Building antifragile manufacturing systems through strategic technology integration
Why this work is in the frame
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Bibliographic record
Abstract
Purpose This study develops and validates, through expert consensus, a framework for achieving antifragility in manufacturing by strategically integrating modern digital technologies with capabilities that enable organizations to grow stronger through disruption. It moves beyond traditional resilience-focused approaches by emphasizing continuous adaptability, sustained growth and competitive advantage in an environment characterized by volatility and rapid technological change. Design/methodology/approach Grounded in the dynamic capability perspective, the study synthesizes insights from an extensive literature review with the results of a Delphi study involving a panel of 14 industry and academic experts. The process identified and refined a set of critical supporting capabilities, including cross-functional governance, interoperability assessment and risk-responsive integration, that enable the alignment of digital transformation initiatives with antifragile objectives. Findings Antifragility is positioned as a higher-order dynamic capability that transforms volatility into a driver of innovation and strategic renewal. The resulting expert-based framework maps emerging technologies such as artificial intelligence, the Internet of Things and big data analytics to specific sensing, seizing and transforming capabilities, providing a structured pathway for operationalizing antifragility in manufacturing contexts. Practical implications The framework offers manufacturers a structured approach for aligning technology investments with antifragile objectives, ensuring that digital transformation enhances rather than undermines adaptability and growth. It encourages a phased, resource-aware implementation strategy that leverages disruptions as strategic assets, fostering both business continuity and long-term competitiveness. Originality/value This research conceptualizes antifragility as a distinct and advanced capability in manufacturing and demonstrates how it can be purposefully developed through strategic technology integration. By combining theoretical grounding with expert validation, it bridges the gap between digital transformation and antifragility, offering a practical roadmap for turning uncertainty and variability into sources of competitive advantage.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it