Examining potential benefits and challenges associated with the Internet of Things integration in supply chains
Bibliographic record
Abstract
Purpose The Internet of Things (IoT) is expected to have a huge impact on businesses and, especially, the way we think about supply chain management (SCM). However, there is still a paucity of studies on the impact of IoT adoption on supply chains and on different aspects of the business in general. The purpose of this paper is to examine the perception of the academic community of the impact of the IoT adoption in organizational supply chains with a view to verify potential key benefits and challenges existent in the literature. The research presents the impact on an organization along with the impact across its entire supply chain. Design/methodology/approach Data were collected through the use of an online survey and 87 participants completed the survey. Participants were mainly from the academic community and were university scholars based in different countries located in six continents. Participants were authors, or co-authors, of academic papers published in the Decision Science Institute 2015 and 2016 annual conference proceedings, the 21st International Symposium of Sustainable Transport and Supply Chain Innovations, the Supply Chain Management: An International Journal 2016 issues, and the Operations and Supply Chain Management: An International Journal 2016 issues. Findings The authors were able to confirm the significance of some of the examined potential benefits to individual organizations and their entire supply chains. However, the study identified other potential benefits that were not seen as a direct impact of IoT adoption. Most of the examined potential benefits were found to contribute to a number of critical success factors for implementing successful SCM. The authors were also able to confirm that some of the examined potential challenges were still perceived as key hinders to IoT adoption but examined potential challenges were not seen as hurdles to IoT adoption. Originality/value To the best of the authors’ knowledge, this is the first study of its kind. Although some literature attempted to provide an overview about the IoT management, no study has specifically explored potential benefits and challenges related to the adoption of IoT in supply chains and ranked them based on their significance. The results can be beneficial to academic scholars interested in the researched topic, business professionals, organizations within different sectors, and any other party interested in understanding more about the impact of adopting IoT on SCM.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".