Claim Missing Document
Check
Articles

Found 1 Documents
Search

Digital Transformation and Operational Performance in Manufacturing: A Systematic Literature Review and Industry-Specific Recommendation Matrix Riyanti Hamdani; Wirawan Endro Dwi Radianto; David Sukardi Kodrat
Equivalent: Jurnal Ilmiah Sosial Teknik Vol. 8 No. 3 (2026): Equivalent: Jurnal Ilmiah Sosial Teknik
Publisher : Politeknik Siber Cerdika Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59261/jequi.v8i3.355

Abstract

Background: Digital transformation has become a key driving force in improving operational performance in the global manufacturing industry.Objectives: This study aims to (1) describe the existing empirical evidence regarding the impact of digital transformation on operational performance in the manufacturing industry and (2) provide recommendations for developing appropriate digital transformation strategies based on the characteristics of different manufacturing industry types. Methods: This study employed a qualitative research design using a systematic literature review approach. Following the PRISMA protocol, 17 articles published between 2015 and 2025 were retrieved from Scopus, Web of Science, and Google Scholar. The selected studies were analyzed using thematic synthesis and coded categorization to identify recurring patterns, technological trends, and industry-specific transformation outcomes.Results: All reviewed studies reported positive impacts of digital transformation on operational performance, with Big Data Analytics and Artificial Intelligence emerging as the dominant technologies. Benefits varied across manufacturing sectors. Discrete manufacturing achieved faster and more measurable improvements, whereas process manufacturing sectors, including pharmaceuticals, agri-food, and semiconductors, experienced longer implementation pathways because of regulatory requirements, biological constraints, and human adoption challenges. Among the 17 studies, Big Data Analytics appeared in 13 articles and Artificial Intelligence in 12, confirming their central role in enhancing manufacturing operations. Conclusions: Based on these findings, this study proposes a five-category recommendation matrix for digital transformation strategies tailored to different manufacturing industry characteristics. This matrix represents the study’s primary practical contribution by providing industry-oriented guidance for organizations seeking to optimize operational performance through digital transformation initiatives.