Maximizing supply chain performance leveraging machine learning to anticipate customer backorders
Bibliographic record
Abstract
The complexity of global supply chains, with their multi-tiered and lengthy structures, presents significant challenges for effectively planning inventory replenishment, accurately forecasting demand, and managing customer backorders. To maintain customer loyalty and avoid extended waiting periods, companies need to have an efficient system for predicting product backlog for customers without overstocking. Traditional statistical techniques like regression analysis can forecast demand and the probability of customer backorders. Nevertheless, they are restricted to modeling linear relationships and may not be well-suited for capturing complex relationships. On the other hand, analytical methods like machine learning (ML) show great promise. However, ML algorithms can be computationally intensive, especially when dealing with many predictors, which can make models complex and increase computational costs. Thus, simpler models with fewer attributes can make data collection and model complexity more manageable. In this study we assess the efficacy and accuracy of simplified ML algorithms, the impact of utilizing limited, high-impact predictors in supply chain backorder prediction. Using publicly available data sets from Kaggle, we developed two sets of models: one with 22 predictors and another with only the top five predictors. The results demonstrate a significant decrease in computational costs, ranging from 30 % to 98 %, with only a marginal reduction in accuracy and F1-score, ranging from 0.6 % to 4.2 %. These findings underscore the potential for simpler backorder prediction models, which helps streamline data collection with lower computational cost. This study enhances the current literature by providing insights into optimizing customer backorder prediction with potential generalization for various types of supply chains. It strikes a balance between accuracy and computational efficiency, making the findings valuable for practical implementation across various industries.
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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 | 0.002 | 0.006 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".