A generalized approach to demand buffering and production levelling for JIT make‐to‐stock applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.000 | 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 it