Finding the Waste: Parts Inventory Analysis Using Lean Methodology
Bibliographic record
Abstract
During a 12-year period (2011-23), the number of staff in the Clinical Engineering (CE) Department at the Children's Hospital of Eastern Ontario (CHEO) increased from five to more than 20 biomedical equipment technicians/technologists. However, despite this increase in staff, processes did not evolve and procedures that used modern technological and shipping advances were not implemented. The absence of standardized procurement and inventory processes for parts created discrepancies between on-hand inventory and the computerized maintenance management system (CMMS). Owing to inaccurate CMMS information and unsystematic parts documentation, time and money were wasted. This situation led to a lack of confidence in CMMS data, causing staff disengagement, loss of accountability, and limited parts tracking. The current article describes a project that used Lean methodologies and use of the define, measure, analyze, improve, and control structure, including tools such as interviews, a survey, process mapping, and Gemba walks, to create a list of prioritized problems. A total of 16 problems were formulated, four of which were identified as prerequisites to be implemented regardless of prioritization. The four prerequisites described the scope of each problem and potential solutions. The goal of this process was to create a workflow that could save time and money while improving the morale of stakeholders involved in the parts procurement and inventory process.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".