To see the other types of publications on this topic, follow the link: AI-Driven Procurement Transformation.
Journal articles on the topic 'AI-Driven Procurement Transformation'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 18 journal articles for your research on the topic 'AI-Driven Procurement Transformation.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Esan, Oluwafunmilayo Janet, Ogechi Thelma Uzozie, Osazee Onaghinor, Grace Omotunde Osho, and Emmanuel Augustine Etukudoh. "Procurement 4.0: Revolutionizing Supplier Relationships through Blockchain, AI, and Automation: A Comprehensive Framework." Journal of Frontiers in Multidisciplinary Research 3, no. 1 (2022): 117–23. https://doi.org/10.54660/.ijfmr.2022.3.1.117-123.
Full textAbstract:
Procurement 4.0 marks a fundamental shift in supplier relationship management by leveraging blockchain, artificial intelligence (AI), and automation to enhance transparency, efficiency, and risk mitigation. This paper provides a comprehensive framework for integrating these technologies into procurement strategies, addressing key challenges in digital transformation while maximizing operational benefits. Blockchain facilitates secure and immutable transaction records, ensuring supplier accountability and seamless contract execution through smart contracts. AI-driven predictive analytics enable data-informed supplier selection, risk assessment, and fraud detection, improving procurement decision-making. Automation, including robotic process automation (RPA), streamlines repetitive tasks, reduces procurement cycle times, and optimizes resource allocation. This study explores the theoretical foundations of Procurement 4.0, focusing on the principles driving its adoption and the impact of digital transformation on supplier relationships. It examines blockchain-enabled transparency, AI-powered risk management, and automation-driven efficiency. A structured framework is proposed to guide organizations in implementing Procurement 4.0, addressing integration challenges and strategic considerations. The paper concludes with policy recommendations and future research directions, emphasizing the need for regulatory alignment, ethical AI deployment, and continued advancements in digital procurement technologies.
2
Esan, Oluwafunmilayo Janet, Ogechi Thelma Uzozie, and Osazee Onaghinor. "Procurement 4.0: Revolutionizing Supplier Relationships through Blockchain, AI, and Automation: A Comprehensive Framework." International Journal of Multidisciplinary Research and Growth Evaluation 3, no. 1 (2022): 900–906. https://doi.org/10.54660/.ijmrge.2022.3.1.900-906.
Full textAbstract:
Procurement 4.0 marks a fundamental shift in supplier relationship management by leveraging blockchain, artificial intelligence (AI), and automation to enhance transparency, efficiency, and risk mitigation. This paper provides a comprehensive framework for integrating these technologies into procurement strategies, addressing key challenges in digital transformation while maximizing operational benefits. Blockchain facilitates secure and immutable transaction records, ensuring supplier accountability and seamless contract execution through smart contracts. AI-driven predictive analytics enable data-informed supplier selection, risk assessment, and fraud detection, improving procurement decision-making. Automation, including robotic process automation (RPA), streamlines repetitive tasks, reduces procurement cycle times, and optimizes resource allocation. This study explores the theoretical foundations of Procurement 4.0, focusing on the principles driving its adoption and the impact of digital transformation on supplier relationships. It examines blockchain-enabled transparency, AI-powered risk management, and automation-driven efficiency. A structured framework is proposed to guide organizations in implementing Procurement 4.0, addressing integration challenges and strategic considerations. The paper concludes with policy recommendations and future research directions, emphasizing the need for regulatory alignment, ethical AI deployment, and continued advancements in digital procurement technologies.
3
Researcher. "THE TRANSFORMATIVE IMPACT OF AI ON PROCUREMENT EXCELLENCE: A TECHNICAL ANALYSIS." International Journal of Computer Engineering and Technology (IJCET) 15, no. 6 (2024): 475–87. https://doi.org/10.5281/zenodo.14178698.
Full textAbstract:
This comprehensive article explores the transformative impact of Artificial Intelligence on procurement operations and strategic decision-making across global enterprises. The article examines how AI-driven solutions revolutionize traditional procurement processes through automation, predictive analytics, and data-driven supplier management. The article investigates the implementation considerations, technical requirements, and critical success factors for AI adoption in procurement. The findings demonstrate that organizations leveraging AI technologies significantly improve operational efficiency, cost reduction, and strategic value creation. The article also explores emerging trends, including the integration of blockchain, IoT, and advanced cognitive computing systems, highlighting their potential to reshape the future of procurement operations and supply chain management.
4
Chandramohan, Pradeep. "Accelerating RFP Evaluation with AI-Driven Scoring Frameworks." European Journal of Computer Science and Information Technology 13, no. 30 (2025): 37–49. https://doi.org/10.37745/ejcsit.2013/vol13n303749.
Full textAbstract:
The evolution of Request for Proposal (RFP) evaluation processes has reached a pivotal moment with the integration of artificial intelligence and machine learning technologies. This advancement addresses longstanding challenges in traditional manual evaluation methods, particularly focusing on efficiency, consistency, and objectivity. Through the implementation of AI-driven scoring frameworks, organizations can now transform qualitative responses into quantifiable insights, enabling faster and more objective assessment of submissions. Natural Language Processing techniques, including named entity recognition and semantic similarity scoring, have revolutionized the extraction of key information and evaluation of alignment with RFP criteria. The integration of rule-based frameworks applies predefined logic to generate transparent scores, ensuring accountability and repeatability throughout the evaluation process. This technological transformation not only reduces evaluator fatigue but also minimizes subjective bias, contributing to fairer procurement outcomes. Additionally, the early detection of incomplete or non-compliant responses through AI systems enhances overall process efficiency. The implementation framework provides organizations with structured guidance for adopting these technologies while maintaining customizable logic, human-in-the-loop design, and compliance with procurement standards.
5
Sreenivasa, Rao Sola. "ERP Cloud and Procurement: Unlocking New Levels of Automation and Integration." International Journal of Leading Research Publication 1, no. 1 (2020): 1–14. https://doi.org/10.5281/zenodo.15259058.
Full textAbstract:
The dynamics of enterprise resource planning (ERP) systems have revolutionized the manner in which organizations manage procurement processes. Oracle ERP Cloud, being one of the leading cloud-based software solutions, has ushered in advanced features that enhance procurement activities via automation, integration, and artificial intelligence (AI)-informed decision-making. This journal addresses the significant contribution of Oracle ERP Cloud applications to optimizing procurement strategies through business ability to automate procurement processes, reduce operation costs, and accelerate procurement cycle times. Levying on automated processes, AI-driven insights, and real-time data integration, companies can make better and faster procurement decisions. The journal points out meaningful features of Oracle ERP Cloud that drive automation, such as supplier collaboration portals, intelligent demand forecasting, and automated purchase order processing. In addition, the integration of machine learning and AI tools in Oracle's cloud platform enables predictive analytics, which allows procurement teams to identify potential disruptions, enhance the performance of suppliers, and steer clear of risks. From industry case studies and prior research, based on developments up to October 2020, this paper provides the actual benefits of deploying ERP cloud solutions for procurement, such as improved supplier relationship management, cost advantages, and improved decision-making capabilities. With more businesses embracing digital transformation, Oracle ERP Cloud is a major driver of efficient, integrated, and low-cost procurement operations, setting new benchmarks in procurement automation and innovation.
6
Fredson, Gracetiti, Babatunde Adebisi, Olushola Babatunde Ayorinde, Ekene Cynthia Onukwulu, Olugbenga Adediwin, and Alexsandra Ogadimma Ihechere. "Enhancing Procurement Efficiency through Business Process Reengineering: Cutting-Edge Approaches in the Energy Industry." International Journal of Social Science Exceptional Research 1, no. 1 (2022): 38–54. https://doi.org/10.54660/ijsser.2022.1.1.38-54.
Full textAbstract:
The energy industry operates in a highly dynamic environment, requiring robust procurement systems to ensure efficiency, cost-effectiveness, and alignment with sustainability goals. However, traditional procurement methods often fail to address emerging challenges such as fluctuating energy markets, supply chain disruptions, and increasing regulatory demands. This study explores the transformative potential of Business Process Reengineering (BPR) in enhancing procurement efficiency within the energy sector. By rethinking and redesigning procurement processes, BPR offers a systematic framework for eliminating inefficiencies, reducing operational costs, and optimizing resource utilization. The study examines cutting-edge approaches such as process automation, digital procurement platforms, data-driven decision-making, and artificial intelligence (AI) integration, which are increasingly being adopted to streamline procurement workflows and improve supplier collaboration. Through an in-depth analysis of case studies and empirical data, the research highlights how energy companies leveraging BPR strategies achieve greater agility and resilience in their procurement operations. Key findings reveal that implementing AI-powered tools for demand forecasting, blockchain for transparent supplier transactions, and digital dashboards for real-time tracking significantly enhances procurement performance. Additionally, the study underscores the importance of fostering organizational culture shifts, change management strategies, and cross-functional collaboration to ensure the successful adoption of reengineered processes. This paper further identifies critical success factors for implementing BPR in the energy sector, including top management support, employee training, and investment in scalable digital infrastructure. It also discusses the role of sustainability in procurement transformation, emphasizing green procurement practices that align with global energy transition goals. By offering actionable insights, this research provides a roadmap for energy companies to achieve procurement excellence, drive operational efficiency, and remain competitive in an evolving market.
7
Bhavikatta, Naga Bharadwaj. "THE INTELLIGENT SUPPLY CHAIN: LEVERAGING AI AND ML FOR OPERATIONAL EXCELLENCE." International Journal of Computer Science and Mobile Computing 14, no. 6 (2025): 56–66. https://doi.org/10.47760/ijcsmc.2025.v14i06.007.
Full textAbstract:
In an increasingly volatile and globalized marketplace, traditional supply chains are under immense pressure to deliver higher efficiency, agility, and resilience. The concept of the intelligent supply chain has emerged as a strategic imperative, driven by the need for real-time insights, adaptive decision-making, and predictive capabilities. Artificial Intelligence (AI) and Machine Learning (ML) are at the forefront of this transformation, enabling end-to-end automation, enhanced forecasting accuracy, and intelligent optimization across procurement, logistics, inventory, and production planning. This study explores the multifaceted role of AI and ML in modernizing supply chain operations, identifying the technological enablers, use cases, and organizational benefits associated with their adoption. Through an in-depth review of current literature, market implementations, and case studies from industry leaders such as Amazon, Maersk, and Unilever, the paper demonstrates how AI-driven systems improve demand forecasting, reduce operational costs, and increase supply chain responsiveness. The findings suggest that the integration of intelligent technologies not only supports operational excellence but also equips enterprises to navigate disruptions with greater strategic foresight.
8
Rainy, Tahmina Akter, and Abdur Razzak Chowdhury. "THE ROLE OF ARTIFICIAL INTELLIGENCE IN VENDOR PERFORMANCE EVALUATION WITHIN DIGITAL RETAIL SUPPLY CHAINS: A REVIEW OF STRATEGIC DECISION-MAKING MODELS." American Journal of Scholarly Research and Innovation 01, no. 01 (2022): 220–48. https://doi.org/10.63125/96jj3j86.
Full textAbstract:
The digital transformation of retail supply chains has fundamentally reshaped how organizations evaluate, manage, and engage with their suppliers. In this context, artificial intelligence (AI) has emerged as a transformative enabler, offering sophisticated tools for enhancing vendor performance evaluation through intelligent automation, predictive modeling, and real-time decision-making support. This systematic review critically examines the integration of AI technologies—such as supervised and unsupervised machine learning, natural language processing (NLP), and deep learning algorithms—into vendor performance assessment frameworks within digital retail ecosystems. Drawing on an analysis of 86 peer-reviewed journal articles, industry white papers, and technical reports published between 2015 and 2022, the study identifies and categorizes the predominant AI-driven models employed to assess key supplier attributes, including reliability, quality assurance, compliance with contractual obligations, cost-efficiency, and operational risk. The review further investigates how these AI tools enable real-time vendor monitoring, dynamic anomaly detection, and the automation of adaptive performance scorecards. Evidence from the literature demonstrates that AI-enabled evaluation systems can significantly enhance the precision, objectivity, and scalability of vendor assessments, while reducing human bias and manual inefficiencies in procurement processes. However, the adoption of AI in this domain is not without challenges. Common barriers include fragmented data architectures, difficulties in integrating AI tools with legacy enterprise systems, concerns over the interpretability and ethical transparency of algorithmic decisions, and a lack of standardization in AI governance practices. In response to these challenges, the review also identifies emergent research opportunities aimed at improving the accountability, fairness, and sustainability of AI applications in retail supply chains. Future research directions include the development of hybrid models combining human expertise with machine learning, reinforcement learning-based adaptive evaluation systems, and the incorporation of ESG (environmental, social, and governance) metrics into AI-based vendor assessments. This review contributes to the growing discourse on AI’s role in shaping agile, data-driven, and ethically sound vendor management practices within the evolving digital retail ecosystem. By synthesizing current findings, the review highlights critical implementation bottlenecks and knowledge gaps in the field, and proposes future research directions for developing explainable, secure, and ethically sound AI solutions that align with sustainable procurement goals and evolving retail strategies.
9
Katuri, Desi Ramananda Kishore. "Streamlining Procure-to-Pay Processes in Large-Scale Companies with ERP Finance Systems." European Journal of Computer Science and Information Technology 13, no. 38 (2025): 174–90. https://doi.org/10.37745/ejcsit.2013/vol13n38174190.
Full textAbstract:
This article examines how Enterprise Resource Planning (ERP) finance systems transform procure-to-pay (P2P) processes in large-scale organizations across various industries. Traditional P2P operations often suffer from inefficiencies including manual data entry, disjointed systems, approval bottlenecks, and limited visibility into spending patterns. ERP solutions address these challenges by integrating procurement with financial operations, automating workflows, and enhancing transparency throughout the P2P lifecycle. It explores implementation across retail, financial services, and educational institutions, highlighting how each sector leverages ERP features to meet unique requirements. Key transformative capabilities discussed include intelligent invoice processing, real-time analytics, and supplier collaboration portals. The article identifies critical implementation challenges—data fragmentation, user resistance, and legacy system integration—along with proven strategies to overcome them. Best practices for P2P excellence are presented, focusing on process standardization before automation, strategic supplier segmentation, and continuous improvement frameworks. Looking ahead, the article examines emerging trends that will shape future P2P transformation: AI-driven procurement intelligence, blockchain for secure transactions, and embedded ESG (Environmental, Social, Governance) considerations. It provides organizations with a roadmap for leveraging ERP finance systems to transform procurement into a strategic asset that delivers operational efficiency, enhanced compliance, and sustainable cost savings.
10
Erceg, Aleksandar, and Santhosh Joseph. "Sourcing efficacy – The role of supportive intelligence." Ekonomski vjesnik 38, no. 1 (2025): 133–49. https://doi.org/10.51680/ev.38.1.10.
Full textAbstract:
Purpose: Globalization has increased the importance of sourcing and procurement strategies and factbased negotiation (FBN). Technological advances such as machine learning (ML) and artificial intelligence (AI) and their integration in FBN are significant transformative steps. The paper explores ML and AI’s role in improving FBN processes that traditionally rely on data-driven perceptions.Methodology: The research used in the paper used a multi-method approach with quantitative and qualitative elements. This research design was chosen to explore the complexity of integrating AI and ML in FBN and to obtain the impact this integration has on sourcing processes in different industries. The research results are based on a survey of 210 participants and 33 in-depth interviews.Results: The research showed that companies use FBN and see it as a beneficial approach to increasing negotiation efficacy. AI and ML integration in FBN significantly improves the negotiation process since it provides predictive modeling and real-time data analysis.Conclusion: The paper’s results align with current scientific studies highlighting the opportunities and barriers to AI and ML integration in negotiation processes. Companies must prioritize planning, education and organizational alignment for further development and optimization of these tools. With this, it is possible to fully realize the possibilities that integrating AI and ML into FBN can bring to the transformation of sourcing processes and the company’s competitiveness.
11
Delina, Radoslav, and Marek Macik. "Quality of Artificial Intelligence Driven Procurement Decision Making and Transactional Data Structure." Quality Innovation Prosperity 27, no. 1 (2023): 103–18. http://dx.doi.org/10.12776/qip.v27i1.1819.
Full textAbstract:
Purpose: Current data driven decision making development calls for the quality assurance based on quality data structure. The paper analyses transactional data structure used in public procurement in Slovakia and the effect of data structure enhancement on prediction performance as crucial part of artificial intelligence (AI) quality assurance standard. We examine the significance of data structure enhancement and attributes transformation for prediction modelling. Methodology/Approach: The research is based on mutli-step model using stacked ensemble machine learning (ML) algorithm and simulating input space of 211 attributes transformed and aggregated according to different perspectives assessed by r2, mean absolute error (MAE) or mean square error (MSE). Findings: The results show that different performance of variable categories to prediction power. The most significant predictors were in category related to sectoral product classifications and in category related to variables aggregated for supplier, what underline the significance of structured information of all suppliers and negotiation participants in public tenders. Research Limitation/Implication: Methodology is based on big data with high complexity. Due to limited computing power, no subjects’ IDs were used as inputs. The complexity behind data and processes call for more complex simulations of all variables and their mutual interaction and interdependencies. Originality/Value of paper: The paper contributes to data science in transactional data domain and assessed the significance of different variables categories with respect to their specific added value to prediction power.
12
Shabnam Sharmin and Rakibul Hasan Chowdhury. "Digital Transformation in Governance: The Impact of e-governance on Public Administration and Transparency." Journal of Computer Science and Technology Studies 7, no. 1 (2025): 362–79. https://doi.org/10.32996/jcsts.2025.7.1.27.
Full textAbstract:
Digital transformation in governance has revolutionized public administration by leveraging emerging technologies such as artificial intelligence (AI), blockchain, big data, and cloud computing to improve efficiency, transparency, and service delivery. E-governance, a crucial component of this transformation, facilitates digital interactions between governments, citizens, businesses, and employees, reducing bureaucratic inefficiencies while promoting accountability. Governments worldwide are adopting e-governance models to enhance service accessibility, streamline administrative processes, and combat corruption through open data initiatives and AI-driven decision-making.This study investigates the impact of e-governance on public administration efficiency and transparency, addressing three key research questions: (1) How does e-governance improve public administration efficiency? (2) What role does e-governance play in enhancing transparency? (3) What challenges and risks are associated with the adoption of e-governance? To answer these questions, the research employs a mixed-methods approach, combining qualitative content analysis of policy documents and quantitative survey data from policymakers and public administrators. A comparative case study analysis examines successful e-governance implementations in Estonia, India, and South Korea. Findings indicate that e-governance significantly improves administrative efficiency by automating workflows, reducing costs, and facilitating citizen engagement. Moreover, digital transparency initiatives such as blockchain-based procurement systems and open data policies contribute to reducing corruption and strengthening public trust. However, challenges such as the digital divide, cybersecurity risks, and bureaucratic resistance hinder full-scale adoption. The study concludes that AI, big data, and blockchain will shape the future of digital governance, but legal and ethical frameworks must be strengthened to ensure secure, inclusive, and citizen-centric governance models. Future research should explore the long-term effects of e-governance on democratic participation and compare adoption patterns between developed and developing nations.
13
Arjunkumar Vijayakumar. "Cloud Technologies in Healthcare Supply Chains: Ensuring Efficiency and Transparency." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 1415–21. https://doi.org/10.32628/cseit251112132.
Full textAbstract:
The integration of cloud technologies in healthcare supply chains has revolutionized the management of critical medical supplies and temperature-sensitive products. This transformation encompasses comprehensive solutions for cold chain management, supplier collaboration, and logistics optimization. Cloud-based systems have enabled healthcare organizations to achieve remarkable improvements in inventory accuracy, order processing efficiency, and regulatory compliance. Through advanced IoT sensor networks and real-time monitoring capabilities, healthcare facilities have enhanced their ability to maintain temperature-sensitive pharmaceuticals within critical thresholds. The implementation of predictive analytics and AI-driven optimization has transformed delivery logistics, particularly benefiting underserved areas. Modern supplier collaboration platforms featuring multi-tenant architectures have streamlined procurement processes and enhanced communication efficiency. Looking ahead, quantum computing integration and 5G network deployment are positioned to further transform healthcare supply chains, while robust security protocols and blockchain-based compliance automation ensure data protection. These technological advancements have resulted in substantial reductions in operational costs, improved inventory accuracy, decreased product wastage, and enhanced overall healthcare delivery efficiency.
14
Omotunde Osho, Grace, Julius Olatunde Omisola, and Joseph Oluwasegun Shiyanbola. "An Integrated AI-Power BI Model for Real-Time Supply Chain Visibility and Forecasting: A Data-Intelligence Approach to Operational Excellence." International Journal of Advanced Multidisciplinary Research and Studies 4, no. 6 (2024): 1463–80. https://doi.org/10.62225/2583049x.2024.4.6.4047.
Full textAbstract:
In today’s dynamic and complex business landscape, achieving real-time visibility and accurate forecasting in supply chain operations is essential for sustaining competitiveness and operational excellence. This study proposes an integrated Artificial Intelligence (AI) and Power BI model designed to enhance real-time supply chain visibility and predictive capabilities through a data-intelligence-driven approach. The framework leverages machine learning algorithms for demand forecasting, anomaly detection, and performance optimization, while utilizing Power BI’s robust data visualization and dashboarding functionalities to offer intuitive insights for decision-makers. The model integrates disparate data sources across procurement, inventory, logistics, and sales channels, transforming raw data into actionable intelligence. Through AI-based predictive analytics, the system forecasts demand patterns, identifies potential disruptions, and prescribes adaptive strategies to optimize resource allocation and reduce lead times. Furthermore, the Power BI component ensures dynamic, user-friendly dashboards that allow supply chain managers to monitor key performance indicators (KPIs), track supplier performance, and assess inventory levels in real-time. To validate the model, a case study was conducted in a mid-sized manufacturing enterprise, where the implementation of the AI-Power BI model led to a 27% improvement in forecasting accuracy, a 19% reduction in stockouts, and a 22% increase in supply chain responsiveness. The real-time data pipeline also enhanced collaboration across departments, resulting in more agile and informed decision-making. This research highlights the significance of integrating AI technologies with business intelligence platforms to overcome traditional supply chain inefficiencies. It contributes to the growing body of knowledge on Industry 4.0 by offering a scalable, adaptable solution that aligns with digital transformation goals. The proposed model empowers organizations to transition from reactive to proactive supply chain management, fostering agility, resilience, and data-driven culture. Future work will focus on incorporating blockchain for enhanced transparency and leveraging edge computing to improve data processing latency. The integrated AI-Power BI model is poised to revolutionize supply chain management by enabling smarter forecasting and end-to-end visibility.
15
Onukwulu, Ekene Cynthia, Joyce Efekpogua Fiemotongha, Abbey Ngochindo Igwe, and Chikezie Paul-Mikki Ewim. "Transforming supply chain logistics in oil and gas: best practices for optimizing efficiency and reducing operational costs." Journal of Advance Multidisciplinary Research 2, no. 2 (2023): 59–76. https://doi.org/10.54660/.jamr.2023.2.2.59-76.
Full textAbstract:
The oil and gas industry operates within a highly complex and dynamic supply chain that demands efficiency, cost-effectiveness, and resilience. This study investigates the latest innovations in oil and gas supply chain logistics, focusing on process optimization techniques to enhance operational efficiency and reduce costs. Given the industry's volatility, optimizing supply chain processes such as cargo clearance, sales efficiency, and inventory management is critical for maintaining profitability and ensuring timely delivery. This paper explores best practices in digital transformation, automation, and data analytics that improve decision-making and streamline logistics operations. Advances such as blockchain-enabled tracking systems, AI-driven predictive analytics, and Internet of Things (IoT) applications in real-time monitoring have revolutionized supply chain visibility, reducing uncertainties and mitigating risks. By leveraging data-driven insights, companies can enhance demand forecasting, optimize transportation routes, and reduce bottlenecks in customs clearance, leading to faster throughput and minimized delays. Furthermore, supply chain resilience in oil and gas requires robust strategies for mitigating disruptions caused by geopolitical tensions, fluctuating oil prices, and environmental regulations. Adopting agile procurement models and strategic supplier partnerships fosters adaptability while ensuring cost-effectiveness. Additionally, integrating cloud-based enterprise resource planning (ERP) solutions with logistics networks improves coordination among stakeholders, reducing redundancies and improving overall efficiency. The study also examines how companies can implement sustainable logistics practices, such as fuel-efficient transportation modes, carbon footprint reduction strategies, and the use of renewable energy in supply chain operations. By incorporating sustainability measures, organizations not only align with global environmental policies but also enhance long-term cost savings. Drawing from real-world case studies and industry benchmarks, this research highlights the effectiveness of these innovations in optimizing oil and gas supply chains. The findingsunderscore the importance of digital transformation, process automation, and data-driven decision-making in achieving operational excellence. By implementing these best practices, oil and gas companies can significantly reduce operational costs, enhance efficiency, and improve delivery timelines, strengthening their competitive edge in a rapidly evolving global market.
16
Thatikonda, Rahul Kumar, and Sucharitha Donepudi. "Enhancing B2B Sales Efficiency: The Role of AI in Automating the Request for Proposal Process." December 12, 2024. https://doi.org/10.5281/zenodo.14433855.
Full textAbstract:
This paper explores the transformative role of Artificial Intelligence (AI) in automating the Request for Proposal (RFP) process within Business B2B operations. Leveraging Natural Language Processing (NLP) and Machine Learning (ML), the study demonstrates how AI reduces response times by 40%, enhances proposal accuracy by 20%, and boosts win rates by 12%. The methodology includes data-driven automation of tasks such as requirement extraction and dynamic pricing optimization. Challenges such as workforce adaptability, data inconsistency, and ethical concerns are critically analyzed. The paper also highlights future trends, including blockchain integration and hyper-personalized proposals, emphasizing AI's strategic impact on business efficiency.
17
BOPPANA, VENKAT RAVITEJA. "Sustainability Practices in IT Infrastructure for Healthcare." EPH-International Journal of Business & Management Science 10, no. 1 (2024). http://dx.doi.org/10.53555/eijbms.v10i1.181.
Full textAbstract:
In recent years, sustainability has become a critical focus across industries, and healthcare is no exception. The growing digitalization in healthcare, while improving efficiency and patient care, has also raised concerns about the environmental impact of IT infrastructure. Healthcare organizations increasingly rely on advanced technologies such as electronic health records (EHR), telemedicine, and AI-driven diagnostics, all of which demand robust IT systems. However, the energy consumption and e-waste generated by these systems can be substantial. To address this, healthcare IT leaders are adopting sustainability practices that include energy-efficient data centers, cloud computing, and hardware recycling programs. Transitioning to green technologies, such as renewable energy-powered data centers and virtualization, helps reduce the carbon footprint of IT infrastructure. Additionally, implementing software solutions that optimize resource use, reducing energy consumption in hospitals through smart systems, and adopting sustainable procurement policies are becoming essential strategies. The healthcare sector is also focusing on reducing e-waste by extending the life of IT equipment through proper maintenance and disposal practices. By integrating sustainability into IT strategies, healthcare organizations not only contribute to environmental protection but also improve operational efficiency and reduce costs. As patient data continues to grow, scalable, environmentally conscious infrastructure will become key. This shift towards sustainable IT solutions is not just an ethical responsibility but also a smart business strategy, aligning healthcare operations with global sustainability goals. By 2024 and beyond, the healthcare industry is set to witness a significant transformation in its IT infrastructure, driven by both technological advancements and an increasing commitment to sustainability.
18
Kale Sakshi, Adamane Rushikesh, Dhotre Sahil, Bhaskarwar Rounak, and Prof. Mrs. Manjushri Raut. "E-Mart : (Wholesale E-Commerce Platform)." International Journal of Advanced Research in Science, Communication and Technology, January 26, 2025, 52–68. https://doi.org/10.48175/ijarsct-23105.
Full textAbstract:
The E-Mart project is a wholesale e-commerce platform designed to bridge the gap between manufacturers, distributors, and retailers by providing a seamless online marketplace. In an era where digital transformation is reshaping industries, wholesale commerce remains a critical area that demands modernization. E-Mart addresses the inefficiencies of traditional wholesale operations by offering a user-friendly, scalable, and secure platform where bulk buyers and sellers can interact and transact with ease. This platform not only simplifies the procurement process but also opens new business opportunities by leveraging technology to create a transparent, efficient, and cost-effective supply chain. The core objective of E-Mart is to streamline B2B (business-to-business) transactions, offering a robust platform for wholesale buyers to access a wide range of products directly from manufacturers and distributors. Key features of the platform include product listing, price comparison, bulk order management, secure payment gateways, and logistics integration for tracking shipments. Sellers can manage inventory, set pricing, and receive real-time analytics to optimize their business strategies, while buyers benefit from competitive pricing, bulk discounts, and an easy-to-navigate interface that reduces the complexity of large-scale procurement. To develop this platform, we employed agile development methodologies and open-source technologies to ensure flexibility, scalability, and security. The system architecture is designed to handle large volumes of transactions and concurrent users while ensuring data integrity and protection through encryption protocols. The backend integrates with various APIs for payment processing, shipping, and inventory management, while the frontend focuses on providing a clean, intuitive user experience. A pilot implementation of E-Mart was conducted with select wholesalers and retailers in different sectors, including electronics, consumer goods, and textiles. The results showed significant improvements in order accuracy, reduced lead times, and cost savings for both buyers and sellers. Furthermore, feedback from early adopters has been overwhelmingly positive, highlighting the platform's potential to transform wholesale e-commerce. In conclusion, E-Mart presents a comprehensive solution to the challenges faced in the wholesale market by integrating technology-driven innovations with industry best practices. As the platform evolves, future enhancements will include AI-driven analytics, dynamic pricing models, and further customization options to enhance user experience and profitability..