MétaCan
Menu
Back to cohort
Record W4229447676 · doi:10.33423/jabe.v24i2.5142

Building Decision Support Systems in Excel for Production and Distribution Planning: A Case Study

2022· article· en· W4229447676 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Applied Business and Economics · 2022
Typearticle
Languageen
FieldComputer Science
TopicSpreadsheets and End-User Computing
Canadian institutionsnot available
Fundersnot available
KeywordsMicrosoft excelProduction (economics)Computer scienceDecision support systemPlan (archaeology)Linear programmingDistribution (mathematics)Operations researchProduction planningIndustrial engineeringSoftware engineeringEngineeringOperating systemData miningAlgorithmEconomicsMathematics

Abstract

fetched live from OpenAlex

We develop a decision support system in Microsoft Excel that integrates production and distribution for a manufacturer of natural fiber-based products in North America. The production and distribution of the company’s products were optimized using a linear programming model, implemented in Excel. The spreadsheet dynamically adjusts the formulation to reflect the user’s current requirements, solves the optimization model in the background, and generates detailed managerial reports. In addition, it allows users to conduct what-if analyses by varying the number of plants and warehouses. It demonstrates the ability of a Linear Programming Model run on an Excel platform to provide the firm with an optimized production plan resulting in significant, cost savings since implementation.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.182
Threshold uncertainty score0.302

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.028
GPT teacher head0.258
Teacher spread0.230 · 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