Towards best management practices for implementing manufacturing flexibility
Bibliographic record
Abstract
Purpose The purpose of this research is to develop a framework and an initial list of best management practices for implementing manufacturing flexibility. Design/methodology/approach To identify these practices, recent frameworks (i.e. 1988 and onward) for implementing manufacturing flexibility in organizations are reviewed. Based on this review, the major management practices for implementing flexibility are identified and synthesized into a new framework. Findings This framework suggests that manufacturing flexibility should be implemented using a three‐stage approach, labeled: identifying required flexibility (i.e. identifying and justifying the flexibility types, measurements and tools needed to achieve the required manufacturing flexibility), achieving required flexibility (i.e. acquiring and implementing the organizational and technological tools needed to achieve the required manufacturing flexibility) and managing required flexibility (i.e. monitoring and changing the required flexibility types and levels, in light of changing uncertainty and competitive, manufacturing and marketing strategies). Based on this framework, a number of potential best management practices are identified. Research limitations/implications This report is conceptual in nature. Future research will focus on empirically testing the practices presented in order to develop a more complete and rigorous list of best management practices for implementing manufacturing flexibility. Practical implications This research provides manufacturing managers with a starting point for developing a formal process for identifying, implementing, and monitoring manufacturing flexibility, thus ensuring that the manufacturing flexibility that exists is continually meeting the manufacturing and competitive strategies of the organization. Various conceptual relationships are identified by the presence of arrows in the framework. As a result, the implications of the conceptual framework for researchers is that it provides a very good starting point for conducting exploratory and confirmatory research on the process of managing manufacturing flexibility. Originality/value This research synthesizes existing frameworks for implementing manufacturing flexibility in organizations, and addresses a gap in the research, specifically the need to identify and empirically test best management practices for implementing manufacturing flexibility.
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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 | 0.075 | 0.085 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.022 | 0.018 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".