Customer Potential Index for Production Program Optimization in Manufacturing SMEs
Bibliographic record
Abstract
This study proposes a methodology to maximize income using a novel Customer Potential Index (CPI) to optimize production programs in small and medium-sized enterprises (SMEs) engaged in customized manufacturing.The research addresses the gap in existing systems that fail to integrate long-term customer value into production planning.Using regression analysis of accounting reports from 33 clients, we developed a CPI model to predict repeat order likelihood.This index was integrated into a mixed-integer linear programming framework that jointly maximizes current marginal profit, projected CPIbased revenue growth, and minimizes opportunity costs from order postponement.In a real-world case with 11 competing orders, the CPI-based optimization identified a highpotential client with a predicted CPI for a higher-margin but low-potential order, resulting in a better strategic allocation of constrained resources.The model was tested using the root mean square error (RMSE) and mean absolute percentage error (MAPE) metrics and was well represented.The novelty of this research lies in bridging customer analytics and production scheduling without proprietary customer relationship management data and using artificial intelligence (AI), while making advanced planning accessible to resourceconstrained SMEs.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".