Transforming Supply Chain Management with Industry 4.0: Post-COVID-19 Digital Advancements
Bibliographic record
Abstract
Industry 4.0 technologies (e.g., Blockchain, Internet of Things (IoT), Artificial intelligence (AI), cloud computing), offer strategic advantages to firms by enhancing cost efficiency and optimizing supply chain performance. The COVID-19 pandemic exposed significant vulnerabilities in global supply chains, prompting firms to adopt Industry 4.0 technologies to improve their supply chain performance. This study aims to examine the impact of Industry 4.0-based digitalization on firms’ supply chain performance, with a focus on integrating digital technologies into Cyber-Physical Systems (CPS). A positivist approach and quantitative research method was employed, using a structured questionnaire distributed to 319 Small and Medium-sized Enterprises (SMEs) in Malaysia’s Electrical and Electronics industry, with 152 valid responses analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM). The study’s research model suggests that Industry 4.0-based digitalization capabilities directly influences firms’ overall supply chain performance, moderated by antifragility capability and enhanced through process innovation and social capital. The study shows that digital transformation improves firms’ integration, communication, and collaboration capabilities using the Dynamic Capabilities View (DCV) and Relational View (RV) of an organization. These capabilities promote firms’ process innovation, antifragility, and social capital, which can lead to improved supply chain performance, post COVID-19. The study’s findings demonstrate the importance of firms advancing their digital competencies to remain competitive in turbulent business environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".