Analysis of supply chain risk in the ceramic industry using the TOPSIS method under a fuzzy environment
Bibliographic record
Abstract
Purpose Risk management has emerged as a critical issue in operating a supply chain effectively in the presence of uncertainties that result from unexpected variations. Assessing and managing supply chain risks are receiving significant attention from practitioners and academics. At present, the ceramic industry in Bangladesh is growing. Thus, managers in the industry need to properly assess supply chain risks for mitigation purposes. This study aims to identify and analyze various supply chain risks occurring in a ceramic factory in Bangladesh. Design/methodology/approach A model is proposed based on a fuzzy technique for order preference using similarity to an ideal solution (fuzzy-TOPSIS) for evaluating supply chain risks. For this, 20 supply chain risk factors were identified through an extensive literature review and while consulting with experts from the ceramic factories. Fuzzy-TOPSIS contributed to the analysis and assessment of those risks. Findings The results of this research indicate that among the identified 20 supply chain risks, lack of operational quality, lack of material quality and damage to inventory were the major risks for the ceramic sector in Bangladesh. Research limitations/implications The impact of supply chain risks was not shown in this study and the risks were considered independent. Therefore, research can be continued to address these two factors. Practical implications The outcome of this research is expected to assist industrial managers and practitioners in the ceramic sector in taking proactive action to minimize supply chain risks. A sensitivity analysis was performed to determine the relative stability of the risks. Originality/value This study uses survey data to analyze and evaluate the major supply chain risks related to the ceramic sector. An original methodology is provided for identifying and evaluating the major supply chain risks in the ceramic sector of Bangladesh.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".