Engineering change request management in a new product development process
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Bibliographic record
Abstract
Purpose The objective of this research was to compare the behavior of two methods of managing an engineering change request (ECR) process, namely, perform changes as they occur or in a batch. Design/methodology/approach This comparison was accomplished by creating a computer model of a new product development (NPD) process and simulating ECR management. The model connects process design and process characteristics (teamwork, parallel activities) to process outcomes (development time, effort). The first method executes the ECR promptly and the rework is done as soon as the ECR is initiated. In the second method, ECRs are batched; in other words, a number of them are accumulated, and processing of the ECRs takes place when a batch of a certain size has accumulated. Thus, the change requests are grouped into a batch, and then, the section(s) of the process to effect the change(s) is (are) reworked. Findings Batching ECRs was found to be superior to doing them one at a time. Research limitations/implications Future work should focus on refining the computer model and differentiating ECRs by assigning priorities to incoming ECRs. Practical implications For product development managers, processing ECRs in batches is preferable than attending to them on an individual basis. Nevertheless, in some situations ECRs require immediate attention. A mechanism will always be needed to deal with situations directly. Also, in terms of batching, ECRs could be processed in groups on a periodic basis. Periodically performing ECRs due to new design versions or prototypes in a timely manner is a good compromise between a random batch mode and doing them individually. Originality/value The paper shows that batch processing is superior to executing ECRs promptly as they are received. This result has been shown through the use of a computer model of NPD. To the authors' knowledge, no other studies have used computer modeling to study this problem.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it