Supply chain management for online pharmacies: An exploration of operations, pricing, counterfeit medicine and technology uptake
Bibliographic record
Abstract
The global online pharmacy sector is set to double its size in the next couple of years; a forecast highlighting its crucial and still rapidly emerging impact in healthcare. Using a theme-based narrative literature review covering academic publications through years 2013 to 2022, this study describes the role of Supply Chain Management (SCM) for online pharmacies by identifying and contextualising four industry-defining themes. These are namely dual-channel operations (i.e., synergies between online and traditional pharmacies), pricing strategies (e.g., balancing profitability and affordability ), legitimacy challenges (i.e., are any medicine counterfeit?), and technology's transformative impact (e.g., AI and blockchain operations are paradigm-changers). The study highlights the urgency of regulatory collaboration and rigorous oversight for secure and efficient online pharmacy supply chains and exposes the reader to the current challenges and opportunities underpinning the online pharmacies’ SCM. This work enhances the academic understanding of an emerging sector and provides practical insights for online pharmacy businesses about tackling barriers that may disrupt or diminish supply chain efficiency. As the sector grows, these evidence-based insights can assist strategic decisions, policy formations, and technological progress, fostering improved global healthcare access.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".