Industrial IoT and AI implementation in vehicular logistics and supply chain management for vehicle mediated transportation systems

• Published in: International journal of system assurance engineering and management Vol. 13; no. Suppl 1; pp. 673 - 680
• Main Authors: Bhargava, Amitabh, Bhargava, Deepshikha, Kumar, P. Naveen, Sajja, Guna Sekhar, Ray, Samrat
• Format: Journal Article
• Language: English
• Published: New Delhi Springer India 01.03.2022; Springer Nature B.V
• Subjects: Company structure; Customer satisfaction; Engineering; Engineering Economics; Industrial applications; Logistics; Maintenance management; Manufacturing; Marketing; Operating costs; Organization; Original Article; Parameter identification; Predictive maintenance; Quality Control; Reliability; Safety and Risk; Scheduling; Supply chain management; Supply chains; Transportation management; Transportation systems; Vibration monitoring; Smart logistics; Supply chain management; Intelligent logistics; Industry 5.0; Artificial intelligence; Industrial IoT
• ISSN: 0975-6809; 0976-4348
• DOI: 10.1007/s13198-021-01581-2

Internet in the present time has deeply impacted the demand and supply of materials. The constant demand of fast supply of logistics at cheapest cost has built a pressure of competitiveness among the manufacturing industries. To fulfil the requirement of present customers has led to the development of IIoT for smart manufacturing and smart logistics. Smart logistics has become the essential need of industry 5.0 to minimise logistic time and cost and maximise the customer satisfaction and organisation profit. This work presents the IIoT model integrated with intelligent logistics, transportation management structure for optimised logistic, betterment of customer experience and customer satisfaction, minimise transportation cost, the present work focus on the certain paraments of logistic scheduling such as optimal route identification, real time monitoring of logistic vehicle parameters such as fuel, wheel axel and engine vibration, temperature monitoring, customise and efficient designing of maintenance schedule and predictive maintenance of logistic vehicle for efficient working. The present work shows the improvement from 77 to 98% in overall performance of the proposed model along with increase in customer satisfaction, process efficiency, decreasing cost of operation with energy efficient and low latency performance of the new IIoT based framework.