Application of cybernetics to manufacturing flexibility: a systems perspective
Bibliographic record
Abstract
Purpose Flexibility continues to be key to the competitiveness of manufacturing firms. However, both in academia and industry, there still exists a lack of understanding regarding the fundamental nature of flexibility. This lack of understanding has often led to overly optimistic expectations regarding the direct transformation of technological flexibility into manufacturing flexibility. A theoretical model of the firm, based on cybernetics, is proposed in this paper. Design/methodology/approach The model relates flexibility to the cybernetic concept of variety and examines a dynamic system in terms of its task structure. Findings The model proves useful both in dispelling some of the misconceptions regarding flexibility, and in providing practical insights into issues of designing flexible manufacturing organizations. Practical implications The paper presents a means by which variety can be measured. Originality/value The conceptual model clarifies certain aspects of system flexibility. The first implication is that the flexibility required at a node is not fixed, but dependent on its connection with other nodes. The degree to which the interconnected nodes are effective regulators determines the variety impinging upon the target node. The second implication is that variety reduction is often a preferred solution over increased variety handling. The third implication is that the seemingly peculiar finding that relatively inflexible nodes in combination can be quite flexible, is easily explained using this theoretical model of the firm. System flexibility depends more on each node possessing requisite variety than on each possessing an enormous number of responses.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".