Enhancing Small and Medium Enterprises: A Hybrid Clustering and AHP-TOPSIS Decision Support Framework
Bibliographic record
Abstract
The vitality of small and medium enterprises (SMEs) is integral to the economic fortification of nations, necessitating refined enhancement mechanisms from governmental bodies.Distinctive in their developmental trajectory, SMEs present a unique challenge in prioritizing interventions.A Decision Support System (DSS), employing a hybridized methodology of Clustering and Analytic Hierarchy Process-Technique for Order Preference by Similarity to Ideal Solution (AHP-TOPSIS), is proposed to facilitate the stratification of SMEs and guide governmental action based on a hierarchical scale of priorities.In this study, K-Means clustering was adopted for the segmentation of SMEs, leveraging its capability to efficiently partition high-dimensional data with minimal error.Subsequently, the TOPSIS method was utilized to rank SMEs within each cluster.However, the critical step of computing criteria weights to ascertain their relative importance was achieved through the AHP method.The latter effectively addresses multivariate considerations encompassing both quantitative and qualitative criteria through pairwise comparison matrices.The research incorporated 11 attributes, encompassing essential characteristics intrinsic to business entities, such as business name, location, operational status, sector, tax identification number, workforce size, average revenue, production costs, operational challenges, credit accessibility, and external financing needs.The clustering process, executed via Self-Organizing Maps (SOM), yielded optimal clusters at 1000 epochs, evidenced by a Davies Bouldin Index (DBI) of 0.74785, translating to an accuracy of 91.2601%.Notably, the SOM's performance in clustering SME data surpassed that of K-Means, as demonstrated by superior results in both Sum of Squared Error (SSE) and DBI metrics, thus showcasing its proficiency in managing data with heterogeneous criteria.The methodology engenders an n-cluster output, from which members are earmarked for priority ranking.AHP-derived weights are calculated, with a Consistency Ratio (CR) exceeding 0 denoting consistency, thereby determining the significance of each SME.TOPSIS calculations then ascribe the final score, delineating the SMEs' standings.This integrated DSS framework presents a robust tool for policymakers, ensuring targeted and efficient allocation of resources towards the advancement of SMEs.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".