Similarly, to determine the most fitting probability distribution with an estimation of the parameters on the basis of observed data is an expensive task. In the literature for the design of fuzzy sets, different approaches can be found, as the interpolation between pairs of observation values and given membership values or the data-based extraction via a clustering algorithm. In the field of data-based fuzzy modeling, the construction of fuzzy sets is often difficult, even for process experts, and practicable for applications with a small number of variables. Indeed, data gathering is one of the major barriers to BPS methods. In practice, in many cases, the available data is inaccurate and not sufficiently precise to parameterize the model, and there are no sufficient sample values to calculate a probability distribution. Unfortunately, process traceability is, in general, a very difficult task. Data is usually collected through discussions with experts and particularly with people involved in the processes to be modeled, through observation of the existing processes and studying the documentation about processes.
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Business Process Parameterizationĭepending on the scale of modeling, a certain amount of important data about the processes needs to be collected and analyzed in order to be incorporated in a model. To choose the right KPIs of a process requires a good understanding of what is important to the organization. For example, waiting time, processing time, cycle time, process cost and resource utilization are commonly used KPIs. The calculated results of the metrics during process monitoring are used to determine whether the target of the KPI has been met. KPIs can be made up of one or more metrics. A KPI may have a target and allowable margins, or lower and upper limits, forming a range of performances that the process should achieve. A KPI is then associated with a specific process and is generally represented by a numeric value. KPIs are created on the basis of business objectives and are the detailed specifications used to track business objectives. KPIs can be related to a marketing-based perspective (e.g., customer satisfaction), to internal quality (non-compliance) and efficiency (cost, duration). In the process-based approach, quantifiable measurements must be defined, so-called key performance indicators (KPIs). In this paper, BPMN is considered a reference standard in BP modeling. However, BPMN provides support to represent the most common control-flow patterns occurring when defining process models. The BPMN structure is similar to well-known flow charts and activity diagrams. Business Process Model and Notation (BPMN) has been an Object Management Group (OMG) standard since 2005, aimed at providing a notation readily understandable by all business stakeholders.
The interested reader is referred to for a comparative analysis of such languages. There is a multitude of languages to support BP modeling, such as textual language (e.g., formal or natural language) and visual language (e.g., flow chart), and there are several representation standards. The development of business process models is very labor-intensive. A conceptual BP model is independent of a particular technology or organizational environment, whereas an executable BP model is specialized to a particular environment. They highlight certain aspects and omit others. BP models describe how BP instances have to be carried out. A BP model is a generic description of a class of BPs. The system has been developed as an extension of a publicly available simulation engine, based on the Business Process Model and Notation (BPMN) standard.īP modeling is an established way of documenting BPs. The resulting process model allows forming mappings at different levels of detail and, therefore, at different model resolutions. In order to compute the interval-valued output of the system, a genetic algorithm is used. Indeed, an interval-valued parameter is comprehensive it is the easiest to understand and express and the simplest to process, among multi-valued representations. The proposed approach exploits interval-valued data to represent model parameters, in place of conventional single-valued or probability-valued parameters. To build and manage simulation models according to the proposed approach, a simulation system is designed, developed and tested on pilot scenarios, as well as on real-world processes. In this paper, a novel approach of BPS is presented. With regard to this problem, currently available business process simulation (BPS) methods and tools are unable to efficiently capture the process behavior along its lifecycle. Simulating organizational processes characterized by interacting human activities, resources, business rules and constraints, is a challenging task, because of the inherent uncertainty, inaccuracy, variability and dynamicity.