Design, building and validating a measuring scale for the supply chain management practices of industrial organizations by assessing their efficiency on SCM measures
Bibliographic record
Abstract
The aim of this study is to design, build and validate a scale for the measurement of Saudi industrial Organizations' SC Management Practices (SCMP), and also to evaluate its efficiency at various SCM measurements. The analysis identified 20 constructs of (SCMPs) based on a comprehensive literature review; namely Strategic Partnership of Suppliers (SPS), Customer Relationship (CR), Information Sharing (IS), Information Quality (IQ), Postponement (PST), Agreed Vision and Goals (AVG), Sharing of Risks and Rewards (SRR), Lean Manufacturing (LM), Total Quality Management (TQM), Organizational Culture (OC), Information and Communication Technology (ICT), Benchmarking and Performance Measurement (BPM), Agile Manufacturing (AM), Outsourcing (OUT), Just In Time Manufacturing (JIT), Green SC Management (GSCM), Reverse Logistics (RL), Vendor Managed Inventory (VMI), Radio Frequency Identification (RFID), and SC Integration (SCI), and four SCM performance structures in particular namely; Flexibility Perspective (FLP), Efficiency Perspective (EFP), Customer’s Perspective (CSP), Product Innovation Perspective (PIP). A survey tool based on the existing literature was developed and relevant data were collected from 351 Industrial Saudi organizations on this tool. In the data analysis the validation of the instrument is mainly carried out with confirmatory factor analysis in terms of unidimensionality, durability, convergent validity, discriminant validity, nomological validity, and the associated validity criteria. A parsimonious instrument that makes an important contribution to the SCM literature is generated by the results of this research. The instrument will allow an enterprise to incorporate various SCMPs, to keep track of the implementation status, and then to evaluate SCM performance to the SCM dimensions.
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How this classification was reachedexpand
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".