IMPROVEMENT OF TOOLS FOR DIAGNOSIS OF INTERNAL BUSINESS PROCESSES OF AGRICULTURAL ENTERPRISES

Abstract

Introduction. Modern agricultural enterprises operate in conditions of high dependence on external risks - seasonality, weather fluctuations, market volatility, which actualizes the need to improve the diagnostics of internal business processes as a tool for ensuring the stability and adaptability of production systems. Traditional approaches (Lean Six Sigma, BPM, BPR), despite their effectiveness, do not fully cover the digital aspects of management, which necessitates the integration of these methodologies with ERP, IoT and Big Data technologies.

Purpose. Theoretical justification and practical improvement of the diagnostic tools for internal business processes of agricultural enterprises by combining process methodologies and digital platforms to increase the efficiency, adaptability and innovative potential of agricultural enterprises.

Materials and methods. The work uses system and process approaches, analysis, comparison methods, BPM modeling, Lean Six Sigma concepts, as well as a review of technological solutions (ERP, IoT, Big Data). The methodological basis is the DMAIC stages, CSF analysis and KPI assessment tools.

Results. It is substantiated that the modernized diagnostic toolkit should function as an integrated platform, where ERP provides a single transactional database, IoT - real-time monitoring of physical parameters, BPM - automation and repeatability of improved processes, and KPI - a strategic measurement of personnel efficiency and motivation. It is shown that the inclusion of management style and organizational maturity in diagnostic parameters improves the accuracy of establishing the causes of inefficiency. The feasibility of prioritizing BPR specifically for processes in the critical CSF zone is proven.

Prospects for further research. It is advisable to develop a methodology for assessing the organizational maturity of agricultural enterprises, form indicators of adaptability of production systems, create risk forecasting models based on Big Data, and study the impact of digital culture on the effectiveness of implementing ERP, IoT, and BPM in the agricultural sector.