Enhancing Supply Chain Resilience and Efficiency through Fuzzy Logic-based Decision-Making Automation in Volatile Environments
Bibliographic record
Abstract
In light of recent mutations and economic volatility stemming from unforeseen global events and increasing security concerns, supply chains are confronted with the complex challenge of fulfilling uncertain customer demands while ensuring sustained value addition.This study introduces a novel approach utilizing fuzzy logic decision-making automation to address and mitigate the impact of current disruptions.By employing if-then scenarios, this methodology facilitates the generation of more accurate predictions and smarter supply planning, enabling effective decision-making, particularly in critical areas such as semiconductor supply sourcing.The integration of Artificial Intelligence within this framework provides dynamic visibility into real-world supply chain operations, thereby aiding in more informed and effective regulatory decisions and fostering continuous improvement.The core innovation of this research lies in the development of a unique Mamdani-fuzzy logic model designed to enhance supply planning.This model extends beyond the realm of efficient inventory management to encompass safer demand forecasting and market segmentation, showcasing its superiority in versatility and effectiveness compared to conventional methods, and leveraging expert knowledge to navigate uncertainties.Through the process of fuzzification, relevant indicators such as Consumption Severity, Market Sensitivity, and Commodity Importance are identified and modeled, with input variables being assigned membership functions and categorized into varying degrees of significance.The Mamdani-inference rules are then formulated, and the output of the fuzzy logic model is defuzzified using the centroid technique to derive precise supply quantities.Simulations conducted in MATLAB demonstrate the model's capacity to convert uncertainty into optimal supply measures across diverse scenarios, thereby enhancing safety and efficiency within the supply chain by minimizing excess inventory and preventing stockouts.This hybrid approach, combining mathematical reasoning with human expertise, validates the efficacy and robustness of fuzzy logic as a potent cognitive and modeling technique for facilitating precise and responsive decision-making in the face of unexpected incidents, imprecision, and qualitative factors affecting 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.007 |
| 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".