Technology transfer model oriented to industry 4.0: proposal and application in the food industry

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Abstract

Goal: This study aimed to propose a Technology Transfer Model oriented to Industry 4.0 (TTM4.0). Design/methodology/approach. Initially, a systematic literature review was conducted to identify existing gaps in Technology Transfer models: Another focus of the review was to determine the requirements for technological implementations in Industry 4.0. Thus, a specific TT model was built. Then, the model was validated through case studies in Brazilian companies (slaughterhouses). The methods employed to measure data and support decisions in the context of TT4.0 were AHP, Likert scale, and Fuzzy-TOPSIS. Results: Focusing on similar results between Companies A and B, thinking about adopting Industry 4.0, the central strategic needs were product quality, reducing human error, and reducing waste. Among the main factors inhibiting the maximum interest and availability of Industry 4.0 for both companies were high investment, lack of knowledge (procedures or in general), and uncertainties in results. Results indicate Big Data Analytics and artificial intelligence technologies. Originality/value: The Poultry Industry strengthens the economy and significantly impacts the country's socioeconomic indices. Despite this, the sector has a low level of technological maturity compared to others. Regarding the TTM4.0, while other models start by recognizing opportunities and then select technologies in the market, this model prioritizes phases of organizational self-knowledge for decision-making support. A primary difference between a maturity model and a Technology Transfer model is in their scope, with the latter being broader. It encompasses technology definition, implementation, and management, rather than being limited to diagnosis.

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APA

da Silva, V. L., Kovaleski, J. L., Pagani, R. N., & Gomes, M. A. S. (2024). Technology transfer model oriented to industry 4.0: proposal and application in the food industry. Brazilian Journal of Operations and Production Management, 21(2). https://doi.org/10.14488/BJOPM.1777.2024

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