A review of agile and lean manufacturing as issues in selected international and national research and development programs and roadmaps
Bibliographic record
Abstract
Purpose The aim of this paper is to analyze international and national research and development (R&D) programs and roadmaps for the manufacturing sector, presenting how agile and lean manufacturing models are addressed in these programs. Design/methodology/approach In this review, several manufacturing research and development programs and roadmaps – national programs from the USA and Canada, and international programs from the European Union and from one international organization – are reviewed. Findings The major finding of this review is that the main concerns in agile manufacturing, as highlighted in these programs, are networks, supply chain and product/service customization, and lean manufacturing's inclination towards achieving better cost efficiency. Although the lean manufacturing approach has been considered in many past and present programs, analysis of the most recent programs shows a greater priority is given to the agile manufacturing approach. The path towards sustainable manufacturing is delineated by pro‐active attitude and action towards customers. Research limitations/implications The study analyzes two national R&D programs from the USA, one international program from the European Union, three international roadmaps from the European Union, one business plan from Canada and one international roadmap from the global organization Intelligent Manufacturing Systems. Practical implications The findings of this paper are intended to help managers, researchers and practitioners from the manufacturing sector to enhance their understanding and define suitable strategy for their organizations' sustainability and identify suitable manufacturing path with respect to agile and lean philosophies. This study could also help academics in defining course curricula for students more coherent with the R&D policies and/or requirements towards sustainable manufacturing with respect to agile and lean philosophies. Originality/value There are reviews comparing agile and lean manufacturing paradigms, but there are no reviews about how the two manufacturing concepts are addressed in manufacturing R&D programs and roadmaps.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".