SCM practices and the health of the SMEs in Pakistan
Bibliographic record
Abstract
Purpose The purpose of this research is to provide a window into the supply chain practices of the small and medium enterprise sector in Pakistan. Design/methodology/approach The Small and Medium Enterprise Center (SMEC) at the Lahore University of Management Sciences undertook a survey in 2003 to gauge the health of this sector. A survey of 650 firms in ten districts in the country was conducted. This paper presents the results and analyses of the supply chain practices of these SMEs. Research limitations/implications There is dearth of independent data and research in the field of SME's in Pakistan. This paper provides a window to the supply chain practices of SME's in Pakistan and will enable future researchers to use this research as a building block in understanding these practices and the factors that pertain to successful firms. Practical implications The implications of this study are far reaching enabling trainers, consultants, donor agencies, and entrepreneurs in the SME sector to learn the practices of successful firms and adopt/help SMEs adopt these in their operations. Findings This paper we report some of the results of the survey and our analysis of factors related to supply chain management practices that seem to correlate with the health of the enterprise. The analysis shows that successful firms on average had more products, more customers as well as more new customers. SMEs into exports were the healthiest and exhibited most dynamic characteristics, followed by those that sold to OEMs. Another interesting insight is that growing firms sold more directly to end users while firms with higher sales per employee sold the least to the end user. Originality/value A major frustration for most policy researchers in the SME area is the virtual non‐existence of scientific data on this sector in the country. This is the first survey of its kind in Pakistan.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".