Multifaceted Modelling of Complex Business Enterprises.

Area of Science:

• Business Management
• Systems Engineering
• Data Science

Background:

• Enterprises are complex systems with diverse data types and stakeholder perspectives.
• Integrating disparate data sources (historic, survey, expert, planning, incomplete) is a significant challenge.
• Existing models often struggle to incorporate the multifaceted nature of business operations.

Purpose of the Study:

• To present a novel, generic, multifaceted complex system approach for modeling complex business enterprises.
• To develop a method for integrating diverse enterprise data into a unified Bayesian probability framework.
• To demonstrate the utility of complex system models as a management tool for enterprise decision-making.

Main Methods:

• Formalization of a multifaceted complex system approach.
• Development of a novel method for converting diverse data types into Bayesian probability forms.
• Application of the approach to a railway transport case study for decision support model development.

Main Results:

• Successful integration of historic, survey, management planning, expert, and incomplete data.
• Demonstration of a structured approach to complex system modeling addressing decision contexts.
• Development of a customized decision support model for a railway transport enterprise.

Conclusions:

• The proposed complex system approach effectively integrates diverse data for enterprise modeling.
• The Bayesian probability framework facilitates the conversion of varied data into usable forms.
• The approach provides a versatile decision support tool applicable across different enterprise operational phases.