Possibilities of digital transformation in food industry

Yuliya S. Otmahova

0000-0001-8157-0029

1. Institute of Economics and Industrial Engineering, Siberian Branch of the Russian Academy of Sciences

2. Novosibirsk state university

otmakhovajs@yandex.ru

Suvaluk Asavasanti

0000-0002-3249-192X

1. King Mongkut’s University of Technology Thonburi (KMUTT)

suvaluk.asa@kmutt.ac.th

Abstract

The paper presents the results obtained during the study of using digital technologies and successful practices of digitalization to increase efficiency of agricultural production and food products. Based on the analysis of economic publications of EconLit database in JEL macro categories as at the end of 2018, it has been revealed that the number of foreign and domestic publications on digitalization of agro-food complex for the period 2010-2018 is generally insufficient, with no any significant increase in the publications in this category being found. The proposed approach for the first time links such categories as digital technology, needs and agro-food complex by taking into account a set of consumer characteristics of agricultural and food products and production technologies. In order to assess the sectoral digital needs, the article considers such significant characteristics as the end-product complexity index, high value added products environment within the product category and their relationship with production technologies. The study proposes a methodology for determining digital priorities of agro-food products based on such characteristics as complexity index of products, product environment and its relationship with production technologies. The proposed approach develops the modern theory of economic complexity and allows us to assess the possibilities of using artificial intelligence technologies and big data analysis based on the linkage between upstream and downstream in value chain cycle activities.

Keywords: digital platforms, technologies, value chains, product complexity index, product environment

Funding The research was financially supported by RFBR, project № 18-29-03086.

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