Working Capital Management of Catering Enterprises Based on the Supply Chain — Taking the Jiumaojiu Group as an Example
Bibliographic record
Abstract
The management of working capital is crucial to the development of a business. In recent years, due to the epidemic, the catering industry has generally suffered from reduced revenue, high costs and financing difficulties. With the increasing competition in the catering industry, it is an important research direction to study the working capital management of listed catering enterprises in China from the perspective of supply chain. As a large catering brand in China, Jiumaojiu was in a poor financial situation in recent years. To better understand and help to tackle the problem existing in the management of its working capital, this paper takes Jiumaojiu Group as a case for study. Based on the working capital management evaluation method proposed by Professor Wang Zhuquan, we analyzed the working capital management of Jiumaojiu Group in recent years from the procurement, production and sales parts of its supply chain. According to the results of financial analysis based on Jiumaojiu Group’s annual reports for the periods from 2018 to 2022, we conclude that, in order to improve the utilization of the funds and the management of the working capital, Jiumaojiu Group need to improve it inventory management capabilities, strengthen the management of its accounts receivable, and slow down its business expansion slightly. It is hoped that this research can provide reference for Chinese catering enterprises to improve their working capital management.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".