The Taguchi method, a robust experimental design technique, establishes a strong connection between input and output variables. Known for its capacity to yield precise results with fewer trials and minimized errors, this method has gained widespread application in various fields such as engineering, physics, chemistry, economics, finance, and more. In this paper, the authors examine the importance of the Taguchi orthogonal array method, its step-by-step optimization procedure, and its potential for future applications. Through a thorough literature review, the authors investigate how the Taguchi method has been effectively employed to identify key factors influencing response variables. The versatility of the Taguchi method becomes apparent when considering its applications across diverse disciplines. Researchers in engineering have successfully utilized this technique to optimize processes and enhance product quality. Furthermore, in scientific fields like physics and chemistry, the Taguchi method has proven invaluable for conducting experiments efficiently, resulting in more accurate and reproducible outcomes. Researchers gain critical insights into the effects of factors on the response variable by employing statistical tools such as mean analysis, variance analysis, and signal-to-noise ratio. The Taguchi method remains a valuable and broadly applicable tool for optimizing experiments and identifying influential factors across multiple disciplines. This paper’s extensive literature review emphasizes its significance in various fields and outlines the step-by-step procedure to leverage its potential for optimization.

This study evaluates the capability of the ARIMA (Auto Regressive Integrated Moving Average) to predict CO₂ emissions in India using data from 1990 to 2023, addressing a critical need for accurate forecasting amid various economic and environmental uncertainties. It is observed that ARIMA yields high accuracy with respect to the prediction, and hence, it is reliable for environmental forecasting. These predictions give policymakers evidence-based information to aid in implementing sustainable climate policies within India. To ensure reliable predictions, the study methodology utilizes the Box–Jenkins approach, which encompasses model identification, estimation, and diagnostic checking. The initial step in the study is the Augmented Dickey–Fuller (ADF) test, which assesses data stationarity as a prerequisite for precise time series forecasting. Model selection is guided by the Akaike Information Criterion (AIC), which balances prediction accuracy with model complexity. The efficiency of the ARIMA model is assessed by comparing the actual observed values to the predicted CO₂ emissions and the results demonstrate ARIMA’s effectiveness in forecasting India’s CO₂ emissions, validated by statistical measures that confirm the model’s robustness. The value of the present study lies in its focused assessment of the relevance of the ARIMA model to the specific environmental and economic context of India, with actionable insight for policymakers. This study enhances prior research by incorporating a focused approach to data-driven policy formulation that increases climate resilience. The establishment of a reliable model for the forecasting of CO₂ will aspire to support informed decision making in environmental policy and help India move forward toward sustainable climate goals. This study only serves to highlight the applicability of ARIMA in terms of environment-based forecasting and permits further emphasis on how much this method can be a useful data-based tool in climate planning.

The distance to default (DD) and the probability of default (PD) are the essential credit risks in the finance world. It provides an estimate of the likelihood that a borrower will be unable to meet its debt obligations. It is crucial to know which parameter effects more on DD and PD so that investor will prevent future risks. The purpose of this study is to investigate the effects of four parameters (asset value of firm V, value of debt X, interest rate r and the volatility of asset σ at one period) on DD and PD. The Black Scholes model is used to estimate the DD and PD. To explore the effects of parameters, the author used Taguchi’s L27 orthogonal array, analysis of variance (ANOVA) and analysis of mean (ANOM), and the analysis will carry out using MINITAB software. The effect of parameters will be discussed with the main effect plot and the average response on a response plot showing the best outcomes. ANOM identified the optimal combination where the DD is a maximum, and the PD is a minimum. The percentage contribution of each input factor on DD and PD was estimated by conducting ANOVA. The above two (DD and PD) exists an inverse relationship. The rank or percentage contribution will vary with change in the data set.

The Design of Experiment (DOE) methodology is a fundamental tool for systematic inquiry and optimization in both scientific and industrial applications. The DOE’s statistical framework is designed to enhance the efficiency and reliability of experimental investigations by systematically planning, conducting, and evaluating controlled experiments. To support well-informed decision-making, process optimization, and quality improvement, the main goal of DOE is to discover and quantify the effects of various variables on a response variable. Various methods such as full factorial, fractional factorial, Taguchi, and response surface methodologies provide powerful tools for optimising processes and enhancing quality. The study covers the application of DOE in several industries, including engineering, manufacturing, agriculture, and medicine. Defect minimization, process optimization, and quality improvement are all aided by DOE in manufacturing. By determining the best dosages and formulations, it helps in drug development in the pharmaceutical industry. In the field of agriculture, DOE facilitates the identification of optimal growing conditions and techniques. It helps in the engineering and assessing new systems and products. To achieve consistency and accuracy in data collection, the experiment must be carried out with strict adherence to the experimental strategy. Analysing data involves using statistical tools to evaluate the findings and make conclusions, such as ANOVA, regression analysis, and graphical approaches. By pointing out important variables and their interactions, these studies aid in process optimization and product quality enhancement.

In today's complex financial environment, managing personal debt effectively has become a significant challenge, often leading to increased loan defaults. This study aims to develop a machine learning-based framework for personalized debt management by analyzing borrower data to identify risk levels and offer tailored financial advice. The scientific novelty of this research lies in its integration of both supervised and unsupervised learning techniques to gain deeper insights into the characteristics of defaulters and predict their risk levels. This methodology improves prediction accuracy and interpretability and applicability in real-world lending. The study offers actionable strategies for debt reduction, optimized spending, and personalized financial planning based on risk profiles. The findings can support financial institutions in refining credit risk assessment models, promoting responsible lending, and contributing to the achievement of broader sustainability goals through improved financial inclusion and stability. Unsupervised learning techniques, such as K-nearest neighbors (KNN), DBSCAN, and rule-based methods, were applied to cluster defaulters based on their risk profiles. These clustering methods allowed us to distinguish various groups of defaulters, providing a nuanced view of risk categories. Financial institutions can use these risk categories to design tailored financial products and adjust lending strategies and policies for lower-risk groups or offer guidance to higher-risk defaulters on areas needing improvement, such as increasing income or enhancing credit scores. Governments in developing countries could make the most use of this study, where most of the population lacks financial knowledge and struggles to get financial help from private institutions once they are categorized as defaulters. For instance, insurance companies have different policies for different age groups, and financial institutions can also make such policies for different risk levels that benefit both parties in the long term. Received: 4 March 2025 | Revised: 20 June 2025 | Accepted: 2 July 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support this work are available upon reasonable request to the corresponding author. Author Contribution Statement Amir Ahmad Dar: Conceptualization, Methodology, Validation, Resources, Writing – review & editing, Supervision, Project administration. Trushal Hirani: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Visualization. Vanshita Arora: Writing – review & editing, Visualization. Sukriti Kakkar: Writing – review & editing, Visualization.