This paper considers the software-based analysis of business process model size and complexity using the network connectivity measurement approach. Business process modeling is a key tool in business process management, software engineering, and information systems management. Business process models that are likely to have errors, may cause extra costs, time losses, or even more harmful impact on activities. Therefore, in this study we propose to improve the size and complexity efficiency of business process models based on their structural analysis using the Coefficient of Network Connectivity (CNC) measurement. The respective software tool is developed to calculate the CNC measure, determine quartile values and corresponding thresholds to classify business process models by their structural efficiency.

This study considers the business process model size and complexity analysis, and improvement. The quality of business process models is critical for the successful projects implementation, connected with the organizational activities and information system workflows. Therefore, this paper considers the main quantitative approaches to business process model assessment and focuses on the density measure, originated from the graph theory. The results provided by the developed software, demonstrate density threshold values for different groups of business process models, organized by their efficiency using the obtained quartiles. Using these threshold values, the business process models can be assessed on their size and complexity efficiency.

This paper proposes an approach to the integration of the Business Process Model and Notation (BPMN) and Economic Order Quantity (EOQ) models for supply chain business process simulation. This approach considers the synergy of Business Process Management (BPM) and Supply Chain Management (SCM) concepts both strive to reduce costs and satisfy customers. Therefore, we consider the extension of BPMN-based models with EOQ-driven parameters to simulate supply chain business processes and support decision-making in the context of supply chain management.

In this paper, Matlab-Simulink and LabView models are constructed for a new nonlinear dynamic system of equations in an eight-dimensional (8D) phase space. For fixed parameters of the 8D dynamical system, the spectrum of Lyapunov exponents and the Kaplan-York dimension are calculated. The presence of two positive Lyapunov exponents demonstrates the hyperchaotic behavior of the 8D dynamical system. The fractional Kaplan-York dimension indicates the fractal structure of strange attractors. We have shown that an adaptive controller is used to stabilize the novel 8D chaotic system with unknown system parameters. An active control method is derived to achieve global chaotic synchronization of two identical novel 8D chaotic systems with unknown system parameters. Based on the results obtained in Matlab-Simulink and LabView models, a chaotic signal generator for the 8D chaotic system is implemented in the Multisim environment. The results of chaotic behavior simulation in the Multisim environment show similar behavior when comparing simulation results in Matlab-Simulink and LabView models.