Differentiating “must–have” and “should–have” supply chain capabilities for enhanced performance: a necessary conditions analysis
Bibliographic record
Abstract
Purpose This study aims to identify the “should have” and “must have” capabilities required to boost a supply chain’s robustness and operational performance. Research on supply chain capabilities and their impact has long been central to the supply chain discipline. However, empirical studies continue to report mixed results regarding the relationship between integration and performance or agility and robustness. Using a novel methodological approach, this study explores how supply chain integration, agility and supply chain risk management activities influence the operational performance and robustness of supply chains. Design/methodology/approach Data was collected through surveys and analyzed using SmartPLS 4 and necessary condition analysis (NCA). This combined approach shifts focus from average trends to identifying the required levels of capabilities, offering insights into the necessity logic of supply chain strategies. Findings The study reveals that supply chain risk management and internal integration significantly influence operational performance and robustness. It also supports agility as a precursor to enhancing supply chain robustness, aligning with contemporary theoretical perspectives. Practical implications The findings suggest the importance of integrating risk management and internal processes to enhance supply chain performance and robustness. Additionally, agility emerges as a critical strategy in navigating disruptions, emphasizing the need to prioritize it in supply chain management. Originality/value By adopting a holistic approach grounded in dynamic capability theory, this study contributes to understanding the interplay of supply chain strategies amid unprecedented challenges. The combined use of SmartPLS 4 and NCA offers a novel perspective, shedding light on the necessary logic of supply chain capabilities.
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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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 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".