Information flow in supply chain: A fuzzy TOPSIS parameters ranking
Bibliographic record
Abstract
Flow of information in supply chain is as prominent as material and financial flows and among these three aspects of a supply chain, information flow could be of great importance since it provides a basis for a steady flow of goods and finance as well. To help managers control the flow of information in an effective way, first, the parameters which affect the flow of information should be determined. Then, if the parameters can be controlled carefully, the information will be shared correctly and in a timely manner among supply chain members. This paper identifies the influencing parameters on proper flow of information in supply chain and provides a list of parameters based on the literature as well as the industrial and academic experts' opinions. Afterwards, in order to define the degree of importance for each parameter from the experts' perspective, fuzzy TOPSIS method is employed and the parameters are ranked based on three criteria, namely "measurability", "being illustrative" and "parameters relevancy" to the issue of information flow. The research findings show that "Supply Chain Hardware Capabilities", "Supply Chain Network Infrastructure", "Information Software Capabilities", "Information Sharing Timeliness", "Information Recency" and "Organizational Rewards" received the highest priorities, while "Power of Internal and Inter-personal Communications", "Users' Trust" and "Users' Tendency" were standing at the bottom of this ranking. The results of this research could be employed as an input for strategy development process for supply chain information management activities. Thus, the awareness of each parameter's importance in proper flow of information, helps us make appropriate strategies to improve information management in the supply chain.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".