transitions as your own model based on Petri nets, thus achieving the required detail in simulation models of data flows. To generate and analyze data flow transactions, this study proposes the use of achievable labeling trees, which allow the analyst to analyze the correctness of working with data within the framework of the developed model, as well as evaluate the possibilities of process reengineering using graph theory or using machine learning tools. The article provides an approach to modeling the components of the DFD notation, as well as an example of a business process simulation model using Petri net theory, and a tree of achievable markings has been constructed that describes the transactional approach used in the studied notation.

Learning Accurate Business Process Simulation Models from Event Logs via Automated Process Discovery and Deep Learning

Learning Accurate Business Process Simulation Models from Event Logs via Automated Process Discovery and Deep Learning

Discovering business process simulation models in the presence of multitasking and availability constraints

Business process simulation is a versatile technique for quantitative analysis of business processes. A well-known limitation of process simulation is that the accuracy of the simulation results is limited by the faithfulness of the process model and simulation parameters given as input to the simulator. To tackle this limitation, various authors have proposed to discover simulation models from process execution logs, so that the resulting simulation models more closely match reality. However, existing techniques in this field make certain assumptions about resource behavior that do not typically hold in practice, including: (i) that each resource performs one task at a time; and (ii) that resources are continuously available (24/7). In reality, resources may engage in multitasking behavior and they work only during certain periods of the day or the week. This article proposes an approach to discover process simulation models from execution logs in the presence of multitasking and availability constraints. To account for multitasking, we adjust the processing times of tasks in such a way that executing the multitasked tasks sequentially with the adjusted times is equivalent to executing them concurrently with the original times. Meanwhile, to account for availability constraints, we use an algorithm for discovering calendar expressions from collections of time-points to infer resource timetables from an execution log. We then adjust the parameters of this algorithm to maximize the similarity between the simulated log and the original one. We evaluate the approach using real-life and synthetic datasets. The results show that the approach improves the accuracy of simulation models discovered from execution logs both in the presence of multitasking and availability constraints.

Simulation Research of Business Processes with Queues Using GPSS Studio Modeling Environment

The purpose of research. The goal of this study is to analyze different domestic software packages intended for modeling of business processes involving queues and time parameters. The main analysis criteria are: the possibility of graphical development or conversion of the business process block diagram to the model; automatic generation of simulation model; quick and convenient construction of domain-specific application; the variety of result analysis tools. The relevance of the problems to choose the optimal modeling tool is determined by the need to use only domestic software for business process investigation in the military and some scientific organizations, and by the lack of information about existing software products. In this paper, the subject of study is the Russian simulation modeling environment GPSS Studio.Materials and methods. In this article, the analysis of the possibility of effective usage of GPSS Studio environment during business processes analysis and synthesis is performed. The study was performed on the available examples of different applications of this tool, and by the creation of the model analyzing business processes that are typical for any HR department.Results. Taking into account the performed analysis of functionality and practical applications of the GPSS Studio environment, the good prospects of its application for modeling of business processes involving queues and time parameters are shown. The further system improvements intended for effective business process modeling are proposed.Conclusion. As a result of the performed study, we can do the following conclusions and recommendations:– The GPSS Studio graphical editor can be successfully used for the graphical description of the business processes when it is required to analyze: queues, the load of resources, and the time characteristics of the processing,– The simulation model is automatically generated from the graphical description. The produced model can be finally debugged and verified with the GPSS Studio,– The standalone simulation application working in terms of the subject domain can be built from the simulation model using the simulation environment,– Using this application, it is possible to perform complex business process simulation modeling by means of varying system parameters and performing of singular experiments, a series of dedicated experiments, and optimizing experiments,– Since the GPSS Studio concepts include the possibility to create domain-specific libraries of standard blocks, it is possible to create a library of standard elements similar to IDEF, BPMN, DFD block charts to facilitate faster description and modeling of business processes. After the creation of such a library, there would be no need to manually write GPSS code, since the business process model could be built by a simple connection of standard blocks.

Automated discovery of business process simulation models from event logs

Business process simulation is a versatile technique to estimate the performance of a process under multiple scenarios. This, in turn, allows analysts to compare alternative options to improve a business process. A common roadblock for business process simulation is that constructing accurate simulation models is cumbersome and error-prone. Modern information systems store detailed execution logs of the business processes they support. Previous work has shown that these logs can be used to discover simulation models. However, existing methods for log-based discovery of simulation models do not seek to optimize the accuracy of the resulting models. Instead they leave it to the user to manually tune the simulation model to achieve the desired level of accuracy. This article presents an accuracy-optimized method to discover business process simulation models from execution logs. The method decomposes the problem into a series of steps with associated configuration parameters. A hyper-parameter optimization method is used to search through the space of possible configurations so as to maximize the similarity between the behavior of the simulation model and the behavior observed in the log. The method has been implemented as a tool and evaluated using logs from different domains.

Met4ITIL: A process management and simulation-based method for implementing ITIL

A key requirement for service providers is to define, manage and deliver Information Technology (IT) services to effectively support business goals and customer needs. Several process reference models for the IT service management (ITSM) have emerged, being Information Technology Infrastructure Library (ITIL) the reference model accepted as de facto standard for ITSM. This paper introduces a novel method for implementing ITIL which is based on business process management lifecycle and simulation modeling. An application of the method to implement the incident management process in a Spanish company is also presented. The method has been reviewed by ITIL experts and evaluated from the process stakeholders’ perspective using a questionnaire. The answers provided to the questionnaire show the usefulness of the method to address critical success factors and to support the application of adequate process management practices.

An approach to business process simulation using mined probabilistic models

There are many approaches on how to analyse business processes, but the simulation is still not widely employed due to high costs associated with simulation model creation. In this paper, an approach on how to automatically generate dynamic business process simulation model is presented. The approach discovers belief network of the process from an event log and uses it to generate a simulation model automatically. Such model then can be further customised to facilitate analysis. For evaluation of the approach, conformance of the simulation results with the source event logs was calculated. The simulation results were event logs that were generated during the simulation of the discovered models. The evaluation showed that the approach could be used for initial simulation model generation.

SELECTION OF ACTIVITIES IN DYNAMIC BUSINESS PROCESS SIMULATION / VEIKLŲ PASIRINKIMAS DINAMINIO VERSLO MODELIO SIMULIACIJOJE

Maintaining dynamicity of business processes is one of the core issues of today's business as it enables businesses to adapt to constantly changing environment. Upon changing the processes, it is vital to assess possible impact, which is achieved by using simulation of dynamic processes. In order to implement dynamicity in business processes, it is necessary to have an ability to change components of the process (a set of activities, a content of activity, a set of activity sequences, a set of rules, performers and resources) or dynamically select them during execution. This problem attracted attention of researches over the past few years; however, there is no proposed solution, which ensures the business process (BP) dynamicity. This paper proposes and specifies dynamic business process (DBP) simulation model, which satisfies all of the formulated DBP requirements. Šiuolaikiniam verslui svarbu vykdyti procesus dinamiškai, norint laiku prisitaikyti prie besikeičiančios aplinkos. Keičiant procesus reikia įvertinti keitimo pasekmes, o įvertinimui galima naudoti dinaminių procesų imitaciją. Siekiant realizuoti procesų dinamiką, reikia imitacijos metu turėti galimybę keisti proceso komponentus. Problema pritraukia daug dėmesio jau kelerius metus, tačiau vis dar nepasiūlytas sprendimas, kuris užtikrintų verslo proceso dinamiškumą. Šis straipsnis siūlo ir pateikia dinaminio verslo proceso imitacinį modelį, kuris atitinka anksčiau suformuotus dinaminio verslo proceso reikalavimus.

The Use of Process Mining in Business Process Simulation Model Construction

The paper focuses on the use of process mining (PM) to support the construction of business process simulation (BPS) models. Given the useful BPS insights that are available in event logs, further research on this topic is required. To provide a solid basis for future work, this paper presents a structured overview of BPS modeling tasks and how PM can support them. As directly related research efforts are scarce, a multitude of research challenges are identified. In an effort to provide suggestions on how these challenges can be tackled, an analysis of PM literature shows that few PM algorithms are directly applicable in a BPS context. Consequently, the results presented in this paper can encourage and guide future research to fundamentally bridge the gap between PM and BPS.

How Close to Reality is the „as-is” Business Process Simulation Model?

Abstract Background and Purpose: Business process simulation (BPS) model is based on real-life data form sources like databases, observations and interviews. It acts as “as-is” business scenario can used for reengineering. The main challenge is to gather relevant data and to develop simulation model. Research aims to elaborate BPS model and to systematically assess how close to reality it is. Design/Methodology/Approach: The research has been performed in Polish telecommunications company. Authors investigate technical process of expanding cellular network. After elaborating “as-is” model, authors use ADONIS simulation tool to run a series simulations and confront simulation results with actual historical events. After this, assessment whether computer simulation model can precisely map real-life business process - and consequently act as credible basis for process improvement - is made. Results: The simulation model has been constructed with data from the WfMS database, observations, staff knowledge and their experience. Fully equipped simulation model is found to allow reconstructing the historical execution of business activity with low margin for error. Some limitation were identified and discussed. Conclusion: BPS is not a popular approach for process reengineering and improvement yet. Data collection issues for BPS that require adopting process mining techniques and additional information sources are among the reasons for that. In our study, computer simulation outputs are compatible with historical events. Hence the model reflects the business reality and can be taken as a reference model while redesigning the process.

Invariant conditions in value system simulation models

This paper presents a framework for the integration of supply chain (or logistics/distribution), value chain (or financial), and business process (or operational/manufacturing) simulation models, which should facilitate assessing the impact of supply chain and operational changes on an enterprise's financial performance. A Design Science approach is taken to demonstrate that the REA ontology, which provides a shared conceptual ground for these three model types, and its axioms, which describe invariant conditions for value systems, can help to build conceptually sound simulation models and identify the integration points between these models. It is further shown how these three types of simulation models can be integrated into one value system model for discrete event simulation, making use of the ExSpecT simulation tool. With this ontology-based framework, simulation model builders should be able to scope their models better and define integration points with other models, which is expected to promote the (re)use of simulation models for different purposes (e.g., simulating logistical, operational and financial performance).

Simulation Modeling of a Check-in and Medication Reconciliation Ambulatory Clinic Kiosk

Gaps in information about patient medication adherence may contribute to preventable adverse drug events and patient harm. Hence, health-quality advocacy groups, including the Joint Commission, have called for the implementation of standardized processes to collect and compare patient medication lists. This manuscript describes the implementation of a self-service patient kiosk intended to check in patients for a clinic appointment and collect a medication adherence history, which is then available through the electronic health record. We used business process engineering and simulation modeling to analyze existing workflow, evaluate technology impact on clinic throughput, and predict future infrastructure needs. Our empiric data indicated that a multi-function healthcare kiosk offers a feasible platform to collect medical history data. Furthermore, our simulation model showed a non-linear association between patient arrival rate, kiosk number, and estimated patient wait times. This study provides important data to help administrators and healthcare executives predict infrastructure needs when considering the use of self-service kiosks.

IDEF method-based simulation model design and development framework

The purpose of this study is to provide an IDEF method-based integrated framework for a business process simulation model to reduce the model development time by increasing the communication and knowledge reusability during a simulation project. In this framework, simulation requirements are collected by a function modeling method (IDEF0) and a process modeling method (IDEF3). Based on these requirements, a common data model is constructed using the IDEF1X method. From this reusable data model, multiple simulation models are automatically generated using a database-driven simulation model development approach. The framework is claimed to help both requirement collection and experimentation phases during a simulation project by improving system knowledge, model reusability, and maintainability through the systematic use of three descriptive IDEF methods and the features of the relational database technologies. A complex semiconductor fabrication case study was used as a testbed to evaluate and illustrate the concepts and the framework. Two different simulation software products were used to develop and control the semiconductor model from the same knowledge base. The case study empirically showed that this framework could help improve the simulation project processes by using IDEF-based descriptive models and the relational database technology. Authors also concluded that this framework could be easily applied to other analytical model generation by separating the logic from the data.

IDEF method-based simulation model design and development framework

The purpose of this study is to provide an IDEF method-based integrated framework for a business process simulation model to reduce the model development time by increasing the communication and knowledge reusability during a simulation project. In this framework, simulation requirements are collected by a function modeling method (IDEF0) and a process modeling method (IDEF3). Based on these requirements, a common data model is constructed using the IDEF1X method. From this reusable data model, multiple simulation models are automatically generated using a database-driven simulation model development approach. The framework is claimed to help both requirement collection and experimentation phases during a simulation project by improving system knowledge, model reusability, and maintainability through the systematic use of three descriptive IDEF methods and the features of the relational database technologies. A complex semiconductor fabrication case study was used as a testbed to evaluate and illustrate the concepts and the framework. Two different simulation software products were used to develop and control the semiconductor model from the same knowledge base. The case study empirically showed that this framework could help improve the simulation project processes by using IDEF-based descriptive models and the relational database technology. Authors also concluded that this framework could be easily applied to other analytical model generation by separating the logic from the data.

Cost Allocation for Business Process Simulation Models

This paper considers how object costing can be incorporated into business process simulation models for decision support. Conventional and activity-based costing (ABC) methods are assessed in order to determine their suitability for business process simulation and decision support. An extension of ABC is proposed, in which costs are divided between resources rather than activities, and cost allocation rates are refreshed after each event so that emergent changes are taken into account. An application built into a simulation model of a UK SME is presented and illustrated through the evaluation of downsizing a sales department.

Developing a Theory to Explain the Insights Gained Concerning Information Systems and Business Process Behaviour: The ASSESS-IT Project

Business Process (BP) literature promotes the value of business processes as essential gearwheels that help organisations to reach their goals. Similarly, many process design approaches claim that information technology is a major enabler of business process, a view also shared by the Information Systems (IS) community. Despite this, BP and IS approaches do not provide clear guidance on how to coordinate the design of BP and IS. Nor is any indication of which modelling techniques could be used to detect and design IT opportunities within a business process context given. The ASSESS-IT project examined this domain and proposed the use of simulation techniques to achieve BP and IT integration. The outcome of this project gave indication that depicting the dynamic behaviour of IS could be very helpful for BP modellers to predict the impact that the insertion of IS may have on organisational processes. This paper proposes a simulation framework to depict IS behaviour within a BP simulation model, namely BPS/ISS, and tests it using the case study that was part of the ASSESS-IT project.

Role-based approach to business process simulation modeling and analysis

This paper presents a new methodology for business process simulation modeling and analysis based on role-based concept. A simulator is developed to systematically construct simulation models and conduct simulation easily and efficiently. A method to analyze the simulation results are suggested with some guidelines to improve the process improvement.