{"title":"基于智能机器学习算法的早期产品成本估算","authors":"R. Lackes, J. Sengewald","doi":"10.1109/ICAIIC57133.2023.10067092","DOIUrl":null,"url":null,"abstract":"Predicting the total manufacturing costs of a new product early in its development is an obstacle for many businesses, especially when selecting between different product designs and their cost implications. Typically, material costs comprise a large part of total manufacturing costs, and therefore obtaining an early estimate of material costs can help businesses in predicting the total manufacturing costs more accurately. At the early stage of product development, with many imponderables and frequent design modifications, it would be impractical to obtain quotations from suppliers. We, therefore, developed a two-stage machine learning scheme estimating the material cost to guide alternative product design choices that yield a lower total manufacturing cost. Our innovative two-stage technique for cost estimation is meant to overcome this issue. In this paper, we demonstrate that neural networks, a prevalent technique in the literature, can be enhanced by adding the concept of modularity to the estimation of the pricing of technical components already during the design process of a new product.","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Early Product Cost Estimation by Intelligent Machine Learning Algorithms\",\"authors\":\"R. Lackes, J. Sengewald\",\"doi\":\"10.1109/ICAIIC57133.2023.10067092\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Predicting the total manufacturing costs of a new product early in its development is an obstacle for many businesses, especially when selecting between different product designs and their cost implications. Typically, material costs comprise a large part of total manufacturing costs, and therefore obtaining an early estimate of material costs can help businesses in predicting the total manufacturing costs more accurately. At the early stage of product development, with many imponderables and frequent design modifications, it would be impractical to obtain quotations from suppliers. We, therefore, developed a two-stage machine learning scheme estimating the material cost to guide alternative product design choices that yield a lower total manufacturing cost. Our innovative two-stage technique for cost estimation is meant to overcome this issue. In this paper, we demonstrate that neural networks, a prevalent technique in the literature, can be enhanced by adding the concept of modularity to the estimation of the pricing of technical components already during the design process of a new product.\",\"PeriodicalId\":105769,\"journal\":{\"name\":\"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-02-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICAIIC57133.2023.10067092\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAIIC57133.2023.10067092","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Early Product Cost Estimation by Intelligent Machine Learning Algorithms
Predicting the total manufacturing costs of a new product early in its development is an obstacle for many businesses, especially when selecting between different product designs and their cost implications. Typically, material costs comprise a large part of total manufacturing costs, and therefore obtaining an early estimate of material costs can help businesses in predicting the total manufacturing costs more accurately. At the early stage of product development, with many imponderables and frequent design modifications, it would be impractical to obtain quotations from suppliers. We, therefore, developed a two-stage machine learning scheme estimating the material cost to guide alternative product design choices that yield a lower total manufacturing cost. Our innovative two-stage technique for cost estimation is meant to overcome this issue. In this paper, we demonstrate that neural networks, a prevalent technique in the literature, can be enhanced by adding the concept of modularity to the estimation of the pricing of technical components already during the design process of a new product.