Hossain Md Shakhawath, Parvin Farjana
Department of Industrial Engineering and Management, Khulna University of Engineering & Technology, Khulna, Bangladesh.
PLoS One. 2025 Jun 4;20(6):e0325449. doi: 10.1371/journal.pone.0325449. eCollection 2025.
Precise demand forecasting has become crucial for merchants due to the growing complexity of client behavior and market dynamics. This allows them to enhance inventory management, minimize instances of stock outs, and enhance overall operational efficiency. In Bangladesh, there is a significant lack of emphasis on demand forecasting to enhance corporate performance. In recognition of these difficulties, the study seeks to produce predictions by employing two statistical models and three machine learning models. The historical sales data was obtained from a restaurant in Bangladesh, and five specific products were chosen for the purpose of predicting sales. The models have been rated according to their average score of deviation from the optimal root mean squared error. The Multilayer Perceptron and Random Forest algorithms have attained the top two positions. Statistical models such as simple exponential smoothing and Croston's method have exhibited superior performance compared to XGBOOST model. This study advances demand forecasting techniques in Bangladesh's restaurant industry by providing valuable insights, comparing different approaches, and suggesting ways to improve forecast accuracy and operational efficiency, thereby demonstrating the practical relevance and applicability of the research to the reader.
由于客户行为和市场动态日益复杂，精确的需求预测对商家来说变得至关重要。这使他们能够加强库存管理，尽量减少缺货情况，并提高整体运营效率。在孟加拉国，对需求预测以提高企业绩效的重视严重不足。认识到这些困难，该研究试图通过采用两种统计模型和三种机器学习模型来进行预测。历史销售数据来自孟加拉国的一家餐厅，为了预测销售情况，选择了五种特定产品。这些模型根据其与最优均方根误差的平均偏差得分进行评级。多层感知器和随机森林算法位居前两位。简单指数平滑法和克罗斯顿法等统计模型与XGBOOST模型相比表现出了更优的性能。本研究通过提供有价值的见解、比较不同方法以及提出提高预测准确性和运营效率的方法，推进了孟加拉国餐饮行业的需求预测技术，从而向读者展示了该研究的实际相关性和适用性。
