Strategic Supply Chain Integration and Competitive Advantage: An Essential Nexus in Vehicle Assembly Firms in Kenya
Bibliographic record
Abstract
This research examined the impact of strategic supply chain integration on the competitive advantage of the vehicle assembly companies in Nairobi City County, Kenya. The Government of Kenya has given attention to the vehicle assembly industry since it forms part of its industrial development strategy, which includes pursued flagship programs such as Vision 2030 and the Big 4 Agenda. Nonetheless, despite these efforts, the Kenya Association of Manufacturers has reported challenges such as severe supply chain inefficiencies and coordination gaps between the assembler, the local suppliers and the modern inventory management systems. The business environment today thrives on rapid technological innovations and charters very robust competitive activity. This scenario has made supply chain integration not only a strategy of excellence, but one of the pillars of organizational success in the long run. In the Kenyan system of industrial development, the flagship initiatives have been emphasized. Nonetheless, even with these measures and efforts, the Kenya Association of Manufacturers notes that it continues to face challenges such as high supply chain inefficiencies, which lead to underutilization of capacity, disjointed coordination among assemblers and local suppliers, and inconsistent uptake of modern inventory management systems. The study focused on the interplay between information sharing, collaborations, lean operations, and inventory control, anchored on the Resource-Based View, Dynamic Capabilities Theory, and Porter Theory of Competitive Advantage. Descriptive research design was adopted, and primary data was collected using questionnaires from 109 stratified supply chain professionals from three large assembly firms (CFAO Mobility Kenya/KVM, Isuzu East Africa). A pilot study was conducted to determine the reliability and validity of the study. Descriptive statistical analysis reported high levels of internal information sharing and strategic collaborations, albeit, their lack of real-time data exchange to suppliers and underuse of automated inventory systems. Multiple regression analysis listed as significant predictors of competitive advantage all four strategic supply chain integration (SSCI) practices, while collaboration was the most substantial as a single predictor. However, the correlation analysis suggested the siloed and isolated nature of the practices, most notably, the gap between strategic information sharing and collaboration integration. Strategic supply chain integration, therefore, is a source of competitive advantage, albeit, remains underexploited and underpinned by the lack of synergistic integration to its components. The study advised companies in the Kenyan automotive industry to enhance the strength and competitiveness of their supply chains by creating consolidated digital supplier portals, developing collaborative operational taskforces, and acquiring automated inventory management systems.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".