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The complexity of Supply Chain Management (SCM) in the Industry 4.0 era demands digital transformation to enhance efficiency, visibility, agility, and real-time decision-making. Traditional SCM systems, hindered by manual workflows, fragmented data, and lack of predictive analytics, result in delays, stock allocation errors, and proof-of-delivery (POD) disputes, affecting operational resilience and cost efficiency. This study proposes a Digital Enterprise Architecture SCM Framework, integrating Enterprise Architecture (EA), Knowledge Management (KM), and the Technology-Organization-Environment (TOE) framework to optimize automation, interoperability, decision intelligence, and predictive capabilities. A Design Science Research (DSR) approach, structured into Preliminary Study, Model Development, and Model Evaluation, ensures systematic analysis, framework development, and empirical validation. The As-Is analysis at MSM Malaysia Holdings Berhad identified inefficiencies in order processing, inventory management, and logistics coordination, while the To-Be framework, integrating Transport Management Systems (TMS), Customer Relationship Management (CRM), AI-powered predictive analytics, and real-time dashboards, enhances supply chain agility, data synchronization, and risk mitigation. Expert validation via the Delphi Method engaged 12 supply chain and digital transformation experts to ensure scalability, industry alignment, and technical feasibility. Usability testing with six industry professionals using the System Usability Scale (SUS) evaluated system functionality, user experience, and operational efficiency, yielding a high usability score. Findings show a 48% reduction in order processing time, a 42.3% decrease in stock allocation errors, and a 70.8% reduction in POD disputes, confirming the framework’s effectiveness in strengthening supply chain resilience, transparency, and cost optimization. This study contributes a structured, knowledge-driven SCM model that leverages AI, real-time intelligence, and enterprise-wide digital integration to foster efficiency, strategic agility, and competitiveness in the FMCG industry.

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A Digital Enterprise Architecture Framework for Supply Chain Transformation: Integrating Knowledge Management and the TOE Framework in the FMCG Industry

  • 会议论文

  • 发表日期：

    2025年

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  • Mira Amielia Jamil,
  • Nur Azaliah Abu Bakar,
  • Surya Sumarni Hussein,
  • Hasimi Salehuddin,
  • Farashazillah Yahya

摘要

The complexity of Supply Chain Management (SCM) in the Industry 4.0 era demands digital transformation to enhance efficiency, visibility, agility, and real-time decision-making. Traditional SCM systems, hindered by manual workflows, fragmented data, and lack of predictive analytics, result in delays, stock allocation errors, and proof-of-delivery (POD) disputes, affecting operational resilience and cost efficiency. This study proposes a Digital Enterprise Architecture SCM Framework, integrating Enterprise Architecture (EA), Knowledge Management (KM), and the Technology-Organization-Environment (TOE) framework to optimize automation, interoperability, decision intelligence, and predictive capabilities. A Design Science Research (DSR) approach, structured into Preliminary Study, Model Development, and Model Evaluation, ensures systematic analysis, framework development, and empirical validation. The As-Is analysis at MSM Malaysia Holdings Berhad identified inefficiencies in order processing, inventory management, and logistics coordination, while the To-Be framework, integrating Transport Management Systems (TMS), Customer Relationship Management (CRM), AI-powered predictive analytics, and real-time dashboards, enhances supply chain agility, data synchronization, and risk mitigation. Expert validation via the Delphi Method engaged 12 supply chain and digital transformation experts to ensure scalability, industry alignment, and technical feasibility. Usability testing with six industry professionals using the System Usability Scale (SUS) evaluated system functionality, user experience, and operational efficiency, yielding a high usability score. Findings show a 48% reduction in order processing time, a 42.3% decrease in stock allocation errors, and a 70.8% reduction in POD disputes, confirming the framework’s effectiveness in strengthening supply chain resilience, transparency, and cost optimization. This study contributes a structured, knowledge-driven SCM model that leverages AI, real-time intelligence, and enterprise-wide digital integration to foster efficiency, strategic agility, and competitiveness in the FMCG industry.