Best Practice Benchmarking in Energy Efficiency: Canadian Automotive Parts Industry
Bibliographic record
Abstract
In 2001, Natural Resources Canada's (NRCan's) Office of Energy Efficiency (OEE) launched its industrial benchmarking and best practices program. Traditionally, energy benchmarking involves the collection and analysis of energy related data that is then used to develop quantitative indicators. These indicators enable industrial companies to assess the energy-efficiency, productivity, and emissions performance of their operations vis-a-vis those of similar operations in the same sector. But when the diversity (or non-uniformity) of a sector makes traditional benchmarking virtually impossible, how then can benchmarking still be used to help companies or a sector achieve any kind of energy-efficiency gain? Such was the challenge faced by the Canadian automotive parts industry, which is diverse with large variations in equipment, industrial processes and operating practices used across the sector, making direct comparisons difficult, if not impossible. The Automotive Parts Manufacturers’ Association (APMA) worked in collaboration with their consultant TdS Dixon and NRCan to adapt the traditional concept of benchmarking such that the analytical results would still enable companies in the sector to comparatively identify deficiencies and adapt to a better practice, thereby improving their energy efficiency. A survey was developed that rated APMA member-sites on their capacity for good energy management; that is their Organisational Capacity, Operational Capacity and Technical Capacity. Companies were rated against performance benchmarks in these three categories that were compiled from numerous sources including Natural Resources Canada, the United States Department of Energy and Action Energy in the United Kingdom. • A company’s organizational capacity for managing its energy use is the degree to which its practices include: formulating an energy policy, positioning energy management in the organizational structure, improving employee skills and knowledge in the area of energy efficiency, managing energy information, producing internal and external communications about energy management, and investing in efficiency measures. • A company’s operational capacity for good energy management is the degree to which it provides its employees with operating procedures and training that will help them to keep energy efficiency in mind during production. Although most operating procedures are written for specific energy-consuming systems, many have common elements that can be implemented plant-wide. • A company’s technical capacity for good energy management is the degree to which it incorporates energy efficiency into its acquisition and operation of individual energyconsuming systems.
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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 | 0.027 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.022 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".