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.

The purpose of this study was to see how silane treatment affected the tensile and impact strength of composites constructed from jute/kenaf/glass fibers with a nano-graphene filler and to investigate the impact of major treatment factors, such as silane concentration, immersion duration, and nano-filler, on composite characteristics. To conduct systematic trials and improve these variables, the Response Surface Methodology (RSM) with central composite designs was used. To precisely forecast the tensile and impact strength of the nano-composite following silane treatment, quadratic models were built. By changing the silane concentration, immersion period, and nano-filler, they discovered ideal conditions for increasing tensile strength. The best ranges for silane concentration and immersion duration were discovered to be 15 wt. % and 30 min, respectively. Given the conditions, the composite impact strength increased by 51% and its tensile strength improved by 22% as compared to the values achieved from RSM optimization. These results highlight the practical importance of silane treatment, especially in improving tensile and impact strength and strengthening the interfacial adhesive characteristics of organic fibers and polymer matrices.

In recent era, the creation of electronic contraptions is expanding step by step, which leads to tremendous electronic waste. This is the condition drives as a result of the current individuals expecting new innovation in a brief time. This circumstance makes the manufacturers in a circumstance to create the new item in a brief time, so the current items need to either be disposed or dismantled. In this circumstance the manufactures wanted to re-manufacture the end-of-life (E-O-L) items to meet the component necessity in the new items production. In this sense in our previous work we exhibited a methodology to choose the optimal number of take back product, so that the aggregate expense needed for the reverse logistic (RL) can be lessened. In this paper, we exhibited a novel methodology for the RL operation of E-O-L items, in view of an optimal scheduling algorithm. In the previous work we have located the optimal number of take back E-O-L products for the disassembly-to-order (D-T-O), and this paper displayed the procedure to legitimately schedule the machines to disassemble the products, so that the aggregate time as well as the total cost needed for the RL operation can be diminished. In the proposed structure artificial bee colony (ABC) algorithm is utilized. The proposed framework drives the preferred performance over the existing scheduling algorithms.