Integrated Neural Network for Ordering Optimization with Intertemporal-Dependent Demand and External Features

This paper introduces an integrated inventory model that employs customized neural networks to tackle the challenge of non-stationary demand for newsvendor-type products, such as vegetables and fashion items. In this newsvendor context, demand is intertemporal-dependent and influenced by external factors such as prices, promotions, and holidays. Contrary to traditional machine-learning-based inventory models that assume stationary and independent demand, our method accounts for the temporal dynamics and the impact of external factors on demand. Our customized neural network model integrates demand estimation with inventory optimization, circumventing the potential suboptimality of sequential estimation and optimization methods. We conduct a case study on optimizing the vegetable ordering decisions for a supermarket. The findings indicate the proposed model’s effectiveness in enhancing ordering decisions, thereby reducing inventory costs by up to 21.14%. By customizing an integrated neural network, this paper presents a precise and cost-effective inventory management solution to address real-world complexities of demand, like seasonality and external factor dependency. The proposed approach is particularly beneficial for retailers in industries dealing with perishable items and market volatility, enabling them to mitigate waste (e.g., unsold vegetables) and stockouts (e.g., seasonal fashion items). This directly confronts challenges related to sustainability and profitability. Furthermore, the integrated framework provides a methodological inspiration for adapting neural networks to other time-sensitive supply chain problems.