Browsing by Author "Kumar, Devesh"

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    Article
    Citation - WoS: 10
    Citation - Scopus: 16
    A hybrid Bayesian approach for assessment of industry 4.0 technologies towards achieving decarbonization in manufacturing industry
    (Elsevier Ltd, 2024) Devesh Kumar; Gunjan Soni; Fauzia Jabeen; Neeraj Kumar Tiwari; Gorkem Sariyer; Bharti Ramtiyal; Ramtiyal, Bharti; Jabeen, Fauzia; Soni, Gunjan; Kumar Tiwari, Neeraj; Sariyer, Gorkem; Kumar, Devesh; Tiwari, Neeraj Kumar
    Since the 1st Industrial Revolution the Earth's atmosphere has warmed due to human activities like deforestation burning fossil fuels for energy generation and livestock raising. Without preventative measures the Earth's atmosphere would warm by 2 °C before the next Industrial Revolution. Thus it has become crucial to move toward a low-carbon economy. Reaching carbon neutrality means cutting our carbon footprint to zero. Innovative research methods and technologies can play a significant role in supporting the economy in its carbon reduction efforts. Industry 4.0 (I4.0) technologies hold great potential for decarbonizing the economy. However there is a need to explore and utilize this potential effectively. This study aims to address this by developing a methodology that identifies relevant attributes and critical measures from existing literature mapping them with I4.0 technologies. Using a MCDM approach each measure is prioritized based on importance. To better understand the interrelationships between these attributes and I4.0 technologies the Bayesian Network (BN) method is employed. This approach enables the exploration of dependencies and influences among variables. By implementing this four-stage strategy economies can make informed decisions and prioritize actions contributing to carbon neutrality while leveraging the benefits of I4.0 technologies. This approach offers a comprehensive framework for guiding economies on their path towards carbon neutrality considering the potential of I4.0 technologies and the importance of various attributes identified through literature. © 2024 Elsevier B.V. All rights reserved.
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    Article
    Citation - WoS: 4
    Citation - Scopus: 7
    A machine learning-based hybrid approach for maximizing supply chain reliability in a pharmaceutical supply chain
    (PERGAMON-ELSEVIER SCIENCE LTD, 2025) Devesh Kumar; Gunjan Soni; Sachin Kumar Mangla; Yigit Kazancoglu; A. P. S. Rathore; Rathore, A. P. S.; Kumar, Devesh; Soni, Gunjan; Mangla, Sachin Kumar; Kazancoglu, Yigit
    In today's interconnected global economy supply chain (SC) reliability is crucial particularly in sectors like the pharmaceutical industry where disruptions can significantly impact public health. SCs have become important to industries due to a customer-driven shift aimed at improving SC reliability especially in terms of delivery performance. It is crucial to define and find the best strategy for reaching the organizational objectives in SC. While designing a SC supplier selection (SS) and order allocation are two decisions that have to be made separately. This study addresses the critical challenges of SS and order allocation within pharmaceutical SCs. It proposes a novel two-phased hybrid approach the first phase integrates machine learning(ML) and multicriteria decision-making(MCDM) method for robust SS. The second phase develops a mathematical model to optimize order allocation while considering SC reliability. This work employs support vector machine (SVM) as the particular ML method in which the training data are historical corporate data that dictate parameters weights. These weights are then used in the measurement of alternatives and ranking according to compromise solution (MARCOS) method to rank the suppliers. A multi- objective mixed integer programming (MOMIP) model is then formulated to identify the right order quantity from the identified suppliers of a pharmaceutical SC in order to minimize SC cost and maximize SC reliability. The results indicate that by optimizing SC reliability and costs orders are directed to high-priority suppliers. This study provides a comprehensive data-driven decision-making framework to assure SC's reliability and cost-efficiency. The implications of the findings are also profound and contribute valuable insights for industry practitioners to improve the performance of SC. To illustrate the proposed methodology an SC example of a pharmaceutical industry is analyzed using the LINGO solver.
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    Article
    Citation - WoS: 19
    Citation - Scopus: 18
    Integrating resilience and reliability in semiconductor supply chains during disruptions
    (ELSEVIER, 2024) Devesh Kumar; Gunjan Soni; Sachin Kumar Mangla; Jiajia Liao; A. P. S. Rathore; Yigit Kazancoglu; Rathore, A. P. S.; Soni, Gunjan; Kumar, Devesh; Liao, Jiajia; Kazancoglu, Yigit; Mangla, Sachin Kumar
    The semiconductor industry a cornerstone of modern technology has been crucial in driving globalization and supporting various sectors from consumer electronics to automotive industries. However in recent years the industry has faced substantial challenges threatening its ability to meet the surging demand for semiconductor chips. Disruptions at any point in the supply chain from raw material sourcing to end-product delivery can substantially influence the semiconductor ecosystem. The intricate nature of such SCs makes them highly vulnerable to various disruptions emphasizing the critical need for building resilient and reliable supply chain strategies. This article presents comprehensive research aimed at addressing critical gaps in the understanding and management of resilience and reliability within the semiconductor supply chain (SSC). This study proposes a multi-objective mixed-integer non-linear programming (MO-MINLP) model to configure an SSC while considering reliability and resilience measures. It emphasizes and draws attention to the importance of resilience and reliability in managing SSC disruptions during a pandemic and potential future epidemic outbreak. Exploring the precise breakdown of batch transportation between two sites shows how disruption can affect product flow along the SC. The applicability of the proposed method is demonstrated through a numerical example of an SSC solved using the LINGO solver. Finally a sensitivity analysis is conducted on the model's parameters to assess the capability and effectiveness of the results from managerial viewpoints.
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    Citation - WoS: 3
    Citation - Scopus: 3
    Modelling and analysis of resilience and reliability in pharmaceutical supply chains
    (PERGAMON-ELSEVIER SCIENCE LTD, 2025) Devesh Kumar; Gunjan Soni; Ajay Pal Singh Rathore; Yigit Kazancoglu; Rathore, Ajay Pal Singh; Kumar, Devesh; Soni, Gunjan; Kazancoglu, Yigit
    The pharmaceutical industry a massive global sector responsible for pharmaceutical production development and marketing has the problem of developing robust supply chains (SCs). These SCs are becoming more complicated while functioning in a global market making them more vulnerable to disruptions. To ensure that the healthcare system operates efficiently and meets the growing demand healthcare organisations must construct resilient and reliable SCs. In this study we develop a multi-objective optimisation model to address the pharmaceutical supply chain (PSC) problem while simultaneously minimising costs and increasing network reliability. We use three essential SC design indicators: node density node complexity and node criticality as well as a network reliability indicator to improve SC resilience and reliability. Our research findings indicate that in the pharmaceutical business improving SC reliability reducing SC costs and managing total SC orders holistically can effectively reduce the risk of SC interruptions.
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    Article
    Citation - WoS: 4
    Citation - Scopus: 3
    On the nature of supply chain reliability: models- solution approaches and agenda for future research
    (EMERALD GROUP PUBLISHING LTD, 2024) Devesh Kumar; Gunjan Soni; Yigit Kazancoglu; Ajay Pal Singh Rathore; Rathore, Ajay Pal Singh; Kumar, Devesh; Soni, Gunjan; Kazancoglu, Yigit
    Purpose - This research aims to update the literature about the importance of reliability in supply chain (SC) and to find out the SC determinants. Design/methodology/approach - This research surveys while contributing to the academic grasp of supply chain reliability (SCR) concepts. The study found 45 peer-reviewed publications using a structured survey technique with a four-step filtering process. The filtering process includes data reduction processes such as an evaluation of abstract and conclusion. The filtered study focuses on SCR and its determinants. Findings - One of the major findings is that most of the study has focused on mathematical and conceptual studies. Also this study provides the answer to a question like how can reliability be better accepted and evolved within the SC after finding the determinants of SCR. Originality/value - The observed methodological gap in understanding and development of SCR was identified and classified into three categories: mathematical conceptual and empirical studies (case studies and survey's mainly). This research will aid academics in developing and understanding the determinants of SCR.