The wastage of perishable items has led to significant health and economic crises, increasing business uncertainty and fluctuating customer demand. This issue is worsened by online food delivery services, where frequent and unpredictable orders create inefficiencies in supply chain management, contributing to the bullwhip effect. This effect results in stockouts, excess inventory, and inefficiencies. Accurate demand forecasting helps stabilize inventory, optimize supplier orders, and reduce waste. This paper presents a Third-Party Logistics (3PL) supply chain model involving restaurants, online food apps, and customers, along with a deep learning-based demand forecasting model using a two-phase Long Short-Term Memory (LSTM) network. Phase one, intra-day forecasting, captures short-term variations, while phase two, daily forecasting, predicts overall demand. A two-year dataset from January 2023 to January 2025 from Swiggy and Zomato is used, employing discrete event simulation and grid search for optimal LSTM hyperparameters. The proposed method is evaluated using RMSE, MAE, and R-squared score, with R-squared as the primary accuracy measure. Phase one achieves an R-squared score of 0.69 for Zomato and 0.71 for Swiggy with a training time of 12 minutes, while phase two improves to 0.88 for Zomato and 0.90 for Swiggy with a training time of 8 minutes. To mitigate demand fluctuations, restaurant inventory is dynamically managed using the newsvendor model, adjusted based on forecasted demand. The proposed framework significantly reduces the bullwhip effect, improving forecasting accuracy and supply chain efficiency. For phase one, supply chain instability decreases from 2.61 to 0.96, and for phase two, from 2.19 to 0.80. This demonstrates the model's effectiveness in minimizing food waste and maintaining optimal restaurant inventory levels.

翻译：易腐物品的浪费已导致严重的健康和经济危机，增加了商业不确定性并加剧了客户需求的波动。在线食品配送服务使该问题进一步恶化，其频繁且不可预测的订单导致供应链管理效率低下，从而加剧了牛鞭效应。该效应会导致缺货、库存积压和效率低下。准确的需求预测有助于稳定库存、优化供应商订单并减少浪费。本文提出了一个涉及餐厅、在线食品应用和客户的第三方物流（3PL）供应链模型，以及一个基于深度学习的、采用两阶段长短期记忆（LSTM）网络的需求预测模型。第一阶段为日内预测，捕捉短期波动；第二阶段为日度预测，预测整体需求。研究使用了来自Swiggy和Zomato的从2023年1月到2025年1月的两年数据集，并采用离散事件模拟和网格搜索来优化LSTM超参数。所提方法使用均方根误差（RMSE）、平均绝对误差（MAE）和决定系数（R-squared）进行评估，其中以R-squared作为主要的准确性度量指标。第一阶段预测中，Zomato和Swiggy的R-squared得分分别为0.69和0.71，训练时间为12分钟；而第二阶段预测中，两者的R-squared得分分别提升至0.88和0.90，训练时间为8分钟。为了缓解需求波动，餐厅库存采用报童模型进行动态管理，并根据预测需求进行调整。所提出的框架显著降低了牛鞭效应，提高了预测准确性和供应链效率。对于第一阶段，供应链不稳定性从2.61降至0.96；对于第二阶段，则从2.19降至0.80。这证明了该模型在最小化食品浪费和维持餐厅最佳库存水平方面的有效性。