Comprehensive Review of Improvement in Inventory Management Methods through Digitalization: Traditional Practices and Emerging Trends
Bibliographic record
Abstract
Efficient inventory management is necessary to enhance supply chain performance.Traditional models such as economic order quantity (EOQ), just-in-time (JIT), material requirements planning (MRP), and reorder point (ROP) fail to satisfy the demands in the current dynamic supply chain.This study tries to perform a comparative review of these methodologies in integration with emerging digital technologies such as the Internet of Things (IoT), artificial intelligence (AI), radio frequency identification (RFID), and blockchain.The research evaluates each method in terms of effectiveness, adaptability, scalability and implementation complexity based on various academic and industry sources.Traditional systems remain cost-effective in a stable context; however, they frequently lack the responsiveness required in technology-driven inventory management.On the other hand, digital tools provide greater transparency and predictive capabilities, but they are more challenging due to performance cost and technical barriers.To address this gap, this paper describes both methods and compares them to identify weaknesses and strengths and offers an insight into a hybrid model that integrates the strengths of both paradigms.This approach may facilitate a slight transition toward digitalization, leading to greater resilience and operational efficiency.The findings tend to inform both practitioners and researchers interested in optimizing inventory strategies.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".