Mapping the dynamics of overall equipment effectiveness to enhance asset management practices
Bibliographic record
Abstract
Purpose The importance of physical assets has been increasingly recognized in recent decades. The significant returns on small improvements in overall equipment effectiveness (OEE) justify investment in the management of physical assets, but the wide variation of OEE across firms raises a question: “Why do these differences persist despite a high return on investments to maximize OEE?”. To address this question the dynamic processes that control the evolution of OEE through time need to be better understood. This paper aims to answer this question. Design/methodology/approach Building on insights from system dynamics and strategy literature, the paper maps the reinforcing feedback loops governing the maintenance function and its interactions with various elements in a firm. Building on strategy literature it hypothesizes that these loops can explain wide variations in observed persistent variations in OEE among otherwise similar firms. The paper draws on previous literature, extensive case studies and consulting projects to provide such mapping using the qualitative mapping tools from system dynamics. Findings The research outlines several reinforcing loops; once active, any of them could lead a firm towards a problematic mode of operation where reactive maintenance, poor morale, and a culture of fire‐fighting dominate. Actions taken to fix problems in the short‐run often activate vicious cycles, erode the capability of the organization over the long run, and lead to a lower OEE. Social implications Knowing the factors affecting the asset management function of a plant increases the plant's safety and limits its environmental hazards. Originality/value Some of the common dynamics of organizations' asset management practices are illustrated and modeled. The strategic importance of OEE and its effect on companies' market capitalization is demonstrated.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".