Demand Driven Material Requirements Planning (DDMRP): A systematic review and classification
Bibliographic record
Abstract
Purpose: Demand Driven Material Requirements Planning (DDMRP) aims to deal with variability by adjusting inventory levels while maintaining, or even increasing, customer service levels. This approach bridges the push and pull approaches. Even though it first made its appearance in 2011, research in this field remains relatively limited. This paper aims to measure the spatiotemporal evolution of the DDMRP, its scope and context of implementation, and the research lines studied in that field in order to identify areas that still need to be addressed by future researchers.Design/methodology/approach: The systematic literature review approach adopted in this paper examines research dealing with the DDMRP approach published in different languages between 2009 and 2020. To-date papers focused on the performance analysis and comparison, what differentiates this study is the focus on the scientific evolution level of DDMRP, the parameters, and contexts that should be more studied.Findings: The results show that DDMRP is not yet a mature method and that the robustness of the approach still needs to be tested. More research is also required to determine scientifically some setting parameters, how the proposed DDMRP could be implemented in different industrial contexts with existing information systems.Originality/value: Based on the evolution analysis of DDMRP, this study outlines its current state of maturity and its different shortcomings under a broader vision to make this method more complete on the scientific and industrial level.
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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 | 0.032 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.042 | 0.040 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".