Critical Success Factors for Supply Chain Sustainability in the Wood Industry: An Integrated PCA-ISM Model
Bibliographic record
Abstract
Due to the increasing popularity of cost-based outsourcing and growing stakeholder concern about environmental, social, and technological issues, supply chain sustainability is vital in both developed and emerging economies. Bangladesh is an emerging economy and wood industry of Bangladesh is suffering from severe sustainability issues besides its growth. Hence, this article aims to examine the critical success factors (CSFs) for sustainability in the Bangladeshi wood industry, which is crucial to help supply chain managers engage in achieving sustainable development goals. This research investigated the CSFs and uncovered their interdependencies through the development of a methodology integrating a literature review, principal component analysis (PCA), interpretive structural modelling (ISM), and Matriced Impacts Croises Multiplication Appliquee aunClassement (MICMAC) techniques. PCA (n = 150) was used to identify and rank the CSFs for sustainability in the Bangladeshi wood industry while ISM (n = 9) and MICMAC were used to determine the driving and dependence power of the CSFs. The findings reveal that research and development, supplier relations, and using eco-friendly technology are the most significant CSFs of the Bangladeshi wood industry. Indispensable links revealing the driving and dependence power among the CSFs were also reported. To the best of our knowledge, this study is the first of its kind that examined the CSFs for supply chain sustainability in the Bangladeshi wood industry. The proposed methodology and findings will help managers in the Bangladeshi wood industry as well as other similar industries to understand the CSFs and reduce the complexity of decision-making in managing business process towards sustainability journey.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".