# A fuzzy ZE-number group decision-making framework using BWM and MABAC for risk assessment in medicinal plant extraction

## TL;DR

This paper introduces a new risk assessment framework using fuzzy logic and decision-making methods to improve the extraction of bioactive compounds from medicinal plants.

## Contribution

The study proposes a novel ZE-number-based decision-making framework combining BWM and MABAC for risk assessment in medicinal plant extraction.

## Key findings

- Sample contamination and improper handling are identified as major risks affecting extraction efficiency and product quality.
- ZE-BWM and ZE-MABAC methods effectively prioritize critical risks in the extraction process.
- Standardization, quality control, and real-time monitoring are recommended to mitigate identified risks.

## Abstract

The extraction of bioactive compounds from medicinal plants is a crucial process in pharmaceutical and herbal medicine industries, yet it presents numerous challenges that can compromise quality, efficiency, and standardization. This study integrates Failure Mode and Effects Analysis (FMEA) with an advanced multi-criteria decision-making framework based on ZE-numbers, employing the ZE-based Best–Worst Method (ZE-BWM) to determine the relative importance of risk criteria and the ZE-MABAC method to rank the most critical risks in medicinal plant extraction. By incorporating decision makers’ assessments, fuzzy logic, and reliability factors, this study enhances traditional risk assessment approaches by accounting for uncertainty and expert reliability, leading to a more precise decision-making framework. The findings indicate that sample contamination, improper handling, suboptimal extraction conditions, and instrumental calibration errors are the most significant failure modes affecting extraction efficiency and product quality. Sample-related risks, such as inconsistencies in raw material quality and contamination, can lead to variability in bioactive compound concentrations. Similarly, inefficient extraction techniques and poor solvent selection impact the purity and yield of the final product, while instrumental errors and inadequate calibration introduce measurement inconsistencies. To mitigate these risks, process standardization, rigorous quality control, and personnel training are essential. Additionally, integrating real-time monitoring systems, sustainable extraction techniques, and AI-driven predictive models can further enhance extraction reliability and efficiency. This research advances risk assessment methodologies by offering a structured and data-driven framework for optimizing medicinal plant extraction, ensuring consistency, and improving product quality. Future studies should focus on automation, real-time risk monitoring, and green extraction technologies to further enhance extraction processes.