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Record W4281775020 · doi:10.22214/ijraset.2022.43144

An interactive GST+ERP System for handling small scale Indian Businesses

2022· article· en· W4281775020 on OpenAlexaff
Harshvardhan R Patil, Nikhil S Madhekar, Sourabh R Kotgire, Priyanka N Shelke, Ishwari Raskar

Bibliographic record

VenueInternational Journal for Research in Applied Science and Engineering Technology · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicERP Systems Implementation and Impact
Canadian institutionsTrinity College
Fundersnot available
KeywordsEnterprise resource planningComputer scienceSimplicityScale (ratio)Resource (disambiguation)Small and medium-sized enterprisesSoftwareBusinessKnowledge managementProcess managementFinance

Abstract

fetched live from OpenAlex

Abstract: An ERP solution, often known as an Enterprise Resource Planning System, has become among of the highly productive and helpful installations. ERPs are incredibly competent of offering a strong organizational structure and simplifying the complete firm with simplicity. ERPs have been increasingly popular in last several decades due to the significant cost savings and excellent administration of an organization's overall business operations. ERP administration is an incredibly expensive operation due to the thorough and comprehensive integration, which is why much of it has been focused on major organizations. This is also why almost all of these new ideas have been out of reach for small to medium sized firms. Several other researchers were also barred from accessing these technologies and applying numerous modifications due to the expensive cost. As a result, an effective method to building a comprehensive approach to an ERP for the involved in administering small to medium enterprises is required. This research article provides an effective approach that enables the manager and the staff to access this software to make purchases, handle client and employee data, view performance charts etc. in much detail in the upcoming sections. Keywords: GST verification, Pan Number Verification, Database connectivity, purchase Entries, Sales Entries.

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)0.003
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.511
Threshold uncertainty score0.703

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.067
GPT teacher head0.387
Teacher spread0.320 · 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 designTheoretical or conceptual
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
Published2022
Admission routes1
Has abstractyes

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