Industrial Maturity of Machine Learning Solutions Within the Food Industry
Ensuring food security is a crucial challenge becoming increasingly complex for society on a global level. Machine learning technology can help to overcome this challenge, however its successful deployment in practice is a mandatory prerequisite and currently achieved only to a limited extent. There...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10949080/ |
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| author | Laura Gradl Luisa Reis Ricardo Buettner |
| author_facet | Laura Gradl Luisa Reis Ricardo Buettner |
| author_sort | Laura Gradl |
| collection | DOAJ |
| description | Ensuring food security is a crucial challenge becoming increasingly complex for society on a global level. Machine learning technology can help to overcome this challenge, however its successful deployment in practice is a mandatory prerequisite and currently achieved only to a limited extent. Therefore, this systematic literature review aims at determining the current state of industrial maturity of machine learning-based approaches in the context of food industry, evaluating their readiness for operational use and deployment. An initial framework for assessment of industrial technology readiness consisting of six technical and human- and process-related dimensions is developed. Existing solutions are categorized according to the addressed process step within the food value chain and the covered dimension of the maturity framework. As the findings demonstrate, the industrial maturity degree is mainly located in the lower to middle range. Regarding all considered dimensions and phases within the food value chain, however particularly regarding the dimensions integrability and usability as well as the phase packaging and logistics, huge progress is required to achieve an overall high or very high industrial maturity degree. Thus, this work highlights the importance of a holistic perspective realized e.g. by cooperation between research and industry in order to achieve application-ready machine learning models with high levels of industrial maturity. |
| format | Article |
| id | doaj-art-6df0d969d7bc41349deac3cb2e74d692 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-6df0d969d7bc41349deac3cb2e74d6922025-08-20T02:11:37ZengIEEEIEEE Access2169-35362025-01-0113628316285510.1109/ACCESS.2025.355809110949080Industrial Maturity of Machine Learning Solutions Within the Food IndustryLaura Gradl0https://orcid.org/0009-0006-6912-5196Luisa Reis1Ricardo Buettner2https://orcid.org/0000-0003-2263-6408Chair of Hybrid Intelligence, Helmut-Schmidt-University/University of the Federal Armed Forces Hamburg, Hamburg, GermanyChair of Hybrid Intelligence, Helmut-Schmidt-University/University of the Federal Armed Forces Hamburg, Hamburg, GermanyChair of Hybrid Intelligence, Helmut-Schmidt-University/University of the Federal Armed Forces Hamburg, Hamburg, GermanyEnsuring food security is a crucial challenge becoming increasingly complex for society on a global level. Machine learning technology can help to overcome this challenge, however its successful deployment in practice is a mandatory prerequisite and currently achieved only to a limited extent. Therefore, this systematic literature review aims at determining the current state of industrial maturity of machine learning-based approaches in the context of food industry, evaluating their readiness for operational use and deployment. An initial framework for assessment of industrial technology readiness consisting of six technical and human- and process-related dimensions is developed. Existing solutions are categorized according to the addressed process step within the food value chain and the covered dimension of the maturity framework. As the findings demonstrate, the industrial maturity degree is mainly located in the lower to middle range. Regarding all considered dimensions and phases within the food value chain, however particularly regarding the dimensions integrability and usability as well as the phase packaging and logistics, huge progress is required to achieve an overall high or very high industrial maturity degree. Thus, this work highlights the importance of a holistic perspective realized e.g. by cooperation between research and industry in order to achieve application-ready machine learning models with high levels of industrial maturity.https://ieeexplore.ieee.org/document/10949080/Maturity dimensionsmaturity degreeassessment frameworkfood value chainmachine learning applications |
| spellingShingle | Laura Gradl Luisa Reis Ricardo Buettner Industrial Maturity of Machine Learning Solutions Within the Food Industry IEEE Access Maturity dimensions maturity degree assessment framework food value chain machine learning applications |
| title | Industrial Maturity of Machine Learning Solutions Within the Food Industry |
| title_full | Industrial Maturity of Machine Learning Solutions Within the Food Industry |
| title_fullStr | Industrial Maturity of Machine Learning Solutions Within the Food Industry |
| title_full_unstemmed | Industrial Maturity of Machine Learning Solutions Within the Food Industry |
| title_short | Industrial Maturity of Machine Learning Solutions Within the Food Industry |
| title_sort | industrial maturity of machine learning solutions within the food industry |
| topic | Maturity dimensions maturity degree assessment framework food value chain machine learning applications |
| url | https://ieeexplore.ieee.org/document/10949080/ |
| work_keys_str_mv | AT lauragradl industrialmaturityofmachinelearningsolutionswithinthefoodindustry AT luisareis industrialmaturityofmachinelearningsolutionswithinthefoodindustry AT ricardobuettner industrialmaturityofmachinelearningsolutionswithinthefoodindustry |