Integration Supply Chain Management, Customer Rela-tionship Management and Accounting Information Systems based Machine Learning to Enhance the Competitive Ad-vantage of MSMEs
Keywords:
Supply Chain Management (SCM), Customer Relationship Management (CRM), Accounting Information Systems (AIS), Machine Learning (ML), Competitive Advantage of MSMEsAbstract
This research examines the integration of Supply Chain Management (SCM), Customer Relationship Management (CRM), and Accounting Information Systems (AIS) based on Machine Learning (ML) to enhance the competitive advantage of SMEs, focusing on three questions: (RQ1) the relevant integration architecture, (RQ2) the integration mechanisms, and (RQ3) its impact on competitiveness. The study uses a Systematic Literature Review (SLR) with the PRISMA 2020 method and bibliometric analysis thru Biblioshiny on Scopus-indexed publications from 2020 to 2025. From the initial 2,923 documents, after deduplication, title/abstract screening, and full-text eligibility, 255 articles were designated as the corpus for analysis. The mapping results show rapid growth (annual growth rate of 44.35%) and a concentration of publications in 2023–2024, indicating the topic of intelligent system integration for SMEs as an emerging research front. The thematic and co-occurrence mapping confirms the interconnection of supply chain, customer/marketing, accounting, and AI/ML themes, with the dominant central outcomes being business/firm/financial performance, competitiveness, and competitive advantage. However, the distribution of domain combinations shows that research is still dominated by single domains, making the simultaneous integration of SCM, CRM and AIS based ML a major gap. The study concludes the need for a modular, cost-effective, and easily adoptable integration architecture, with a focus on data standardization, lightweight cloud modules, and strengthening the competencies of MSME actors, as well as encouraging further research for implementable artifacts and more measurable impact evaluations.
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