Inventory Lot Sizing Decisions for Material Requirements Planning to Minimize Inventory Costs
Bibliographic record
Abstract
Inventory control is one of the most important factors in achieving optimal organizational performance. Material Requirement Planning (MRP) is a common method used by businesses to manage inventories. This study focuses on a hydraulic firm that has been in operation since 2016. This research examines the planning of eleven components to get the best planning for the company. This study contributes to the integration of Moving Average (MA) and Exponential Smoothing (ES) forecasting techniques alongside the MRP and three lot sizing techniques, such as LFL, EOQ, and LUC. The minimum error values between MA and ES are evaluated and followed by the comparison between three lot sizing techniques. The result shows that ES (α=0.1) is selected as the best forecasting technique, and LUC presents the lowest total inventory cost. However, LUC is only 0.05 percent lower than what LFL presents. A larger difference is shown by EOQ with 14.57 percent higher than LUC which makes EOQ unlikely to be selected.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".