Identification of factors affecting reverse chain performance in relation to customer satisfaction using ISM Modelling & MICMAC Analysis
Bibliographic record
Abstract
Reverse Logistics has become buzzword in today's industrial word and big differentiating factor in quest of success for any organization word wide.Whole world is suffering from scarcity of resources and we have finite area available for the waste disposal.One of the sustainable solutions is to design product in such a way so that we can maximize its reuse through reverse logistics.Reverse logistics is fast becoming industrial design standard for majority of organizations worldwide.Reverse logistics has become a need of an hour.Some of the organization do it voluntarily and some because of the environmental rules and legislations.In order to achieve total customer satisfaction, one has to take care of product for its entire life cycle through activities of reverse logistics.In this research I tried to investigate the factors affecting performance of reverse logistics in relation to customer satisfaction.I have used interpretive structural modelling technique to understand the complex pattern of contextual relationship amongst the most important factors through diagraph, which explain performance of reverse logistics in relation to customer satisfaction.I have also used MICMAC analysis to classify these factors according to their driving power and dependency power.MICMAC analysis facilitate in identification of most important factors in explaining performance of reverse logistics in relation to customer satisfaction.Further this research may helpful in reverse logistics design process planning, driven by customer requirement.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".