Profit Analysis of Green Products in New Product Development
Bibliographic record
Abstract
The increasingly negative effects of product development and manufacture on the environment are pushing firms to move towards producing new generations of products, i.e. green products. However, the financial effect of green products in the future is a main concern for managers in charge of new product development (NPD) projects. This research focuses on profit analysis of a new product, which is designed based on recyclability, environmentally friendly disposal, and an energy and emission efficiency strategy, in order to equip decision makers with a proper forecast of profitability of such green new products. In this research, first, a primary mixed integer model is proposed to assess the trade-off analysis of green products based on the Cost Volume Profit model in a life-cycle framework. Then, the model is developed based on dynamic programming and a quantitative choice model is applied in the automotive industry. Finally, two numerical examples are used to evaluate the models. The results show the interaction between profitability and environment-friendly attributes of products based on recyclability, disassembly, and an environmentally friendly disposal strategy. It also provides a decision support methodology for management in order to decide about the future of the project during the business analysis stage of the new product development process.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".