Blockchain-Enhanced Inventory Management in Decentralized Supply Chains for Finite Planning Horizons
Bibliographic record
Abstract
This research introduces a decentralized supply chain optimization model that incorporates blockchain technology.The model, implemented through an optimized iterative method, integrates ordering, holding, and purchasing costs to offer a comprehensive view of total costs for both retailers and suppliers.The model's uniqueness and optimality are demonstrated through theoretical analysis, highlighting the optimal ordering interval as the sole solution to the derived equation.Employing an algorithmic methodology, optimal replenishment schedules are efficiently calculated using Wolfram Mathematica 13.0.A numerical example and sensitivity analysis illustrate the impact of key parameters on replenishment cycles, order quantity, and costs, encompassing wholesale prices, demand uncertainty, and holding/ordering costs.Managerial insights derived from sensitivity analysis guide decision-makers in optimizing supply chain management, emphasizing strategies such as wholesaler price balance and strategic blockchain information management.In essence, this research contributes to an enhanced understanding of decentralized supply chain models with blockchain, providing a systematic decisionmaking optimization approach for increased efficiency and resilience.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".