On Specifying an Information Management Tool to Support Manufacturing Process Planning in Aerospace: A Case Study
Bibliographic record
Abstract
Facing increasing product complexity and pressure to reduce time to market, manufacturing process planning (MPP) engineers must be able to quickly access reliable information in order to make swift and correct decisions. Organizations therefore turn to information management tools, such as PDM (Product Data Management), MPM (Manufacturing Process Management) and ERP (Enterprise Resource Planning), to support their product development processes. These various information management tools compete by offering similar features, while MPP engineers have to manipulate multiple tools to access the information they need. This paper aims to take a fresh look at a fundamental question: what are the specifications of an ideal information management tool that would help MPP engineers efficiently define manufacturing work instructions (process plan) from the product definition? This paper thus presents the approach and the results of a research work conducted within the process planning department of a manufacturing company operating in the aerospace sector. The study was conducted so as to direct the effort toward documenting the MPP process and the MPP engineer’s information needs. The approach that is presented primarily relies on a comprehensive documentation and modeling of the MPP development process. Two processes have been modeled, a reference process of the MWI (Manufacturing Work Instructions) development and a change management process impacting the MWI development. These process models offer a sound basis to conduct an analysis of the MPP engineers’ information needs. This analysis next leads to the specifications of a Dashboard solution aimed at MPP engineers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".