Characterizing and structuring a new make‐to‐forecast production strategy
Bibliographic record
Abstract
Abstract To date, the theory of production in operations management has lacked a production strategy for one major segment of the manufacturing industry. For large engineered equipment, a relatively recent but increasingly common production strategy has arisen to better meet today's competitive pressures for faster delivery of more customized products without increasing costs. A hybrid of the make‐to‐order (MTO) and make‐to‐stock (MTS) production strategies, manufacturers launch major product models to a demand forecast (MTS) and then modify the partially completed products as the actual orders arrive (MTO), a production strategy we refer to as make‐to‐forecast (MTF). This paper has two purposes: (1) it describes and conceptualizes the MTF situation in a variety of industries and places the MTF strategy among the other major production strategies in the theory of production and (2) it analyzes decision rules for matching partially completed units to incoming customer orders—one of the unique and perhaps most demanding challenges of the MTF environment. It shows that two order matching rules developed in the paper outperform the ad hoc rules commonly used in practice. We test and confirm the generalizability of the superior performance of these two rules in 13 different industry variations of the MTF production situation. Last, the insights provided by the model are discussed in terms of their practical relevance.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".