Digital Transformation: Improving Operation Efficiencies Through AI-Predictive Analysis Network at a Vancouver Catering Services

Main Article Content

Innocent Mafiele
Rustam Nigmatullin
Farzaneh Tajikfilestan
Gaurav Pawar
Anu Chawla

Abstract

An enormous global food firm is growing quickly and getting ready to move to a new location, but it needs more software infrastructure and antiquated technologies, which present serious problems. This paper suggests a thorough digital transformation plan to overcome these obstacles and facilitate the company's growth. The concept is centred on deploying the AI-driven Predictive Analytics Network (APAN), a technological solution intended to boost productivity overall, streamline workflows, and improve the efficiency of food delivery. APAN seeks to solve inefficiencies from the company's expanded scale and staff by automating repetitive tasks and optimizing essential business processes. The company's technological infrastructure will be modernized, employee collaboration and communication will increase, customer service will be improved, and operating expenses will be decreased with the proposed digital transformation. This idea is valuable since it can help the business expand, enhance customer satisfaction, and guarantee more economical and efficient operations. Employees, clients, and business partners will all profit from this change, which will ultimately create an organizational culture that is more creative and effective.

Article Details

How to Cite
Digital Transformation: Improving Operation Efficiencies Through AI-Predictive Analysis Network at a Vancouver Catering Services. (2024). International Journal of Management and Data Analytics, 4(1), 55-66. https://ijmada.com/index.php/ijmada/article/view/62
Section
Student Paper

How to Cite

Digital Transformation: Improving Operation Efficiencies Through AI-Predictive Analysis Network at a Vancouver Catering Services. (2024). International Journal of Management and Data Analytics, 4(1), 55-66. https://ijmada.com/index.php/ijmada/article/view/62

References

Abdullah, F., & Ward, R. (2015, December 14). Developing a general extended technology acceptance model for e-learning (GETAMEL) by analyzing commonly used external factors. ScienceDirect: Computers in Human Behavior. https://www.sciencedirect.com/science/article/pii/S074756321530251X?via%3Dihub.

Abrahams, N. T. O., Ewuga, N. S. K., Dawodu, N. S. O., Adegbite, N. a. O., & Hassan, N. a. O. (2024). A REVIEW OF CYBERSECURITY STRATEGIES IN MODERN ORGANIZATIONS: EXAMINING THE EVOLUTION AND EFFECTIVENESS OF CYBERSECURITY MEASURES FOR DATA PROTECTION. Computer Science & IT Research Journal, 5(1), 1–25. https://doi.org/10.51594/csitrj.v5i1.699

Alahmadi, A.N., Rehman, S.U., Alhazmi, H.S., Glynn, D.G., Shoaib, H. and Solé, P., 2022. Cyber-security threats and side-channel attacks for digital agriculture. Sensors, 22(9), p.3520. https://doi.org/10.3390/s22093520

Analytics8. (2024, August 8). Data & Analytics Strategy Services Page. Retrieved from Analytics8 website: https://www.analytics8.com/services/data-strategy-consulting-services/

AWS. (2024). Intrusion Detection and Prevention. Retrieved from Amazon Web Services, Inc. website: https://aws.amazon.com/mp/scenarios/security/ids/

AZoSensors. (2024). AZoSensors Equipment. Retrieved from AZoSensors website: https://www.azosensors.com/equipment-index.aspx

Barz, N., Benick, M., Dörrenbächer-Ulrich, L., & Perels, F. (2024). Students’ acceptance of e-learning: Extending the technology acceptance model with self-regulated learning and affinity for technology-discover education. SpringerLink. https://link.springer.com/article/10.1007/s44217-024-00195-7

Buffalo. (2024). TeraStationTM 71210RH Series - Rackmount | Buffalo Americas. Retrieved September 10, 2024, from Buffalotech.com website: https://www.buffalotech.com/products/terastation-7010-series-rackmount

Butt, A., Imran, F., Helo, P., & Kantola, J. (2024). Strategic design of culture for digital transformation. Long Range Planning, 57(2), 102415. https://doi.org/10.1016/j.lrp.2024.102415

Camaréna, S. (2022). Artificial Intelligence (AI) for Sustainable Institutional Food Systems: Implementing AI Tools for School Nutrition Program Management in the United States of America. Frontiers in Sustainable Food Systems, 6. https://doi.org/10.3389/fsufs.2022.743810

Charles, V., Rana, N. P., & Carter, L. (2022). Artificial Intelligence for data-driven decision-making and governance in public affairs. Government Information Quarterly, 39(4), 101742. https://doi.org/10.1016/j.giq.2022.101742

Chen, S., & Ye, J. (2023). Understanding consumers’ intentions to purchase Smart Clothing using PLS-sem and fsQCA. PLOS ONE. https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0291870

Chen, T.-C., & Yu, S.-Y. (2021). The review of food safety inspection system based on artificial intelligence, image processing, and robotic. Food Science and Technology. https://doi.org/10.1590/fst.35421

Chu, H., Zhang, W., Bai, P., & Chen, Y. (2021). Data-driven optimization for last-mile delivery. Complex & Intelligent Systems, 9(3), 2271–2284. https://doi.org/10.1007/s40747-021-00293-1

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1988). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. ResearchGate. https://www.researchgate.net/publication/227446117_User_Acceptance_of_Computer_Technology_A_Comparison_of_Two_Theoretical_Models

Deep, N. G. (2023). Digital transformation’s impact on organizational culture. International Journal of Science and Research Archive, 10(2), 396–401. https://doi.org/10.30574/ijsra.2023.10.2.0977

D-Link. (2024). Nuclias Cloud-Managed AC2600 Wave-2 PoE Access Point - DBA-2820P. Retrieved from D-Link Shop Canada website: https://dlink.ca/products/nuclias-cloud-managed-ac2600-wave-2-access-point-dba-2820p

Elkan, C. (2010). Predictive analytics and data mining.

Galanakis, C. M. (2024). The Future of Food. Foods, 13(4), 506–506. https://doi.org/10.3390/foods13040506

Google Cloud. (2024). Google Cloud Pricing Calculator. Retrieved from Google.com website: https://cloud.google.com/products/calculator?dl=CiRlZjUwMTc0Mi03YjljLTRkNTAtYjU3MS05MzBiOTA5MmYwZWMQCRokRTk1Q0E3OTctMzVBNC00N0M4LUE4NEUtNjA5MEZCMkM1OTE2

Jagtap, S., Garcia-Garcia, G. and Rahimifard, S., 2021. Optimisation of the resource efficiency of food manufacturing via the Internet of Things. Computers in Industry, 127, p.103397. https://doi.org/10.1016/j.compind.2021.103397

Jiang, X. (2020). Digital economy in the post-pandemic era. Journal of Chinese Economic and Business Studies, 18(4), 333–339. https://doi.org/10.1080/14765284.2020.1855066

Kaldero, N. (2018). Data Science for Executives: Leveraging Machine Intelligence to Drive Business ROI. Lioncrest Publishing. https://dl.acm.org/doi/10.5555/3307102

Konur, S., Lan, Y., Thakker, D., Morkyani, G., Polovina, N. and Sharp, J., 2023. Towards design and implementation of Industry 4.0 for food manufacturing. Neural Computing and Applications, pp.1-13. https://doi.org/10.1007/s00521-022-07768-3

Kudashkina, K., Corradini, M. G., Thirunathan, P., Yada, R. Y., & Fraser, E. D. G. (2022). Artificial Intelligence technology in food safety: A behavioral approach. Trends in Food Science & Technology, 123, 376–381. https://doi.org/10.1016/j.tifs.2022.03.021

Leachman, L. (2023b, March 17). Using technology to create a better customer experience. Harvard Business Review. https://hbr.org/2023/03/using-technology-to-create-a-better-customer-experience

Lenovo. (2024). IdeaCentre AIO I (24″ Intel) All in One. Retrieved from Lenovo.com website: https://www.lenovo.com/ca/en/p/desktops/ideacentre/aio-500-series/ideacentre-aio-i-gen-9-(24-inch-intel)/len102d0035

Lenovo. (2024). ThinkSystem ST250 V3. Retrieved from Lenovo.com website: https://www.lenovo.com/ca/en/p/servers-storage/servers/towers/thinksystem-st250-v3-tower-server/7dcea01jna

Leso, B. H., & Cortimiglia, M. N. (2021, July 22). The influence of user involvement in information system adoption: An extension of Tam - Cognition, Technology & Work. SpringerLink. https://link.springer.com/article/10.1007/s10111-021-00685-w

Lin, Y., Ma, J., Wang, Q., & Sun, D.-W. (2022). Applications of machine learning techniques for enhancing nondestructive food quality and safety detection. Critical Reviews in Food Science and Nutrition, 1–21. https://doi.org/10.1080/10408398.2022.2131725

Liu, H., Wang, Y., & Yan, Z. (2024). Artificial Intelligence and Food Processing Firms Productivity: Evidence from China. Sustainability, 16(14), 5928–5928. https://doi.org/10.3390/su16145928

LinkedIn Learning. (2024). LinkedIn Learning. Retrieved from Linkedin.com website: https://learning.linkedin.com/content/me/author-training/en-us/author-test-pages/adobe-target-staged-content/lls-72/nav-var-1. https://cseweb.ucsd.edu/~elkan/255/dm.pdf

Microsoft. (2024). Microsoft Defender for Business | Microsoft Security. Retrieved from Microsoft.com website: https://www.microsoft.com/en-ca/security/business/endpoint-security/microsoft-defender-business#Microsoft-defender-plans-and-pricing

Microsoft Azure. (2024). Pricing - Machine Learning | Microsoft Azure. Retrieved from azure.microsoft.com website: https://azure.microsoft.com/en-us/pricing/details/machine-learning/

Musa, H. G., Fatmawati, I., Nuryakin, N., & Suyanto, M. (2024). Marketing research trends using technology acceptance model (TAM): a comprehensive review of researches (2002–2022). Cogent Business & Management, 11(1). https://doi.org/10.1080/23311975.2024.2329375

Nassibi, N., Fasihuddin, H., & Hsairi, L. (2023). Demand Forecasting Models for Food Industry by Utilizing Machine Learning Approaches. International Journal of Advanced Computer Science and Applications, 14(3). https://doi.org/10.14569/ijacsa.2023.01403101

Oludapo, S., Carroll, N., & Helfert, M. (2024). Why do so many digital transformations fail? A bibliometric analysis and future research agenda. Journal of Business Research, 174, 114528. https://doi.org/10.1016/j.jbusres.2024.114528

Oracle. (2024). Cloud Cost Estimator | Oracle Canada. Retrieved from Oracle.com website: https://www.oracle.com/ca-en/cloud/costestimator.html

Pacolli, M. (2022). Importance of Change Management in Digital Transformation Sustainability. ScienceDirect-IFAC-PapersOnLine. https://www.sciencedirect.com/science/article/pii/S2405896322030749

Palakurti, N. (2022, September 1). AI Applications in Food Safety and Quality Control. ESP Journal of Engineering & Technology Advancements ER. 10.56472/25832646/JETA-V2I3P111

Palfreyman, J., & Morton, J. (2022). The benefits of agile digital transformation to innovation processes. Journal of Strategic Contracting and Negotiation, 6(1), 26-36. https://journals.sagepub.com/doi/full/10.1177/20555636221079943

Pandey, Ms. S., & Shanker Mishra, Dr. R. (2024). UNRAVELING THE ROLE OF INFORMATION AND COMMUNICATION TECHNOLOGY (ICT) IN FOOD SYSTEMS. Futuristic Trends in Information Technology Volume 3 Book 1, 3, 279–290. https://doi.org/10.58532/v3bbit1p4ch1

Pragmatic Institute. (2024). Data Science for Product Teams | Pragmatic Institute. Retrieved 2024, from Pragmatic Institute - Data website: https://www.pragmaticinstitute.com/data/

Režek Jambrak, A., Nutrizio, M., Djekić, I., Pleslić, S. and Chemat, F., 2021. Internet of nonthermal food processing technologies (Iontp): Food industry 4.0 and sustainability. Applied Sciences, 11(2), p.686. https://doi.org/10.3390/app11020686

Scherer, R., Siddiq, F., & Tondeur, J. (2018). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in Education. ScienceDirect: Computers & Education. https://www.sciencedirect.com/science/article/abs/pii/S0360131518302458?via%3Dihub

Suthar, A., Sindhi, S., Kathiriya, J., Sharma, A., Singh, V., & Bhedi, K. (2024). Use of artificial intelligence (AI) in ensuring quality and safety of food of animal origin: A review. International Journal of Veterinary Sciences and Animal Husbandry, 9(2S), 240–247. https://doi.org/10.22271/veterinary.2024.v9.i2sd.1294

Tanriverdiyev, E. (2022). THE STATE OF THE CYBER ENVIRONMENT AND NATIONAL CYBERSECURITY STRATEGY IN DEVELOPED COUNTRIES. National Security Studies, 23(1), 19–26. https://doi.org/10.37055/sbn/149510

Trung, N.D., Huy, D.T.N. and Le, T.H., 2021. IoTs, machine learning (ML), AI and digital transformation affects various industries-principles and cybersecurity risks solutions. Management, 18(10.14704). https://doi.org/10.14704/107.2021

Van Der Linden, D., Michalec, O.A. and Zamansky, A., 2020. Cybersecurity for smart farming: socio-cultural context matters. IEEE Technology and Society Magazine, 39(4), pp.28-35. https://doi.org/10.1109/MTS.2020.3026468

Venkatesh, V., & Davis, F. D. (2020). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. ResearchGate. https://www.researchgate.net/publication/227447282_A_Theoretical_Extension_of_the_Technology_Acceptance_Model_Four_Longitudinal_Field_Studies

World Health Organization (WHO). (2022). Food safety. Who.int; World Health Organization: WHO. https://www.who.int/news-room/fact-sheets/detail/food-safety

Wu, P.-J., & Chien, C.-L. (2021). AI-based quality risk management in omnichannel operations: O2O food dissimilarity. Computers & Industrial Engineering, 160, 107556. https://doi.org/10.1016/j.cie.2021.107556

Yucel, S. (2018). Estimating the benefits, drawbacks and risk of digital transformation strategy. In 2018 International Conference on Computational Science and Computational Intelligence (CSCI) (pp. 233-238). IEEE. https://www.sciencegate.app/document/10.1109/csci46756.2018.00051

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