Framework for the Design of a Demand-Driven MRP (DDMRP) Model from Case Study
Bibliographic record
Abstract
Demand-Driven Material Requirements Planning (DDMRP) represents a significant advancement in the evolution of Material Requirements Planning (MRP) by addressing the challenges of variability in manufacturing environments.Unlike traditional methods that focus on managing variability, DDMRP proactively optimizes production and inventory management by strategically positioning and sizing inventory buffers within complex Bills of Materials (BOMs).This paper aims to achieve three primary objectives: (i) to identify the theoretical foundations for the application of DDMRP through a systematic literature review; (ii) to develop a comprehensive framework outlining key Manufacturing Planning and Control (MPC) systems essential for DDMRP implementation; and (iii) to provide a detailed analysis of the methodology's core components.The research employs the PRISMA statement to enhance the clarity and transparency of the systematic review process.The analysis categorizes the reviewed literature into five critical themes: strategic inventory positioning, safety stock sizing and management, buffer profiles and level determination, and demand-driven planning.The proposed framework uncovers significant gaps in the current literature and highlights valuable opportunities for further research.Additionally, it serves as a guide for policymakers and supply chain professionals, providing insights into the selection of sustainable strategies for improving operational efficiency and responsiveness in supply chain management.
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.
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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".