BITS Faculty Publications

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    Identification of success clusters using principal component analysis for oil and gas industry
    (Taylor & Francis, 2025-02) Kakadea, Vijay
    Project success factors are key elements that contribute to the successful completion of any mega project. As project success factors vary from one industry to another, it is essential to tailor the success factors specific to a particular industry type. The oil and gas industry-specific success factors provide information to investing agencies of the steps to be undertaken to make the project a success. These success factors for the complete life cycle of the project offers a unique opportunity to the management in planning manpower and resources to meet the desired objectives. This study identifies 66 success factors for large oil and gas projects. A questionnaire survey was carried out to establish the most important factors or critical success factors/success clusters. One hundred forty-two responses were received from various stakeholders. Principal component analysis (PCA) was carried out to identify the clusters of success factors. This study recommends that for successful project implementation of a mega project, stakeholders should strive to focus on the eight key clusters identified by PCA. These eight key clusters may be utilized in similar mega projects such as chemical process industry, mining and metallurgical process industry and mega infrastructure projects.
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    Critical Success Factors of Blockchain adoption in Green Supply Chain Management: Contribution through an Interpretive Structural Model
    (Taylor & Francis, 2022-02) Kota, Srinivas
    The main purpose of this paper is to study the critical success factors (CSFs) of the blockchain technology (BCT) adoption in green supply chain management (GSCM) which might be literally afirst attempt and also propose aconceptual framework of aGSCM model adopting the BCT, which will be promoting the combination of these two areas in the future. Acritical literature review of the BCT, GSCM, and BCT-based GSCM was conducted to identify the most relevant factors of BCT adoption, followed by the model formulation with the help of interpretive structural modelling (ISM) consisting of CSFs and relationships between those based on experts’ views. The overall results emphasized that ‘recording and trading’-related factors may contribute to the BCT adoption, while others like smart contract must be enhanced. This study supports previous conceptual work on BCT and GSCM and could serve as astarting point to assist in decision-making.
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    Decision modelling of critical success factors for cold chains using the DEMATEL approach: a case study
    (Emerald, 2022-08) Jasti, Naga Vamsi Krishna
    The objective of the present study is to identify and analyze a set of critical success factors (CSFs) for ice-cream industry [cold chain management (CCM)] that helps in increasing the efficacy, quality, performance and growth of the supply chain organization.
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    Development of Framework for Lean Implementation: An Interpretive Structural Modeling and Interpretive Ranking Process Approach
    (SAE, 2021) Mishra, Rajesh P
    Today’s explosive condition of the market is compelling the manufacturing organizations to switch from traditional manufacturing (TM) to lean manufacturing (LM) to create a footprint in this competi- tive era. In this article, 16 critical success factors (CSFs) for LM implementation are identifi ed through a vast literature review, the opinion of academicians and industry experts and interpretive structural modeling (ISM) is used to create interrelationships among the identifi ed CSFs, and interpretive ranking process (IRP) rank these CSFs based on dominance with respect to performance dimen- sions. Leadership and management made the foundation of an ISM model while the training and people development have secured the fi rst rank in the IRP model. Implementation of such ISM- and IRP-based models of CSF would give a clear understanding of these CSFs so that LM researchers, decision-makers, managers, and practitioners of LM will use their resources more effi ciently.
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    Development and validation of quality management constructs for software industries: an empirical investigation from India
    (Inder Science, 2014-03) Digalwar, Abhijeet K.
    Quality management (QM) is a powerful technique for managing, monitoring, analysing and improving the operational performance. Various studies have been done in the manufacturing and service sectors to find out the relation between quality management practices and operational performance. Very little studies are found from software industries. The aim of this paper is to present results of a survey on QM in the software industries. The focus is on understanding the critical success factors (CSFs) for successful implementation of QM in the software industry. In total, 12 critical success factors (CSFs) with 114 variables were developed from the literature and discussions with software quality professionals. To validate the data obtained from a survey of software industries in India, reliability and validity analysis was carried out. These results provide an increased understanding of how to better implement QM in the software industry, and provide managers with improved guidelines for identifying the most important factors that will lead to success. Indian software companies are leading exporters to Europe and the USA. Considering the growth of the Indian software industry and increased inclination towards acquiring quality certifications and quality improvement techniques, a better understanding of the implementation of QM can provide companies with a stronger competitive advantage.
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    Green manufacturing performance measures: an empirical investigation from Indian manufacturing industries
    (Emerald, 2013-11) Digalwar, Abhijeet K.
    The purpose of the paper is to explore the performance measures for the green manufacturing practices in the Indian manufacturing industries.
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    Evaluation of world-class manufacturing systems: a case of Indian automotive industries
    (Inder Science, 2008-06) Digalwar, Abhijeet K.; Sangwan, Kuldip Singh
    Many industries are implementing World-Class Manufacturing (WCM) systems to compete in the global market. However, the researchers in the area of WCM have not paid much attention to the techniques/methodologies for the evaluation of WCM systems. This paper presents a set of validated critical success factors and their performance variables for WCM industries. The reliability and validity analyses were carried out by using the SPSS? 11.5 statistical tool on the data obtained from the Indian automotive industries. A multiattribute decision model, i.e., Performance Value Analysis (PVA), was developed for the evaluation of WCM systems. The usefulness of the model is demonstrated using a case situation of the Indian automotive industry. By utilising this model, it is expected that managers/decision makers will acquire sufficient confidence in evaluating world-class automotive industries.
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    Development and validation of performance measures for world class manufacturing practices in India
    (World Scientific, 2007) Sangwan, Kuldip Singh; Digalwar, Abhijeet K.
    This paper aims to develop and validate performance measures for world class manufacturing (WCM) in Indian context that could be used by managers/ practitioners in assessing and improving their manufacturing performance. Using a thorough synthesis of the world class manufacturing literature, sixteen performance measures — top management commitment, knowledge management, employee training, innovation and technology, employee empowerment, environmental health and safety, supplier management, production planning and control, quality, flexibility, speed, cost, customer involvement, customer satisfaction, customer services and company growth — of world class manufacturing and their 89 variables have been developed. Using the data obtained from a survey of manufacturing industries in India, the identified performance measures were subjected to appropriate statistical tests to establish reliability and validity. Statistical computing package SPSS 11.5 for Windows was used for reliability and validity analysis. The validated instrument of world class manufacturing measures developed here may be used by manufacturing organizations to prioritise their management efforts to assess and implement WCM. The validated results are in Indian context, however, the instrument developed can be used in global context.
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    Evaluating the critical success factors of supplier development: a case study
    (Emerald, 2013-05) Routroy, Srikanta
    The purpose of this paper is to identify and evaluate the critical success factors (CSFs) responsible for supplier development (SD) in a manufacturing supply chain environment.
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    Modelling resilience of truckload transportation industry
    (Emerald, 2018-10) Sharma, Satyendra Kumar
    The purpose of this paper is to study the supply chain resilience of Indian truckload transportation industry, in the event of potential disasters that affect the normalcy of their services. This study helped to identify factors affecting the two important dimensions of resilience, namely, resistive capacity and restorative capacity.