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Record W2001048774 · doi:10.1108/95740930480000354

Adapting the Analytical Hierarchy Process to Identify Inventory Risk

2004· article· en· W2001048774 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.

Bibliographic record

VenueThe International Journal of Logistics Management · 2004
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicQuality and Supply Management
Canadian institutionsMitel (Canada)
Fundersnot available
KeywordsAnalytic hierarchy processProduct (mathematics)BusinessNew product developmentProcess (computing)Process managementComputer scienceSafety stockRisk analysis (engineering)MarketingOperations managementSupply chainOperations researchEconomicsEngineering

Abstract

fetched live from OpenAlex

The telecommunications industry is characterized by short product life cycles, driven by rapid market development and sometimes by new technologies emerging from internal and external research and design activities. These innovations cause product changes ranging from cosmetic, such as re‐packaging an existing product, to fundamental, such as introducing a completely new concept. The challenge for telecommunications manufacturers is to have the correct inventory in place for product launch and subsequent consumer demand. However, there are some categories of components that can cause serious inventory management problems and risk. We use the Analytical Hierarchy Process (AHP) in a specific telecommunications case study, and propose new strategies to manage high risk categories of stock. We identify ways of containing those risks through product design strategies, adapting MRP systems, better supplier control and a closer liaison between marketing and manufacturing activities to better anticipate change.

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)0.003
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.809
Threshold uncertainty score0.676

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0020.001
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 head0.054
GPT teacher head0.334
Teacher spread0.279 · 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