A Case Study on Strategic Digital Transformation in Traditional Manufacturing

Authors

  • Tai-Yu Lin Department of Business Administration, National Cheng Kung University
  • Sheng-Pin Wu National Cheng Kung University
  • Chien-Chih Wang Ming Chi Univeristy of Technology
  • Kun-Zhong Lin National Cheng Kung University
  • Yu-Hsuan Cheng National Cheng Kung University
  • Wei-Yu Shen National Cheng Kung University
  • Chiu-Han Chuang Far East Precision Machinery Corp
  • Meng-Hsien Chen Far East Precision Machinery Corp

DOI:

https://doi.org/10.52731/lbds.v005.366

Keywords:

Strategic Digital Transformation, Resource-Based Theory, Managerial Competency Development, ERP and AI Integration, Traditional Manufacturing SMEs

Abstract

The present study conducts a longitudinal single-case analysis guided by Resource-Based Theory (RBT), Dynamic Capabilities, and Organizational Learning Theory to investigate the strategic digital transformation (DT) of a traditional Taiwanese manufacturing firm. The research examines how the phased integration of ERP systems, real-time data platforms, and AI-driven decision support tools has facilitated improvements in operational efficiency, managerial competencies, and environmental sustainability. A multi-source methodology was employed, comprising structured interviews, expert panel evaluations, and organizational performance metrics. This methodology was applied across three stages of transformation: digitization, optimization, and transformation. The findings indicate significant advancements in processing efficiency, profit margins, and the development of managerial aptitude. Additionally, the transition to electronic documentation has nearly eliminated the use of paper. The study further identifies organizational learning cycles and adaptive capabilities as key enablers in overcoming internal resistance and technological inertia. This work makes a notable contribution to the existing DT literature by integrating strategic capability-building frameworks with practical implementation insights. It provides a contextualized model for transformation in resource-constrained traditional industries.

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Published

2025-10-02