{"title":"公司范围内的差异减少","authors":"L. Hare","doi":"10.1080/08982112.2022.2132403","DOIUrl":null,"url":null,"abstract":"Abstract We illustrate the Statistical Engineering (SE) approach to the solution of large unstructured problems through some years-long experiences with improving productivity and quality using only a relatively small technical staff to elicit a major manufacturing culture change. The invention of a program for Process Variation Reduction (PVR) paralleling the SE steps of opportunity identification, problem definition, operating within the organizational context, strategy development, identification of tactics and creating sustainable solutions, enabled staff at all levels to drive down costs while improving customer satisfaction. Examples here involve advances in the food industry, but the variation reduction planning and strategies are applicable to most organizations.","PeriodicalId":20846,"journal":{"name":"Quality Engineering","volume":"34 1","pages":"482 - 488"},"PeriodicalIF":1.3000,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Corporation-wide variation reduction\",\"authors\":\"L. Hare\",\"doi\":\"10.1080/08982112.2022.2132403\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract We illustrate the Statistical Engineering (SE) approach to the solution of large unstructured problems through some years-long experiences with improving productivity and quality using only a relatively small technical staff to elicit a major manufacturing culture change. The invention of a program for Process Variation Reduction (PVR) paralleling the SE steps of opportunity identification, problem definition, operating within the organizational context, strategy development, identification of tactics and creating sustainable solutions, enabled staff at all levels to drive down costs while improving customer satisfaction. Examples here involve advances in the food industry, but the variation reduction planning and strategies are applicable to most organizations.\",\"PeriodicalId\":20846,\"journal\":{\"name\":\"Quality Engineering\",\"volume\":\"34 1\",\"pages\":\"482 - 488\"},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2022-11-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Quality Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1080/08982112.2022.2132403\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, INDUSTRIAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Quality Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/08982112.2022.2132403","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
Abstract We illustrate the Statistical Engineering (SE) approach to the solution of large unstructured problems through some years-long experiences with improving productivity and quality using only a relatively small technical staff to elicit a major manufacturing culture change. The invention of a program for Process Variation Reduction (PVR) paralleling the SE steps of opportunity identification, problem definition, operating within the organizational context, strategy development, identification of tactics and creating sustainable solutions, enabled staff at all levels to drive down costs while improving customer satisfaction. Examples here involve advances in the food industry, but the variation reduction planning and strategies are applicable to most organizations.
期刊介绍:
Quality Engineering aims to promote a rich exchange among the quality engineering community by publishing papers that describe new engineering methods ready for immediate industrial application or examples of techniques uniquely employed.
You are invited to submit manuscripts and application experiences that explore:
Experimental engineering design and analysis
Measurement system analysis in engineering
Engineering process modelling
Product and process optimization in engineering
Quality control and process monitoring in engineering
Engineering regression
Reliability in engineering
Response surface methodology in engineering
Robust engineering parameter design
Six Sigma method enhancement in engineering
Statistical engineering
Engineering test and evaluation techniques.