Apply GM(0,N) and Grey Relational Grade in the Relational Analysis of Business Items-An Example on the Chain Store 7-11
Bibliographic record
Abstract
7-11 was originally started from the franchise, and it was founded in the United States. Now it is the largest chain convenience store worldwide. In November, 2005, the Japanese 7&I holding company had purchased the American 7-11. After that, 7-11 was positioned as a Japanese company. Nowadays, modern people are busy working, and they can't deal with the basic issues, like food, clothing, shelter and transportation. Hence, it enables the growth of 7-11. Currently, 7-11 has about 46,000 stores worldwide, and they distribute in the countries of Japan, the United States, Canada, Hong Kong, China, Macau, Taiwan, Singapore, Thailand, Malaysia, the Philippines and South Korea. 7-11 can not only solve the general problems, like food, clothing, shelter and transportation, it also provides collection business services in Taiwan. For example, it can deal with telephone bill, gas bill, parking bill, water bill, electricity bill, insurance bill, etc. In addition, 7-11 provides coupons, i-cash, Easy card, small-paid card and self-payment service. From the above explanations, we can understand that 7-11 is the company which makes a lot of profits, has marketing ability and vast network system. Hence, how to develop operating policies has become the first priority. In the past, there were many relevant studies and they provided good results. However, this paper started from the regional 7-11 business concept and proposed soft-calculation math method to analyze the bulk of purchasing items. Therefore, the paper used seven 7-11 convenience stores in a county in southern Taiwan as the research subject. Then the paper used globalization grey relational grade and GM(0,N) method in the grey system theory to analyze the yearly six business items, including beverages, bread, tobacco, alcohol, books and stationery. Also, the total turnover of the year was calculated as the output. The weighting of each impact factor can be reached and the main operation points can be found. Through the practical analysis and step by step math calculation, the relevant weighting of each factor in the turnover can be obtained. The clustering of the result by using two methods are quite same, which the stationary and books were ranked as the first and the tobacco is the last one, respectively. 7-11 stores’ main function is to provide convenient access to various materials and this is in line with the real situation. Hence, the results not only provide purchasing and stocking policies to operate chain enterprises, they also enhance operational efficiency and increase high income.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".