Forecasting of Radio Frequency Identification Entropy Viscosity Parking and Forwarding Algorithm Flow Risks and Costs: Integrated Supply Chain Health Manufacturing System (ISCHMS) Approach

Our research hypothesis is that we could improve traditional supply chain management (SCM) health by designing a new integrated manufacturing enterprise system based on parking and forwarding algorithms, radio frequency identification (RFID) feedback and a Markov decision process. The goal of our research was to optimize a cost function that depends on RFID system viscosity. The viscosity of the RFID can be defined as the flow of data through the RFID network. This process can be measured by the entropy of the parking and forwarding of the signal, leading to a sustainable supply chain system. The dynamics of this system are governed by differential equations. Using this entropy model, we verified the integrated supply chain health manufacturing system risk failure reduction via simulation. Our method employs a fuzzy Kalman filter, improves the system, reduces backorders and demonstrates that RFID viscosity is an effective means to this end. A Markov blanket entropy approach demonstrated that it can capture and provide a theory of RFID viscosity parking and forwarding algorithms as a networking solution to lowering SCM costs, reducing waste, and improving sustainability.