Optimal Inventory Management with Buy-One-Give-One (BOGO) Promotion
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recently, the Buy-One-Give-One (BOGO) model, where the firm donates one unit of its product for every unit purchased, has emerged as a viable option to practice corporate social responsibility. Despite growing public attention to the BOGO model, optimal inventory management and profitability associated with BOGO has not yet been explored adequately in the academic literature. Under the BOGO promotion, inventory management naturally becomes a key decision, since the firm has to produce an extra unit for each unit sold. In this article, we examine optimal inventory management of the BOGO model under stochastic demand and compare it to the standard newsvendor model as well as a model with cash donation. Analogous to the standard newsvendor model, we clearly define the BOGO fractile and optimal stocking quantity. We show that, counterintuitively, it is not necessarily optimal to produce more units under BOGO, due to the trade-off between give-away commitment and reduced product margin. Moreover, although the BOGO model invariably yields a lower profit than the classic newsvendor model or cash donation model if demand remains the same, there often exists a certain level of positive demand shift that renders BOGO more profitable, which helps explain growing presence of BOGO in the marketplace.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it