The optimization of resources and reduction of costs through efficient inventory management are paramount to organizational success. This study undertakes a comparative analysis of two distinct forecasting methodologies, Exponential Smoothing (ES) and Gradient Boosting (GB), within the framework of materials forecasting aimed at inventory minimization. Our study introduces innovation by methodically scrutinizing these approaches within a unified framework, shedding light on their merits and shortcomings. This comparative analysis gives practitioners a practical roadmap for the optimal forecasting strategy to streamline inventory management operations. Methodologies are evaluated based on their efficiency in predicting material demand, encompassing metrics such as accuracy, computational efficiency, and suitability across various inventory management scenarios. Response surface methodology entails refining processes to modify factorial variables’ configurations to attain a desired peak or trough in response. The SPSS results show that the ES method has 43.20%, surpassing the accuracy of the inventory optimization model, which stood at 65.08%. The response surface methodology results show that 45.20% profit was achieved for the variable and operational cost process parameters. This research seeks to unveil the traces of each method, facilitating decision-makers in selecting an optimal forecasting strategy tailored to their specific inventory management requirements. The analysis shows that the ES method surpasses the accuracy of the GB machine learning for material forecasting to minimize inventory.

Offering intended products at an affordable price is a highly challenging task in a business environment. To meet such challenges, industrial experts and researchers have different approaches, such as shifting the purchase of materials from import mode to local vendors and optimizing machining cost by optimizing process parameters, waste reduction, rework reduction, and technological improvements. The novelty of this study lies in reducing material cost by accurate materials forecasting to minimize storage cost, ablation cost, average annual consumption cost, etc. This study aims to compare the efficacy of the Seasonal Decomposition Method (SDM) against Multi-Echelon Inventory Optimization (MEIO) for minimizing inventory through accurate materials forecasting. Through rigorous evaluation and analysis, this research seeks to clarify the strengths and weaknesses of each approach, thereby providing insights into their applicability and effectiveness in addressing inventory management challenges across diverse seasonal demand patterns. The sample size was taken as 50 per group. The G-power applied is about 80%. The significance value obtained is 0.001 (p < 0.05), indicating a statistically significant difference between the two algorithms used for inventory reduction and materials forecasting. SDM (63.43) outperforms MEIO (48.57) in terms of accuracy. SDM yielded a better accuracy when compared to MEIO for inventory minimization and materials forecasting.

This study examines the effect of different infill patterns and percentages on the compressive strength attributes of carbon fiber-reinforced PETG samples printed using fused deposition modeling, employing response surface methodology. Carbon fiber-enhanced PETG (polyethylene terephthalate glycol) composites represent a cutting-edge advancement in additive manufacturing, drawing significant interest due to their impressive mechanical attributes. The experimentation involves modifying printing parameters such as the infill pattern (tri-hexagon, cubic, or line) and infill density (40%, 60%, and 80%). These parameter values were obtained through a central composite experimental design utilizing response surface methodology. The compressive strength of the 3D-printed carbon fiber-reinforced PETG specimens is assessed following ASTM D695 standards. Research indicates that increasing the density of the infill results in enhanced compressive strength. Specifically, specimens featuring an 80% infill density with a tri-hexagon pattern demonstrate a notable compressive strength of 39.16 MPa. By employing regression analysis and optimization techniques, the study predicts experimental outcomes accurately. These findings offer valuable insights into refining the manufacturing process of carbon fiber-reinforced PETG components. This advancement holds potential benefits across various engineering fields, particularly in automotive and aerospace industries, where strength and durability are essential.

The purpose of this study is to examine the mechanical properties of components produced through the Response Surface Methodology for polylactic acid, utilizing the Fused Deposition Modeling 3D printing technique. Polylactic acid is a commonly employed biodegradable polymer, making it a desirable substance for diverse applications. This study involves carrying out experiments to vary process printing parameters like layer height or thickness, part orientation, and infill density. The values of these parameters were obtained using a Response Surface Methodology Box–Behnken experimental design. The mechanical performance of the 3D Printed polylactic acid fabricated was assessed by evaluating their flexural and tensile strength. The test samples for measuring tensile and flexural strength are fabricated according to American Society for Testing and Material standards. The findings suggest that higher strength is achieved when using increased layer height and infill levels. The experimental results indicated that specimens with a filling ratio of 80% exhibited greater tensile strength, while the flexural strength of samples with 50% infill was observed to be higher. Regression analyses and multi-optimization techniques were employed to predict the experimental results. This study provides valuable insights that can significantly impact various industries. Our research on the complex interactions between process variables and mechanical properties has major implications for improving high-strength component manufacturing. As demand for dependable and efficient 3D-printed materials rises, our discoveries improve material design and manufacturing methods, making a significant contribution to the field.

Poor planning and scheduling increase buying, storage, and obsolescence expenses. Material shortages increase labor, machine optimum time, etc. Industrial raw materials, semi-finished items, spares, and consumables have distinct consumption patterns, reorder points, purchase lead times, quantity limits, discounts, etc. To save money, machine learning predicts demand and prepares materials. This study employs ARIMA or Support Vector Machine (SVM) machine learning-based forecasting approaches to forecast materials for less inventory. Feature engineering eliminates seasonality, time series, and external demand and ignores data irregularities, missing figures, and disparities. This approach needs to adapt traits to factors, separate test and training data, and consider many future models to represent the best forecasts. Forecast reliability and consistency were examined for each model. Inventory management systems were evaluated for computational complexity and installation ease and found implementation issues. Both models’ input data and resilience were examined using sensitivity analysis. Accurate prediction SVM and ARIMA predict material demand differently. Meaningful statistics show the optimal model. Performance differences between SVM and ARIMA enhance model selection. Thinking about the execution of high inventory system integration and computational complexity, response surface methodology chooses factorial variables with the highest or lowest responses. Analysis of variance, factor analysis, and effect modeling expansions demonstrated for the response.