Lean manufacturing is one of the most popular improvement agents in the pursuit of perfection. However, in today’s complex and dynamic manufacturing environments, lean tools are facing an inevitable death. Industry 4.0 can be integrated with lean tools to avoid their end. Therefore, the primary purpose of this paper is to introduce an Industry 4.0-based lean framework called dynamic value stream mapping (DVSM) to digitalize lean manufacturing through the integration of lean tools and Industry 4.0 technologies. DVSM with its powerful features is proposed to be the smart IT platform that can sustain lean tools and keep them alive and effective. This paper specifically tackles the scheduling and dispatching in today’s lean manufacturing environments, where the aim of this research is developing a smart lean-based production scheduling and dispatching model to achieve the lean target through optimizing the flow along the VSM and minimizing the manufacturing lead time. The developed model, called the real-time scheduling and dispatching module (RT-SDM), runs on DVSM. The RT-SDM is represented through a mathematical model using mixed integer programming. Part of the testing and verification process, a simplified IT-based software, has been developed and applied on a smart factory lab.

Since the introduction of the novel Corona Virus (The COVID-19) to the Chinese city Wuhan in the Hubei province during the late December 2019, the effectiveness of the deadly disease, its human infection, spreading severity and the mortality rate of the infection has been an issue of debate. The outbreak of the virus along the time has become a massive threat to the global public health security and has been declared as a pandemic. Accounting the radical number of increases in the infected cases and the death due to COVID-19 infections around the globe, there is a need to predict the infections among the people by making proper optimization and using various Infectious Disease modelling (IDM) methods, in order to challenge the outcome. In comparison with previous diseases like SARS and Ebola viruses, the new corona virus (COVID-19) infections are infectious during the incubation period. In addition to that, naturally produced droplets from humans (e.g. droplets produced by breathing, talking, sneezing, coughing) and Person-to-person contact transmission are reported to be the foremost ways of transmission of novel corona virus. By considering the above two factors, a modified SEIR (Susceptibility-Exposure-Infection-Recovery) method have been used for predicting the spread of the infections in the state of Tamil Nadu which is located in the southern part of India. Further, we have utilized the current surveillance data from Health and Family Welfare Department – Government of Tamil Nadu to accurately predict the spreading trend of the infection on a state level.

We report a comprehensive study carried out for the robust detection of leakages in gas pipelines based on, simultaneous distributed sensing of acoustics and temperature due to the leakage event. Such signatures corresponding to the leakage event have been simulated using a computational fluid dynamics (CFD) model. Our CFD analysis reveals that low-porosity soil like clay transmit acoustic waves efficiently thereby facilitating ease of detection using distributed acoustic sensing (DAS), while high-porosity soil like sand exhibit stronger thermal conductivity favoring distributed temperature sensing (DTS). Such observations highlight the importance of simultaneous measurements using DAS and DTS for consistent leak detection across diverse soil conditions. The detection of such signatures through a simultaneous DAS/DTS measurement system are then simulated using a unified numerical model of the Rayleigh/Raman scattering in a standard singlemode fiber and the various noise components. Finally, the simulation results are experimentally validated using controlled experiments conducted over a 43 km-long standard telecom-grade single-mode optical fiber with a spatial resolution of 10 m. Further validation was performed on a pressurized buried pipeline testbed under desert soil conditions, confirming the systems effectiveness in detecting controlled leak events under realistic field conditions. These findings clearly demonstrate the advantage of combining DAS and DTS technologies for reliable and consistent leak detection across diverse underground environments.