Manufacturing in Industry 4.0: A Scoping Review of Open Innovation Practices and Future Research
Bibliographic record
Abstract
The Fourth Industrial Revolution (Industry 4.0), first introduced in 2011, has enabled manufacturing firms to integrate smart, technology-driven processes into their Product Development (PD) lifecycles. At the same time, advancements and increased acceptance of web-based platforms have enabled manufacturing leaders to leverage open innovation practices. This approach provides firms with access to pools of external knowledge and skills, accelerating their product innovation and enabling faster responses to market and consumer demands. This paper aims to examine the literature on open innovation in manufacturing, in the context of the Industry 4.0 era (i.e., since its widespread acceptance in 2014) and provides directions for future study. Through a scoping review of 891 papers collected from the Web of Science (WoS) database covering 2014-2023, co-occurrence analysis was performed using the VOSviewer software. By following this approach, four main themes were identified i.e., innovation management practices, performance measurement, strategic challenges, and the balance between innovation exploration and exploitation. Findings demonstrate a shift among firms towards collaborative and externally inclusive innovation. This study highlights several research gaps and provides practical insights for manufacturing leaders on the opportunities and barriers to open innovation adoption, outlining the importance of absorptive capacity and a balanced innovation strategy. Recommendations for future research include empirical studies on open innovation’s long-term impacts, cross-industry comparisons, and how technological advancements can be integrated into innovation strategies.
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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 | 0.022 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.036 | 0.037 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".