Automated inventory tracking system prototype in cloud
Bibliographic record
Abstract
In this paper we investigate possible solutions for alleviating retail manufacturers of logistical concerns by using inexpensive cell phones with WAP and WiFi capabilities, low resolution digital cameras, and open source applications for web hosts in the cloud to store and process business information. The proposed inventory tracking system prototype is aimed at the company's agents whose responsibilities are to track and manage the retailer's merchandise as it flows between suppliers and consumers. The system can eliminate inefficiencies in the process of tracking inventory and orders processing, while doing so with minimal economic cost by utilizing inexpensive cell phones from one side and inexpensive web hosting in the cloud on the other side. This means to use inexpensive options in terms of both hardware and software, and services in the cloud for data processing and storage as well as to automate the process of physically tracking inventory so less time is spent on this particular task. Such a system with further development can also address business critical question of monitoring sales personnel adherence to the assigned sales routes, collection of other information from the retail outlets (products distribution, pricing, shelving, out-of-stock situations etc.). There are several areas where the proposed solution can be used: on-shelf availability check and inventory calculation (used both by retailers' personnel and by the manufacturer's sales force); orders taking (to automate the process); retail audit (used by the specialized commercial or governmental agencies) and by consumer protection rights agencies. From the technical point of view the goal is to investigate the available open source solutions so they may be integrated with a new proposed system for business utilization. The paper outlines the design of the proposed system and the prototype implementation results, as well as our problems during prototype design and development, and our future plans.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".