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• USING PROJECT PLANNING TOOLS: GANTT CHART AND NETWORK DIAGRAM

  А.А. Bognyukov , D.Y. Zorkin , I.А. Tarasova

  102-110

  2025-10-01

  Abstract ▼

  An integrative model has been developed that combines calendar planning methods with software functionality (Excel, MS Project) for multi-level project optimization. Central focus is placed on three complementary methodologies: the Gantt chart, network diagram, and critical path analysis, which form the conceptual foundation for effective coordination of project processes. The study details the algorithm for creating a Gantt chart, which visualizes timeframes and task sequences, with emphasis on the functional capabilities of specialized software solutions, including Microsoft Project and Excel, enabling automated construction and adjustment of schedules. Further, the principles of constructing a network diagram, interpreted as a directed graph with edges (tasks) and vertices (events), are elaborated. This approach allows for identifying logical dependencies between project stages and determining the critical path – a sequence of operations with zero time reserves, defining the project’s minimum duration. Practical illustrations of critical path calculations are supported by examples demonstrating its role in optimizing time resources. A key aspect of the study is the analysis of time reserves, aimed at minimizing deadline risks through rational resource reallocation. The methodological framework is supplemented by visualization tools: resource requirement graphs and resource load diagrams, ensuring operational control over material and personnel assets across all project phases. The final element of the planning system is the calendar plan, which structures data on work titles, chronological intervals, and resource intensity. This document serves as an integrative foundation for synchronizing operational activities, ensuring adherence to established deadlines

• DEVELOPMENT AND IMPLEMENTATION OF A CORPORATE INFORMATION SYSTEM AT THE AvtoVAZ INDUSTRIAL ENTERPRISE

  D.Y. Zorkin , А.А. Bognyukov , Т. Е. Kozhanova

  2026-02-27

  Abstract ▼

  In the context of global industrial digitalization, the development and implementation of corporate information systems (CIS) have become strategically critical for enhancing operational efficiency and competitiveness of enterprises. This study examines the integration case of the ERP system "1C: Enterprise Management" at the AvtoVAZ plant – a key player in the Russian automotive industry. The research aimed to optimize management and production processes through the automation of planning, resource accounting, and coordination of cross-functional interactions. The methodological framework combined analytical, graphical, and comparative approaches, as well as practical testing of solutions in the "1C" software environment. The focus was on designing algorithms for managing production cycles, forming resource specifications, and configuring planning scenarios. The study developed demand forecasting models, analyzed production capacities, and balanced output based on model prioritization (Lada Granta, Vesta, Largus). The system implementation reduced order processing time by 30%, minimized warehouse downtime by 18–22% through synchronized logistics schedules, and improved quality control accuracy via integrated diagnostic tools (CAN-bus, spectrophotometry). Special emphasis was placed on overcoming institutional and technological barriers, including modernizing outdated planning methods, training employees in ERP interfaces, and deploying hybrid cloud solutions to ensure system scalability. The practical significance of the research was confirmed by achieving resource allocation transparency, reducing operational costs, and forming an adaptive production strategy aligned with market dynamics. The results demonstrate that CIS implementation not only optimizes current business processes but also lays the foundation for sustainable enterprise development in the digital transformation era. The acquired experience can be extrapolated to other engineering and industrial enterprises facing challenges in management automation and data integration under competitive pressure. Future research prospects involve analyzing the long-term effects of ERP system adoption, including their impact on innovation potential and supply chain ecosystems.

• INTELLIGENT DATA ANALYSIS IN ENTERPRISE MANAGEMENT BASED ON THE ANNEALING SIMULATION ALGORITHM

  E.V. Kuliev, А.V. Kotelva, М.М. Semenova, S.V. Ignateva, А.P. Kukharenko

  2022-11-01

  Abstract ▼

  The article considers an analytical review of the annealing simulation algorithm for the
  problem of efficient enterprise management. The optimization of the annealing simulation algorithm
  for the problem of efficient enterprise management has been carried out. For the analysis of
  cases, the optimization of the work schedule of workers in the organization was used. Established
  worker scheduling model with strong and weak constraints. The simulated annealing algorithm is
  used to optimize the strategy for solving the staff scheduling model. The simulated annealing algorithm
  is an algorithm suitable for solving large-scale combinatorial optimization problems. It also
  evaluates and obtains the optimal scheduling strategy. The simulated annealing algorithm has a
  good effect on the data mining of human resource management. Big data mining can help companies
  conduct dynamic analysis in talent recruitment, and the talent recruitment plan is carried out
  in a quality and standard way to analyze the characteristics of various talents from many angles
  and improve the level of human resource management. An algorithm has been developed that implements
  the operation of the annealing simulation algorithm. The simulated annealing algorithm
  makes new decisions based on the Metropolis criterion, so in addition to making an optimized
  decision, it also makes a reduced decision in a limited range. The Metropolis algorithm is a sampling
  algorithm mainly used for complex distribution functions. It is somewhat similar to the variance
  sampling algorithm, but here the auxiliary distribution function changes over time. Experimental
  studies have been carried out that show that a worker scheduling model based on strong
  and weak constraints is significantly better than a manual scheduling model, achieving an effective
  balance between controlling wage costs in an organization and increasing employee satisfaction.
  The successful application of a workforce scheduling model based on a simulated annealing
  algorithm brings new insights and insights to solve large-scale worker scheduling problems.
  The results presented can serve as a starting point for studying personnel management systems
  based on data mining technology.