Analysis of Postponement Strategies in Supply Chains
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Inventory is an important part of supply chain management as it directly impacts both cost and service. As demand is more or less uncertain and it takes time to manufacture and deliver the goods, some amount of inventory is required somewhere in the chain to provide the required service to the end customer. Increasing supply chain inventories increases customer service and consequently the revenue, but it comes at a higher cost. The aim of supply chain inventory management is to optimize the inventories and to shift the current customer service curve outward through improved inventory strategies and redesigning the supply chain. This article is aimed at studying the effectiveness of various factors in the supply chain environment with and without postponement strategies. Analysis of these factors enables a better understanding of the supply chains and will help to design these systems more effectively. Simulation models are developed using Arena and are used to capture the system dynamics with probability distribution which provides valuable insight into which variables are the most important and how variables interact. It also helps to capture the uncertainty and stochastic nature of the model. Two-level Fractional Factorial Experimental designs are used to study and analyze the performance of service and inventory levels and to determine which variables are the most influential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it