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Stream/Area:
-
Subject:
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Paper ID:
IJSREM63036
Volume:
10 |
5 |
May 2026
Pages:
1-11
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of Computer Science and Engineering
Bhagwan Parshuram Institute Of Technology
Department of Computer Science and Engineering
Bhagwan Parshuram Institute Of Technology
Department of Computer Science and Engineering
Bhagwan Parshuram Institute Of Technology
Department of Computer Science and Engineering
Bhagwan Parshuram Institute Of Technology
India's informal retail sector, comprising over 12 million kirana stores, relies extensively on paper-based credit ledger systems (bahi-khata) for managing customer credit ('udhaar'). These manual systems produce critical operational inefficiencies including record loss, calculation errors, context amnesia, and the absence of systematic credit risk assessment. This paper presents VendorFlow, a mobile-first Progressive Web Application (PWA) that reconceptualizes informal credit management through the lens of event-sourced, multi-tier architecture operating on a React 18 and Supabase Backend-as-a-Service foundation. Each vendor operates within an isolated "Bounded Commercial Universe" with independently managed transaction context, inheriting compressed customer reputation state from ancestral payment behavior. The system implements: (i) an automated customer reputation scoring engine executed as PostgreSQL database triggers, eliminating role confusion inherent in manual classification; (ii) WhatsApp-native payment reminder workflows leveraging URL-scheme deep-linking; (iii) client-side PDF ledger generation using jsPDF; and (iv) analytics dashboards with Recharts visualization. Row Level Security (RLS) enforces declarative multi-tenant data isolation at the database engine level, ensuring complete auditability of all commercial transactions. A dynamic context pipeline assembles lineage summaries, recent transactions, and reputation-based filtering before each business intelligence query. Performance evaluation on the deployed Vercel instance under simulated Slow 4G conditions yielded a Lighthouse mobile performance score of 60, First Contentful Paint of 4.7 s, Largest Contentful Paint of 9.1 s, and Cumulative Layout Shift of 0. The reputation scoring trigger executes in under 6 ms per transaction. VendorFlow demonstrates that modern web technologies can be effectively composed to digitize informal economy workflows while eliminating the context pollution and reliability decay inherent in paper-based systems.
KEYWORDS
This paper has presented VendorFlow, a commercial operating system that reconceptualizes informal retail credit management through the lens of structured, event-sourced, multi-tier web architecture. By replacing the linear paper ledger paradigm with an event-sourced transaction log served through Bounded Commercial Universes (vendor-isolated contexts), the system directly addresses the four structural failure modes—context amnesia, reliability decay, role confusion, and context pollution—that constrain conventional manual ledger systems. The event-sourcing data model provides a principled foundation for auditability and point-in-time balance reconstruction. The database-trigger-based reputation scoring eliminates role confusion through architectural specialization, achieving sub-6 ms execution latency. The dynamic context assembly pipeline ensures each analytical operation receives maximally relevant, minimally polluted commercial context. Together, these contributions constitute a meaningful step toward web-based systems capable of supporting sustained, complex commercial credit relationships in India's informal economy at scale. Performance evaluation established baseline metrics under Slow 4G conditions (Lighthouse score 60, FCP 4.7 s, LCP 9.1 s, CLS 0) and identified actionable optimization targets. Future work addresses lazy-loading optimizations, empirical calibration of the reputation classifier against longitudinal payment outcome data, and evaluation of WhatsApp Business API integration for automated reminders.
[1] Ministry of Commerce and Industry, Government of India, "Annual Report on Retail Trade Statistics 2022-23," Dept. for Promotion of Industry and Internal Trade, New Delhi, 2023. [2] Reserve Bank of India, "Report on Trend and Progress of Banking in India 2021-22," RBI, Mumbai, 2022. [3] Gupta, R. "The Influence of Technology on Accounting Practices in India: A Survey of Accounting Software Utilization by Micro Enterprises in Delhi," European Journal of Theoretical and Applied Sciences, vol. 1, no. 6, pp. 1041-1053, 2023. [4] Kumar, J. A. and Babu, S. R. "Enhancing Credit Scoring with Alternative Data and Machine Learning for Financial Inclusion," South-Eastern European Journal of Public Health, vol. XXVI, pp. 511-518, 2025. [5] Berg, T., Burg, V., Gombovic, A., and Puri, M. "On the Rise of FinTechs: Credit Scoring Using Digital Footprints," Review of Financial Studies, vol. 33, no. 7, pp. 2845-2897, Jul. 2020. [6] Amanuel, A. "Supabase vs Firebase: Evaluation of Performance and Development of Progressive Web Apps," B.Eng. thesis, Metropolia University of Applied Sciences, Helsinki, May 2022. [7] Berg, T., Fuster, A., and Puri, M. "FinTech Lending," Annual Review of Financial Economics, vol. 14, pp. 187-207, 2022. [8] Google Developers, "Progressive Web Apps," Web.dev, https://web.dev/progressive-web-apps/, accessed May 2026.
Publication Date
5/11/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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