AI-Assisted Demand Forecasting and Production Scheduling for High-Volume Food Manufacturing in Saudi Arabia: A Food Security Framework for Vision 2030

References

[1] F. Angizeh, H. Montero, A. Vedpathak, and M. Parvania, “Optimal production scheduling for smart manufacturers with application to food production planning,” Computers & Electrical Engineering, vol. 84, Art. no. 106609, 2020, doi: 10.1016/j.compeleceng.2020.106609. ScienceDirect

[2] F. Kamhuber, T. Sobottka, B. Heinzl, J. Henjes, and W. Sihn, “An efficient hybrid multi-criteria optimization approach for rolling production smoothing of a European food manufacturer,” Computers & Industrial Engineering, vol. 147, Art. no. 106620, 2020, doi: 10.1016/j.cie.2020.106620. ScienceDirect

[3] G. P. Georgiadis, B. Mariño Pampín, D. A. Cabo, and M. C. Georgiadis, “Optimal production scheduling of food process industries,” Computers & Chemical Engineering, vol. 134, Art. no. 106682, 2020, doi: 10.1016/j.compchemeng.2019.106682. ScienceDirect

[4] C. G. Palacin, C. Vilas, A. A. Alonso, J. L. Pitarch, and C. de Prada, “Closed-loop Scheduling in a canned food factory,” IFAC-PapersOnLine, vol. 53, pp. 10791–10796, 2020.

[5] S. Punia and S. Shankar, “Predictive analytics for demand forecasting: A deep learning-based decision support system,” Knowledge-Based Systems, vol. 258, Art. no. 109956, 2022.

[6] D. Ni, Z. Xiao, and M. K. Lim, “A review and analysis of artificial intelligence methods for demand forecasting in supply chain management,” Procedia CIRP, vol. 107, pp. 1126–1131, 2022.

[7] O. N. Arunkumar and D. Divya, “Deep learning techniques for demand forecasting: A review and future directions,” Information Resources Management Journal, vol. 35, 2022.

[8] V. L. Migueis, A. Pereira, J. Pereira, and G. Figueira, “Reducing fresh fish waste while ensuring availability: Demand forecast using censored data and machine learning,” Journal of Cleaner Production, vol. 359, Art. no. 131852, 2022.

[9] J. Huber, A. Gossmann, and H. Stuckenschmidt, “Daily retail demand forecasting using machine learning with emphasis on calendric special days,” International Journal of Forecasting, vol. 36, pp. 1420–1438, 2020.

[10] K. Posch, C. Truden, P. Hungerländer, and J. Pilz, “A Bayesian approach for predicting food and beverage sales in staff canteens and restaurants,” International Journal of Forecasting, vol. 38, pp. 321–338, 2022.

[11] F. Tangour, M. Nouiri, and R. Abbou, “Multi-Objective Production Scheduling of Perishable Products in Agri-Food Industry,” Applied Sciences, vol. 11, Art. no. 6962, 2021.

[12] V. Solina and G. Mirabelli, “Integrated production-distribution scheduling with energy considerations for efficient food supply chains,” Procedia Computer Science, vol. 180, pp. 797–806, 2021.

[13] M. E. Samouilidou, G. P. Georgiadis, and M. C. Georgiadis, “Food Production Scheduling: A Thorough Comparative Study between Optimization and Rule-Based Approaches,” Processes, vol. 11, Art. no. 1950, 2023.

[14] R. Mousavi, M. Bashiri, and E. Nikzad, “Stochastic production routing problem for perishable products: Modeling and a solution algorithm,” Computers & Operations Research, vol. 142, Art. no. 105725, 2022.

[15] V. Polotski, A. Gharbi, and J.-P. Kenne, “Production control in manufacturing systems with perishable products under periodic demand,” Journal of Manufacturing Systems, vol. 63, pp. 288–303, 2022.

[16] A. Alvarez, P. Miranda, and S. U. K. Rohmer, “Production routing for perishable products,” Omega, vol. 111, Art. no. 102667, 2022.

[17] Koulouris, N. Misailidis, and D. Petrides, “Applications of process and digital twin models for production simulation and scheduling in the manufacturing of food ingredients and products,” Food and Bioproducts Processing, vol. 126, pp. 317–333, 2021.

[18] P. Maheshwari, S. Kamble, A. Belhadi, M. Venkatesh, and M. Z. Abedin, “Digital twin-driven real-time planning, monitoring, and controlling in food supply chains,” Technological Forecasting and Social Change, vol. 195, Art. no. 122799, 2023.

[19] S. K. Jauhar, S. Harinath, V. Krishnaswamy, and S. K. Paul, “Explainable artificial intelligence to improve the resilience of perishable product supply chains by leveraging customer characteristics,” Annals of Operations Research, vol. 354, pp. 103–142, 2025.

[20] N. Hübner, J. Caspers, V. C. Coroamă, et al., “Machine-learning-based demand forecasting against food waste: Life cycle environmental impacts and benefits of a bakery case study,” Journal of Industrial Ecology, vol. 28, pp. 1117–1131, 2024, doi: 10.1111/jiec.13528. Wiley

[21] K. Ait Ben Hamou, Z. Jarir, and S. Elfirdoussi, “Using machine learning for production scheduling problems in the supply chain: A review,” Computers & Industrial Engineering, vol. 206, Art. no. 111243, 2025.

[22] A. Brintrup, E. Kosasih, P. Schaffer, G. Zheng, G. Demirel, and B. L. MacCarthy, “Digital supply chain surveillance using artificial intelligence: Definitions, opportunities and risks,” International Journal of Production Research, vol. 62, pp. 4674–4695, 2024.

[23] H. Wu, J. Liu, and B. Liang, “AI-driven supply chain transformation in Industry 5.0: Enhancing resilience and sustainability,” Journal of the Knowledge Economy, vol. 16, pp. 3826–3868, 2024.

[24] A. Rejeb, K. Rejeb, and A. Hassoun, “The impact of machine learning applications in agricultural supply chain: A topic modeling-based review,” Discover Food, vol. 5, Art. no. 141, 2025.

[25] Saudi Vision 2030, Annual Report 2025. Riyadh, Saudi Arabia, 2025.

[26] National Industrial Development and Logistics Program, Delivery Plan: Food Processing and Industrial Development Priorities. Riyadh, Saudi Arabia, 2024.

[27] Saudi Vision 2030, National Industrial Development and Logistics Program: Food Processing Sub-Sector Strategy. Riyadh, Saudi Arabia, 2022.

[28] General Authority for Statistics, Food Security Statistics 2024. Riyadh, Saudi Arabia, 2024.

[29] Food and Agriculture Organization of the United Nations, GIEWS Country Brief: Kingdom of Saudi Arabia. Rome, Italy, 2024.

[30] F. Avishan, İ. Yanıkoğlu, and M. Soysal, “Adaptive optimization approach for production and distribution planning of perishable food products under demand uncertainty,” Annals of Operations Research, 2025, doi: 10.1007/s10479-025-06552-5. Springer

@article{1723457,
      author = {Atif Maqbool Syed},
      title = {AI-Assisted Demand Forecasting and Production Scheduling for High-Volume Food Manufacturing in Saudi Arabia: A Food Security Framework for Vision 2030},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {3},
      pages = {2950-2961},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1723457.pdf},
      abstract = {High-volume food manufacturing in Saudi Arabia is increasingly exposed to volatile demand, short product life cycles, promotion-driven peaks, imported input dependencies, capacity bottlenecks, and the operational consequences of seasonal and religious events. These conditions make demand forecasting and production scheduling inseparable rather than sequential planning tasks. This review synthesizes research published between 2020 and 2025 on artificial intelligence-assisted demand forecasting, perishable-product planning, finite-capacity scheduling, digital-twin-enabled decision support, and food supply-chain resilience. The study develops an integrated framework in which demand sensing produces probabilistic forecasts that are translated into production decisions through capacity, shelf-life, sanitation, changeover, labour, inventory, and service constraints. Evidence indicates that machine-learning and deep-learning methods can improve forecast accuracy when promotions, weather, calendar events, regional variation, and censored demand are represented appropriately, while optimization models can convert these signals into feasible schedules that reduce changeovers, waste, shortages, and unstable resource use [1-8]. The review also identifies a persistent implementation gap: forecasting studies often stop at predictive accuracy, whereas scheduling studies frequently assume demand inputs are fixed and reliable. The proposed framework closes this gap by linking forecast uncertainty, rolling-horizon scheduling, execution feedback, and food-security outcomes. For Saudi manufacturers, the value of this integration lies not only in lower cost, but also in improving product availability, freshness, localized processing capacity, shock recovery, and evidence-based management under Vision 2030. The paper concludes with a staged implementation roadmap and research priorities for explainability, cross-site scheduling, event-aware forecasting, data governance, and resilient planning.},
      month = {September},
  }