Applying quality function deployment in food safety management
Bibliographic record
Abstract
Purpose This paper aims to report on a case study conducted to help plan a rollout process for hazard analysis and critical control point (HACCP) type food safety policies at a frozen pie facility in Calgary, Alberta, Canada. Design/methodology/approach Existing company policies were prioritized using a quality function deployment tool, which quantified the qualitative material in the original manual based on a number of developed criteria. Interrelations between the different required tasks were also quantified to facilitate effective implementation. Findings The use of quality function deployment was shown to be useful in speeding up the implementation of food safety policies in the facility. Practical implications Quality function deployment, originally from new product design, proved useful when applied to HACCP implementation. Originality/value This paper discusses the use of product development tools to facilitate the effective introduction of HACCP like procedures. Thus it will be of use to academics and practitioners interested in HACCP implementation.
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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 | 0.001 | 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".