A novel framework of ERP implementation in Indian SMEs: Kernel principal component analysis and intuitionistic Fuzzy TOPSIS driven approach
Bibliographic record
Abstract
Over the years, organizations have witnessed a transformational change at global market place. Integration of operations and partnership have become the key success factors for organizations. In order to achieve inclusive growth while operating in a dynamic uncertain environment, organizations irrespective of the scale of business need to stay connected across the entire value chain. The purpose of this paper is to analyze Enterprise Resource Planning (ERP) implementation process for Small and Medium Enterprises (SMEs) in India to identify the key enablers. Exhaustive survey of existing literature as a part of secondary research work, has been conducted in order to identify the critical success factors and usefulness of ERP implementation in different industrial sectors initially and examines the impact of those factors in Indian SMEs. Kernel Principal Component Analysis (KPCA) has been applied on survey response to recognize the key constructs related to Critical Success Factors (CSFs) and tangible benefits of ERP implementation. Intuitionistic Fuzzy set theory based Technique of Order Preference by Similarity to Ideal Solution (TOPSIS) method is then used to rank the respective CSFs by mapping their contribution to the benefits realized through implementing ERP. Overall this work attempts to present a guideline for ERP adoption process in the said sector utilizing the framework built upon KPCA and Intuitionistic Fuzzy TOPSIS. Findings of this work can act as guidelines for monitoring the entire ERP implementation project.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".