Assessing the Value of Data: An Approach to Evaluate the Technology Driven Benefits of Smart Product Data

G. Schuh, Ramon Kreutzer, Marc Patzwald
{"title":"Assessing the Value of Data: An Approach to Evaluate the Technology Driven Benefits of Smart Product Data","authors":"G. Schuh, Ramon Kreutzer, Marc Patzwald","doi":"10.23919/PICMET.2017.8125431","DOIUrl":null,"url":null,"abstract":"Data-centered and customer-oriented business models shape the digitalization to be the central social, economic and technological development of current knowledge societies. A great share of these new business models is enabled by smart product field data, which is generated during the product's phase of utilization. To capture the diluting value and commodification of mechanical products, companies within the manufacturing industry as the provider of smart products have to position themselves strategically against new competitors, e.g. from the IT sector, to avoid being victim to the \"digital Darwinism\". A growing number of manufacturing companies already starts to see the revenue and differentiation potential of smart product data as a competitive advantage. Similar to other intangible assets, a comprehensive understanding of the quality, scope and value of smart product field data is a basic prerequisite for its monetization. However, so far the majority of companies are lacking this basic understanding for the value of their smart product data, thus not being able to lever its potentials. Hence, this paper develops a model, which supports manufacturing companies in assessing, if a generic set of field data generated by a smart product is able to provide value added for the user. In the model itself, field data of smart products as well as \"digitally enabled functionalities\" will be examined extensively. Subsequently, generic sets of field data as carrier of information will be mapped regarding their suitability for enabling functionalities, which represent a demand of information.","PeriodicalId":438177,"journal":{"name":"2017 Portland International Conference on Management of Engineering and Technology (PICMET)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 Portland International Conference on Management of Engineering and Technology (PICMET)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/PICMET.2017.8125431","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

Data-centered and customer-oriented business models shape the digitalization to be the central social, economic and technological development of current knowledge societies. A great share of these new business models is enabled by smart product field data, which is generated during the product's phase of utilization. To capture the diluting value and commodification of mechanical products, companies within the manufacturing industry as the provider of smart products have to position themselves strategically against new competitors, e.g. from the IT sector, to avoid being victim to the "digital Darwinism". A growing number of manufacturing companies already starts to see the revenue and differentiation potential of smart product data as a competitive advantage. Similar to other intangible assets, a comprehensive understanding of the quality, scope and value of smart product field data is a basic prerequisite for its monetization. However, so far the majority of companies are lacking this basic understanding for the value of their smart product data, thus not being able to lever its potentials. Hence, this paper develops a model, which supports manufacturing companies in assessing, if a generic set of field data generated by a smart product is able to provide value added for the user. In the model itself, field data of smart products as well as "digitally enabled functionalities" will be examined extensively. Subsequently, generic sets of field data as carrier of information will be mapped regarding their suitability for enabling functionalities, which represent a demand of information.
评估数据的价值:一种评估智能产品数据技术驱动效益的方法
以数据为中心和以客户为导向的商业模式使数字化成为当今知识社会的核心社会、经济和技术发展。这些新业务模式的很大一部分是由智能产品现场数据实现的,这些数据是在产品的使用阶段生成的。为了抓住机械产品的稀释价值和商品化,作为智能产品供应商的制造业公司必须在战略上定位自己,以对抗新的竞争对手,例如来自IT行业的竞争对手,以避免成为“数字达尔文主义”的受害者。越来越多的制造企业已经开始将智能产品数据的收入和差异化潜力视为一种竞争优势。与其他无形资产类似,全面了解智能产品领域数据的质量、范围和价值是其货币化的基本前提。然而,到目前为止,大多数公司都缺乏对其智能产品数据价值的基本理解,因此无法发挥其潜力。因此,本文开发了一个模型,该模型支持制造公司评估智能产品生成的通用现场数据集是否能够为用户提供附加值。在模型本身中,将广泛检查智能产品的现场数据以及“数字化功能”。随后,将对作为信息载体的一般现场数据集进行映射,以确定它们是否适合实现代表信息需求的功能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信