Automated Transportation Systems in Supply Chains: Assessment of Operational and Energy Performance

Supply Chain Management emphasizes the need for estimating lead times as a performance metric to assess the supply chain operations. In particular, synchronizing the sub-assembly arrivals, supplier collaborations, transportation logistics, manufacturing operations, dispatching and delivery reliabilities are the ongoing challenges in supply chains. Automation has been implemented in material flow improvement in manufacturing processes, warehousing, material loading, docking operations. IOT integration in supply chain operations can streamline material, information, cash flows through the supply chain improving visibility across the network. This paper explores ongoing research on methods of automation accomplished within the supply chain, influence of implementing automation on transportation costs, lead times, carbon footprint supporting sustainable supply chain operations. The Queuing theory models for lead time and variability estimations is a classic analytical computation technique which has been researched in many assembly lines, service systems, computer networks, supply chain research. Existing literature in queueing theory investigates manufacturing operations as a network of workstations with material flow as the entities within the queue network. The traditional energy efficiency computations have been investigated using standard M/M/c or general distribution queue models with forklifts or machining centres as servers and energy control policies established in the manufacturing or warehousing system. This paper extends fork join queue network models with energy consumption queueing systems to develop an end-to-end energy aware fork join queue supply chain network with transportation nodes defined in the network represented by automated trucks as servers in the system. The fork join queue network approximations compute lead times and energy consumption as performance metrics for the supply chain. The approximations are computed for studying supply chain performance for generalized queue networks (Generalized Jackson’s Network) and Jackson’s Networks for the supply chain model across a range of utilizations (45-50%, 70 – 80%, 90 – 95%) with low, moderate and high variability in the system. To illustrate the supply chain model, an experimental supply chain network setup is established with an automated truck prototype in the FAME Laboratory at Penn State University. The fabricated prototypes are utilised in extracting instantaneous power profiles across the supply chain network. The data extraction, pre-processing and analysis yields an end-to-end energy aware supply chain model computing lead times and energy consumptions. The power profiles for fabricated prototypes are scaled using standard United Nations ECE Group of Rapporteurs on Pollution and Energy (UN ECE GRPE) testing cycles to classify and approximate a full-sized electric vehicle power profile. This power profile is extended in analysing a real supply chain in approximating lead times and energy consumption using multimodal transportation tool.

Advisor: Dr Vittaldas Prabhu