The Significance of IoT Technology in Improving Logistical Processes and Enhancing Competitiveness: A Case Study on the World’s and Slovakia’s Wood-Processing Enterprises
Bibliographic record
Abstract
The wood-processing industry currently does not sufficiently use modern technologies, unlike the automotive sector. The primary motive for writing this article was in cooperation with a Slovak wood processing company, which wanted to improve its logistics processes and increase competitiveness in the wood processing sector through the implementation of new technologies. The aim of this article was to identify the positives and limitations of the implementation of Internet of Things (IoT) technology into the wood processing industry, based on a secondary analysis of case studies and the best practice of American wood processing companies such as West Fraser Timber in Canada, and Weyerhaeuser in the USA. The selection of case studies was conditional on criteria of time relevance, size of the sawmills, and production volume in m3. These conditional criteria reflected the conditions for the introduction of similar concepts for wood-processing enterprises in Slovakia. The implementation of the IoT can reduce operating costs by up to 20%, increase added value for customers, and collect real-time data that can serve as the basis for support of management and decision-making at the operational, tactical, and strategic levels. In addition to the secondary analysis, methods of comparison of global wood processing companies, synthesis of knowledge, and summarization of positives and limitations of IoT implementation or deduction were used to reach our conclusions. The results were used as the basis for the design of a general model for the implementation of IoT technology for Slovak wood processing enterprises. This model may represent best practice for the selected locality and industry. The implications and verification of the designed model in practice will form part of other research activities, already underway in the form of a primary survey.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".