Faculty Publications

Now showing 1 - 3 of 3

Do wholesale pricing strategies matter during asymmetric disruptions? A game theoretic analysis
(Emerald Publishing, 2024) Raju, S.; Tm, R.; S, P.K.; Jacob, J.
Purpose: In most economies, there are rules from the market regulators or government to sell at an equal wholesale price (EWP). But when one upstream channel is facing a negative demand disruption and another positive, EWP can create extra pressure on the disadvantageous supply chain partner, which faces negative disruption. The purpose of this study is to analyse the impact of EWP and the scope of the discriminatory wholesale price (DWP) during disruptions. Design/methodology/approach: For the study, the authors used a dual-channel supply chain consisting of a manufacturer, online retailer (OR) and traditional brick-and-mortar (BM) retailer. Stackelberg game is used to model the interaction between the upstream and downstream channel partners, and the horizontal Nash game to analyse the interaction within downstream channel partners. For modelling asymmetric disruption, the authors took instances from the lock-down and post-lock-down periods of the COVID-19 pandemic, where consumers flow from BM retailer to OR store. Findings: By analysing the disruption period, the authors found that this asymmetric disruption is detrimental to the BM channel, favourable to OR and has no impact on the manufacturer. But with DWP, the authors found that the profit of the BM channel and manufacturer can be increased during disruption. Though the profit of the OR decreased, it was found to be higher than in the pre-disruption period. Under DWP, the consumer surplus increased during disruption, making it favourable for the customers also. Thus, DWP can aid in creating a win-win strategy for all the supply chain partners during asymmetric disruption. Later as an extension to the study, the authors analysed the impact of the consumer transfer factor and found that it plays a crucial role in the optimal decisions of the channel partner during DWP. Originality/value: Very scant literature analyses the intersection of DWP and disruptions. To the best of the authors’ knowledge, this study, for the first time uses DWP as a tool to help the disadvantageous supply chain partner during asymmetric disruptions. The study findings will assist the government, market regulators and manufacturers in revamping the wholesale pricing policies and strategies to help the disadvantageous supply chain partner during asymmetric disruption. © 2023, Emerald Publishing Limited.
Pricing strategies for dual-channel supply chain members under pandemic demand disruptions
(Springer Science and Business Media Deutschland GmbH, 2025) Raju, S.; Rofin, T.M.; Kumar, S.P.
The COVID-19 pandemic created an unprecedented disruption and has checked the robustness of the global supply chains. This article, for the first time, addresses a Dual Channel Supply Chain (DCSC) competition between an upstream manufacturer and downstream traditional retail stores (r-store) and electronic stores (e-store) under pandemic-induced demand disruptions. We employed the Stackelberg game to model the multi-agent interaction among the upstream manufacturer and downstream r-store and e-store. The competitive subgame between r-store and e-store was modelled using a horizontal Nash game, assuming their comparable channel power. To assess the impact of demand disruption, the benchmark pre-pandemic setting was compared against panic buying, lock-down, and post-lock-down situations in alignment with the actual occurrence of events. The optimal pricing strategies and consequent profit functions of all the channel members were derived by conducting an equilibrium analysis. Further, a computational analysis using Monte-Carlo simulation was conducted to obtain managerial insights. The study found that r-store profited the most during the panic buying period, except for high-cost products. On the other hand, the e-store benefited significantly during the post-lock-down period. The lock-down period was unfavorable for both r-store and e-store. Manufacturers achieved maximum profits during panic buying, especially for essential goods. Both lock-down and post-lock-down periods were less favourable for the upstream channel partner. Findings from the study will aid the management practitioners in developing policies to make the DCSC robust during pandemic disruptions. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.
Machine Learning Framework for Classification of COVID-19 Variants Using K-mer Based DNA Sequencing
(John Wiley and Sons Inc, 2025) Kumar, S.; Raju, S.; Bhowmik, B.
Accurate classification of viral DNA sequences is essential for tracking mutations, understanding viral evolution, and enabling timely public health responses. Traditional alignment-based methods are often computationally intensive and less effective for highly mutating viruses. This article presents a machine learning framework for classifying DNA sequences of COVID-19 variants using K-mer-based tokenization and vectorization techniques inspired by Natural Language Processing (NLP). DNA sequences corresponding to Alpha, Beta, Gamma, and Omicron variants are obtained from the Global Initiative on Sharing All Influenza Data (GISAID) database and encoded into feature vectors. Multiple classifiers, including Extra Trees, Random Forest, Support Vector Classifier (SVC), Decision Tree, Logistic Regression, Naive Bayes, K-Nearest Neighbor (KNN), Ridge Classifier, Stochastic Gradient Descent (SGD), and XGBoost, are evaluated based on accuracy, precision, recall, and F1-score. The Extra Trees model achieved the highest accuracy of 93.10% (Formula presented.) 0.42, followed by Random Forest with 92.60% (Formula presented.) 0.38, both demonstrating robust and balanced performance. Statistical significance tests confirmed the robustness of the results. The results validate the effectiveness of K-mer-based encoding combined with traditional machine learning models in classifying COVID-19 variants, offering a scalable and efficient solution for genomic surveillance. © 2025 Wiley Periodicals LLC.

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