Performance Analysis Of Hungarian Food Industry Enterprises Using The Dea Method

This study examines the performance of Hungarian food industry enterprises using the Data Envelopment Analysis (DEA) method. In today's rapidly evolving economic environment, assessing operational efficiency and cost-effectiveness is of paramount strategic importance. The primary objective of this research is to investigate how performance measurement and benchmarking can enhance informed decision-making and drive business development within the sector. The empirical analysis is based on secondary data retrieved from the EMIS database, covering 611 companies over the financial years from 2018 until 2023. The selected input variables include the value of tangible assets, material and personnel-related expenses, other operational costs, and interest paid. Output variables consist of total revenue and gross value added (GVA). The linear programming-based DEA model calculates a relative efficiency score for each decision-making unit, comparing firms with similar input-output structures. The study investigates efficiency differences across four dimensions: regional location, industry sub-sector, company size (based on number of employees), and enterprise age. The results reveal considerable heterogeneity. Companies in the Budapest region show the highest efficiency levels, while those in Pest County perform the weakest. Among industry sub-sectors, the efficiency gap between the best and worst performers reaches 36%. Larger firms, especially those employing more than 250 people, consistently outperform smaller ones. In terms of age, the youngest (0-5 years) and the oldest (over 50 years) enterprises exhibit the highest technical efficiency, whereas mid-aged firms tend to lag behind. The findings offer valuable insights for both academic research and practical applications by identifying key performance drivers and benchmarking opportunities within the Hungarian food industry.