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Record W2607456581 · doi:10.1504/ijitm.2017.10004644

Providing custom enterprise resource planning solutions: benefits and challenges

2017· article· en· W2607456581 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

VenueInternational Journal of Information Technology and Management · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicERP Systems Implementation and Impact
Canadian institutionsCarleton University
Fundersnot available
KeywordsEnterprise resource planningFlexibility (engineering)Key (lock)Resource (disambiguation)Enterprise systemKnowledge managementProcess managementArchitectureComputer scienceEnterprise architectureBusinessComputer securityManagementEconomics

Abstract

fetched live from OpenAlex

Present-day enterprise resource planning systems (ERPs) cater to the ever-increasing need for real-time information and instant running analysis of financial trends and operational data. Out-of-the-box ERPs, while providing sturdy back-end architecture are still quite expensive and lack the flexibility of customised modules. Lightweight custom ERP solutions are cost-effective and favour the needs of modern businesses with unique reporting hierarchy and core processes. This study of a custom-solutions business partner of a US-based Fortune 500 company examines two cases to analyse key benefits and challenges that govern providing custom ERP solutions to organisations. Our results indicate that businesses realise optimal benefits from custom ERPs developed as modules around conventional solutions, providing the best of both worlds and a unique cost advantage.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.927
Threshold uncertainty score0.522

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 communication0.0010.004
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 head0.043
GPT teacher head0.284
Teacher spread0.242 · 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