AI-Driven Dynamic Capacity Optimization for Custom Cake Operations in Large-Scale Retail

Abstract

Production of custom cakes in large-scale retail businesses poses a highly complicated issue. Demand patterns are difficult to predict because they depend on customer preferences, seasons, and promotional events. On the other hand, production is limited by factors including skilled personnel availability and dependencies in the supply chain. Therefore, static methods of capacity planning do not work in this situation, which means that the organization may lose some business or reduce the level of customer satisfaction. To address the identified problems, an artificial intelligence-based model is proposed here. The solution combines several aspects into a single framework, which evaluates incoming orders in relation to the available capacity and chooses the most efficient strategy in terms of profit. Thus, it is possible to determine how many orders each store should accept or reject based on current needs. The real-time nature of this solution will ensure high efficiency and allow the organization to maximize its revenue. Furthermore, there are benefits for customers because their orders can be fulfilled faster, which improves customer experience. While the proposed framework is contextualized within custom cake operations, its underlying principles are broadly applicable to any retail domain involving high-variability, personalized production—including custom jewelry, specialty paint formulation, and designer apparel and accessories