The extent of logistics outsourcing among small and medium-sized manufacturing enterprises in Nairobi
Bibliographic record
Abstract
Background: Small and medium-sized manufacturing enterprises (manufacturing SMEs) can facilitate economic growth and development by creating employment and spurring economic activities at low levels of the economy. The performance of SMEs in Kenya has, however, been poor, despite their significance. This poor performance is attributed to the high costs of logistics operations in the country. Manufacturing SMEs can, however, improve the performance of their logistics operations by adopting appropriate logistics outsourcing strategies.Aim: The purpose of this study is to determine the extent of logistics outsourcing among manufacturing SMEs in Nairobi.Setting: Manufacturing SMEs in Nairobi operate from the industrial zones of the Nairobi City County. The enterprises’ logistics operations are characterised by long cycle times, high transportation costs and limited resources. The high operational costs experienced by the SMEs threaten their survival, and they are therefore required, among other things, to manage their logistics more efficiently to improve overall performance.Method: A quantitative research design was used in this study. Data were collected from 163 manufacturing SMEs using a structured questionnaire. Descriptive statistics and (a one-way analysis of variance) ANOVA were used to analyse the data.Results: Most (94%) of the manufacturing SMEs opted to outsource their logistics operations, although the extent of outsourcing was limited (1% – 50% of logistics operations were outsourced). Logistics outsourcing by the SMEs is intended to reduce logistics costs and supplement the limited in-house capabilities. In addition, there were significant differences in the extent of outsourcing of operational, information processing and value-added categories of logistics activities.Conclusion: The results motivate SME owners and managers to acquire logistics resources and capabilities that are lacking in-house through logistics outsourcing to achieve the required efficiencies. Although the majority of SMEs have embraced logistics outsourcing, the low extent of its usage within the enterprises might have limited the ability to achieve high 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".