Cost and Complexity as Barriers to RTLS Adoption in SMEs: A Survey and Analysis
Bibliographic record
Abstract
Real-time location systems (RTLSs) are central to Industry 4.0 and emerging Industry 5.0, providing the spatiotemporal data required for asset tracking, workflow optimization, safety, and integration with WMS, MES, and digital twins. While large enterprises increasingly deploy RTLSs, adoption among small and medium-sized enterprises (SMEs) remains limited. This paper examines whether cost and installation complexity are primary barriers to SME adoption. We position RTLSs within the broader Industry 4.0 and Logistics 4.0 landscape and summarize their operational value. We then synthesize evidence from the literature on technical, financial, and organizational constraints, with emphasis on infrastructure requirements, calibration effort, integration with legacy systems, and human factors. To complement this analysis, we report results from an online survey of sixteen manufacturing and technology professionals in Canada and the United States. Respondents report strong perceived value for real-time tracking but identify upfront cost, installation effort, integration difficulty, and reliance on multiple anchor nodes as dominant obstacles. Most indicate acceptable upfront investments below $10,000 and express a clear preference for low-infrastructure deployments with minimized anchor counts. Building on these findings, we outline design directions for SME-focused RTLSs, including wireless and modular architectures, cloud-managed and self-calibrating systems, standardized integration interfaces, and anchor-minimizing or anchor-free localization methods. Overall, the results show that limited SME adoption stems less from insufficient perceived value than from misalignment between current RTLS deployment models and SME resource constraints.
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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".