Management. Economics. Informatics (M.E.I.)

Introduction. Russia has enormous historical experience in implementing large-scale projects. This resource can not only inspire new successes, but also serve as an example for modern managers. The material is devoted to the successful implementation of the project to create the largest brick fortress in the world - the Smolensk fortress wall.

Materials and methods. The Smolensk fortress wall, which is currently the largest brick fortress on the planet, was built in 6 years, in 1596-1602. Construction projects of this scale and incredible dynamics of implementation are phenomenal examples of the coherence of management and production teams. An appeal to archival materials and the works of researchers who examined the process of building the Smolensk fortress wall in historical, art history, technical and other contexts allow us to analyze the management practices of the turn of the 16th-17th centuries.

Research Findings. As a result of the study, the process of construction of the Smolensk fortress wall was analyzed for the first time within the framework of the classical structure of project implementation, including the stages of initiation, planning, execution, control, and completion. Methods for risk management by developing management decisions were identified. Particular attention was paid to the planning stage, which in the case of the construction of the Smolensk fortress wall lasted for a long time.

Discussion and conclusion. Based on the analysis, it was revealed that the management practices applied more than 400 years ago continue to remain relevant to this day. The nature and scale of the work carried out in Smolensk, as well as the volume of accompanying organizational measures, allow us to consider the construction of the fortress wall as the first national project of Russia.

Introduction. In their day-to-day operations, companies face the need to provide sufficient current assets to support the production process. The challenge lies in accounting for the specifics of the company’s activities and determining the optimal amount of current assets while avoiding both overspending and shortages. Particular attention in current asset management is given to managing accounts receivable.

Materials and methods. A methodological analysis of approaches and methods for managing current assets was conducted. The key features of managing accounts receivable as a core element of current assets are identified. The specific characteristics of current asset usage in energy companies are examined.

Research findings. Using the example of accounts receivable management at Mosenergo, a methodology for managing accounts receivable was tested. The relevance of the issues under consideration is due to the fact that the organization of the production process in companies requires ensuring both fixed and current assets. Overspending on raw materials and supplies, as well as insufficient funding, can lead to force-majeure circumstances. It is also necessary to take into account the specifics of the company’s operations, especially when managing accounts receivable.

Discussion and conclusion. Current asset management requires significant attention. Excessive spending on raw materials and supplies “immobilizes” financial resources, while shortages may lead to production disruptions. A crucial element of current asset management is the management of accounts receivable. The methods to be applied and their effectiveness are examined using the example of Mosenergo’s accounts receivable management.

Introduction. This research focuses on addressing the scientific and practical task of implementing machine learning (ML) algorithms into marketing and pricing processes. The relevance of the study is driven by the inability of traditional approaches to effectively process growing volumes of data and facilitate a transition to proactive and predictive business strategies. The aim of the work is to investigate the practical application of ML algorithms for solving two key tasks: pricing optimization through sales forecasting and enhancing marketing strategy efficiency by evaluating the effectiveness of promotional campaigns. The paper presents a methodology for building and training models, along with an analysis of their applicability based on retail trade data.

Materials and Methods. The study focused on historical data from a retail chain, including daily sales, customer traffic, and marketing activities. The study covered data from 1,115 stores, with a total sample size of over 1 million observations. The study employed a combination of machine learning techniques, including regression analysis for sales forecasting and binary classification for evaluating the effectiveness of marketing campaigns. The research architecture is based on the sequential application of gradient boosting algorithms - CatBoostRegressor for the regression task and CatBoostClassifier for the classification task. The pandas library was used for data processing, and feature engineering involved generating time-based features and calculating the performance of stocks based on comparisons with baseline sales. The models were validated using cross-validation and a test set divided by time intervals.

Results. As a result of the study, two machine learning prediction models were developed and tested. The sales prediction model achieved a determination coefficient of R² = 0.837 with a root mean squared error of RMSE = 1254.38, indicating a high accuracy in predicting daily turnover. The stock performance classification model demonstrated balanced accuracy with an F1-score of 0.65 and revealed a significant difference in the effectiveness of promotions between different store types, ranging from 73.6% to 99.7%. It was found that effective promotions lead to an increase in average sales by 87% compared to days without promotions (8244.31 vs. 4406.05). Feature importance analysis identified key influencing factors: number of customers (42.8%), distance to a competitor (15.4%), and store type (12.5%) for the effectiveness of promotions; store ID, day of the week, and the fact of a promotion for predicting sales.

Discussion and Conclusion. The practical significance of the study lies in the creation of a toolkit for optimizing marketing strategies and inventory management in retail chains. The implementation of the developed models allows for a transition from reactive to proactive sales management, improving the accuracy of demand forecasting by 15-20%, and optimizing the allocation of marketing budgets by focusing on the most effective promotion channels. The results obtained demonstrate a significant dependence of the effectiveness of promotions on the type of store and its competitive environment, indicating the need for a differentiated approach to marketing activity planning. Prospects for further research include the development of dynamic pricing systems, the integration of external factors (seasonality, macroeconomic indicators), and the creation of recommendation systems for selecting optimal stock parameters. The developed methodology can be adapted for other retail segments and service industries.

Ключевые слова: машинное обучение, ценообразование, прогнозирование продаж, маркетинг, тестовая выборка, торговля.

Keywords: machine learning, ricing, sales forecasting, marketing, test sample, trading.

Goal

The aim of the study is to create and evaluate a transfer learning model for multiclass classification of electric guitar types.

Methods

The work uses an open dataset consisting of 7 classes, 605 images. MobileNetV2 was used as the architecture, followed by the addition of global averaging, dropout, and fully connected output layers. The training is performed in two stages: first, only the classification heads, then the fine-tuning of the last 40 layers. Data augmentation has been applied to increase generalizing ability.

Results

The model achieved an accuracy of 73.77% on an independent test sample. The best metrics were shown by the Telecaster and Les Paul classes. The main errors occurred between visually similar pairs: ES and SG.

Conclusions

The results confirm the effectiveness of MobileNetV2 in small data tasks. The solution has practical implications for mobile applications, cataloging, and learning. In the future, it is planned to expand the dataset, use Grad-CAM and optimize the model for peripheral computing devices.