Using qualitative interviewing to examine human factors in warehouse order picking: technical note
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The effect of human factors on the performance of labour-intensive order picking processes has thus far been relatively understudied in the operations and logistics management literature. This technical note offers guidance to researchers and managers regarding how qualitative methods can be used to assess human factors in order picking. The paper first discusses manual tasks in this process and highlights where human factors influence the outcomes of time, quality and worker health. This discussion is used to inform the development of a qualitative example interview guide to investigate the order picking system. The paper provides step-by-step guidance for using interviewing to assist researchers and logistics managers that emphasises considering human factors in the planning of order picking processes. Using qualitative methods to integrate human factors into order picking processes can help to avoid workers' exposure to musculoskeletal disorders and improve the quality and efficiency of order picking systems.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 it