Logo

Conceptual Framework for Efficient Inbound Supply Chain Analytics

MIRABEL, MIKIAVONTY ENDRAWATI and Yuliana, Oviliani Yenty and Yahya, Bernardo Nugroho (2023) Conceptual Framework for Efficient Inbound Supply Chain Analytics. [UNSPECIFIED]

[img] PDF
Download (397Kb)
    [img] PDF
    Download (1201Kb)
      [img]
      Preview
      PDF (paper - Oviliani Y)
      Download (963Kb) | Preview
        [img]
        Preview
        PDF (cek plagiasi - Oviliani Y)
        Download (1197Kb) | Preview

          Abstract

          Industry 4.0 is a terminology that denotes the era of industrial digitization with the emergence of new technologies in which data is the main focus of increasing company competitiveness in all aspects, including supply chain management systems. It has become one of the main focuses of companies to build resilience when dealing with the risk of uncertainties while still meeting the critical goal of improving the efficiency and responsiveness of customer needs. Therefore, supply chain analytics become essential for facilitating data-driven decision-making in planning, sourcing, making, and delivering functions. However, implementing supply chain analytics in developing countries limits only the traditional application silos and ignores disruptive emerging technologies such as cloud computing. This paper explores cases from the manufacturing and retail domains in Indonesia and discusses in detail the conceptual framework for efficient inbound supply chain analytics, which embodies the three characteristics of adequate supply chain visibility such as automation (implementation of automation technology), information (good data management), transformational (analytic application to display information) to meet the organization�s need for consolidated reports in all branches/subsidiaries. The aspect of inbound supply chain analytics is specified in the plan and source functions, consisting of eight supplier and inventory key performance indicators through the analytical descriptive data visualization aspect in the Analytics Dashboard.

          Item Type: UNSPECIFIED
          Subjects: H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
          Divisions: Graduate Program > Economic Management
          Depositing User: Admin
          Date Deposited: 13 Jan 2024 00:45
          Last Modified: 31 Jul 2024 09:10
          URI: https://repository.petra.ac.id/id/eprint/20780

          Actions (login required)

          View Item