Three Essays on The Interface of Sales and Operations Management
Bibliographic record
Abstract
This dissertation investigates three profit maximization models for coordinating Sales and Operations (S\\&OP) management.\nThe first problem considers implementing a customer education strategy for the digital channel users of a multi-channel service provider. Using a customer network flow model, I investigate the effect of customer education in the digital channel on the number of users of digital and in-person channels. Further, I define a customer value metric that characterizes the optimal level of education effort. Finally, I determine conditions under which the effect of positive word-of-mouth regarding the quality of education dominates the lifetime value of customers.\nThe second problem studies a supply chain in which a distributor procures perishable products, transports them along the supply chain through distribution centers, and sells them at retail stores. The objective is to jointly optimize quality-based transportation decisions and pricing policies. I propose two frameworks to study this problem under sequential and integrated systems. Pricing and transportation decisions are made separately and then coordinated through an iterative process in the sequential model but jointly optimized in the integrated system.\nI exploit the special structure of the integrated model formulation and develop a decomposition-based solution method. I tested the model and solution methodology on a lettuce product distribution network in Eastern Canada.\nFinally, the third problem investigates the category space allocation at the macro-level and explores how considering location-based and product-based attractiveness can improve a retailer's overall space profitability. I consider both location-based and product-based attractiveness factors in a mixed-integer quadratic problem. As large-scale instances of this problem are computationally challenging to solve, I further provide a decomposition-based heuristic solution method for solving large instances of the problem.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".