Advancing Healthcare Service Efficacy by Optimizing Pharmaceutical Inventory Management: Leveraging ABC, VED Analysis for Trend Demand
Bibliographic record
Abstract
Background: The modern world has witnessed significant advancements across various industries such as food, healthcare, fashion, economics, and education. Among these sectors, healthcare is essential, given its critical role in promoting the well-being of individuals and communities. Purpose: Pharmaceuticals are a significant part of the healthcare system, as they are a crucial factor in increasing life expectancy and are often considered the heart of the health industry. Maintaining effective inventory management for drugs is essential for pharmacists to provide efficient and reliable services to their patients. Methodology: The study thoroughly analyzes the cost and consumption data for each type of demand, to develop a well-suited review and issuance policy for the apothecary. Research Limitations/Implications: The paper delves into the ABC analysis, VED analysis, and trend demand for medical stores, making it a valuable resource for pharmacy stores seeking to optimize their operations and inventory management. Originality/Value: A total of 564 drugs were included in this study, and data were collected from random strip sales between October 2022 and Mar 2023. The study's findings can be used to make informed decisions about inventory planning and classification strategies. The model utilized in this study is based on three categories of medicines: high priority, medium priority, and low priority. By analyzing the demand for these medicines, they can be categorized based on their priority within the three core groups. Pharmacists can use the model to detect shortages and take proactive measures to avoid them by analyzing demand patterns and inventory levels.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".