A Structured Approach to Supplier Performance Measurement
Bibliographic record
Abstract
The start of the new millennium will see companies in the oil and gas industry faced with a dual challenge. Not only will they have to undertake exploration in more demanding terrain and environments, but they also face far more competition in what they previously regarded as their traditional marketplace. The goal of meeting both shareholder and customer needs, while simultaneously attempting to increase market share by becoming more competitive, will be paramount if this success is to be achieved. While a number of strategies have been developed over the last decade in an attempt to achieve and balance these financial goals, the control and reduction of costs play a significant part in all such ‘cost effective’ programs. Past approaches have targeted the organisational structure, internal processes and strategic advantage through acquisitions, mergers and downsizing. However, any gains realised by such programs must be continuously improved upon by implementing innovative approaches to future reductions and controlling costs. Some companies have shifted the focus from internal cost scrutiny to influencing and ultimately controlling external factors of cost. The supply chain offers a tremendous opportunity to drive out costs, one such approach being to partner with the best suppliers of key components to shorten delivery times while minimizing life cycle costs. It is therefore paramount that one distinguishes between those who are simply suppliers and that smaller group who are the best suppliers, all the while fostering a win-win relationship by sharing growth and profitability. This paper will introduce the concepts of the Supplier Performance Measurement Process (SPMP), which NOVA / TransCanada introduced in late 1997 to measure and manage its suppliers’ performance in the provision of a few strategically critical commodities. To provide context for this paper two such commodities, high pressure line pipe and high integrity pipe coatings are addressed in some detail. The application of the process to these commodities alone yielded a capital cost reduction of 6%. The paper explains in practical terms, the steps involved in the implementation of SPMP, and provides a simple process for eliciting feedback on the efficacy of the procurement process.
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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 | 0.018 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".