Optimization of Pricing and Service Location Decisions for Do-It-Yourself Products
Bibliographic record
Abstract
This study is motivated by a real-world case in China. Affected by the geopolitical tensions in the Middle East, a Chinese mechanical watch component manufacturer has suffered a sales decline of nearly 50% since 2024, largely due to its reliance on a single B2B channel. To address this, the study proposes a dual distribution strategy integrating the B2B channel with online B2C sales supported by offline services. The online product range mainly features Do-It-Yourself (DIY) mechanical watch kits, designed with modularity and personalization to boost enthusiasts’ engagement and enjoyment. To match customers’ varying assembly abilities, products are categorized as loose-part kits, semi-finished kits, and fully assembled watches. This study focuses on four decisions for the new B2C channel: (1) optimal pricing across product types; (2) evaluation of offline service models, comparing in-house and outsourced centers; (3) service network location planning; and (4) determining who bears the service fee in outsourced centers. A mixed-integer nonlinear programming (MINLP) model is developed by simulating demographic and geographic data from all 297 cities at the prefecture level and above in China. A decomposition-coordination strategy is employed to decompose the complex problem into three interrelated subproblems: pricing, service network design, and payment allocation, which is solved by CPLEX. The study is expected to provide guidance on these decisions, enhancing customer experience while maximizing corporate profitability. The proposed methods can also be applied to other situations, such as IKEA’s furniture supply chains.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".