Deconstructing Risk Factors for Predicting Risk Assessment in Supply Chains Using Machine Learning

Abstract

Risk management is an ongoing process that includes several stages of mapping and identification, analysis, and evaluation, planning, and implementation to reduce risks and ensure ongoing control. Risk management along the supply chains has become more significant in recent years due to an increased complexity of the relationships between components in the chain as well as various disruptions such as climate change, COVID-19, or geo-political scenarios. The current literature alongside the increase in complexity and frequency of risk events, leads us to the single, most prominent challenge in risk management today: the auditor’s subjectivity in determining the risk levels. Simply stated, two different auditors may assess a given situation differently due to their specific history and experience. Specifically, it seems to be extremely difficult to find cases in which different auditors, working on the same organization, made the same risk assessment. With that in mind, this research aims to reduce the human subjectivity bias and reach a risk evaluation that is as objective as possible, by using the machine learning approach. For this aim the paper introduces a new risk assessment framework based on factors analysis and artificial neural network as the predictive model. We first introduced a new approach of deconstructing the risk factors into their basic elements and analyzing them as a feature vector. Next, we collected unique, real-world data of risk surveys and audit reports from 60 industrial companies of various industries (from plastic and metal factories to logistic and medical devices companies). Lastly, we constructed a neural network to predict the risk levels of operational processes in the industry. We trained our model on 42 samples and managed to achieve a R2 score of 0.9 on the test set of 18 samples. Our model was validated and managed to predict the risk accuracy with R = 0.95 in accordance with the human auditor results.

Uncertainty Reduction in Operational Risk Management Process

“…The risk matrix is the primary tool for assessing and prioritizing risks based on their likelihood and impact today (Burstein and Zuckerman 2023). It visually represents potential risks, allowing organizations to make informed decisions about allocating resources for risk mitigation.…”

“…Assessments of likelihood and consequence are the basic steps in determining the calculated risk score level in the matrices (risk = likelihood × consequence) (Burstein and Zuckerman 2023). Due to the biases, any small error in the likelihood directly affects the computed result.…”

“…Our research is based on developing a simple-to-apply framework for deconstructing the likelihood factor into its essential elements of sub-factors. The intuition behind the segmentation into sub-factors arose due to the understanding that some sources of uncertainty are related to an aleatory process and some lack of knowledge regarding the process parameters (Burstein and Zuckerman 2023). We can also relate free will and human factor interests and judgment as a source of uncertainty and unpredictability.…”

The Emotion Magnitude Effect: Navigating Market Dynamics Amidst Supply Chain Events

“…Rather than just looking at pairs of companies, we connect emotional magnitude to the supply chain through commodities and companies impacted to derive more complex relationships. We further leverage insights and techniques from Burstein and Zuckerman (2023) to deepen our understanding of supply chain risks for companies. This approach enables us to create a more comprehensive view of the impacts of supply chain events.…”

A Novel Framework of Public Risk Assessment Using an Inte-Grated Approach Based on AHP and Psychometric Paradigm

“…Researchers from various disciplinary backgrounds (e.g., management, psychology, engineering, environmental sciences, and statistics) have been performing risk analysis from two main perspectives: risk assessment and risk management. Researchers within the risk assessment stream have focused on identifying and quantifying risks and hazards associated with induvial and organizational activities [1][2][3][4][5]. In comparison, the latter stream of research is focused on mitigating and communicating risks and decision making under uncertainty [6][7][8][9][10].…”