Harnessing Artificial Intelligence (AI) to Mitigate Food Waste: Innovative Strategies for Sustainable Consumption
Abstract
The capacity of artificial intelligence (AI) to transform food waste management is rooted in its capability to analyse extensive data sets and enhance processes throughout the food supply chain. Despite the potential of AI to transform food waste management, many organisations and consumers remain uninformed about its capabilities and the innovative strategies it can provide to effectively reduce food waste. This study aimed to review innovative strategies for sustainable consumption by harnessing AI to mitigate food waste. This study employed a review analysis as a methodological approach to synthesise existing literature on the intersection of AI and food waste management. The review analysis revealed several innovative strategies for sustainable consumption by harnessing AI to mitigate food waste, including: (a) smart inventory management; (b) recipe suggestions based on available ingredients; (c) automated waste tracking; (d) predictive analytics for meal preparation; and (e) consumer behaviour insights. In conclusion, utilising AI to reduce food waste offers a viable strategy for promoting sustainable consumption practices throughout the food supply chain. Future research should prioritise longitudinal analyses to assess the long-term effectiveness of AI interventions across sectors including agriculture, retail, and hospitality.
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Abdallah, M., Talib, M. A., Feroz, S., Nasir, Q., Abdalla, H., & Mahfood, B. (2020). Artificial intelligence applications in solid waste management: A systematic research review. Waste Management, 109, 231-246. https://doi.org/10.1016/j.wasman.2020.04.057
Adegbola, A. E., Adegbola, M. D., Amajuoyi, P., Benjamin, L. B., & Adeusi, K. B. (2024). Advanced financial modeling techniques for reducing inventory costs: A review of strategies and their effectiveness in manufacturing. Finance & Accounting Research Journal, 6(6), 801-824. https://doi.org/10.51594/farj.v6i6.1180
Ameh, B. (2024). Digital tools and AI: Using technology to monitor carbon emissions and waste at each stage of the supply chain, enabling real-time adjustments for sustainability improvements. International Journal of Science and Research Archive, 13(1), 2741-2754.. https://doi.org/10.30574/ijsra.2024.13.1.1995
Amosu, O. R., Kumar, P., Ogunsuji, Y. M., Oni, S., & Faworaja, O. (2024). AI-driven demand forecasting: Enhancing inventory management and customer satisfaction. World Journal of Advanced Research and Reviews, 23(2), 100-110. https://doi.org/10.30574/wjarr.2024.23.2.2394
Anwar, H., Anwar, T., & Mahmood, G. (2023). Nourishing the future: AI-driven optimization of farm-to-consumer food supply chain for enhanced business performance. Innovative Computing Review, 3(2), 14-29. https://doi.org/10.32350/icr.32.02
Arowosegbe, O. B., Ballali, C., Kofi, K. R., Adeshina, M. K., Agbelusi, J., & Adeshina, M. A. (2024). Combating food waste in the agricultural supply chain: A systematic review of supply chain optimization strategies and their sustainability benefits. World Journal of Advanced Research and Reviews, 24(1), 122-140. https://doi.org/10.30574/wjarr.2024.24.1.3023
Azevedo, S. G., Pimentel, C. M., Alves, A. C., & Matias, J. C. (2021). Support of advanced technologies in supply chain processes and sustainability impact. Applied Sciences, 11(7), 3026. https://doi.org/10.3390/app11073026
Blešić, I., Petrović, M. D., Gajić, T., Tretiakova, T. N., Syromiatnikova, J. A., Radovanović, M., Popov-Raljić, J., & Yakovenko, N. V. (2021). How the extended theory of planned behavior can be applied in the research of the influencing factors of food waste in restaurants: learning from Serbian urban centers. Sustainability, 13(16), 9236. https://doi.org/10.3390/su13169236
Carmody, J., Shringarpure, S., & Van de Venter, G. (2021). AI and privacy concerns: A smart meter case study. Journal of Information, Communication and Ethics in Society, 19(4), 492-505. https://doi.org/10.1108/jices-04-2021-0042
Chelh, S. (2023). Improving green supply chain resilience through blockchain innovation: A risk mitigation perspective. E3S Web of Conferences, 412, 01056. https://doi.org/10.1051/e3sconf/202341201056
Chen, C. R., & Chen, R. J. (2018). Using two government food waste recognition programs to understand current reducing food loss and waste activities in the US. Sustainability, 10(8), 2760. https://doi.org/10.3390/su10082760
Chen, M. F. (2023). Integrating the extended theory of planned behavior model and the food-related routines to explain food waste behavior. British Food Journal, 125(2), 645-661. https://doi.org/10.1108/bfj-07-2021-0788
Cubric, M. (2020). Drivers, barriers and social considerations for AI adoption in business and management: A tertiary study. Technology in Society, 62, 101257. https://doi.org/10.1016/j.techsoc.2020.101257
Del Prete, M. (2022). Mindful sustainable consumption and sustainability chatbots in fast fashion retailing during and after the COVID-19 pandemic. Journal of Management and Sustainability, 12(1), 19. https://doi.org/10.5539/jms.v12n1p19
Elamin, M. O. I. (2024). Modernizing the charitable sector through artificial intelligence: Enhancing efficiency and impact. Journal of Ecohumanism, 3(4), 3426-3443. https://doi.org/10.62754/joe.v3i4.3856
Fadiji, T., Bokaba, T., Fawole, O. A., & Twinomurinzi, H. (2023). Artificial intelligence in postharvest agriculture: Mapping a research agenda. Frontiers in Sustainable Food Systems, 7, 1226583. https://doi.org/10.3389/fsufs.2023.1226583
Faezirad, M., Pooya, A., Naji-Azimi, Z., & Amir Haeri, M. (2021). Preventing food waste in subsidy-based university dining systems: An artificial neural network-aided model under uncertainty. Waste Management & Research: The Journal for a Sustainable Circular Economy, 39(8), 1027-1038. https://doi.org/10.1177/0734242x211017974
Fesenfeld, L., Rudolph, L., & Bernauer, T. (2022). Policy framing, design and feedback can increase public support for costly food waste regulation. Nature Food, 3(3), 227-235. https://doi.org/10.1038/s43016-022-00460-8
Frehner, A., De Boer, I. J., Muller, A., Van Zanten, H. H., & Schader, C. (2022). Consumer strategies towards a more sustainable food system: Insights from Switzerland. The American Journal of Clinical Nutrition, 115(4), 1039-1047. https://doi.org/10.1093/ajcn/nqab401
Gao, H., Jia, H., & Guo, B. (2024). Resources available for me versus us: Implications for mitigating consumer food waste. Journal of Marketing Research, 61(4), 619-637. https://doi.org/10.1177/00222437231162615
Ghinoi, S., Silvestri, F., & Steiner, B. (2020). Toward the creation of novel food waste management systems: A network approach. Journal of Cleaner Production, 246, 118987. https://doi.org/10.1016/j.jclepro.2019.118987
Gómez-Talal, I., González-Serrano, L., Rojo-Álvarez, J. L., & Talón-Ballestero, P. (2024). Avoiding food waste from restaurant tickets: a big data management tool. Journal of Hospitality and Tourism Technology, 15(2), 232-253. https://doi.org/10.1108/jhtt-01-2023-0012
Hernandez, D., Pasha, L., Yusuf, D. A., Nurfaizi, R., & Julianingsih, D. (2024). The role of artificial intelligence in sustainable agriculture and waste management: Towards a green future. International Transactions on Artificial Intelligence, 2(2), 150-157. https://doi.org/10.33050/italic.v2i2.552
Herrera Burstein, Y. E., & Goñi Avila, N. M. (2024). Promoting sustainable consumption among university students: a systematic literature review. International Journal of Sustainability in Higher Education, 25(2), 265-288. https://doi.org/10.1108/ijshe-09-2022-0298
Hussein, A. H. A., Jabbar, K. A., Mohammed, A., & Jasim, L. (2024). Harvesting the future: AI and IoT in agriculture. E3S Web of Conferences, 477, 00090. https://doi.org/10.1051/e3sconf/202447700090
Jarašūnienė, A., Čižiūnienė, K., & Čereška, A. (2023). Research on impact of IoT on warehouse management. Sensors, 23(4), 2213. https://doi.org/10.3390/s23042213
Jones-Garcia, E., Bakalis, S., & Flintham, M. (2022). Consumer behaviour and food waste: understanding and mitigating waste with a technology probe. Foods, 11(14), 2048. https://doi.org/10.3390/foods11142048
Kim, J., Rundle-Thiele, S., Knox, K., Burke, K., & Bogomolova, S. (2020). Consumer perspectives on household food waste reduction campaigns. Journal of Cleaner Production, 243, 118608. https://doi.org/10.1016/j.jclepro.2019.118608
Kim, M., Lee, J., Lee, C., & Jeong, J. (2022). Framework of 2D KDE and LSTM-based forecasting for cost-effective inventory management in smart manufacturing. Applied Sciences, 12(5), 2380. https://doi.org/10.3390/app12052380
La Barbera, F., Amato, M., Riverso, R., & Verneau, F. (2022). Social emotions and good provider norms in tackling household food waste: an extension of the theory of planned behavior. Sustainability, 14(15), 9681. https://doi.org/10.3390/su14159681
Liu, Z., Wang, S., Zhang, Y., Feng, Y., Liu, J., & Zhu, H. (2023). Artificial intelligence in food safety: A decade review and bibliometric analysis. Foods, 12(6), 1242. https://doi.org/10.3390/foods12061242
Luu, T. T. (2020). Reducing food waste behavior among hospitality employees through communication: Dual mediation paths. International Journal of Contemporary Hospitality Management, 32(5), 1881-1904. https://doi.org/10.1108/ijchm-09-2019-0779
Mehta, M. (2024). Intelligent waste management: Maximize profits and minimize waste using IoT and AI. World Journal of Advanced Research and Reviews, 24(1), 693-701. https://doi.org/10.30574/wjarr.2024.24.1.3062
Mondol, E. P. (2021). The impact of block chain and smart inventory system on supply chain performance at retail industry. International Journal of Computations, Information and Manufacturing, 1(1), 56-76. https://doi.org/10.54489/ijcim.v1i1.30
Nwokediegwu, Z. Q. S., & Ugwuanyi, E. D. (2024). Implementing AI-driven waste management systems in underserved communities in the USA. Engineering Science & Technology Journal, 5(3), 794-802. https://doi.org/10.51594/estj.v5i3.903
Nwokediegwu, Z. Q. S., Ugwuanyi, E. D., Dada, M. A., Majemite, M. T., & Obaigbena, A. (2024). AI-driven waste management systems: A comparative review of innovations in the USA and Africa. Engineering Science & Technology Journal, 5(2), 507-516. https://doi.org/10.51594/estj.v5i2.828
Onyeaka, H., Tamasiga, P., Nwauzoma, U. M., Miri, T., Juliet, U. C., Nwaiwu, O., & Akinsemolu, A. A. (2023). Using artificial intelligence to tackle food waste and enhance the circular economy: Maximising resource efficiency and minimising environmental impact: A review. Sustainability, 15(13), 10482. https://doi.org/10.3390/su151310482
Ouro Salim., O., Guarnieri, P., & Leitão, F. (2021). Food waste from the view of circular economy: A systematic review of international literature. Revista de Gestão Social e Ambiental, 15, e02579.. https://doi.org/10.24857/rgsa.v15.2579
Ozcil, I. E. (2024). Reducing food waste using machine learning models: Forecasting and optimization approaches. Open Access Journal of Data Science and Artificial Intelligence, 2(1), 000136. https://doi.org/10.23880/oajda-16000136
Pilarski, S., Sidhu, A., & Varró, D. (2023). A simulation environment for reducing food waste via reinforcement learning. In M. J. Blas, & G. Alvarez (Eds.), 2023 Annual modeling and simulation conference (pp. 332-344). IEEE.
Pong, V. (2021). Global versus local framing of the issue of food waste: The role of identification with all humanity and the implications for climate change communication. Asian Journal of Social Psychology, 24(2), 221-231. https://doi.org/10.1111/ajsp.12453
Purwanto, E., Yulianto, A., Biasini, N., Octavia, J., & Wati, V. (2023). Environmental awareness and intention to reduce food waste among urban people. IOP Conference Series Earth and Environmental Science, 1168(1), 012048. https://doi.org/10.1088/1755-1315/1168/1/012048
Rejeb, A., Keogh, J. G., & Treiblmaier, H. (2019). Leveraging the internet of things and blockchain technology in supply chain management. Future Internet, 11(7), 161. https://doi.org/10.3390/fi11070161
Reyes, V., Cahill, E., & Mis Solval, K. E. (2024). The potential for reducing food waste through shelf-life extension: Actionable insights from data digitization. Sustainability, 16(7), 2986. https://doi.org/10.3390/su16072986
Santos‐Corrada, M. D. L. M., Méndez‐Tejeda, R., Flecha‐Ortiz, J. A., & Lopez, E. (2024). An analysis of sustainable consumption practices through the role of the consumer behavior in the circular economy. Journal of Consumer Behaviour, 23(1), 229-242. https://doi.org/10.1002/cb.2183
Septianto, F., Kemper, J. A., & Northey, G. (2020). Thanks, but no thanks: The influence of gratitude on consumer awareness of food waste. Journal of Cleaner Production, 258, 120591. https://doi.org/10.1016/j.jclepro.2020.120591
Shaw, P. J., Smith, M. M., & Williams, I. D. (2018). On the prevention of avoidable food waste from domestic households. Recycling, 3(2), 24. https://doi.org/10.3390/recycling3020024
Somlai, R. (2023). Insights into business strategies for reducing food waste in the Australian food industry. Business Strategy and the Environment, 32(6), 3151-3164. https://doi.org/10.1002/bse.3292
Taneja, A., Nair, G., Joshi, M., Sharma, S., Sharma, S., Jambrak, A. R., Roselló-Soto, E., Barba, F.J., Castagnini, J.M., Leksawasdi, N., & Phimolsiripol, Y. (2023). Artificial intelligence: Implications for the agri-food sector. Agronomy, 13(5), 1397. https://doi.org/10.3390/agronomy13051397
Tonini, P., Odina, P. M., & Durany, X. G. (2023). Predicting food waste in households with children: Socio-economic and food-related behavior factors. Frontiers in Nutrition, 10, 1249310. https://doi.org/10.3389/fnut.2023.1249310
Trabelsi, M., Casprini, E., Fiorini, N., & Zanni, L. (2023). Unleashing the value of artificial intelligence in the agri-food sector: Where are we?. British Food Journal, 125(13), 482-515. https://doi.org/10.1108/bfj-11-2022-1014
Trevisan, C., & Formentini, M. (2024). Digital technologies for food loss and waste prevention and reduction in agri-food supply chains: A systematic literature review and research agenda. IEEE Transactions on Engineering Management, 71, 12326-12345. https://doi.org/10.1109/tem.2023.3273110
Tsai, W. C., Chen, X., & Yang, C. (2020). Consumer food waste behavior among emerging adults: Evidence from China. Foods, 9(7), 961. https://doi.org/10.3390/foods9070961
Tutore, I., Parmentola, A., di Fiore, M. C., & Calza, F. (2024). A conceptual model of artificial intelligence effects on circular economy actions. Corporate Social Responsibility and Environmental Management, 31(5), 4772-4782. https://doi.org/10.1002/csr.2827
Urugo, M. M., Teka, T. A., Gemede, H. F., Mersha, S., Tessema, A., Woldemariam, H. W., & Admassu, H. (2024). A comprehensive review of current approaches on food waste reduction strategies. Comprehensive Reviews in Food Science and Food Safety, 23(5), e70011. https://doi.org/10.1111/1541-4337.70011
Van Klompenburg, T., Kassahun, A., & Catal, C. (2020). Crop yield prediction using machine learning: A systematic literature review. Computers and Electronics in Agriculture, 177, 105709. https://doi.org/10.1016/j.compag.2020.105709
Vittuari, M., Falasconi, L., Masotti, M., Piras, S., Segrè, A., & Setti, M. (2020). ‘Not in my bin’: Consumer’s understanding and concern of food waste effects and mitigating factors. Sustainability, 12(14), 5685. https://doi.org/10.3390/su12145685
Waltersmann, L., Kiemel, S., Stuhlsatz, J., Sauer, A., & Miehe, R. (2021). Artificial intelligence applications for increasing resource efficiency in manufacturing companies—a comprehensive review. Sustainability, 13(12), 6689. https://doi.org/10.3390/SU13126689
Wang, L., He, Y., & Wu, Z. (2022). Design of a blockchain-enabled traceability system framework for food supply chains. Foods, 11(5), 744. https://doi.org/10.3390/foods11050744
Wu, P. J., & Tai, Y. C. (2024). Artificial intelligence-based food-quality and warehousing management for food banks' inbound logistics. Journal of Enterprise Information Management, 37(1), 307-325. https://doi.org/10.1108/jeim-10-2022-0398
Yang, C., Feng, Y., & Whinston, A. (2022). Dynamic pricing and information disclosure for fresh produce: An artificial intelligence approach. Production and Operations Management, 31(1), 155-171. https://doi.org/10.1111/poms.13525
Zhao, N., Hong, J., & Lau, K. H. (2023). Impact of supply chain digitalization on supply chain resilience and performance: A multi-mediation model. International Journal of Production Economics, 259, 108817. https://doi.org/10.1016/j.ijpe.2023.108817
