Dr. Sachin Gupta

RESEARCH, TEACHING, or OTHER INTERESTS

Organizational Behavior and Human Resource Management, Mathematics, Statistics, Probability and Uncertainty

Scopus Publications

The analysis of performance due to impact of bullwhip effect in Covid: select Indian sector perspective
Sachin Gupta, Sakshi Goel, Santosh Kumar, and Gaurav Nagpal
Emerald
Purpose The purpose of the study is to analyze and measure the impact of disruption in demand which causes the bullwhip effect. The bullwhip effect impacts the performance of firm. Just like everything else, covid has had an impact on the disruption of supply chain too leading to the need of measuring the bullwhip effect of select Indian sectors. The comparison on bullwhip effect is drawn in pre- and during covid era in major sectors. The study helps to understand, analyze and measure the impact of covid and its challenges to supply chain. Design/methodology/approach The empirical study is carried out on five major select Indian sectors which have the largest market capitalization in Indian economy, namely, FMCG (fast-moving consumer goods), automobile, utility, consumer durable and IT (information technology). The disruption in the supply chain is measured in terms of bullwhip effect. The novel metric ratio of bullwhip effect is computed which is based on demand–supply mismatch and analyzed based on 10 years of observations. The data is analyzed twice, first from 2011 to 2019 (pre-covid era) and second from 2019 to 2021 (during covid era). Each time, Bombay Stock Exchange (BSE) sectoral indices are used to compute the bullwhip ratio, and empirical data is collected using Prowess. The firms listed in BSE represent most of the sector. Such panel data helps us to analyze inter- and intraindustry bullwhip effect. The changes in the bullwhip effect for various BSE listed firms are analyzed pre- and during covid era. These changes are specifically studied at the manufacturer end of the supply chain. Later regression analysis is performed to study the changes required in production based on the demand. The various strategies that cause or mitigate the impact of covid in intraindustry can be derived from the study. The disruption in production is analyzed based on the disruption in demand and profit before interest and tax (PBIT). Findings In pre-covid era, the percentage of demand disruption was low in select sectors but not exactly zero. Covid caused the disruptions in supply chain across the globe which resulted in bullwhip effect in Indian sectors too. Yet some of the sectors were able to cope better with the situation as compared to others. In the present study, same is analyzed statistically, and results are derived for practical significance. Research limitations/implications The empirical data is having the observations of past 10 years to analyze the pattern of demand disruption in the firms and hence the sectors. The impact of covid is studied on performance, which is analyzed in terms of PBIT. The impact of other factors (political, social, marketing policies, etc.) that may cause disruption in the supply chain of a firm is not considered in the study. Originality/value Study is unique, as it measures disruption and provides a peerless way to study the inter- and intrasectors. To analyze the impact of bullwhip effect on sector performance, it is very much required to first measure the bullwhip; this measure of bullwhip as a ratio of the slopes of demand and supply is a novel approach. The study emphasizes that the impact of covid is not the same among the firms, and hence among the sectors. Also, it is found that the impact of such adversities can be mitigated, and performance of firm can remain intact in turbulent times too.


What determines the performance of pharmaceutical firms in India on account of COVID-19 interventions?
Ashu Lamba, Priti Aggarwal, Sachin Gupta, and Mayank Joshipura
Emerald
Purpose This paper aims to examine the impact of announcements related to 77 interventions by 46 listed Indian pharmaceutical firms during COVID-19 on the abnormal returns of the firms. The study also finds the variables which explain cumulative abnormal returns (CARs). Design/methodology/approach This study uses standard event methodology to compute the abnormal returns of firms announcing pharmaceutical interventions in 2020 and 2021. Besides this, the multilayer perceptron technique is applied to identify the variables that influence the CARs of the sample firms. Findings The results show the presence of abnormal returns of 0.64% one day before the announcement, indicating information leakage. The multilayer perceptron approach identifies five variables that explain the CARs of the sample companies, which are licensing_age, licensing_size, size, commercialization_age and approval_age. Originality/value The study contributes to the efficient market literature by revealing how firm-specific nonfinancial disclosures affect stock prices, especially in times of crisis like pandemics. Prior research focused on determining the effect of COVID-19 variables on abnormal returns. This is the first research to use artificial neural networks to determine which firm-specific variables and pharmaceutical interventions can influence CARs.


An empirical investigation on mitigation of bullwhip effect: practices perspective
Sachin Gupta and Anurag Saxena
Inderscience Publishers



A new theoretical framework of shopping motives and channel preference behaviour in the digital era
Durgesh Kumar Agrawal and Sachin Gupta
Wiley
AbstractThe unprecedented dissemination of digital technology has changed people's psychology including their shopping behaviour in the last two decades. Smartphone led digital applications and advancements have disrupted consumers' shopping processes, purchase decisions, and priorities as well as increased their exposure, aspirations and expectations inevitably. Therefore, it is imperative to examine the relevance of various elements of shopping motives holistically. Therefore, the present study aims to develop a new theoretical framework based on significant elements of shopping motives for physical products in the digital technology era. For this purpose, an exploratory study, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modelling (SEM) were used. EFA generated five latent factors by grouping 18 independent variables. CFA validated latent factor construct and measured model fit. SEM visualized the path analysis and portrayed the pattern of relations between latent factors into a single factor structural model (consumer shopping motives framework). Results show that consumers' shopping priorities are changing as 3 conventional variables (‘role‐playing’, ‘status and authority’, and ‘pleasure of bargaining’) became obsolete and 3 new (‘anywhere and any‐time shopping’, ‘safe and secure digital transaction’, and ‘unbiased reviews and ratings”) came into existence prominently. The format and perspective of ‘fun, entertainment and recreation’ ‘social interaction and communication’, and ‘exposure to new and latest trends' have been changing. Consumers are becoming more technology‐dependent in their shopping processes and purchase decisions. The validation of the framework on channel preference behaviour revealed that consumers largely prefer offline channels for the fulfilment of risk‐free and social sub‐motives, and online channels for convenience sub‐motive. Therefore, broad structural change and clarity, specification of priorities, a shift in the format and perspective of few elements of shopping motives, intrinsic passion for the use of digital technology and web service in the shopping journey, and simplification of antecedents for growing popularity of multi‐channel shopping paradigm are the key novelty of this study.


Operations-based classification of the bullwhip effect
Sachin Gupta and Anurag Saxena
Emerald
Purpose Present study deals with the most discussed rather than addressed yet still an unsolved problem of supply chain known as the bullwhip effect. Operational variables affecting the bullwhip effect are identified and their role in causing the bullwhip effect has been explored using artificial neural networks. The purpose of this study is to analyze the impact of identified operational reasons that affect the bullwhip effect and to analyze the bunch of variables that are more prominent in explaining the phenomenon of the bullwhip effect. Design/methodology/approach Ten major sectors of the Indian economy are analyzed for the bullwhip effect in the present study, and the operational variables affecting the bullwhip effect in these sectors are identified. The bullwhip metric is developed as the ratio of variance in production to the variance in the demand. The impact of identified operation variables on the bullwhip effect has been discussed using the artificial neural network technique known as multilayer perceptron. The classification is also performed using neural network, logistic regression and discriminant analysis. Findings The operation variables are found to be varying with respect to sectors. The study emphasizes that analyzing the right set of operation variables with respect to the sector is required to deal with the complex problem, the bullwhip effect. The operational variables affecting the bullwhip effect are identified. The classification result of the neural network is compared with those of the logistic regression and discriminant analysis, and it is found that the dynamism present in the bullwhip effect is better classified by neural network. Research limitations/implications The study used 11 years of observations to analyze the bullwhip effect on the basis of operational variables. The bullwhip effect is a complex phenomenon, and it is explained on the basis of an extensive set of operational variables which is not exhaustive. Further, the behavioral aspect (bullwhip because of decision-making) is not explored in the present study. Practical implications The operational aspect plays a gigantic role to explain and deal with the bullwhip effect. Strategies to mitigate the bullwhip effect must be in accordance with the operational variables impacting the sector. Originality/value The study suggests a novel approach to study the bullwhip effect in supply chain management using the application of neural networks in which operational variables are taken as predictor variables.


Predicting the impact of operational and financial variables on bullwhip effect using threshold regression: Indian context
Sachin Gupta and Anurag Saxena
Emerald
Purpose The operational aspects of supply chain, when handled correctly, results in diminishing the impact of the bullwhip effect. The purpose of this study is to analyze the impact of operational and financial variables on the bullwhip effect. Various operational factors that contribute to the bullwhip effect in a supply chain are identified and their impact on variability in production is measured at manufacturer’s end in the supply chain. Design/methodology/approach Ten different sectors of the Indian economy are identified and analyzed on the basis of bullwhip effect. The ratio of change in production with respect to change in demand is taken as a metric to measure the bullwhip effect. Initially, the impact of identified variables on bullwhip effect is analyzed using the linear regression analysis and then to gain more insights, the threshold regression model is applied according to the change in bullwhip ratio. Findings The study identifies four threshold regions in which bullwhip ratio is changing its slope considerably. The operational and financial variables impacting bullwhip effect differently in these four regions provide useful insights about how the variables are impacting the bullwhip effect. Research limitations/implications Past 11 years of observations on identified operational and financial variables are studied for ten different sectors. The operational and financial variables are identified on basis of available literature but may not be exhaustive in nature. Practical implications The present study implies that the emphasis must be given to the magnitude of the bullwhip ratio. Strategies must be adopted that result in mitigation of bullwhip effect. Such mitigation strategies must not only be restricted on the basis of type of product or sector, perhaps they must be on the basis of threshold region of bullwhip ratio. Originality/value The study suggests a novel approach to study the bullwhip effect in supply chain management using the application of threshold regression considering the bullwhip ratio as a threshold variable.


Classification of operational and financial variables affecting the bullwhip effect in indian sectors: A machine learning approach
Sachin Gupta and Anurag Saxena
Bentham Science Publishers Ltd.
Background: The increased variability in production or procurement with respect to less increase of variability in demand or sales is considered as bullwhip effect. Bullwhip effect is considered as an encumbrance in optimization of supply chain as it causes inadequacy in the supply chain. Various operations and supply chain management consultants, managers and researchers are doing a rigorous study to find the causes behind the dynamic nature of the supply chain management and have listed shorter product life cycle, change in technology, change in consumer preference and era of globalization, to name a few. Most of the literature that explored bullwhip effect is found to be based on simulations and mathematical models. Exploring bullwhip effect using machine learning is the novel approach of the present study. Methods: Present study explores the operational and financial variables affecting the bullwhip effect on the basis of secondary data. Data mining and machine learning techniques are used to explore the variables affecting bullwhip effect in Indian sectors. Rapid Miner tool has been used for data mining and 10-fold cross validation has been performed. Weka Alternating Decision Tree (w-ADT) has been built for decision makers to mitigate bullwhip effect after the classification. Results: Out of the 19 selected variables affecting bullwhip effect 7 variables have been selected which have highest accuracy level with minimum deviation. Conclusion: Classification technique using machine learning provides an effective tool and techniques to explore bullwhip effect in supply chain management.


Audio watermarking with reduced number of random samples
Rohit Anand, Gulshan Shrivastava, Sachin Gupta, Sheng-Lung Peng, and Nidhi Sindhwani
IGI Global
Digital signal watermarking is an indiscernible and safe transmission of freehold data through host signal that includes immersing into and extrication from the actual host. Some algorithms have been investigated for the strong and secure embedding and extraction of watermarks within the host audio files but they do not yet yield good results in compression and re-sampling. In this chapter, an excellent method is suggested for the compressed wave files that uses random carrier to immerse the watermark in the sequence of an audio signal. The watermark is embedded lucently in audio stream after adaptive differential pulse code modulation (ADPCM) before quantization. The proposed scheme has been implemented and its parameters are compared with the finest auditory watermarking method known. A tool has been used to measure the parameters to produce the results and tabular values. The larger PSNR and smaller BER prove that the suggested scheme is robust.

RECENT SCHOLAR PUBLICATIONS

The analysis of performance due to impact of bullwhip effect in Covid: select Indian sector perspective
S Gupta, S Goel, S Kumar, G Nagpal
Journal of Global Operations and Strategic Sourcing 2024



A Comparative Analysis of Task Scheduling Algorithms for Resource Management in Cloud Environments
S Gupta
Empirical Economics Letter 23 2024



Enhancing Agricultural Decision Support with AIoT: A Research Travelogue
S Gupta
Empirical Economics Letter 23 2024



What determines the performance of pharmaceutical firms in India on account of COVID-19 interventions?
A Lamba, P Aggarwal, S Gupta, M Joshipura
International Journal of Pharmaceutical and Healthcare Marketing 18 (3), 353-374 2023



AI with Consciousness: Advancing Explainability in the Healthcare Sector
A Koul, S Gupta, B Gupta
2023 International Conference on Communication, Security and Artificial 2023



An empirical investigation on mitigation of bullwhip effect: practices perspective
S Gupta, A Saxena
International Journal of Public Sector Performance Management 11 (4), 401-434 2023



A new theoretical framework of shopping motives and channel preference behaviour in the digital era
DK Agrawal, S Gupta
International Journal of Consumer Studies 47 (1), 400-418 2023



Operations-based classification of the bullwhip effect
S Gupta, A Saxena
Journal of Modelling in Management 17 (1), 134-153 2022



Does Cross-Functional Pedagogy of Teaching a Course Help in Management Education?: Evidence From a Supply Chain Management Course
G Nagpal, NVK Jasti, A Kumar, S Gupta
International Journal of Adult Education and Technology (IJAET) 13 (1), 1-18 2022



Multi-Period EOQ Model for Multi-Generation Technology Products With Short Product Life Cycles
G Nagpal, U Chanda, NVK Jasti, S Gupta
International Journal of E-Adoption (IJEA) 14 (1), 1-22 2022



Predicting the impact of operational and financial variables on bullwhip effect using threshold regression: Indian context
S Gupta, A Saxena
Journal of Global Operations and Strategic Sourcing 13 (2), 211-227 2020



Factors affecting the effective implementation of MOOCs in India
A Saxena, A Saxena, S Gupta
Global Journal of Enterprise Information System 12 (4), 9-20 2020



Countering bullwhip effect in supply chain management: a literature review
S Goel, M Toufeeq, A Saxena, S Gupta
Journal of Supply Chain Management Systems 9 (1), 14 2020



The Insights on Marketing Initiatives Impacting the Bullwhip Effect: Sectoral Study.
S Gupta, A Saxena
International Journal of Business & Applied Sciences 9 (1) 2020



Classification of operational and financial variables affecting the bullwhip effect in Indian sectors: a machine learning approach
S Gupta, A Saxena
Recent Patents on Computer Science 12 (3), 171-179 2019



Performance modeling and evaluation of transportation systems using analytical recursive decomposition algorithm for cyclone mitigation
S Gupta, B Gupta
Journal of Information and Optimization Sciences 40 (5), 1131-1141 2019



Factors affecting bullwhip effect: Indian sectoral study
S Gupta, A Saxena
Journal of Supply Chain Management Systems 8 (3), 1-12 2019



A study of change in bullwhip effect: An Indian sectoral study
S Gupta, A Saxena
Journal of Supply Chain Management Systems 8 (2), 24-31 2019



Interpretive structural modelling of critical operational factors for mitigation of bullwhip effect in supply chain management
S Gupta, A Saxena
International Journal of Comparative Management 2 (3-4), 297-328 2019



Optimizing Next Release of Software: A Quality Perspective
H Sharma, S Madan, N Joshi, S Gupta
International Journal of Pure and Applied Mathematics 120 (6), 25-37 2018

MOST CITED SCHOLAR PUBLICATIONS

Audio watermarking with reduced number of random samples
R Anand, G Shrivastava, S Gupta, SL Peng, N Sindhwani
Handbook of Research on Network Forensics and Analysis Techniques, 372-394 2018
Citations: 80



A new theoretical framework of shopping motives and channel preference behaviour in the digital era
DK Agrawal, S Gupta
International Journal of Consumer Studies 47 (1), 400-418 2023
Citations: 17



Classification of operational and financial variables affecting the bullwhip effect in Indian sectors: a machine learning approach
S Gupta, A Saxena
Recent Patents on Computer Science 12 (3), 171-179 2019
Citations: 17



Mergers and acquisitions for enhancing supply chain competitiveness
S Gupta
Journal of Marketing and Operations Management Research 2 (3), 129 2012
Citations: 13



Operations-based classification of the bullwhip effect
S Gupta, A Saxena
Journal of Modelling in Management 17 (1), 134-153 2022
Citations: 10



Performance modeling and evaluation of transportation systems using analytical recursive decomposition algorithm for cyclone mitigation
S Gupta, B Gupta
Journal of Information and Optimization Sciences 40 (5), 1131-1141 2019
Citations: 7



Countering bullwhip effect in supply chain management: a literature review
S Goel, M Toufeeq, A Saxena, S Gupta
Journal of Supply Chain Management Systems 9 (1), 14 2020
Citations: 5



Factors affecting bullwhip effect: Indian sectoral study
S Gupta, A Saxena
Journal of Supply Chain Management Systems 8 (3), 1-12 2019
Citations: 4



Empirical evaluation of human factors that affect design of the product
R Bakshi, S Gupta
International Journal of Computer Applications 100 (9) 2014
Citations: 4



Factors affecting the effective implementation of MOOCs in India
A Saxena, A Saxena, S Gupta
Global Journal of Enterprise Information System 12 (4), 9-20 2020
Citations: 2



Demand estimation under push marketing strategy: Tool to mitigate bullwhip effect
S Gupta
International Journal of Research in Commerce and Management 3 (3), 93-98 2012
Citations: 2



What determines the performance of pharmaceutical firms in India on account of COVID-19 interventions?
A Lamba, P Aggarwal, S Gupta, M Joshipura
International Journal of Pharmaceutical and Healthcare Marketing 18 (3), 353-374 2023
Citations: 1



Predicting the impact of operational and financial variables on bullwhip effect using threshold regression: Indian context
S Gupta, A Saxena
Journal of Global Operations and Strategic Sourcing 13 (2), 211-227 2020
Citations: 1



The Insights on Marketing Initiatives Impacting the Bullwhip Effect: Sectoral Study.
S Gupta, A Saxena
International Journal of Business & Applied Sciences 9 (1) 2020
Citations: 1



A study of change in bullwhip effect: An Indian sectoral study
S Gupta, A Saxena
Journal of Supply Chain Management Systems 8 (2), 24-31 2019
Citations: 1



Interpretive structural modelling of critical operational factors for mitigation of bullwhip effect in supply chain management
S Gupta, A Saxena
International Journal of Comparative Management 2 (3-4), 297-328 2019
Citations: 1



A Literature Review of Designing, Modality and Psychological perspective in Human-Computer Interaction
R Bakshi, S Gupta
International Journal of Engineering Science Invention 3 (5), 18-26 2014
Citations: 1