Optimizing Process-Improvement Efforts for Supply Chain Operations under Disruptions: New Structural Results
Bibliographic record
Abstract
Rampant disruptions have probed the fragility of supply chains: Renewed perspectives and comprehensive operational models are needed to enhance resiliency and sustainability in business. This paper proposes a new inventory management model that explicitly integrates process improvement efforts to improve supply chain sustainability through the better use of capital (materials, assets, and technology) and labor (workforce and know-how). Under a desired service-level constraint, we study reducing setup (fixed) costs when they are expressed in terms of economic production functions of two (input) decision variables: the level of capital (e.g., process change, and technology investments) and the level of labor required. This research is motivated by lean manufacturing practices, which rely on shaping the operating environment and operating optimally within that business environment. Based on mathematical modeling and analysis, we provide closed-form optimality expressions and structural results that lend themselves to decision insights. In particular, we provide, along with illustrative numerical examples, results on the sensitivity of setup-reduction efforts to demand rates, variability, and explicit expressions for determining the required labor and capital resources. A generalization of the model for carbon emissions is also presented.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".