Work Order and Subassembly Identification and Tracking System
Bibliographic record
Abstract
The research encompassed in this thesis includes the development of a work order and subassembly identification and tracking software system created in-house and tested at SPM Automation (Canada) Inc. The research is motivated by the significant losses the company is enduring and the recurring problems occurring at the facility (i.e., excess inventory, late ordering, reordering, misplaced components, etc.). These problems are critical in the progress and profits of the company.\nAn extensive literature review was completed, and the research gaps were presented. The optimal work order and subassembly process was created using Process Mapping Methodology, Cause-and-Effect Diagrams, and 5Why Analysis. The software architecture diagrams were developed and used to code and program the software. The software was tested on five work orders and results were compared against a previous job. Application of the developed system minimized late ordering by 67%, reordering by 50%, and number of changes to project timelines by 71%. The occurrences of misplaced components for the specific job tested were eliminated using the developed solution.\nA cost structure model was used to illustrate the associated costs and benefits of the developed system. There would be a one-time cost for training SPM employees, however, the benefits outweigh the training cost significantly. It was estimated that the implementation of this software system could give the company an average annual cost saving of approximately $40,884 [≈ 1965 average production worker pay] [≈ Toyota Prius] and a time decrease of 77.78%. Since the software system is created in-house, there would be minimal additional costs for implementation and continuous software support. Work order and subassembly tracking issues are not unique to SPM Automation (Canada) Inc. Many small and medium size manufacturing companies are facing these challenges. The methodologies developed in this research would be applicable and useful to other discrete parts manufacturers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".