MEASURING SENSITIVITY OF PROCUREMENT DECISIONS USING SUPERIORITY AND INFERIORITY RANKING
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Wrong decisions or inappropriate selection of equipment may lead to increase in cost and reduction in efficiency and effectiveness. Selecting right equipment has always been a key factor in the success of the process it is used for. In this study, superiority and inferiority ranking (SIR) methodis utilized for evaluation of most suitable offer for procurement of equipment installed inside a facility, whereas, analytical hierarchy process (AHP) is used to calculate the weights of factors that influence procurement decision. To achieve this target, a methodological framework of a series of interviews are conducted, then two questionnaire surveys are developed for identifying the important factors affecting the selection process of equipment and determining their relative importance. A solution of the problem is then designed in a model using AHP and SIR methods in addition to using the simple additive weighting (SAW) and technique for order preference by similarity to the ideal solution (TOPSIS) procedures to generate the superiority and inferiority flows. The model is generic and flexible and is used for the application of the multiple criteria decision making (MCDM) methods in the procurement process. The model also offers an efficient and convenient tool that aids its users to act in an orderly and methodical thinking, and guides them in making logical and robust decisions. A case study is presented to demonstrate the use of the developed model and sensitivity analysis is carried out to measure the robustness of the model in different scenarios.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it