AI-Driven Quoting: Enhancing Customer Forecasting & Procurement Optimization |
||
 |
 |
|
| © 2023 by IJCTT Journal | ||
| Volume-71 Issue-4 |
||
| Year of Publication : 2023 | ||
| Authors : Sharda Kumari, Ravi Dave, Avinash Malladhi | ||
| DOI : 10.14445/22312803/IJCTT-V71I4P102 | ||
How to Cite?
Sharda Kumari, Ravi Dave, Avinash Malladhi, "AI-Driven Quoting: Enhancing Customer Forecasting & Procurement Optimization," International Journal of Computer Trends and Technology, vol. 71, no. 4, pp. 8-13, 2023. Crossref, https://doi.org/10.14445/22312803/IJCTT-V71I4P102
Abstract
The quoting process is a crucial aspect of customer relationship management and supply chain operations, as it directly impacts customer satisfaction, inventory management, and procurement efficiency. The research paper, "AI-Driven Quoting: Enhancing Customer Forecasting & Procurement Optimization," delves into the applications of artificial intelligence (AI) and machine learning algorithms to enhance the quoting process, with a particular focus on improving customer forecasting and streamlining procurement proposal creation. By leveraging AI and machine learning, organizations can efficiently capture and analyze vast amounts of customer data, enabling them to predict demand accurately and make data-driven decisions. This, in turn, leads to more precise procurement proposals, minimizing inventory carrying costs and improving supplier relationships. The paper also explores the challenges associated with integrating these advanced technologies into existing CRM and supply chain management systems, addressing the potential barriers to adoption and the importance of ensuring data security and privacy. Through a comprehensive examination of the latest developments in AI and machine learning, this paper aims to provide valuable insights and best practices for organizations seeking to harness the power of these transformative technologies in the quoting process, ultimately driving greater efficiency, competitiveness, and customer satisfaction.
Keywords
Customer Relationship Management, Forecasting, AI, Machine Learning, Forecasting.
Reference
[1] Ming-Hui Huang, and Roland T. Rust, “Artificial Intelligence in Service,” Journal of Service Research, vol. 21, no. 2, pp. 155-172, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[2] İlker Mutlu et al., “A New Demand Forecasting Framework Based on Reported Customer Forecasts and Historical Data,” Information Systems Frontiers, pp. 839-848, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Truong Nguyen et al., “Big Data Analytics in Supply Chain Management: A State-of-the-Art Literature Review,” Computers & Operations Research, pp. 98, 254-264, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Hsinchun Chen, Roger H.L. Chiang, and Veda C. Storey, “Business Intelligence and Analytics: From Big Data to Big Impact,” JSTOR Journal, vol. 36, no. 4, pp. 1165-1188, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Florian Kache, and Stefan Seuring, “Challenges and Opportunities of Digital Information at the Intersection of Big Data Analytics and Supply Chain Management,” International Journal of Operations & Production Management, vol. 37, no. 1, pp. 10-32, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Gang Wang et al., “Big Data Analytics in Logistics and Supply Chain Management: Certain Investigations for Research and Applications,” International Journal of Production Economics, vol. 176, pp. 98-110, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[7] V. Kumar, “Transformative Marketing: The Next 20 Years,” Journal of Marketing, vol. 82, no. 4, pp. 1-12, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Sasha Romanosky, “Examining the Costs and Causes of Cyber Incidents,” Journal of Cybersecurity, vol. 2, no. 2, pp. 121-135, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Yang-Im Lee, and Peter Trim, “Enhancing Marketing Provision through Increased Online Safety That Imbues Consumer Confidence: Coupling AI and ML with the AIDA Model,” Big Data and Cognitive Computing, vol. 6, no. 3, p. 78, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Seth Siyuan Li, and Elena Karahanna, “Recommendation Systems in a B2C E-commerce Context: A Review and Future Directions,” Journal of the Association for Information Systems, vol. 16, no. 2, pp. 489-519, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Salman Bahoo, Marco Cucculelli, and Dawood Qamar, “Artificial Intelligence and Corporate Innovation: A Review and Research Agenda,” Journal of Interactive Marketing, vol. 188, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Cristina Ledro, Anna Nosella, and Andrea Vinelli, “Artificial Intelligence in Customer Relationship Management: Literature Review and Future Research Directions,” Emerald Journal, vol. 37, no. 13, pp. 48-63, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Michael Chui, James Manyika, and Mehdi Miremadi, “Where Machines Could Replace Humans—and Where They cannot (yet),” Mckinsey, pp. 1-9, 2016.
[Publisher Link]
[14] Alireza Faed et al., “Intelligent Customer Complaint Handling Utilizing Principal Component and Data Envelopment Analysis (PDA),” Applied Soft Computing, vol. 47, p. 614-630, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Pritam Gundecha, and Huan Liu, “Mining Social Media: A Brief Introduction,” Tutorials in Operations Research, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Zhaojun Yang et al., “Understanding SaaS Adoption from the Perspective of Organizational Users: A Tripod Readiness Model,” Decision Support Systems, vol. 45, pp. 254-264, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Rich Hein, “A New Era of AI Transformation in the CRM Industry Arrives,” From Data to Action: The Future of AI-Enabled CRM, CMS Wire.
[Publisher Link]
[18] H. T. Nguyen, N. Nguyen, and T. H. Cao, “Artificial Intelligence in Customer Relationship Management: An Overview,” Journal of Science and Technology Development, pp. 92-104, 2018.
[19] Surajit Bag et al., “Big Data Analytics and Artificial Intelligence Technologies Based Collaborative Platform Empowering Absorptive Capacity in Health Care Supply Chain: An Empirical Study,” Journal of Business Research, vol. 154, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Thuy D. Nguyen, Audhesh Paswan, and Alan J. Dubinsky, “Allocation of Salespeople's Resources for Generating New Sales Opportunities Across Four Types of Customers,” Industrial Marketing Management, vol. 68, pp. 114-131, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Michela Guida et al., “The Role of Artificial Intelligence in the Procurement Process: State of the Art and Research Agenda,” Journal of Purchasing and Supply Management, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Dr. Rameshwaran Byloppilly, "An Empirical Study on the Influence of Customer Relationship Management on Customer Loyalty: A Special Reference to the Hotels in India," SSRG International Journal of Economics and Management Studies, vol. 8, no. 5, pp. 107- 119, 2021.
[CrossRef] [Publisher Link]
[23] Sam Ransbotham et al., “Reshaping Business with Artificial Intelligence,” MIT Sloan Management Review, vol. 59, no. 1, 2017.
[Google Scholar] [Publisher Link]
[24] Dmitry Ivanov, and Alexandre Dolgui, “Viability of Intertwined Supply Networks: Extending the Supply Chain Resilience Angles Towards Survivability, A Position Paper Motivated by the COVID-19 Outbreak,” International Journal of Production Research, vol. 58, no. 10, pp. 2904-2915, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Dishant Banga, and Kiran Peddireddy, "Artificial Intelligence for Customer Complaint Management," International Journal of Computer Trends and Technology, vol. 71, no. 3, pp. 1-6, 2023.
[CrossRef] [Publisher Link]
[26] Maciel M. Queiroz et al., “Impacts of Epidemic Outbreaks on Supply Chains: Mapping A Research Agenda amid the COVID-19 Pandemic through a Structured Literature Review,” Annals of Operations Research, vol. 139, pp. 1159-1196, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Rohit Sharma et al., “The Role of Artificial Intelligence in Supply Chain Management: Mapping the Territory,” International Journal of Production Research, vol. 60, no. 24, pp. 7527-7550, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[28] T.S. Deepu, and V. Ravi, “A Review of Literature on Implementation and Operational Dimensions of Supply Chain Digitalization: Framework Development and Future Research Directions,” International Journal of Information Management Data Insights, vol. 3, no. 1, 2023.
[CrossRef] [Google Scholar] [Publisher Link]