Neuromorphic Optimization in Construction Supply Chain Logistics Management

Abstract

The inefficiencies in the logistics of the supply chain in the construction industry often result in project delays, increased expenses, and resource wastage. Traditional management methods are inappropriate for the dynamic and complex environment in which construction projects are undertaken. This research delves into the potential of leveraging neuromorphic computing, inspired by the human brain's neural architecture, to optimize supply chain operations. The primary objective is to scrutinize how neuromorphic systems can elevate inventory management, logistics optimization, demand forecasting, and supplier relationship management within supply chain operations. In pursuit of this goal, A structured questionnaire survey was administered to 182 respondents from Saudi Arabia to conduct data analysis via Principal Component Analysis (PCA). PCA exposed that the AVE values at 0.554, 0.607, 0.709, and 0.655 for Inventory Management Optimization, Logistics Optimization, Demand Forecasting, and Supplier Management, respectively, established the validity and reliability of the constructs. The established path coefficients were 0.43, 0.337, 0.477, and 0.135, at p < 0.05. Policymakers in the field of construction logistics are encouraged to promote the adoption of neuromorphic computing in order to enhance operational efficiency, reduce costs, and achieve improved outcomes. Future research endeavours should prioritize investigating challenges associated with integration and the assurance of data security.