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Record W4416876742 · doi:10.37665/smuddnb87082

Developing a Business Case for Implementing RFID in Manufacturing

2005· article· W4416876742 on OpenAlex
William M. Scott

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

VenueSMTA International · 2005
Typearticle
Language
FieldEngineering
TopicRFID technology advancements
Canadian institutionsIBM (Canada)
Fundersnot available
KeywordsBusiness caseResource (disambiguation)Business ruleBusiness processCompetitive advantageBusiness analysisEMIBusiness information

Abstract

fetched live from OpenAlex

ABSTRACT There have been several developments in recent years in RFID technologies and data collection systems. In manufacturing, there are some RFID solutions that use fairly standard products and other solutions that use specialized engineering for environments that handle large amounts of metals or liquids or that have environmental issues such as EMI and RF. Whether the RFID solution is standard or specialized, the importance of this business case evaluation approach is critical to success. Most projects require at least some elements of a business case to gain approvals for resource usage, schedules, and funding. The business case must be in a clear, understandable form so that management can easily recognize the business benefits associated with the project—the competitive advantages that will result. To this end, IBM/LGS have developed a Business Case Methodology for RFID and other information related technology projects. This approach results in a concise, complete view of the scope, benefits, costs, and other elements of the project.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.812
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.026
GPT teacher head0.305
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