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Record W4388036418 · doi:10.5430/bmr.v13n1p18

Challenges in the Receiving and Inspection of Supplies & Equipment in an International Organization in West Africa

2023· article· en· W4388036418 on OpenAlexvenueno aff
Ralyn E. Bermudez

Bibliographic record

VenueBusiness and Management Research · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicLaw, logistics, and international trade
Canadian institutionsnot available
Fundersnot available
KeywordsDocumentationInvoiceBusinessOperations managementQuality (philosophy)Process (computing)Order (exchange)Unit (ring theory)Computer scienceProcess managementMarketingAccountingFinanceEngineering

Abstract

fetched live from OpenAlex

The warehouse operation has enabled physical inspection activities, including receiving and inspection of materials and equipment. It has been found that there are certain challenges faced by inspectors during the receiving and inspection of materials. The warehouse custodian has performed the physical inspection, but not all cross-checking activities are accurate. Therefore, receiving and inspection are crucial to the delivery process, and distinct approaches must be implemented to deal with internal and external clients, including international and local suppliers. To effectively deal with these challenges, a process for entry document processing must be established. This process should include harmonizing the purchase order's terms and supporting it with the delivery note, packing list, invoice, bill of lading, and airway/waybill provided by the supplier. This will ensure that the material's quantity, quality, and specifications are accurately documented. A total of 14 responses were collected among managers and supervisors of receiving and inspection unit through interviews and survey questionnaires in the seven geographical locations of Mali, West Africa. The independent variables, namely (PI) physical inspection, (HOV) handover, and (DOC) documentation, significantly impact the dependent variable.

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.

How this classification was reachedexpand

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.556
Threshold uncertainty score0.331

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.219
GPT teacher head0.354
Teacher spread0.135 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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