{"title":"工程系统:同类最佳/同类最差","authors":"Donald M. Beckett","doi":"10.1080/1941658X.2014.922905","DOIUrl":null,"url":null,"abstract":"Measurement’s goal is to help assess performance—to determine which methods are productive or counterproductive. Metrics are tools used to identify and implement practices that lower costs, reduce time to market, and improve product quality. But process improvement is not accomplished through measurement or metrics alone. Rather, one must use the data to make conscious decisions that change the way business is done. In fact, one of best ways to make those decisions is by studying the characteristics of best- and worst-in-class software projects. Referencing Quantitative Software Management’s database of 10,000+ completed software projects, this article evaluates the common factors that define the most and least successful engineering projects—drawn from the database’s System Software, Scientific, Telecom, and Command and Control application domains. Presenting a thorough analysis of project staffing, effort, duration, cost, and quality data, this article gives project managers a solid, scientific framework for evaluating potential projects and identifying winning strategies.","PeriodicalId":390877,"journal":{"name":"Journal of Cost Analysis and Parametrics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Engineering Systems: Best-in-Class/Worst-in-Class\",\"authors\":\"Donald M. Beckett\",\"doi\":\"10.1080/1941658X.2014.922905\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Measurement’s goal is to help assess performance—to determine which methods are productive or counterproductive. Metrics are tools used to identify and implement practices that lower costs, reduce time to market, and improve product quality. But process improvement is not accomplished through measurement or metrics alone. Rather, one must use the data to make conscious decisions that change the way business is done. In fact, one of best ways to make those decisions is by studying the characteristics of best- and worst-in-class software projects. Referencing Quantitative Software Management’s database of 10,000+ completed software projects, this article evaluates the common factors that define the most and least successful engineering projects—drawn from the database’s System Software, Scientific, Telecom, and Command and Control application domains. Presenting a thorough analysis of project staffing, effort, duration, cost, and quality data, this article gives project managers a solid, scientific framework for evaluating potential projects and identifying winning strategies.\",\"PeriodicalId\":390877,\"journal\":{\"name\":\"Journal of Cost Analysis and Parametrics\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-05-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Cost Analysis and Parametrics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/1941658X.2014.922905\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Cost Analysis and Parametrics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/1941658X.2014.922905","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Measurement’s goal is to help assess performance—to determine which methods are productive or counterproductive. Metrics are tools used to identify and implement practices that lower costs, reduce time to market, and improve product quality. But process improvement is not accomplished through measurement or metrics alone. Rather, one must use the data to make conscious decisions that change the way business is done. In fact, one of best ways to make those decisions is by studying the characteristics of best- and worst-in-class software projects. Referencing Quantitative Software Management’s database of 10,000+ completed software projects, this article evaluates the common factors that define the most and least successful engineering projects—drawn from the database’s System Software, Scientific, Telecom, and Command and Control application domains. Presenting a thorough analysis of project staffing, effort, duration, cost, and quality data, this article gives project managers a solid, scientific framework for evaluating potential projects and identifying winning strategies.