From Decision Models to Prediction Models: Enhancing Supplier Selection with Machine Learning and Deep Learning Article Swipe

MAKE IMPACT

OSHODIN, Osamudiamen David , EHICHOYA, Martins · YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.5281/zenodo.17857004

This study extends the traditional Multi-Criteria Decision-Making (MCDM) framework for supplier selection in circular supply chains by integrating predictive machine learning (ML) and deep learning (DL) models. Conceptually, it bridges the gap between interpretable, theory-driven decision models and powerful, data-driven prediction, proposing a hybrid intelligence framework where MCDM provides the foundational "expert judgment" for ML to learn and automate. Building on a foundational MCDM analysis that ranked 100 raw material suppliers for a Nigerian beverage firm using Weighted Sum (WSM), Weighted Product (WPM), and TOPSIS methods, this study treats the derived composite MCDM score as a target variable for predictive modeling. We implemented and compared several ML models, Linear Regression, Random Forest, XGBoost, and Support Vector Regression (SVR), alongside a Deep Neural Network (DNN) to predict supplier performance. The results revealed that a simple Linear Regression model achieved the highest predictive accuracy (MAE = 0.0103), outperforming more complex ensemble and deep learning models. Feature importance analysis from the Random Forest model confirmed the critical role of Sustainability Efforts and Financial Stability, validating the pre-defined weights in the original MCDM framework. The core conceptual contribution lies in demonstrating a viable pathway "from decision to prediction," establishing a novel, automated pipeline for supplier scoring that learns from expert-derived MCDM rankings. This demonstrates that robust predictive analytics can effectively streamline and enhance supplier evaluation processes in circular supply chains, with simpler models often providing superior performance on structured operational data.

Type

article

Language

en

Landing Page

https://doi.org/10.5281/zenodo.17857004

OA Status

green

OpenAlex ID

https://openalex.org/W7111341452

Raw OpenAlex JSON

OpenAlex ID

https://openalex.org/W7111341452

Canonical identifier for this work in OpenAlex

DOI

https://doi.org/10.5281/zenodo.17857004

Digital Object Identifier

Title

From Decision Models to Prediction Models: Enhancing Supplier Selection with Machine Learning and Deep Learning

Work title

Type

article

OpenAlex work type

Language

en

Primary language

Publication year

2025

Year of publication

Publication date

2025-12-08

Full publication date if available

Authors

OSHODIN, Osamudiamen David, EHICHOYA, Martins

List of authors in order

Landing page

https://doi.org/10.5281/zenodo.17857004

Publisher landing page

Open access

Yes

Whether a free full text is available

OA status

green

Open access status per OpenAlex

OA URL

https://doi.org/10.5281/zenodo.17857004

Direct OA link when available

Concepts

Artificial intelligence, Multiple-criteria decision analysis, Machine learning, Computer science, Supply chain, Support vector machine, Random forest, Pipeline (software), Artificial neural network, Deep learning, Feature selection, Decision tree, Predictive analytics, Model selection, Supplier evaluation, Lasso (programming language), Predictive modelling, Decision support system, Selection (genetic algorithm), Evidential reasoning approach, Decision model, Linear model, Gradient boosting, Data mining, Product (mathematics), Ensemble learning, Ensemble forecasting, Big data, Decision analysis, Supply chain management, Linear regression, Regression analysis, Sustainability, Boltzmann machine, AdaBoost, Curse of dimensionality, Principal component analysis, Deep belief network, TOPSIS

Top concepts (fields/topics) attached by OpenAlex

Cited by

0

Total citation count in OpenAlex

Full payload