BITS Faculty Publications

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Author: search.filters.author.Nagpal, GauravSubject: search.filters.subject.Mechanical Engineering

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    Inventory Modelling for technology generation products under uncertain trade credit terms and imprecise procurement costs
    (OSCM, 2022) Nagpal, Gaurav; Chanda, Udayan; Jasti, Naga Vamsi Krishna
    The inventory policies for any product under the trade credit mechanism are influenced by the procurement price per unit and the credit period offered by the seller to the buyer. This paper develops an inventory model for the technology generations under the imprecise trade credit period and the imprecise procurement cost. It considers the demand that is credit-linked and governed by innovation diffusion as well. The imprecise nature of the parameters is captured by the use of fuzzy numbers. The trapezoidal membership function has been used to fuzzify the profit function with the imprecise parameters, and then the centroid method is used to de-fuzzify the profit. The numerical illustrations have been performed, followed by the sensitivity analysis with the launch timing of the second generation product. A few important implications for the inventory practitioners and the possible extensions of this work have also been discussed
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    Ore Grade Estimation in Mining Industry from petro-physical data using neural networks
    (ACM Digital Library, 2022) Nagpal, Gaurav; Nagpal, Ankita; Jasti, Naga Vamsi Krishna
    The grade of the ore in mining industry plays a very important role. From the petro-physical data, the grade of the ore can be predicted with reasonable accuracy. However, the existing literature is silent on the techniques of data analytics that can be used for ore-grade estimation with the help of data. The study uses multi-layer neural network perceptron model and neural network regression models for predicting the grade on the basis of Petro-physical data that was collected by doing borehole geophysical survey capturing twenty-one properties of the ore. The research study is able to estimate the grade of the ore with reasonable accuracy using the data.
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    Predictive Analytics based Modeling of the purchase intention of electric vehicles, and understanding the drivers and risks in their adoption for the people of Tamil Nadu in India
    (IEEE, 2023) Jasti, Naga Vamsi Krishna; Nagpal, Gaurav; Nagpal, Ankita
    While the adoption of electric vehicles by the World is very important to address the issues of climate change, the rate of adoption of EVs is substantially low due to some of the barriers in their adoption by the population. While several studies have been done in the past using different theories to measure the perception of people towards the electric vehicles in the context of several countries including India, there is no such study for a specific Indian state. Since India is the land of diversity where people with different lifestyles and values co-exist, the state-specific studies can generate more insights for the policy makers, manufacturers and marketers of EVs. Therefore, this study measures the perception of people of Tamil Nadu, one of the prominent automotive manufacturing hubs in the country, towards the adoption of electric vehicles through a structured primary survey. A logistic regression model has also been developed to measure the purchase intention of the potential adopters. The important findings of the study and managerial implications have also been discussed.
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    An empirical study on implementation of sustainable production practices in Indian manufacturing industry
    (Emerald, 2023-10) Jasti, Naga Vamsi Krishna; Nagpal, Gaurav; Kota, Srinivas
    Sustainable production (SP) is an efficient and influential approach of production for Indian manufacturing industries as it preserves the social, environmental and economic aspects of production activities altogether. The objective of this research work is to investigate the implementation status of SP practices in Indian manufacturing industries by utilizing empirical survey methodology.