Supplier selection considering product structure and product life cycle cost
Bibliographic record
Abstract
Purpose Supplier selection is a complex decision that involves not only the consideration of unit purchasing cost but also product life-cycle cost (LCC), which affects the company’s after-sale costs over the life cycles of their products. Product structure and its impact on the supplier selection evaluation process are rarely investigated in the literature. Therefore, product structure for a multi-criteria multi-product supplier selection problem with uncertainty is considered. In the model, we address product structure, the competitive supply environment, diverse criteria, and standard requirements. The objective is to choose suppliers that minimize LCC and maximize the reliability of the finished products. Design/methodology/approach Our model provides straightforward representation of interrelationships among multi-objectives and analysis of tradeoffs among conflicting objectives affected by product structure. We illustrate our model by using real life data from lubrication systems in the offshore reliability data (OREDA) handbook. Sensitivity analysis is provided for the case study in which various scenarios that describe product structure, the uncertainties in purchasing prices, reliabilities of purchased components, machine down-time due to poor quality components, suppliers’ capacity and delivery times. Different priority ranking among objectives is also tested to examine the impact of each objective on the overall objective. Findings Our computational results are based on real data and would provide useful guidelines for the management in OEM to choose right suppliers. Originality/value Product structure and its impact on the supplier selection evaluation process are rarely investigated in the literature. Therefore, product structure for a multi-criteria multi-product supplier selection problem with uncertainty is considered.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".