Managing a high-tech startup: A case of machine vision for the poultry industry

Abstract

High-tech startups face a number of insurmountable problems that prevent them from turning innovative ideas into new products. The article investigates the managerial aspects of implementation and commercialization of high-tech startups in Russia using the case of an automated computer vision analytical system for industrial poultry farming. Entrepreneurship theory and the concept of strategic management constitute the theoretical basis of the study. Among the research methods used in the paper are the POCD framework in combination with SWOT analysis involved in the formation of startup management strategies, as well as Sandelovsky and Barroso’s Meta-Synthesis method applied to identify factors that determine the successful implementation of a startup. The empirical evidence of the work was a bank of video data collected at the VNITIP Federal Research Center of the Russian Academy of Sciences and covering the full life cycle of broiler chickens. The paper established three main sources of funding for high-tech start-ups to introduce machine vision systems in the poultry industry: the state, industrial corporations, and venture capital. At that, none of the enterprises, including the global leaders, has yet reached the IPO stage. We identify two central lines to launch and commercialize a Russian start-up in poultry farming, these are association with developers of integrated digital solutions and/or integration with poultry meat producers. Recommendations are formulated regarding the implementation of a high-tech start-up: to form a public-private partnership, actively interact with research centers and universities, cooper ate with business angels, expand the entrepreneurial competencies of startupers, and clarify the business model of the project. The mass introduction of machine vision technology in the poultry industry is possible due to the acceleration of technological progress and the elimination of the main obstacle, i.e., the high cost of components for machine vision systems.