The Integration of Human Factors to Warehousing and Manual Order Picking Operations
Bibliographic record
Abstract
<p>Human Factors (HF) identified as the main concern of studies since poor HF and system design interaction has a negative impact on quality, error occurrence, system performance, and worker’s well-being. This dissertation takes literature review and sensitivity analysis to assess human- system errors by focusing on: 1) finding the most suitable model to estimate human-system errors, 2) identifying failure modes causing human-system errors, 3) creating a tool that capable of identifying the source of pick errors and degraded quality at the initial stage of a design, 4) evaluating the usability, functionality, and usefulness of the developed tool. The most common Human Reliability Assessment (HRA) models were studied to identify the most suitable model to estimate human error probability (HEP) in various contexts. The qualitative and quantitative analysis among the most common HRA models showed that these models are suffering from lack of guideline on Performance Indicating Factors (PIFs) selection and multiplier values allocation. Hence, a need for creating and validating new empirically based models in different sectors were recognized. Therefore, the focus of the research study was changed from finding a suitable model to determining the source of errors in manual order picking operation. The developed Warehouse Design Error Prevention (WDEP) checklist tool integrated HF aspects to both Order Picking (OP) design elements and warehouse management elements based on the identified failure modes from literature and field studies as elaborated in chapter 3 of this dissertation. Tool usability, functionality, and usefulness were evaluated through the survey study and qualitative interviews. The outcome of the user evaluation found the created tool useful, functional, and useable both at the initial stage of a design as well as during process improvement process. These dissertation findings illustrate the need for developing HF measures that contribute to pick errors and optimize OP system design strategies concerning HF demands by creating and validating the error prevention checklist tool. </p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".