Stochastic Game Network-inspired intelligent framework for quality assessment in logistic industry

Abstract

The widespread utilization of IoT-Fog-Cloud computing has revolutionized customer-oriented service delivery aspects in the global logistics industry. With industrial globalization, the logistics industry is often challenged to address quality issues. Moreover, with improper product management and transportation, the logistics industry has to face considerable financial loss. Conspicuously, a novel Stochastic Game Network (SGN) is proposed in the current study to ensure enhanced service product quality in the logistics industry. Specifically, the smart logistic industry-based decision-making framework is presented by incorporating several parameters of product quality. In the proposed method, each IoT device acts as a game player with a preset action set and strategy. Moreover, each IoT sensor at 4 levels of the logistic industry including Production Management Sensor (IMS), Inventory Management Sensor (IMS), Transportation Management Sensor (TMS), and Delivery Management Sensor (DMS) is incorporated as a game player node. Quality parameters are analyzed using the proposed Bayesian Belief Model (BBM) technique for effective classification. Moreover, Game-theoretic decision-making is used to automate enhanced product quality management and control units in IoT-based logistic industries for effective analysis and customer-oriented service delivery. For validation purposes, experimental results are compared with state-of-the-art decision-making techniques. Based on the results, the proposed model has registered enhanced statistical measures in terms of Precision (92.98%), Specificity (93.78%), Sensitivity (96.78%), Reliability (96.54%), and Stability (78%). To assess the theoretical efficacy of the proposed strategy, mathematical analysis is performed to quantify customer satisfaction. Conclusively, the proposed model presents an effective mechanism for quality assurance by incorporating stochastic behavior in the logistics industry.