Proceeding 6th International Conference on Operations and Supply Chain Management (AN INTEGRATED MODELING OF HUMAN, MACHINE, AND ENVIRONMENTAL ASPECTS IN SUPPLY CHAIN PLANNING AND OPERATIONS USING FUZZY LOGIC \n )
Bibliographic record
Abstract
Supply chain planning and operations is deeply dependent on human endeavor. The performance of a supply chain is determined by the human that is involved in the process of planning and operation. Supply chain planning involves activities such as demand forecasting, developing various plans that includes production plan, procurement plan, and distribution plan. Supply chain operations are essentially executing such supply chain processes such as procurement, production, transportation, and warehousing. In all of the above processes, the roles of human are critical, although the specific roles played from one process to another are different. Human performance problems identified in real operational events often involve operators performing actions that are not required for accident response. Analyses of the major failure/accidents during recent decades have concluded that human errors on part of operators, designers or managers have played a major role. On the other hand, the effectiveness of human in planning as well as operations of a supply chain is affected by two other factors, namely the tools used and the working environment. In this paper we present a simulation modeling that establish a linkage between human, tools, and working environments in supply chain planning and operations to reduce or eliminate human error. The analysis of these relations is complex, involving vagueness and uncertainty data. Fuzzy Logics (FL) provides a mathematical framework for the systematic treatment of vagueness and imprecision data. This paper presents a simulation modeling using fuzzy logics in reducing human error.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.007 |
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".