The Sustainable Logistics: Big Data Analytics and Internet of Things
Bibliographic record
Abstract
The presence of IoT technology in Indonesia has made many industries grow rapidly, especially in data management in large companies.The aim of this journal is to discuss one of the 'smart' solutions that can be recognized as an innovative solution in both the technology and organizational fields presented by a telecommunications company in Indonesia.This solution can be implemented in the logistics industry, which in the era of globalization plays a very important role.But not only in the logistics industry, the smart logistic solutions discussed can also be used in several other industries such as retail, warehousing, transportation, manufacturing and mining.The feature of the smart logistic device already uses big data analysis which is known to process and use real time data.All of the features carried by the smart logistics are not only aimed at reducing distribution costs but also optimizing the distribution system of goods.The direct impact felt by user companies is that their productivity has increased dramatically because they can set delivery destinations that have been adjusted according to the zoning system, look for traffic-free paths, and so on.The pace of modern economic development encourages companies to introduce more new solutions, resulting in innovations that drive market progress.This research aim is to discuss about how to resolve the problem that was encountered by logistic companies by of implementing logistics IT solutions which consists of Big Data Analytic and Internet of Thing.So big data IoT like that requires an appropriate analytical framework to generate knowledge to measure operational efficiency, distribution, machine renovation, and so on inside the enterprise.The research method used in this article is library research or literature review, this research begins with the problem identification, hence looking source and information, data collection and information, analysis and processing the gathered information and create a conclusion.One of conclusions of this research is All the features of the Nextfleet application as well as the development of Advanced Driver Assisted System (ADAS) technology and Intelligent Telematics Surveillance enable the Nextfleet application to be applied not only to the logistics industry but also other industries such as retail, warehousing, transportation, manufacturing, and mining.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".