Managing Demand to Optimize Production Costs
Bibliographic record
Abstract
Manufacturing firms often have to deal with a demand pattern that is characterized by fluctuations. Demand fluctuations may be caused by factors such as seasonalities, the dynamics of a particular industrial sector or of the entire economy, change in demand levels over a short product life cycle, promotions carried out by marketing, or by contracts between an organization and its customers. Typically, a firm would be able to exercise a high degree of control over its choice of contracts and over marketing promotions and very little control over the remaining factors. This paper focuses on situations in which demand fluctuations are caused by "controllable" factors. The paper develops an analytic model to examine the impact of demand fluctuations (resulting from "controllable" factors) on the production system. The objective is to manage demand to ensure that production costs are minimized for a given amount of aggregate demand over the planning horizon. The paper examines the validity of the notion that demand fluctuations always have an adverse impact on manufacturing. The results indicate that optimal demand and production patterns tend to be well-behaved monotone series that are characterized by little, if any, fluctuation. These results thus support the notion that fluctuations are undesirable. It is shown, however, that a level demand pattern is not necessarily optimal.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".