Measuring the Dynamic Efficiency of Food Industry using by Data Envelopment Analysis(DEA)

Purpose: By deriving the DEA Window model and Malmquist productivity index, this study attempted to find ways to improve efficiency by analyzing the dynamic efficiency of large Korean food companies, and to find improvements for sustainable development. Research design, data, and methodology: The data used in the analysis were 21 companies announced in the production performance of food and food additives, and among them, companies with excellent management performance for five years were selected. Capital, debt, sales and management costs were selected as input variables, and sales were selected as output variables. The DEA Window model was used for efficiency and stability, and the Malmquist productivity index was used for dynamic efficiency. Results: As a result of DEA Window’s analysis, the same stability values were derived for 2019 a nd 2 020. T he s tability of the target DMU o ver the last 5 years has been repeated with a decrease and an increase. Among them, DMU 18 showed the highest efficiency by period during the analysis period, followed by DMU 13, 20, 7, 11, and 21 in order. Inefficient companies were derived as DMU 5, 4, 1, 9, and 8. The Malmquist Productivity Index analyzes productivity from 2017 to 2021. As a result of the analysis, it was analyzed that productivity in 2018 decreased compared to productivity in 2017, and productivity improved in 2020 and 2021 compared to productivity in 2018 and 2019, respectively. Implications: This study used the Malmquist productivity index to analyze dynamic efficiency through productivity analysis of the DEA Window model for efficiency according to period changes in food companies. Based on the analysis of this study, small food companies in Korea can be quantified and applied to diagnosis to improve efficiency and improve overall management efficiency based on stability and productivity over time.