Verification of Normative-Reference Information for Building a Production Simulation Model

Managing a modern industrial enterprise requires the application of innovative methods and technologies to achieve set goals with maximum efficiency. In the context of a dynamically changing market, companies face the need to rethink management strategies aimed at enhancing competitiveness, financial stability, and product quality. Particular importance is placed on the implementation of flexible production systems capable of adapting to changes in demand, product modifications, and issues in the production process. The concept of the digital twin, proposed by M. Grieves in 2003, has become a key element of the digital transformation of enterprises. It allows for the creation of virtual models of production systems to conduct experiments without risk to real operations. When building simulation models and digital twins for production management and modeling across different decision-making horizons, there arises the issue of the relevance of reference information, particularly inaccurate operation time norms. The use of incorrect or incomplete data leads to false conclusions during plan feasibility analysis, erroneous identification of bottlenecks, and improper formulation of shift schedules. In modern conditions of managing industrial enterprises, it is necessary to implement new technologies and methods to improve production efficiency, competitiveness, and business process flexibility. This article analyzes key aspects of production system digitalization, including the use of APS (Advanced Planning & Scheduling) software and the «digital twin» concept. Special attention is given to updating regulatory and reference information (reference data), which is fundamental for creating accurate simulation models of production. The author examines three approaches to solving the problem of inflated operation time norms, ranging from simple methods of analyzing employee attendance data to complex mathematical models using the least squares method. It is demonstrated that the accuracy of reference data directly impacts the quality of forecasting and optimization of production processes. Both foreign solutions (e.g., Siemens Tecnomatix Plant Simulation) and domestic developments (the BFG platform, products from the company «RightStep») are considered. The article emphasizes the importance of integrating various production management methodologies, such as Lean, MRP-II, and Theory of Constraints (TOC). The study demonstrates the practical applicability of the proposed solutions for enterprises of various scales and profiles.