Product Portfolio Optimization Using Multi-Criteria Decision Analysis
Bibliographic record
Abstract
Globalization increases the potential for business expansion, which creates more opportunities in the market. The competition and expansion in the market create varieties of products to satisfy human needs and make it more difficult for the product planner to maintain huge piles of stock, resulting in high inventory costs to meet customer demands on time. Product portfolio optimization, as a tool of strategic management for companies in the competitive market, plays a vital role, helping businesses to constantly strive to maximize their returns while minimizing risks. This study explores Multi-Criteria Decision Analysis (MCDA) by using real-world applications of the fuzzy Analytical Hierarchy Process (AHP) and fuzzy TOPSIS, which offer a comprehensive approach by integrating quantitative and qualitative factors, to evaluate and prioritize products within a company’s portfolio, specifically focusing on the product of lead–acid storage batteries from a renowned organization in Bangladesh, aiming to provide valuable insights for businesses seeking to enhance their decision-making processes and achieve sustainable growth.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".