Case study: company analysis Chatime / Nor Faraain Mohd Tahir … [et al.]
Bibliographic record
Abstract
Although there are many dangers and difficulties when starting a business, Malaysia has a variety of businesses, particularly those that provide food and beverages. So, in learning this subject will learn, practice and relate the theory to a practical situation found in business by using the Business Model Canvas (BMC) in the case study. In this case study, we chose business Chatime in Kangar, Perlis. Chatime is a Taiwanese franchise launched in 2005, with over 1000 retail shops in Taiwan, Malaysia, China, India, Macau, Vietnam, Singapore, Hong Kong, Thailand, Korea, Philippines, Indonesia, Australia, Dubai, Vietnam, Canada, UK, Mexico, and the United States. However, every business must deal with challenges and problems, and Chatime is no exception. Besides that, in this case study, we explain about company analysis. Inside the company analysis is divided into nine parts. In those nine sections there are key partners, key activities, key resources, value propositions, customer relationships, channels, customer segments, cost structure and revenue streams. Furthermore, in this case study, we can see the findings and solutions about the Chatime business that need to be known and need to be improved. Chatime is a business that is less popular and less bought by the public. It is due to the lack of promotion that can attract people to buy drinks at the store compared to their competitor. Therefore, Chatime can get more collaboration and get involved in the social media marketing by associating with influencers to attract more customers. Furthermore, the Chatime product's packaging is less appealing since it appears gloomy, and the flavor of the beverages offered is less fulfilling due to the less sweet taste for certain drinks or the too sweet taste for some drinks. Other than that, the prices offered by Chatime for its drinks are a little expensive compared to their competitors. So, Chatime needs to review the cost so that the product sold can get a more reasonable price for their target customer. In addition, for the recommendation we decided to make ice mango for our product after we analysis product from Chatime. As a result, we improved our product to make it appealing for customers to purchase. The packaging for our product is one of the ideas that will be used for our ice mango. We opt to use a large plastic straw. To make it simpler for our customers to sip the drink with toppings like whipping cream, mango slices, and nata de coco, we use plastic spoon straws. Last but not least, we put topping for our drinks with three different kinds of toppings. The topping's main function is to give flavor to our drinks and to make them more appealing to consume.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".