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Record W4309716668 · doi:10.5267/j.ijiec.2022.9.001

Investigating the collective impact of postponement, scrap, and external suppliers on multiproduct replenishing decision

2022· article· en· W4309716668 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

VenueInternational Journal of Industrial Engineering Computations · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicProduct Development and Customization
Canadian institutionsnot available
Fundersnot available
KeywordsPostponementScrapOutsourcingQuality (philosophy)Production (economics)Process (computing)Industrial organizationProduct (mathematics)Finished goodProduction planningBusinessManufacturing engineeringOperations managementComputer scienceRisk analysis (engineering)EngineeringMarketingEconomicsMicroeconomics

Abstract

fetched live from OpenAlex

This study examines the collective impact of postponement, scrap, and subcontracting standard components on the multiproduct replenishing decisions. Rapid response, desirable quality, and various goods guide the client’s demands in today’s competitive market. Therefore, many manufacturing firms search for alternative fabrication and outsourcing strategies during the production planning stage to satisfy the client’s expectations, minimize fabrication-inventory costs, and smoothen machine utilization. To effectively help producers meet today's client's needs and enhance their competitive advantage, we develop a two-stage multiproduct replenishing system incorporating scraps, standard parts subcontracting, commonality, and delayed differentiation. To reduce the production uptime, stage one has a hybrid fabrication process for the common components (i.e., a partial outsourcing strategy), and stage two manufactures the finished multiproduct. In-house fabrication processes in both stages are imperfect; a screening process detects and removes scraps to maintain the finished batch quality. We determine the cost-minimized operating cycle. The findings reveal the collective impact of postponement, scrap, and external suppliers on this multi-product replenishment problem and can be used to facilitate production planning and decision-making.

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)0.001
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 score0.281
Threshold uncertainty score0.351

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.025
GPT teacher head0.255
Teacher spread0.231 · 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