Logistics Sustainability?: Long Term Technology Investments and Integration
Bibliographic record
Abstract
This study examines the investment in technology over time in an effort to achieve sustainability and secondarily to support green initiatives by minimizing environmental impacts. The literature suggests integrated supply chain contractors emphasize efficiency and flexibility which leads to organizational performance improvements. This study compares productivity changes in the logistics industry measures after significant RFID investments over time. Productivity ratios collected for the fiscal years of 2000-2013 from financial statements are used to investigate technology investments effects.Since 2003, many end users (Wal-Mart specifically) mandated the implementation of RFID technology as one part of a larger system in reaching long term sustainability objectives. Historically, B2B customers and retailers have either built in-house logistic systems or have relied on either shippers services, i.e., third party logistic suppliers (3PL) or independent fourth party logistic suppliers (4PL). Logistic companies that have invested in logistical technologies aimed at sustainability strategies have improved financial ratios during the period studied. Interestingly, companies who have not fully implemented technology enhancements because of logistic service type have not seen improvements in productivity at the same level as others in the supply chain during this same period.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".