Joint optimization of product design and manufacturing inventory in a C2M supply chain
Bibliographic record
Abstract
Motivated by the prevalence of Consumer-to-Manufacturer (C2M) programs in retail platforms, this study considers a Vendor Managed Inventory (VMI) supply chain system, consisting of a retail platform and a manufacturer, which designs and sells a new product to a market of customers. Particularly, the manufacturer’s design of the product’s attributes would affect the conversion rate of the customers who click the web links to the product, and customer demand for the product eventually. The manufacturer jointly plans the product design and inventory control in this model. By exploring the model, we demonstrate that the manufacturer makes inventory decisions following a modified base-stock policy dependent on the conversion rate, and adjusts the product design as the inventory cost rate increases. Furthermore, we find the manufacturer’s separate plan of product design and inventory control harms the manufacturer but can benefit the retail platform and the entire supply chain. This finding can explain why the manufacturer’s separate plan is a widely observed phenomenon in practical C2M programs. We further propose a hybrid contracting scheme, which combines quantity discount and cost sharing contracts, to coordinate the supply chain system. Two-fold extensions of the main model, heterogeneous customer valuation and dynamic product design, have also been considered.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".