Success factors and organizational approaches for the implementation and the operational use of energy management systems according to ISO 50001: Paper presented at the 6th Canadian Quality Congress, September 29-30, 2014, University of Manitoba, Winnipeg, Manitoba, Canada
Bibliographic record
Abstract
Due to rising energy costs, increasing global competitive pressure and the demand for environmentally friendly products companies all over the world consider the implementation of an energy management system (EnMS) to meet future challenges. The purpose of this paper is to identify main success factors for the effective implementation, operation and certification of an EnMS in accordance with ISO 50001, which represents the fastest growing standard for management systems in the world. For that reason a survey among already certified German companies has been conducted focusing on organizational, teambuilding and technical aspects. The study provides best practice knowledge and gives interested companies the advantage to benefit from both the positive experiences of the participants as well as to prevent potential contra productive activities during the implementation, certification and operation of an EnMS. Results indicate that EnMSs are most commonly built on already existing management structures and therefore staff organization is crucial for the success of the project. Still, monetary aspects such as energy related cost savings seem to be the decisive criterion for the operation of an EnMS. Regarding teambuilding aspects specific technical expertise is required which leads to cross-functional teams focusing on the field of production. In addition key technical and administrative measures for an effective EnMS were identified.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".