A generalized approach to demand buffering and production levelling for JIT make‐to‐stock applications
Bibliographic record
Abstract
Abstract Heijunka is a Just‐In‐Time scheduling technique that strives to level variety and/or volume over a fixed period in order to maintain low inventories and to avoid excessive batching of product types and/or volume fluctuations. Businesses that use heijunka scheduling and immediately fulfill customer orders upon receipt require a finished goods inventory to service that demand. This inventory must be appropriately sized to adequately balance the customers' variable demand against the level production rate from manufacturing. However, even producing for long periods of time at the true mean demand rate will not guarantee low inventories due to the random walk nature by which inventories are unavoidably generated. The production process must be willing and able to flex their production to eliminate this random walk. This paper develops the simple production control law that will allow manufacturing to reliably operate their JIT heijunka process. It determines the trade offs that must be made between: the time between fixed production rate changes (N), the finished goods inventory needed to provide a user specified level of backorders, and the flex needed in production capacity to guarantee that the process will continue to function effectively.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".