Bibliographic record
Abstract
This serial cases comprise Case A and Case B that focus on how Baman Tech (Baman), a catering company can engage in business model innovation in the background of China’s consumption upgrading and Internet era, specifically how to utilize community marketing and omni-channel operations to reach the consumers on the demand side effectively and how to realize efficient operations and supply chain management (SCM) on the supply side. Case A and Case B have the first major problem of Baman in SCM as the dividing line. Case A reviews the venturing experience of Baman. It successfully became an Internet popular brand in the catering industry through a single product strategy and community marketing and satisfied the consumers’ desire to eat authentic Hunan beef rice noodles anywhere and anytime through take-away, home delivery, and retailing services. Meanwhile, the share of customer base between catering business and retailing business and between online channels and offline channels constituted Baman’s “multidimensional battle” business model. As catering and retailing had two distinct supply chains, Baman had increasing inventory costs with the increase in its retailing SKUs so that its supply chain encountered “pain” at the end of 2017. Case B introduces how Baman gradually increase the SCM capability and optimize the supply chain network from supply to delivery in the last two years after realizing the importance of SCM. Some of the practices include establishing a clear supply chain strategy, segmenting and differentiating the supply chains of catering business and retailing business, and continuously improving the operational efficiency at different links of the supply chain, reducing costs and shortening the lead time through multiple approaches. At the end of the case, there is an open discussion: How will Baman manage a larger-scale, more complex and volatile supply chain and further increase its efficiency in the future? The cases focus on a startup company, introduce business model innovations in a more comprehensive manner and how the supply chain strategy and capabilities support the business model innovations, thus providing references to the supply chain and business model innovations of other catering/retailing companies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".