Technology diffusion planning for ERP in aircraft manufacturing industry
Bibliographic record
Abstract
The Enterprise resource planning (ERP) and material requirement planning (MRP/MRP-II) are few of the key considerations in any complex manufacturing industry. The requirement of ERP and MRP for aircraft industry is growing at a phenomenal rate. The advancement in material and manufacturing management systems has changed the dynamics of shop-floor scene. The induction of smart materials and nano-technology, ultra-high speed machining technologies and psychometric testing of highly skilled labor in a target focused team environment has tremendously enhanced the performance expectation from Man, Machine and Resources. The resource-management and supply-chain-management is becoming extremely complex and require dedicated ERP modules for better management and effective control over the industrial and financial activities through integrated business intelligence (BI) software. The implementation of ERP in industry is a cumbersome process and takes years before it yields and reveals its effectiveness. Research in all these areas is making a phenomenal addition to the volume of knowledge in limited time. The concept of competitiveness demands that the integrated framework for ERP adoption be planned for aircraft industry prior to its deployment. So as to minimize its deployment-span in terms of time and to curtail financial overheads. A number of working principles and guidelines have already been developed in other industries and can be employed in a variety of ways in aircraft industry for optimum performance and to earn competitiveness through ERP suites. This paper provides guidelines for planning ERP and detailed mapping of all activities for ERP in aerospace-industry for effective production planning and control.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".