Digital Technologies in Cold Chain Pharmaceutical Supply Chain: A Systematic Literature Review
Bibliographic record
Abstract
The cold chain pharmaceutical supply chain (CCPSC) plays a crucial role in maintaining the safety and integrity of temperature-sensitive pharmaceuticals such as vaccines and drugs. With growing emphasis on temperature control in the healthcare industry, digital technologies are being widely adopted in CCPSC due to their ability to enhance real-time monitoring, improve traceability, and increase transparency across the supply chain. This study aims to identify major digital technologies in the field of CCPSC and to identify trends and gaps of digital technology application in the identified literature. A systematic literature review was conducted using PRISMA guidelines to identify relevant articles, which were further analyzed to determine whether they presented implementable frameworks. These frameworks were then examined to understand the technologies used and their application across five functional areas: manufacturing, storage & inventory management, distribution, transportation, and monitoring & control. Technologies such as blockchain, IoT, RFID, and smart contracts were found to be widely used. The study also identified that digital technologies are predominantly used in monitoring & control, in stark contrast to manufacturing. By mapping technologies to specific functions, this study offers insights for academics and decision-makers and lays the groundwork for future research.
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.
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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.021 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".