Pioneering Firms in India and Their Climatic Challenges: A Case Study of ITC Limited Sujit Singh, Ezutah Udoncy Olugu, Sumit Gupta Climate Risk and Emerging Markets Valuation Volatility and Portfolio Strategies, 2026 This chapter presents a case of one of the pioneering corporations, Indian Tobacco Company (ITC) Limited’s sustainable business strategy to address the effects of climate change. ITC’s sustainability initiatives in water stewardship, renewable energy, climate-smart agriculture, and paperboard recycling are comprehensively explored. The results of the study are based on the data collected from ITC’s financial and socio-cultural projects which aimed at enhancing agricultural production, empowering women and creating rural employment. Some of the key milestones that ITC has achieved include water positivity, over 50% renewable energy, and climate-smart agriculture, all because of their sustainable approach. The company also reduced its environmental effects while increasing its socio-economic value, particularly in rural areas of India. This case points out the necessity of the stakeholder’s approach and the long-term perspective for a sustainable business. This chapter provides a rich and practical understanding of how organizations can incorporate sustainability strategies to tackle climate change and thrive. The practices of ITC are exemplary, and this case provides a clear understanding of how sustainability can be incorporated into business strategies. The study adds to the growing discussion on corporate sustainability. This study offers insights for other companies on managing the conflict between profit, people, and the environment.
Introduction to AI in Finance: Foundational Concepts and Emerging Challenges Afzalur Rahman, Sruthi Sivakumar, Uvesh Husain, Sujit Singh Foundations of Artificial Intelligence in Finance Insights for Practitioners with Applications and Case Studies, 2026 Artificial intelligence (AI) has profoundly altered the financial services industry by offering new paradigms for data analysis, decision-making, and operational efficiency. This chapter provides an extensive review of the core concepts, recent advancements, and emerging applications of AI in the financial sector. Based on a critical review of 162 high-impact scientific articles, the chapter identifies and evaluates key theme areas, including financial forecasting, fraud detection, explainable AI (XAI), and the nexus between AI and FinTech. The findings demonstrate the importance of state-of-the-art AI techniques such as deep learning, reinforcement learning, and hybrid models for enhancing risk assessment, raising forecast precision, and ensuring regulatory compliance. The chapter also discusses how AI could transform several financial processes, including portfolio management, credit assessment, and anti-money laundering efforts. The creation of interpretable AI frameworks, the incorporation of multimodal data sources, and the examination of AI's societal ramifications regarding sustainability and financial inclusion are just a few of the new opportunities and research gaps that are highlighted in this chapter. By bridging the gap between academic research and industry practice, this chapter provides useful insights for both researchers and practitioners. To harness AI's transformative potential while upholding moral standards and public trust, it encourages ethical, interdisciplinary collaboration. The chapter concludes with a strategic roadmap for the innovative and long-term use of AI technology in contemporary financial ecosystems.
AI- Driven Risk Sensing and Anomaly Detection for Predictive Project Management: Designing Early Warning Systems to Enhance Resilience and Decision- Making Vennela Mamidi, Harini Edala, Joel Pramod Manekshaw, Sujit Singh, Afzalur Rahman Agile AI Powered Project Management for Modern Delivery Organizations, 2026 The objective of this chapter is to propose a modernized framework for IT project management that addresses the shortcomings of traditional methodologies. It will argue for a shift from rigid, reactive models to an adaptive, proactive approach. The chapter demonstrates how integrating real-time data analytics and continuous risk monitoring can enhance flexibility, improve decision-making, and prevent the accumulation of unseen risks. Ultimately, it aims to outline the principles of a data-driven management strategy capable of delivering successful project outcomes in today's volatile technological landscape.
Modelling of critical success factors for procurement of AI systems: a study in the purview of the Indian public sector Sujit Singh, Rajkumari Mittal, Parul Sinha Journal of Public Procurement, 2025 Purpose This study aims to determine and prioritize the critical success factors (CSFs) that drive the procurement process of artificial intelligence (AI) systems in the public sector in the Indian context. It addresses the peculiar challenges and exigencies of procurement processes of AI systems in public sector organizations and develops a hierarchical model for guiding policymakers. Design/methodology/approach This study uses a research methodology that includes: identifying potential CSFs through literature review and expert consultations, developing a structural self-interaction matrix and performing cross-impact matrix multiplication applied to classification analysis for classifying CSFs based on driving and dependence powers. The study also uses interpretive structural modelling to establish relationships among these factors. Findings Fourteen CSFs are found in this study, which shape public procurement of AI. These influential factors cut across technical, organizational, regulatory as well as socio-economic aspects. This model is arranged hierarchically to indicate how these identified CSFs fit together thereby providing a roadmap towards successful procurement of AI systems. All other CSFs depend on or are influenced by stakeholders’ coordination and collaboration, which means that it is the most important one. Practical implications The results present a hierarchical model for navigating the intricacies of public sector AI procurement. Consequently, this model may support public organizations to enhance service delivery, optimize resource allocation and improve decision-making processes. It also addresses the need for an organized approach to the procurement of AI systems in developing countries such as India where extant guidelines are either insufficient or non-existent. Implementing these CSFs can result in more efficient, transparent and accountable procurement practices of AI systems, ultimately impacting positively on public service outcomes. Social implications The hierarchical model and classification of CSFs provide an alternative view regarding what entails successful AI systems’ acquisition stressing out particular requirements and obstacles associated with it within developing countries’ public sector agencies. It is in line with achieving sustainable development goals (SDGs) 9 which is industry, innovation and infrastructure and SDG 12 which is responsible consumption and production. Originality/value This study provides a new perspective by developing a comprehensive structured framework for the procurement of AI systems in the public sector. It fills gaps existing in research by offering a structural model on how to increase organization efficiency through the procurement of AI systems. The hierarchical model and classification of CSFs provide an alternative view regarding what entails successful AI acquisition, stressing particular requirements and obstacles associated with it within developing countries’ public sector agencies.
Barriers to industry 4.0 implementation in Indian manufacturing smes using integrated AHP-VIKOR framework Sujit Singh, Afzalur Rahman Sustainable Advanced Manufacturing and Logistics in Asean, 2025 This study explores the barriers that Indian Small and Medium Enterprises (SMEs) encounter when implementing Industry 4.0 technologies. Industry 4.0, marked by cyber-physical systems, data analytics, and automation, offers significant potential for enhancing efficiency and productivity. Despite this, Indian SMEs face substantial challenges in implementing these technologies. The research utilizes a combined Multi-Criteria Decision Making (MCDM) approach, integrating the Analytic Hierarchy Process (AHP) and VIKOR methods. AHP helps prioritize various barriers based on expert opinions and their relative importance. After identifying and weighing the barriers, VIKOR is used to rank them. VIKOR evaluates the “closeness” to the ideal solution and the “maximum group utility distance” from other options. This integrated approach offers a comprehensive way to identify the most critical barriers and develop strategies to address them. The results of this study can aid in designing targeted interventions to overcome these challenges. By understanding the specific issues Indian SMEs face, policymakers, industry leaders, and technology providers can create a more favorable environment for adopting Industry 4.0. This, in turn, can help Indian SMEs harness the transformative power of this new industrial revolution.
AI-Based Early Warning System for Preventing Thermal Incidents in Electric two-Wheeler Battery Packs Hemanthasai Madugula, Sujit Singh, Venkat Reddy 2025 IEEE International Conference on Smart Power Energy Renewables and Transportation Spert 2025 Proceedings, 2025 Thermal instability in electric two-wheeler battery packs continues to be a major safety issue, influencing rider protection, system reliability, and confidence in electric mobility. As batteries age and operate under varying loads and environments, traditional threshold-driven Battery Management Systems (BMS) often miss the early, gradual indicators that precede critical failures. In this work, a realtime early warning framework is introduced to address this gap. The system tracks key operating parameters—temperature, voltage, current, and State of Charge (SoC)—and evaluates them using data-driven anomaly detection and predictive models. These models adapt to changing conditions and help recognize unusual thermal patterns before they escalate. Experiments were conducted on a 48 V, 24 Ah NMC lithium-ion pack over more than 250 charge–discharge cycles. The developed method reached a detection accuracy of 96.8% with a high-risk recall of 95.4%, offering an advance warning of approximately 8-10 minutes before critical thermal limits were reached. It also forecasted State of Health (SOH) deterioration 12-15 cycles before significant failure symptoms appeared. With its combination of intelligent diagnostics and continuous monitoring, the proposed system reduces false alerts, supports timely maintenance, increases battery longevity, and strengthens the overall safety performance of electric two-wheeler batteries.
AI-Driven Supply Chain Optimization for Two-Wheeler EV Batteries: Enhancing Sustainability and Efficiency Hemanthasai Madugula, Aishvaria Gorityala, Sujit Singh, Sudha Radhika 5th IEEE International Conference on Sustainable Energy and Future Electric Transportation Sefet 2025, 2025 Despite their growing popularity, electric two-wheelers face barriers such as the expensive and environmentally taxing nature of lithium-ion batteries. The widespread implementation of electric two-wheelers is constrained by the high cost and environmental impact of lithium-ion batteries (LIBs). Second-life LIBs offer a sustainable solution by extending battery lifecycle through reuse in applications such as last-mile delivery fleets. This study develops a data-driven framework to predict the State of Health (SoH) of recycled lithium iron phosphate (LiFePO₄) batteries using Random Forest, Extreme Gradient Boosting (XG Boost), and Long Short-Term Memory (LSTM) models. The models were trained and tested on a dataset of 200 recycled batteries, with an 80:20 train-test split. Random Forest achieved the highest accuracy with a Mean Absolute Error (MAE) of 7.78% and Root Mean Squared Error (RMSE) of 9.24%, outperforming XG Boost-MAE: 7.98%, RMSE: 9.64% and LSTM-MAE: 9.85%, RMSE: 11.25%. Using the predicted SoH, batteries were allocated for long-distance delivery (>75% SoH), local use (60–75%), or refurbishment (<60%), enabling optimized resource utilization. This allocation strategy demonstrated potential cost savings exceeding ₹1.6 million in battery procurement and reduced charging costs by approximately ₹219,000 over two years for a fleet of 200 vehicles. The proposed approach provides a scalable framework for the sustainable integration of second-life batteries in commercial EV operations, contributing to circular economy goals and reduced environmental impact.
A lotus-optimized Radial basis function framework for explainable and energy-efficient battery health prediction in electric vehicles H Madugula, A Gorityala, S Singh, VR Muppani, S Radhika Energy 347, 13 , 2026 2026
Pioneering Firms in India and Their Climatic Challenges: A Case Study of ITC Limited S Singh, EU Olugu, S Gupta Climate Risk and Emerging Markets: Valuation, Volatility, and Portfolio … , 2026 2026
AI-Driven Cognitive Robotics for Autonomous Multi-Material Sorting and High-Value Recovery in Circular Supply Chains AK Sri, A Allapur, S Singh, AU Sri, A Rahman Waste Management, Reduction, and Sustainable Practices in Modern Societies … , 2026 2026
Logistics Vulnerabilities in Conflict Zones: Insights From the MENA Region KPS Bhargav, AV Reddy, BLN Sanjeev, S Singh Sustainable and Resilient Supply Chain Management in MENA: Challenges … , 2026 2026
AI-Driven Risk Sensing and Anomaly Detection for Predictive Project Management: Designing Early Warning Systems to Enhance Resilience and Decision-Making V Mamidi, H Edala, JP Manekshaw, S Singh, A Rahman Agile AI-Powered Project Management for Modern Delivery Organizations, 217-244 , 2026 2026
Introduction to AI in Finance: Foundational Concepts and Emerging Challenges A Rahman, S Sivakumar, U Husain, S Singh Foundations of Artificial Intelligence in Finance, 1-10 , 2026 2026
AI-Based Early Warning System for Preventing Thermal Incidents in Electric two-Wheeler Battery Packs H Madugula, S Singh, V Reddy 2025 IEEE International Conference on Smart Power, Energy, Renewables, and … , 2025 2025
Modelling of critical success factors for procurement of AI systems: a study in the purview of the Indian public sector S Singh, R Mittal, P Sinha Journal of Public Procurement 25 (4), 449-475 , 2025 2025
Assessing the Sustainability Performance of Indian Manufacturing SMEs with an Interval Valued Fuzzy Framework P Sinha, S Singh, R Mittal, AL Roshan, A Rahman 2025
AI-Driven Supply Chain Optimization for Two-Wheeler EV Batteries: Enhancing Sustainability and Efficiency M Hemanthasai, G Aishvaria, S Singh, R Sudha 2025 IEEE 5th International Conference on Sustainable Energy and Future … , 2025 2025
AI and ML revolution in last-mile delivery optimization : A bibliometric analysis R Nalluri, VR Muppani, S Singh Journal of Information and Optimization Sciences 40 (8), 2487–2497 , 2025 2025
Seeds of Change: Empowering Women Agri-Entrepreneurs in India through Key Drivers S Singh, VR Muppani, Y Atmakuri Journal of Information & Optimization Sciences 46 (8), 10.47974 , 2025 2025
The Looming Labyrinth: Risks of Artificial Intelligence in Financial Sector S Singh, A Rahman, SK Johl Green Horizons: Role of AI in Sustainable Finance, 215-235 , 2025 2025 Citations: 1
Legal Landscapes of Blockchain and Digital Twins: A Comparative Analysis Between India and Global Perspectives A Rahman, S Singh, M Saleem, U Husain Green Horizons: Role of AI in Sustainable Finance, 255-268 , 2025 2025
Burberry: Victim of Price, Perception, or Plunge? S Parul, S Sujit, M Rajkumari, D Smita Ivey Publication , 2025 2025
INTEGRATION OF COMPUTER VISION AND AI FOR ENHANCED INVENTORY MANAGEMENT S Singh, R Afzalur Urban India 45 (1), 25-38 , 2025 2025
Barriers to Industry 4.0 Implementation in Indian Manufacturing SMEs Using Integrated AHP-VIKOR Framework S Singh, A Rahman Sustainable Advanced Manufacturing and Logistics in ASEAN, 249-268 , 2025 2025
AI-DRIVEN SUPPLY CHAIN OPTIMISATION USING MULTI-AGENT REINFORCEMENT LEARNING: A REVIEW S Singh, A Rahman URBAN INDIA 44 (2), 54-71 , 2024 2024
Futuristic Technology for Sustainable Manufacturing S Singh, S Gupta, S Jagtap 2024 Citations: 2
Fuzzy Based Principal’s Leadership Skills Assessment Model for Secondary Schools M Niqab, S Singh, S Shaikh JISR management and social sciences & economics 17 (2), 67-84 , 2019 2019 Citations: 6
MOST CITED SCHOLAR PUBLICATIONS
Fuzzy-based sustainability evaluation method for manufacturing SMEs using balanced scorecard framework S Singh, EU Olugu, SN Musa, AB Mahat Journal of Intelligent Manufacturing , 2018 2018 Citations: 196
Fuzzy-based sustainable manufacturing assessment model for SMEs S Singh, EU Olugu, A Fallahpour Clean Technologies and Environmental Policy 16 (5), 847-860 , 2014 2014 Citations: 167
Development of sustainable manufacturing performance evaluation expert system for small and medium enterprises S Singh, EU Olugu, SN Musa Procedia CIRP 40, 609-614 , 2016 2016 Citations: 142
Strategy selection for sustainable manufacturing with integrated AHP-VIKOR method under interval-valued fuzzy environment S Singh, EU Olugu, SN Musa, AB Mahat, KY Wong The International Journal of Advanced Manufacturing Technology, 1-17 , 2015 2015 Citations: 97
Electrical Discharge Machining of AISI 329 stainless steel using copper and brass rotary electrode P Sharma, S Singh, DK Mishra Procedia Materials Science 5 (2014), 1771-1780 , 2014 2014 Citations: 50
System Dynamics as a tool for Green Supply Chain Management: A Theoretical Ransom S Singh, PC Sharma, PFP Barcellos, MR de Carvalho Borella International Journal of Humanities and Social Science 5 (4(1)), 121-133 , 2015 2015 Citations: 12
Optimization of Multi-Response Parameters of Inconel 600 on EDM using Rotary Brass Hollow Tubular Electrode by Taguchi Method P Sharma, DR Sharma, S Singh, DR Mishra Journal of Science & Technology Research 2 (2), 20-30 , 2012 2012 Citations: 7
Fuzzy Based Principal’s Leadership Skills Assessment Model for Secondary Schools M Niqab, S Singh, S Shaikh JISR management and social sciences & economics 17 (2), 67-84 , 2019 2019 Citations: 6
Supplier selection under fuzzy environment: a hybrid model using KAM in DEA A Fallahpour, EU Olugu, SN Musa, D Khezrimotlagh, S Singh Recent developments in data envelopment analysis and its applications, 342-348 , 2014 2014 Citations: 6
Proposition of key performance measures for sustainable manufacturing in SMEs S Singh, EU Olugu, SN Musa, AB Mahat MSME Conclave Cum Conference on Sustainable Supply Chain Capabilities of … , 2014 2014 Citations: 4
GREEN: RACE TO COMPETE OR IMAGINATION TO RIOT : A REVIEW IN INDIAN PERSPECTIVE R Mittal, S Pareek, S Singh, MA Khair International Journal of multidisciplinary Reseach and advances in … , 2011 2011 Citations: 3
Futuristic Technology for Sustainable Manufacturing S Singh, S Gupta, S Jagtap 2024 Citations: 2
Development of key performance measures for sustainable manufacturing in global SMEs S Singh, EU Olugu, SN Musa Decision Management: Concepts, Methodologies, Tools, and Applications, 1000-1008 , 2017 2017 Citations: 2
The Looming Labyrinth: Risks of Artificial Intelligence in Financial Sector S Singh, A Rahman, SK Johl Green Horizons: Role of AI in Sustainable Finance, 215-235 , 2025 2025 Citations: 1
A lotus-optimized Radial basis function framework for explainable and energy-efficient battery health prediction in electric vehicles H Madugula, A Gorityala, S Singh, VR Muppani, S Radhika Energy 347, 13 , 2026 2026
Pioneering Firms in India and Their Climatic Challenges: A Case Study of ITC Limited S Singh, EU Olugu, S Gupta Climate Risk and Emerging Markets: Valuation, Volatility, and Portfolio … , 2026 2026
AI-Driven Cognitive Robotics for Autonomous Multi-Material Sorting and High-Value Recovery in Circular Supply Chains AK Sri, A Allapur, S Singh, AU Sri, A Rahman Waste Management, Reduction, and Sustainable Practices in Modern Societies … , 2026 2026
Logistics Vulnerabilities in Conflict Zones: Insights From the MENA Region KPS Bhargav, AV Reddy, BLN Sanjeev, S Singh Sustainable and Resilient Supply Chain Management in MENA: Challenges … , 2026 2026
AI-Driven Risk Sensing and Anomaly Detection for Predictive Project Management: Designing Early Warning Systems to Enhance Resilience and Decision-Making V Mamidi, H Edala, JP Manekshaw, S Singh, A Rahman Agile AI-Powered Project Management for Modern Delivery Organizations, 217-244 , 2026 2026
Introduction to AI in Finance: Foundational Concepts and Emerging Challenges A Rahman, S Sivakumar, U Husain, S Singh Foundations of Artificial Intelligence in Finance, 1-10 , 2026 2026
