The Role of Supply Chain in Demand Forecasting and Comparison of the Current and Desired State in the Iranian Book Printing and Publishing Industry

Nowadays, supply chain management and accurate demand forecasting are key factors in improving productivity, reducing operational costs, and increasing flexibility in various industries. The book printing and publishing industry, which is highly affected by demand fluctuations, requires efficient strategies for supply chain management and optimal resource allocation. This research examines the impact of the supply chain on demand forecasting processes in this industry and analyzes the differences between the current and desired conditions. In this regard, machine learning methods, particularly artificial neural networks, have been utilized to enhance forecast accuracy and reduce supply chain fluctuations. The findings indicate that machine learning models, especially feedforward neural networks with backpropagation learning algorithms, perform better compared to traditional methods such as time series models. Implementing these models can mitigate the bullwhip effect, improve coordination among supply chain members, and enable more precise strategic decision-making in the publishing industry. This study introduces a hybrid model that explores the practical application of neural networks in book demand forecasting and provides recommendations for optimizing the supply chain in this industry.

کلیدواژه ها:

Demand Forecasting ، Supply Chain ، Machine Learning ، Artificial Neural Networks ، Bullwhip Effect ، Printing and Publishing Industry