LEARNING PRODUCT DEVELOPMENT PROCESS MODELING FROM A REAL SCALE CASE
Bibliographic record
Abstract
New product development is an information based process, therefore quite intangible. Frequently, people involved with product project, develop tacit knowledge that, if not made explicit may be lost if a member of the team leaves the organization. The process graphical modeling is recommended not only to explicit individual tacit knowledge but also to give a good visibility of the process to every team member, creating the basis to process improvement. For this reason the product development process (PDP) modeling can be considered an important subject in design and engineering graduation courses. The aim of this paper is to propose a way of motivating students to learn PDP modeling from a real case. This study was conducted within the Product Development Management discipline from the Industrial Engineering graduation course in a Federal University from south Brazil. Modeling this process in a company is a non trivial task that depends on interviews with PDP team in order to achieve information and to convert it in a model. Time and organization information access restrictions would make this task impossible to be performed by graduation students. In order to overcome the mentioned restrictions an interview with the project manager of a large Brazilian shoe company was recorded in video to be used in a practical modeling class. In this video, she describes shortly how the organization’s PDP is. The interview was performed following a semi structured questionnaire and attended the qualitative research theory. The results show that there are differences between students groups mapping and between the groups and the real representation. Moreover, the learning achieved by the students from this exercise can be divided in two categories: the use of swim lanes as a modeling tool that allows converting tacit into explicit knowledge and the perception of the importance of structured interviews to obtain useful information.
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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 | 0.001 |
| 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".