Supply chain performance, profitability and Liquidity: An analytical study of Indian pharmaceutical sector
Bibliographic record
Abstract
The study aims to find out the relationship between Supply Chain Performance (SCP), profitability, and liquidity of selected leading Indian pharmaceutical companies. The study is based upon the secondary data available on the website of the concerned Indian pharmaceutical companies. The SCP defines the operational velocity and is measured by the manufacturing efficiency (inventory days), ability of recovery from the debtors (accounts receivables days), and payment to creditors (account payables days). Profitability is the relative measurement of the earning capacity of the business organization and facilitates the comparison among the business organization of similar industries. The liquidity in a business organization refers to the state of pay ability of the short term liabilities in ordinary business activities. Profitability and liquidity are the bi-polar concepts in the business organization. There is an optimum balance between liquidity and profitability is expected for the growth and development of the business organization. Ratio analysis is to be used to analyze the SCP, profitability, and liquidity while Karl Pearson’s correlation and Spearman’s rank correlation applied to get the correlation between SCP, profitability, and liquidity of the companies, and relative relationship between a correlation of profitability and profitability to liquidity ratio of all selected companies. It is observed that there is moderate relationship gross profitability, profitability on the owner’s fund, and liquidity. But there is a negligible relationship between liquidity and return on total resources or profitability on total assets. The Indian pharmaceutical companies with higher profitability are much sensitive about the co-movement of profitability and liquidity. The SCP of the Indian pharmaceutical companies negatively and positively but negligibly affects the profitability and liquidity of the Indian pharmaceutical companies.
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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 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".