Supply Chain Management Optimization based on Deep Learning Approach

One of the main challenges in industrial engineering is supply chains. The supply chain is a set of activities that optimize the economic value of goods and services from source to consumption in order to meet the demands and satisfy customers by coordinating physical, financial, and informational flows. While the flow of things is moving forward, the flow of information has a backward movement. This means that information about demand, cost, quality, etc., is provided by the customer to the system. In this research, we try to present a new model for optimizing supply chain management using the Deep Learning with Convolutional Neural Network which combined with a fuzzy logic structure to confront four criteria: profit, risk, time capacity constraints, capacity constraints (suppliers, production centers, centers distribution, warehouses, and labor force). This study considers the Iranian e-commerce website www.digikala.com as a case study with real data.

Keywords:

Supply Chain , Convolutional Neural Network (CNN) , Deep Learning , Optimization , Fuzzy Logic