The Impact of ICT on Supply Chain Management Efficiency and Effectiveness: A Literature Review
Bibliographic record
Abstract
ICT plays a vital role in enhancing supply chain operations, leading to improved efficiency and productivity within the industry.This study aims to examine how information and communication technologies can improve supply chain management's efficiency and effectiveness through a comprehensive literature review.The study utilized various databases such as Scopus, Emerald, Sage, spring link and Taylor.The study employed a predefined search strategy that used "ICT in Supply Chain" as the primary keyword search term.Additionally, the study examined research articles published from 2018 to 2023.Initially, 1086 references were retrieved, but eventually, only 10 articles were deemed eligible and met the inclusion criteria for the analysis.The key findings and suggestions of each selected publication have been carefully considered and synthesized.The study outlines future research opportunities and highlights the consequence of continued leveraging ICT for sustainable and competitive supply chain management practices.Nevertheless, the study suggested that there is a need to strengthen the implementation of ICT in supply chain management to enhance the performance and efficiency of supply chain operations and their members.Furthermore, the study emphasizes the importance of implementing ICT tools in supply chain sectors while considering factors hindering ICT utilization in supply chains.
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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".