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DIRECT PRODUCT PROFITABILITY AND RETAIL SHELF‐SPACE ALLOCATION MODELS

2001· article· en· W2135485623 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Business Logistics · 2001
Typearticle
Languageen
FieldDecision Sciences
TopicOperations Management Techniques
Canadian institutionsWilfrid Laurier UniversityUniversity of WaterlooMinistry of Transportation of Ontario
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsProfitability indexGross marginMargin (machine learning)Space (punctuation)Product (mathematics)BusinessIndustrial organizationOperations researchComputer scienceMarketingEconometricsMathematicsFinance

Abstract

fetched live from OpenAlex

Given an assortment of k products, a retailer must make an important decision on the percentage of space allocated to each of them. Optimization models for this problem have previously shown that an item's space should not simply be given in proportion to its sales or its gross margin (GM). Calculations of Direct Product Profitability (DPP) have improved upon the GM concept, but the present paper is the first (of which we are aware) to integrate the DPP approach into a shelf‐space optimization model.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.935
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.181
GPT teacher head0.366
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it