Review of The Application of Digital Transformation in Food Industry


  • Galih Nugroho Food Technology Department, Faculty of Engineering, Bina Nusantara University, Jakarta, 11480, Indonesia
  • Felicia Tedjakusuma Food Technology Department, Faculty of Engineering, Bina Nusantara University, Jakarta, 11480, Indonesia
  • Diana Lo Food Technology Department, Faculty of Engineering, Bina Nusantara University, Jakarta, 11480, Indonesia
  • Andreas Romulo Food Technology Department, Faculty of Engineering, Bina Nusantara University, Jakarta, 11480, Indonesia
  • Dimas Hanung Pamungkas PT Nutrifood Indonesia, Jakarta, 13920, Indonesia
  • Sigit Aditya Kinardi PT Nutrifood Indonesia, Jakarta, 13920, Indonesia



Artificial intelligence, block chain, food industry, machine learning, internet of things


In recent years, food companies have been facing various challenges related to fluctuating demand and constantly evolving customer and supplier requirements. These challenges have made it necessary for food companies to adapt to new technological advancements and implement new solutions to optimize their manufacturing systems. One such solution is digital transformation, which is a suite of technologies aimed at creating smart ecosystems that can transform industrial processes. Digital transformations are designed to exploit the potential of rapidly advancing information and communication technology (ICT) in the food industry. These include digital tools such as machine learning, artificial intelligence, blockchain, and IoT or the Internet of Things. These technologies have several applications in the food industry, ranging from food supply chain management to food safety, production, and consumption.


Ahani, A., Nilashi, M., Ibrahim, O., Sanzogni, L., & Weaven, S. (2019). Market segmentation and travel choice prediction in Spa hotels through TripAdvisor’s online reviews. International Journal of Hospitality Management, 80, 52–77.

Ali, I., Arslan, A., Khan, Z., & Tarba, S. Y. (2021). The role of industry 4.0 technologies in mitigating supply chain disruption: Empirical evidence from the Australian food processing industry. IEEE Transactions on engineering management, 1-10.

Amenaghawon, A. N., Evbarunegbe, N. I., & Obahiagbon, K. (2021). Optimum biodiesel production from waste vegetable oil using functionalized cow horn catalyst: a comparative evaluation of some expert systems. Cleaner Engineering and Technology, 4, Article 100184.

Ammar, M., Haleem, A., Javaid, M., Bahl, S., & Nandan, D. (2022). Improving the performance of supply chain through industry 4.0 technologies [Conference presentation]. Advancement in Materials, Manufacturing and Energy Engineering, Vol. II: Select Proceedings of ICAMME 2021. Singapore: Springer Nature Singapore.

Andersen, E., Johnson, J. C., Kolbjørnsrud, V., & Sannes, R. (2018). Chapter 2: The data-driven organization: Intelligence at SCALE. In At the Forefront, Looking Ahead: Research-Based Answers to Contemporary Uncertainties of Management (pp. 23-42). Oslo: Universitetsforlaget.

Apolo-Apolo, O. E., Martínez-Guanter, J., Egea, G., Raja, P., & Pérez-Ruiz, M. (2020). Deep learning techniques for estimation of the yield and size of citrus fruits using a UAV. European Journal of Agronomy, 115, Article 126030.

Appio, F. P., Frattini, F., Petruzzelli, A. M., & Neirotti, P. (2021). Digital transformation and innovation management: A synthesis of existing research and an agenda for future studies. Journal of Product Innovation Management, 38(1), 4-20.

Arel, I., Rose, D. C., & Karnowski, T. P. (2010). Deep machine learning-a new frontier in artificial intelligence research [research frontier]. IEEE computational intelligence magazine, 5(4), 13-18.

Asif, M., Searcy, C., & Castka, P. (2022). Exploring the role of industry 4.0 in enhancing supplier audit authenticity, efficacy, and cost effectiveness. Journal of Cleaner Production, 331, Article 129939.

Astiningrum, M., Wijayaningrum, V. N., & Putri, I. K. (2021). Forecasting Model of Staple Food Prices Using Support Vector Regression with Optimized Parameters. Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI), 7(3), 441-452.

Bankins, S., & Formosa, P. (2023). The ethical implications of artificial intelligence (AI) for meaningful work. Journal of Business Ethics, 185, 725-740.

Barandun, G., Soprani, M., Naficy, S., Grell, M., Kasimatis, M., Chiu, K. L., ... & Güder, F. (2019). Cellulose fibers enable near-zero-cost electrical sensing of water-soluble gases. ACS sensors, 4(6), 1662-1669.

Basheer, I. A., & Hajmeer, M. (2000). Microbiological Methods. Journal of Microbiological Methods, 43(1), 3-31.

Bayraktar, O., & Ataç, C. (2018). The effects of Industry 4.0 on Human resources management. Globalization, Institutions and Socio-Economic Performance. Globalization, Institutions and Socio-Economic Performance (pp.337-359). Lausanna: Peter Lang.

Beker, I., Delić, M., Milisavljević, S., Gošnik, D., Ostojić, G., & Stankovski, S. (2016). Can IoT be used to mitigate food supply chain risk?. International Journal of Industrial Engineering and Management, 7(1), 43-48.

Ben-Daya, M., Hassini, E., Bahroun, Z., & Banimfreg, B. H. (2020). The role of internet of things in food supply chain quality management: A review. Quality management journal, 28(1), 17-40.

Bersani, C., Ruggiero, C., Sacile, R., Soussi, A., & Zero, E. (2022). Internet of Things Approaches for Monitoring and Control of Smart Greenhouses in Industry 4.0. Energies, 15(10), Article 3834.

Bhat S, M., Prabhakar, A., Rao R, R. K., GM, M., & G. H, R. (2010). Statistical optimization and neural modeling of amylase production from banana peel using Bacillus subtilis MTCC 441. International Journal of Food Engineering, 6(4).

Bhatia, M., & Ahanger, T. A. (2021). Intelligent decision-making in smart food industry: quality perspective. Pervasive and Mobile Computing, 72, Article 101304.

Bi, Z., Da Xu, L., & Wang, C. (2014). Internet of things for enterprise systems of modern manufacturing. IEEE Transactions on industrial informatics, 10(2), 1537-1546.

Bunge, J. (2017). Latest use for a bitcoin technology: Tracing turkeys from farm to table. The Wall Street Journal. Retrieved January 15, 2023, from

Chen, M., Tian, Y., Fortino, G., Zhang, J., & Humar, I. (2018). Cognitive internet of vehicles. Computer Communications, 120, 58-70.

Chen, R. Y. (2015). Autonomous tracing system for backward design in food supply chain. Food control, 51, 70-84.

Chiu, Y. C., Cheng, F. T., & Huang, H. C. (2017). Developing a factory-wide intelligent predictive maintenance system based on Industry 4.0. Journal of the Chinese Institute of Engineers, 40(7), 562-571.

Creydt, M., & Fischer, M. (2019). Blockchain and more-Algorithm driven food traceability. Food Control, 105, 45-51.

Das, A., Shukla, A., Manjunatha, R., & Lodhi, E. A. (2021, February 4-6). IoT based solid waste segregation using relative humidity values [Conference presentation]. 2021 3rd international conference on intelligent communication technologies and virtual mobile networks (ICICV). Tirunelveli, India.

de Medeiros Esper, I., From, P. J., & Mason, A. (2021). Robotisation and intelligent systems in abattoirs. Trends in Food Science & Technology, 108, 214-222.

De Pilli, T. (2022). Application of fuzzy logic system for the pizza production processing optimisation. Journal of Food Engineering, 319, Article 110906.

Demartini, M., Pinna, C., Tonelli, F., Terzi, S., Sansone, C., & Testa, C. (2018). Food industry digitalization: from challenges and trends to opportunities and solutions. IFAC-PapersOnLine, 51(11), 1371-1378.

Du, C. J., & Sun, D. W. (2004). Recent developments in the applications of image processing techniques for food quality evaluation. Trends in food science & technology, 15(5), 230-249.

Duong, L. N., Al-Fadhli, M., Jagtap, S., Bader, F., Martindale, W., Swainson, M., & Paoli, A. (2020). A review of robotics and autonomous systems in the food industry: From the supply chains perspective. Trends in Food Science & Technology, 106, 355-364.

Fernando, Y., Wahyuni-TD, I. S., Gui, A., Ikhsan, R. B., Mergeresa, F., & Ganesan, Y. (2022). A mixed-method study on the barriers of industry 4.0 adoption in the Indonesian SMEs manufacturing supply chains. Journal of Science and Technology Policy Management, 14(4), 678-695.

Galanakis, C. M. (2021). Food Technology Disruption. Massachusetts: Academic Press.

Galvez, J. F., Mejuto, J. C., & Simal-Gandara, J. (2018). Future challenges on the use of blockchain for food traceability analysis. TrAC Trends in Analytical Chemistry, 107, 222-232.

Goel, R., & Gupta, P. (2020). Robotics and industry 4.0. A Roadmap to Industry 4.0: Smart Production, Sharp Business and Sustainable Development (pp. 157-169). Springer Cham.

Gorny, C., & Wedel, F. H. (2022). Strategic Data Acquisition–a literature review. Seminar IT-Management in the Digital Age. Germany: FH Wedel.

Gupta, K., & Rakesh, N. (2018). IoT-based solution for food adulteration [Conference presentation]. Proceedings of First International Conference on Smart System, Innovations and Computing: SSIC 2017, Jaipur, India. Springer Singapore.

Hartarto, A., & Antara, N. T., (2018). Indonesia Industry 4.0 Readiness Index. Ministry of Industry, Indonesia. Badan Penelitian dan Pengembangan Industri Kementrian Perindustrian.

Hassoun, A., Aït-Kaddour, A., Abu-Mahfouz, A. M., Rathod, N. B., Bader, F., Barba, F. J., ... & Regenstein, J. (2022a). The fourth industrial revolution in the food industry—Part I: Industry 4.0 technologies. Critical Reviews in Food Science and Nutrition, 2022, 1-17.

Hassoun, A., Alhaj Abdullah, N., Aït-Kaddour, A., Ghellam, M., Beşir, A., Zannou, O., ... & Regenstein, J. M. (2022b). Food traceability 4.0 as part of the fourth industrial revolution: key enabling technologies. Critical Reviews in Food Science and Nutrition, 2022, 1-17.

Helwan, A., Sallam Ma’aitah, M. K., Abiyev, R. H., Uzelaltinbulat, S., & Sonyel, B. (2021). Deep learning based on residual networks for automatic sorting of bananas. Journal of Food Quality, 2021, Article 5516368.

Hong, I., Park, S., Lee, B., Lee, J., Jeong, D., & Park, S. (2014). IoT-based smart garbage system for efficient food waste management. The Scientific World Journal, 2014, Article 646953.

Huang, Y., Lan, Y., Thomson, S. J., Fang, A., Hoffmann, W. C., & Lacey, R. E. (2010). Development of soft computing and applications in agricultural and biological engineering. Computers and electronics in agriculture, 71(2), 107-127.

Ireland Craft Beers. (2017). Downstream beer. Retrieved January 15, 2023, from

Islam, M. S., & Dey, G. K. (2019, December 24-25). Precision agriculture: renewable energy based smart crop field monitoring and management system using WSN via IoT [Conference presentation]. 2019 International Conference on Sustainable Technologies for Industry 4.0 (STI). Dhaka, Bangladesh.

Jagtap, S., & Rahimifard, S. (2019). The digitisation of food manufacturing to reduce waste case study of a ready meal factory. Waste Management, 87, 387-397.

Jahanbakhshi, A., & Salehi, R. (2019). Processing watermelon waste using Saccharomyces cerevisiae yeast and the fermentation method for bioethanol production. Journal of Food Process Engineering, 42(7), Article e13283.

Jiang, M., & Chen, Z. (2021). Symmetry detection algorithm to classify the tea grades using artificial intelligence. Microprocessors and Microsystems, 81, Article 103738.

Jiang, Y., & Stylos, N. (2021). Triggers of consumers’ enhanced digital engagement and the role of digital technologies in transforming the retail ecosystem during COVID-19 pandemic. Technological Forecasting and Social Change, 172, Article 121029.

Jin, H., Qin, Y., Liang, H., Wan, L., Lan, H., Chen, G., ... & Hong, Z. L. (2017). A Mobile-Based High Sensitivity On-Field Organophosphorus Compounds Detecting System for IoT Based Food Safety Tracking. Journal of Sensors, 2017, Article 8797435.

Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260.

Kakatkar, C., Bilgram, V., & Füller, J. (2020). Innovation analytics: Leveraging artificial intelligence in the innovation process. Business Horizons, 63(2), 171-181.

Kalinowska, K., Wojnowski, W., & Tobiszewski, M. (2021). Smartphones as tools for equitable food quality assessment. Trends in Food Science & Technology, 111, 271-279.

Kittipanya-Ngam, P., & Tan, K. H. (2020). A framework for food supply chain digitalization: lessons from Thailand. Production Planning & Control, 31(2-3), 158-172.

Klerkx, L., & Begemann, S. (2020). Supporting food systems transformation: The what, why, who, where and how of mission-oriented agricultural innovation systems. Agricultural Systems, 184, Article 102901.

Konstantinidis, F. K., Kansizoglou, I., Santavas, N., Mouroutsos, S. G., & Gasteratos, A. (2020). Marma: A mobile augmented reality maintenance assistant for fast-track repair procedures in the context of industry 4.0. Machines, 8(4), Article 88.

Konur, S., Lan, Y., Thakker, D., Morkyani, G., Polovina, N., & Sharp, J. (2021). Towards design and implementation of Industry 4.0 for food manufacturing. Neural Computing and Applications, 2021, 1-13.

Kutyauripo, I., Rushambwa, M., & Chiwazi, L. (2023). Artificial intelligence applications in the agrifood sectors. Journal of Agriculture and Food Research, 11, Article 100502.

Lehmann, R. J., Reiche, R., & Schiefer, G. (2012). Future internet and the agri-food sector: State-of-the-art in literature and research. Computers and Electronics in Agriculture, 89, 158-174.

Limketkai, B. N., Mauldin, K., Manitius, N., Jalilian, L., & Salonen, B. R. (2021). The age of artificial intelligence: use of digital technology in clinical nutrition. Current surgery reports, 9(7), Article 20.

Lin, C. S., Pan, Y. C., Kuo, Y. X., Chen, C. K., & Tien, C. L. (2021). A study of automatic judgment of food color and cooking conditions with artificial intelligence technology. Processes, 9(7), Article 1128.

Liu, Y., Han, W., Zhang, Y., Li, L., Wang, J., & Zheng, L. (2016). An Internet-of-Things solution for food safety and quality control: A pilot project in China. Journal of Industrial Information Integration, 3, 1-7.

Liu, Y., Ma, X., Shu, L., Hancke, G. P., & Abu-Mahfouz, A. M. (2020). From Industry 4.0 to Agriculture 4.0: Current status, enabling technologies, and research challenges. IEEE Transactions on Industrial Informatics, 17(6), 4322-4334.

Lu, B. H., Bateman, R. J., & Cheng, K. (2006). RFID enabled manufacturing: fundamentals, methodology and applications. International Journal of Agile Systems and Management, 1(1), 73-92.

Marquez, A. J., Herrera, M. A., Ojeda, M. U., & Maza, G. B. (2009). Neural network as tool for virgin olive oil elaboration process optimization. Journal of Food Engineering, 95(1), 135-141.

Matindoust, S., Baghaei-Nejad, M., Abadi, M. H. S., Zou, Z., & Zheng, L. R. (2016). Food quality and safety monitoring using gas sensor array in intelligent packaging. Sensor Review, 36(2), 169-183.

Mayer, M., & Baeumner, A. J. (2019). A megatrend challenging analytical chemistry: biosensor and chemosensor concepts ready for the internet of things. Chemical reviews, 119(13), 7996-8027.

Mededjel, M., Belalem, G., & Neki, A. (2017). Towards a traceability system based on cloud and fog computing. Multiagent and Grid Systems, 13(1), 47-68.

Misra, N. N., Dixit, Y., Al-Mallahi, A., Bhullar, M. S., Upadhyay, R., & Martynenko, A. (2020). IoT, big data, and artificial intelligence in agriculture and food industry. IEEE Internet of things Journal, 9(9), 6305-6324.

Moeuf, A., Pellerin, R., Lamouri, S., Tamayo-Giraldo, S., & Barbaray, R. (2018). The industrial management of SMEs in the era of Industry 4.0. International journal of production research, 56(3), 1118-1136.

Mueller, B., & Mueller, C. (2016). The political economy of the Brazilian model of agricultural development: Institutions versus sectoral policy. The quarterly review of economics and finance, 62, 12-20.

Nirenjena, S., BalaSubramanian, D. L., & Monisha, M. (2018). Advancement in monitoring the food supply chain management using IOT. International Journal of Pure and Applied Mathematics, 119(14), 1193-1196.

Nita, S. (2015). Application of big data technology in support of food manufacturers commodity demand forecasting. NEC Technical Journal, 10(1), 90-93.

Oltra‐Mestre, M. J., Hargaden, V., Coughlan, P., & Segura‐García del Río, B. (2021). Innovation in the Agri‐Food sector: Exploiting opportunities for Industry 4.0. Creativity and Innovation Management, 30(1), 198-210.

Oluyisola, O. E., Bhalla, S., Sgarbossa, F., & Strandhagen, J. O. (2022). Designing and developing smart production planning and control systems in the industry 4.0 era: a methodology and case study. Journal of Intelligent Manufacturing, 33(1), 311-332.

Pathan, M., Patel, N., Yagnik, H., & Shah, M. (2020). Artificial cognition for applications in smart agriculture: A comprehensive review. Artificial Intelligence in Agriculture, 4, 81-95.

Rahimifard, S., Woolley, E., Webb, D. P., Garcia-Garcia, G., Stone, J., Jellil, A., ... & Trollman, H. (2017, April 26-28). Forging new frontiers in sustainable food manufacturing [Conference presentation]. Sustainable Design and Manufacturing 2017. SDM 2017. Smart Innovation, Systems and Technologies, Springer, Cham.

Ramesh, M. V., & Das, R. N. (2012, August 1-3). A public transport system based sensor network for fake alcohol detection [Conference presentation]. Wireless Communications and Applications: First International Conference, ICWCA 2011, Springer Berlin Heidelberg.

Remondino, M., & Zanin, A. (2022). Logistics and agri-food: Digitization to increase competitive advantage and sustainability. Literature review and the case of Italy. Sustainability, 14(2), Article 787.

Roldán, J. J., Crespo, E., Martín-Barrio, A., Peña-Tapia, E., & Barrientos, A. (2019). A training system for Industry 4.0 operators in complex assemblies based on virtual reality and process mining. Robotics and computer-integrated manufacturing, 59, 305-316.

Rowan, N. J. (2019). Pulsed light as an emerging technology to cause disruption for food and adjacent industries–Quo vadis?. Trends in food science & technology, 88, 316-332.

Saguy, I. S., Roos, Y. H., & Cohen, E. (2018). Food engineering and food science and technology: Forward-looking journey to future new horizons. Innovative Food Science & Emerging Technologies, 47, 326-334.

Sahni, V., Srivastava, S., & Khan, R. (2021). Modelling techniques to improve the quality of food using artificial intelligence. Journal of Food Quality, 2021, 1-10.

Samad, S., Ahmed, F., Naher, S., Kabir, M. A., Das, A., Amin, S., & Islam, S. M. S. (2022). Smartphone apps for tracking food consumption and recommendations: Evaluating artificial intelligence-based functionalities, features and quality of current apps. Intelligent Systems with Applications, 15, Article 200103.

Sanaeifar, A., Bakhshipour, A., & De La Guardia, M. (2016). Prediction of banana quality indices from color features using support vector regression. Talanta, 148, 54-61.

Sander, F., Semeijn, J., & Mahr, D. (2018). The acceptance of blockchain technology in meat traceability and transparency. British Food Journal, 120(9), 2066-2079.

Shahi, C., & Sinha, M. (2020). Digital transformation: challenges faced by organizations and their potential solutions. International Journal of Innovation Science, 13(1), 17-33.

Shih, C. W., & Wang, C. H. (2016). Integrating wireless sensor networks with statistical quality control to develop a cold chain system in food industries. Computer Standards and Interfaces,45, 62-78.

Shufutinsky, A., Beach, A. A., & Saraceno, A. (2020). OD for robots? Implications of Industry 4.0 on talent acquisition and development. Organization Development Journal, 38(3), 59-76.

Smiljkovikj, K., & Gavrilovska, L. (2014). SmartWine: Intelligent end-to-end cloud-based monitoring system. Wireless personal communications, 78, 1777-1788.

Smith, P. D. (2018). Hands-On Artificial Intelligence for Beginners: An introduction to AI concepts, algorithms, and their implementation. Packt Publishing Ltd.

Tabrizi, B., Lam, E., Girard, K., & Irvin, V. (2019). Digital transformation is not about technology. Harvard business review, 13, 1-6.

Tasca, P., & Tessone, C. J. (2017). Taxonomy of blockchain technologies. Principles of identification and classification. arXiv preprint arXiv:1708.04872.

Trinh, C., Meimaroglou, D., & Hoppe, S. (2021). Machine learning in chemical product engineering: The state of the art and a guide for newcomers. Processes, 9(8), Article 1456.

van Geest, M., Tekinerdogan, B., & Catal, C. (2021). Design of a reference architecture for developing smart warehouses in industry 4.0. Computers in industry, 124, Article 103343.

Verma, A., Bansal, M., & Verma, J. (2020). Industry 4.0: Reshaping the future of HR. Strategic Direction, 36(5), 9-11.

Walter, A., Finger, R., Huber, R., & Buchmann, N. (2017). Smart farming is key to developing sustainable agriculture. Proceedings of the National Academy of Sciences, 114(24), 6148-6150.

Wang, Z., Hu, M., & Zhai, G. (2018). Application of deep learning architectures for accurate and rapid detection of internal mechanical damage of blueberry using hyperspectral transmittance data. Sensors, 18(4), Article 1126.

Wang., J, & Yue, H. (2017). Food safety pre-warning system based on data mining for a sustainable food supply chain. Food Control, 73, 223-229.

Yan, B., Hu, D., & Shi, P. (2012). A traceable platform of aquatic foods supply chain based on RFID and EPC Internet of Things. International Journal of RF Technologies, 4(1), 55-70.

Yaqub, M. Z., & Alsabban, A. (2023). Industry-4.0-Enabled Digital Transformation: Prospects, Instruments, Challenges, and Implications for Business Strategies. Sustainability, 15(11), Article 8553.

Yasin, M. Z. (2022). Technical efficiency and total factor productivity growth of Indonesian manufacturing industry: does openness matter?. Studies in Microeconomics, 10(2), 195-224.

Zhang, M., Fan, B., Zhang, N., Wang, W., & Fan, W. (2021). Mining product innovation ideas from online reviews. Information Processing & Management, 58(1), Article 102389.

Zhang, X., Zhou, T., Zhang, L., Fung, K. Y., & Ng, K. M. (2019). Food product design: a hybrid machine learning and mechanistic modeling approach. Industrial & Engineering Chemistry Research, 58(36), 16743-16752.

Zhang, Y., Chen, B., & Lu, X. (2011, August 1-3). Intelligent monitoring system on refrigerator trucks based on the internet of things [Conference presentation]. International conference on wireless communications and applications (ICWCA 2011). Sanya, China.

Zhao, G., Guo, Y., Sun, X., & Wang, X. (2015). A system for pesticide residues detection and agricultural products traceability based on acetylcholinesterase biosensor and internet of things. International Journal of Electrochemical Science, 10(4), 3387-3399.

Zhong, R. Y., Li, Z., Pang, L. Y., Pan, Y., Qu, T., & Huang, G. Q. (2013). RFID-enabled real-time advanced planning and scheduling shell for production decision making. International Journal of Computer Integrated Manufacturing, 26(7), 649-662.

Zhou, L., Zhang, C., Liu, F., Qiu, Z., & He, Y. (2019). Application of deep learning in food: a review. Comprehensive reviews in food science and food safety, 18(6), 1793-1811.




How to Cite

Nugroho, G., Tedjakusuma, F., Lo, D., Romulo, A., Pamungkas, D. H., & Kinardi, S. A. (2023). Review of The Application of Digital Transformation in Food Industry. Journal of Current Science and Technology, 13(3), 774–790.