The effects of strategic planning, purchasing strategy and strategic partnership on operational performance
Bibliographic record
Abstract
The global competition in the manufacturing industry has obliged the companies to adopt an efficient and effective business process and adaptability of the company's competitive strategy following the external uncertainty conditions. The competitive strategy should enhance the competitiveness of the company, which is formulated during the strategic planning process. This paper investigates the impact of strategic planning, purchasing strategy, strategic partnership, on operational performance. The research has surveyed, using a questionnaire with a five-point Likert scale, 135 manufacturing companies domiciled in the region of East Java, Indonesia. Data analysis used the PLS technique. The objective of the analysis is to assess the measurement model for validity and reliability. Besides, the analysis also examines six hypotheses developed. The result reveals that all six hypotheses were empirically supported. The manufacturing company's strategic planning influences the purchasing strategy and strategic partnership. The result also shows that purchasing strategy through periodic evaluation of supplier capability, influences the strategic partnership in terms of involvement of suppliers in the business process of the company. Overall, strategic planning, purchasing strategy, and strategic partnership affect operational performance. It was also found that purchasing strategy and strategic partnerships mediate the influence of strategic planning on the performance. The results presented here may facilitate improvements in operational performance in the context of supply chain management. This paper also contributes to the ongoing research in the supply chain management theory.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".