A multi-criterion decision model for advanced manufacturing technology acquisition in supply chain networks
Bibliographic record
Abstract
Supply chains are the key to successful business performance in manufacturing and service organizations. One of the biggest challenges faced by these organizations in the chain is the need to respond to frequent and unpredictable changes in the market. Acquisition of advanced manufacturing technology (AMT) in the manufacturing plants constituting the supply chain network can be used as a strategy to respond to these changes. We propose a mixed integer programming model (MIP) that can determine the appropriate combination of various components and levels of AMT by minimizing the total technology acquisition costs under conflicting technical, managerial and financial objectives in order to meet the demand in the respective time periods. The model can aid the manufacturing plants/supply chain driver in various ways: (1). by generating and comparing various AMT alternatives for the manufacturing plant, (2). to assess the overall impact of change in performance level of one component of technology, and (3). run sensitivity analysis on technology capacity requirements, performance levels of various components and levels of AMT and various associated costs.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".