Leveraging a Process-Oriented Perspective on Frugal Innovation Through the Linkage of Lean Product Development (LPD) Practices and Waste
Bibliographic record
Abstract
Frugal innovation aims to attain significantly cheaper solutions on the market. The literature points out a lack of studies addressing frugal innovation from a process-oriented (ex-ante) perspective, as well as how to structure and leverage the frugal product development process. This paper explores how lean principles and fundamentals, and more particularly lean product development (LPD) can help fill this gap. The incidence of different types of waste has been observed throughout the new product development (NPD) process, and their mitigation could certainly leverage frugal innovation from the process perspective. However, to operationalize waste mitigation, an evaluation of how different types of waste are related to the various existing LPD practices is necessary. We accordingly build a relationship matrix between LPD practices and waste mitigation throughout the NPD process, based on a literature review encompassing 310 studies. By correlating LPD practices and waste, this paper proposes and discusses a conceptual model that brings insights to understanding how LPD can drive frugal innovations from an ex-ante perspective. The contributions of this paper are the categorization and classification of waste within NPD; the assessment and classification of the lean practices most studied in the literature; the association of the categories of waste with the corresponding lean practices; and the discussion of the possible contribution of LPD literature to leverage frugal innovation from a process-oriented perspective.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".