A hierarchical model for critical success factors in apparel supply chain
Bibliographic record
Abstract
Purpose Supply chain management plays an important role in sustaining businesses in today's competitive environment. Therefore, industrial managers are focusing on exploring the key performance improvement attributes of supply chain management to achieve a better position in the global market. Aimed at ensuring best supply chain management practices, this study presents the key performance improvement attributes, known as critical success factors (CSFs), within the context of the apparel supply chain of Bangladesh. Design/methodology/approach In this paper, the interpretive structural modeling method (ISM) has been applied to develop a structural framework to analyze the contextual relationship among the factors under consideration. MICMAC (Matriced' Impacts Croise´s Multiplication Applique´e a´ unClassement) analysis has also been performed to define the classification of the CSFs in terms of their driving and dependence power. Findings The research findings reveal that supply chain collaboration/partnership and customer satisfaction are of crucial importance to success in the context of supply chain management of the readymade (RMG) garments industry of Bangladesh. Further evidence suggests that these, along with other success factors, can assist in achieving a competitive advantage and better market position. A number of theoretical and managerial implications have been provided for managers and practitioners, and for further evaluation of the study. Originality/value This paper considers a new supply chain problem which identifies and evaluates critical success factors. This paper also develops a new structural model for evaluating critical success factors.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".