Measuring the relative efficiency of the food and drink industry in the chosen EU countries using the data envelopment analysis with missing data

Kedžo, Margareta Gardijan; Lukač, Zrinka

doi:10.1007/s10100-018-0540-0

Cited by 21 publications

(15 citation statements)

References 19 publications

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“…Moreover, the study found that pure technical inefficiencies are the main cause of overall technical inefficiencies. A study conducted by Gardijan and Lukač [31] used the BCC output-an oriented model to evaluate 7000 companies from the F&B industry in the 19 EU countries between 2011 and 2015. They found the leading countries in the F&B industry and which countries have the largest percentage of inefficiency companies.…”

Section: Methodology Discussion and Related Literature Reviewmentioning

confidence: 99%

A DEA Resampling Past-Present-Future Comparative Analysis of the Food and Beverage Industry: The Case Study on Thailand vs. Vietnam

Wang

Nguyen

et al. 2020

Mathematics

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The food and beverage industry plays a significant role in the economic development of developing and emerging countries in Asia through an immense contribution to the national income, employment, value-added inducement, and foreign exchange earnings. Among the developing countries in Asia, Thailand and Vietnam have recently experienced a significant growth in the industry due to their many advantages. However, the nascent stage of this industry was found to be lacking sustainable competitiveness in both countries. Therefore, this study aims to evaluate and forecast the performance efficiency of the food and beverage industry in Thailand and Vietnam to understand how efficient the food and beverage industry to these countries is and formulate suggestions to improve their productivity in accordance with the research findings. To achieve the research objectives, the resampling method in the data envelopment analysis is applied to measure and forecast the efficiency of 20 Vietnamese companies and 20 Thailand firms over the period of 2016 to 2023. The Malmquist productivity index is deployed to calculate the efficiency change over observed periods. The results reveal that Vietnam is found to have a higher efficiency than Thailand due to the outstanding performance of one company but have performed quite poorly due to low scores in technical and productivity change. The findings of this research can give useful information and practical suggestions to improve performance for inefficient companies as well as enhance competitiveness of the efficient companies trying to operate and reach global markets.

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Section: Methodology Discussion and Related Literature Reviewmentioning

confidence: 99%

A DEA Resampling Past-Present-Future Comparative Analysis of the Food and Beverage Industry: The Case Study on Thailand vs. Vietnam

Wang

Nguyen

et al. 2020

Mathematics

View full text Add to dashboard Cite

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“…In the period between the years 2005–2012, Europe and Asia addressed 100% of the publications, while, in the remaining regions, the first articles began to emerge in 2013. The large number of studies in Europe can be attributed to the fact that the food and beverages industry is the largest manufacturer in the European Union, is a leader in generating employment and is also an important contributor to the economy of the region, as Gardijan and Lukač (2018) state.…”

Section: Literature Reviewmentioning

confidence: 99%

Measuring productivity of dairy industry companies: an approach with data envelopment analysis

Guzmán

Lozano

Domínguez

2021

JADEE

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PurposeThis research had two main objectives: To measure the productivity of companies in the Colombian dairy industry and to identify efficient decision-making units (DMUs) that can be used as work sample in future case studies.Design/methodology/approachIn the measurement of productivity, financial variables were considered for a sample of 19 DMUs. Efficient companies were identified through the data envelopment analysis (DEAs) methodology with the VRS model oriented to inputs and outputs. The input variables analyzed were “current asset,” “property, plant and equipment,” “non-current liability” and “equity,” while the output variables were “revenue” and “profit.”FindingsFindings revealed that seven DMUs are efficient in the input and output orientation and that companies of different sizes and with or without quality certifications are efficient in the sample analyzed. Additionally, the benchmark efficient DMUs were identified for each of the non-efficient DMUs.Research limitations/implicationsThe implications for the research include the contribution to the theory, on the one hand, with the analysis of the current state of the literature on the use of DEA in the food sector, and on the other, with the use of DEA to measure the productivity of Colombian dairy industry companies and with the identification of a sample of efficient units that can be analyzed in future case studies.Originality/valueThis article is novel and pioneering because it measures for the first time the productivity of DMUs of the Colombian dairy industry, in addition to including the current state of the literature on the application of the DEA methodology in the food sector. These findings contribute to the consolidation of the theory and also provide inputs for researchers, practitioners, managers, and policy makers.

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“…Kuosmanen used dummy entries (zero for output variables and large enough numbers for input variables) to reduce the effects of DMUs with missing data on the relative efficiency of the other DMUs [25]. Gardijan and Lukač applied the dummy entries method and proposed DEA models (the dummy entries DEA) to measure the efficiency of the food and drink industry [26]. Interval DEA approach is another widely used method in which missing data are replaced with a lower bound and an upper bound so that the lower and upper bounds of efficiency can be evaluated [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Additive integer-valued data envelopment analysis with missing data: A multi-criteria evaluation approach

et al. 2020

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Traditional data envelopment analysis (DEA) models assume that all the inputs and outputs data are available. However, missing data is a common problem in data analysis. Although several scholars have developed techniques to conduct DEA with missing data, these techniques have some disadvantages. A multi-criteria evaluation approach is proposed to measure the efficiency of decision making units (DMUs) with missing data. In this approach, analysts first estimate the upper and lower bounds of DMUs' efficiency using the proposed I-addIDEA-U models (interval additive integer-valued DEA models with undesirable outputs) that can be applied to address integer-valued variables and undesirable outputs. Then, DMUs' "relative" efficiency is evaluated using the proposed "Halo + Hot deck" DEA method (if there is no correlation between variables) or regression DEA techniques (if there is a correlation between variables). Finally, the multi-index comprehensive evaluation method is applied to determine which scenario (the lower bound of efficiency, the "relative" efficiency, or the upper bound of efficiency) should be selected. With a case study, it is shown that the proposed multicriteria evaluation approach is more effective than traditional approaches such as the mean imputation DEA method, the deletion DEA method, and the dummy entries DEA method.

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Measuring the relative efficiency of the food and drink industry in the chosen EU countries using the data envelopment analysis with missing data

Cited by 21 publications

References 19 publications

A DEA Resampling Past-Present-Future Comparative Analysis of the Food and Beverage Industry: The Case Study on Thailand vs. Vietnam

A DEA Resampling Past-Present-Future Comparative Analysis of the Food and Beverage Industry: The Case Study on Thailand vs. Vietnam

Measuring productivity of dairy industry companies: an approach with data envelopment analysis

Additive integer-valued data envelopment analysis with missing data: A multi-criteria evaluation approach

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