Pub Date : 2019-11-01DOI: 10.1109/WITCONECE48374.2019.9092920
Mridula Singhal, Shruti V. Hegde, R. Mohan
The idea is to build a smart stock management and supplier chain management system. The users have to place the load sensors at different locations for monitoring their stocks. Weights will be mapped to the number of stocks/ any other suitable unit using scale factor. When the stock falls below a certain set threshold, the users will automatically be notified through an app. The users will be able to view the current levels of stocks at sensor locations dynamically on the app. The collected sensor data of the number of stocks sold can then be used by the manufacturer to forecast the future requirements to meet demands. On uploading the file containing the orders with the number of stocks and location for a specific period of time, a heatmap visualization can be created which can be used to draw conclusions about how to do transport optimization or choosing a proper location for the warehouse. This app connects the wholesalers to the manufacturers to order stocks in the required quantities. This system can also be used in restaurants, pharmaceuticals and manufacture industrial stores where it maybe a critical situation if some item is not available or is lesser than the prescribed quantity. So, we can maintain the optimum quantity as per JIT (Just in Time) system. This product eliminates manual labor required for frequent stock checks and the error associated with it.
{"title":"Smart Industrial Supply Chain Management and Prediction System","authors":"Mridula Singhal, Shruti V. Hegde, R. Mohan","doi":"10.1109/WITCONECE48374.2019.9092920","DOIUrl":"https://doi.org/10.1109/WITCONECE48374.2019.9092920","url":null,"abstract":"The idea is to build a smart stock management and supplier chain management system. The users have to place the load sensors at different locations for monitoring their stocks. Weights will be mapped to the number of stocks/ any other suitable unit using scale factor. When the stock falls below a certain set threshold, the users will automatically be notified through an app. The users will be able to view the current levels of stocks at sensor locations dynamically on the app. The collected sensor data of the number of stocks sold can then be used by the manufacturer to forecast the future requirements to meet demands. On uploading the file containing the orders with the number of stocks and location for a specific period of time, a heatmap visualization can be created which can be used to draw conclusions about how to do transport optimization or choosing a proper location for the warehouse. This app connects the wholesalers to the manufacturers to order stocks in the required quantities. This system can also be used in restaurants, pharmaceuticals and manufacture industrial stores where it maybe a critical situation if some item is not available or is lesser than the prescribed quantity. So, we can maintain the optimum quantity as per JIT (Just in Time) system. This product eliminates manual labor required for frequent stock checks and the error associated with it.","PeriodicalId":350816,"journal":{"name":"2019 Women Institute of Technology Conference on Electrical and Computer Engineering (WITCON ECE)","volume":"44 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133753427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/WITCONECE48374.2019.9092925
Priyesh Kumar, Sarita Yadav, P. Pal
This paper presents an analysis of Nanosheet Field Effect Transistor (NSFET) and Nanowire FET for device and circuit perspective with 7nm channel length. While simulating the above device TiO2is used as a dielectric. In this device sheet thickness is 5nm and sheet pitch is 10nm. TiN is used as a gate metal. These devices are termed as gate all around transistor. Theyhave bettercontrol over short channel effect. High k material is used for reducing short channel effect by increasing gate control for channel length below 20 nm. But this also increases fringes capacitance due to which RC delay is increased. However, Nanosheet Field Effect Transistor (NSFET)have better RC delay and drive current. In these devices sheet width is variable and drive current increases with increase in sheet width. In this paper, 6T SRAM cell is explained. Static noise margin is found from the butterfly curve from which we realized that read noise margin of Nanowire is better than that of NSFET but write noise margin is better in NSFET.N curve is more appropriate for finding SRAM parameters because in thisinformation about both voltage and current are available.
{"title":"Analysis of Nanosheet Field Effect Transistor (NSFET) for device and circuit perspective","authors":"Priyesh Kumar, Sarita Yadav, P. Pal","doi":"10.1109/WITCONECE48374.2019.9092925","DOIUrl":"https://doi.org/10.1109/WITCONECE48374.2019.9092925","url":null,"abstract":"This paper presents an analysis of Nanosheet Field Effect Transistor (NSFET) and Nanowire FET for device and circuit perspective with 7nm channel length. While simulating the above device TiO2is used as a dielectric. In this device sheet thickness is 5nm and sheet pitch is 10nm. TiN is used as a gate metal. These devices are termed as gate all around transistor. Theyhave bettercontrol over short channel effect. High k material is used for reducing short channel effect by increasing gate control for channel length below 20 nm. But this also increases fringes capacitance due to which RC delay is increased. However, Nanosheet Field Effect Transistor (NSFET)have better RC delay and drive current. In these devices sheet width is variable and drive current increases with increase in sheet width. In this paper, 6T SRAM cell is explained. Static noise margin is found from the butterfly curve from which we realized that read noise margin of Nanowire is better than that of NSFET but write noise margin is better in NSFET.N curve is more appropriate for finding SRAM parameters because in thisinformation about both voltage and current are available.","PeriodicalId":350816,"journal":{"name":"2019 Women Institute of Technology Conference on Electrical and Computer Engineering (WITCON ECE)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127441731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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