Data envelopment analysis for investigating the relative efficiency of supply chain management.
Bibliographic record
Abstract
Despite the growing popularity of supply chain management (SCM) both among managers and scholars, the efficiency of implementation of SCM has been barely assessed in an analytic way. The purpose of this study is to shift the attention of SCM scholars towards an in-depth investigation of SCM efficiency by reporting procedure and outcomes of one possible methodological approach. The current study investigates the relative efficiency of SCM implementation (in terms of ratio of various outputs to inputs) and subsequently identifies influencing factors. This procedure is illustrated by an empirical application based on a European sample of manufacturing plants as Decision Making Units (DMUs), following a two-step approach. (1) Data envelopment analysis (DEA) assesses the relative efficiencies of SCM implementation of DMUs. (2) Subsequently, factors fostering or impeding SCM efficiency are explored through a bootstrapped truncated regression model. Our analysis finds that factors influencing relative SCM efficiency refer to country affiliation, characteristics of manufacturing plants, characteristics of production, buyer's purchasing situation, and buyer-supplier relationship characteristics, confirming previous literature that highlight complex and contingent interrelation between investments into buyer-supplier relationships and performance. Going beyond previous research, our study reframes the strategic implementation of SCM from the distinct angle of the economic principle of efficiency. It provides a novel approach of assessing the efficiency of SCM implementation in an analytic way, thus guiding managers in their strategic decision-making regarding the input-output ratio of SCM. Simultaneously, our study adds to SCM theory by conceptualizing strategic SCM as an input-output system with varying transformation efficiencies.
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.
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 | 0.028 | 0.083 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".