Causes and consequences of bullwhip effect on the boutique industry of Dhaka city
Bibliographic record
Abstract
The phenomenon of the bullwhip effect (BWE) has become a pressing concern in contemporary supply chain management. Every echelon of the supply chain faces the negative consequences of BWE somehow. So, it is crucial to determine the reasons responsible for the BWE to mitigate the consequences. The boutique industry in Bangladesh is a rapidly growing industrial sector. In this study, we focused on finding the reasons and consequences of the bullwhip effect on the boutique industry in Dhaka city. The main targets of this study are to examine the underlying reasons for the BWE, identify the most significant causes from the perspective of Dhaka city, and determine the major consequences of the bullwhip effect. Studies of previous literature and consultation with experts have identified sixteen common causes behind the bullwhip effect. This study uses a survey-based method; respondents are chosen through clustered sampling. Necessary data have been collected with a semi-organized inquiry form. Among all the 16 causes, six causes are found to be the most significant causes from the perspective of retailers and wholesalers. SPSS Version 26 has been used for statistical analysis to make the final decision. We also found ten consequences commonly faced by these two echelons of the boutiques' supply chain because of the bullwhip effect. These are high inventory costs, workforce wastages and higher labor costs, higher replenishment lead-time, higher transportation costs, tension in the buyer-supplier relationship, product unavailability, loss of profit, poor customer service, etc.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".