An integrated approach for supplier selection and purchasing decisions
Bibliographic record
Abstract
Purpose The paper seeks to provide academic researchers and practitioners with a better understanding about purchasing strategies through an integrated approach to supplier selection and purchasing decisions. Design/methodology/approach This paper views supplier selection as a multi‐criteria problem. Through the analytical hierarchy process (AHP), in consideration of both quantitative and qualitative criteria, a set of candidate suppliers is identified. A multi‐objective linear programming (MOLP) model, with multiple objectives and a set of system constraints, is then formulated and solved to allocate the optimum order quantities to the candidate suppliers. Findings The paper provides tradeoffs among different objectives, which are more consistent with the complexity and nature of the real‐world decision‐making environment. It also offers better information and solutions supporting effective purchasing decisions. Research limitations/implications The main concept of the proposed approach can be applicable to any organization with a purchasing function. However, its implementation will be very specific to a particular organization of interest, as each individual organization must define its own subjective criteria and constraints. The area of decision support system development, which automates (or computerizes) the input process of the proposed models and integrates with other databases in a company, will provide great opportunities for future research. Practical implications The paper provides practitioners with flexibility and effectiveness in their supplier selection and purchasing decision process and with a better understanding about their future purchasing strategies. The results from the application of the proposed models to the supplier selection problem at a high‐technology firm in Taiwan show that the models are effective and applicable. Originality/value This paper takes an integrated approach to problem analysis (i.e. multi‐objectives with both quantitative and qualitative information), uses a sound scientific methodology in model development (i.e. integrating AHP with MOLP), and provides practical use of the models. It offers additional knowledge and value to both academics and practitioners.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".