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2017 | OriginalPaper | Buchkapitel

More Observations, More Variables or More Quality? - Data Acquisition Strategies to Enhance Uncertainty Analytics for Industrial Service Contracting

verfasst von : Björn Schmitz, Gerhard Satzger, Ralf Gitzel

Erschienen in: Exploring Services Science

Verlag: Springer International Publishing

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Abstract

Service business models expose industrial service providers to an increasing amount of uncertainties. In order to design profitable offerings, providers need to understand how uncertainties affect contract profitability. Both, access to data and algorithms are key requirements for accurate analyses.

While current research focuses on developing algorithms to derive insights from data that already exist, the need for strategically acquiring relevant data sets has been neglected so far. In this article, we develop a method for defining data acquisition strategies to improve uncertainty analyses for industrial service contracting. We explain how lacking observations, variables and quality of data affect uncertainty analyses, propose data acquisition strategies as a systematic plan to acquire relevant data and develop an approach for ranking acquisition strategies by measuring their acquisition effort and business benefit.

The method is applied in an industrial use case to demonstrate its benefit for assessing cost uncertainties in full-service repair contracts.

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Vorheriges Kapitel Creative Industries and Big Data: A Business Model for Service Innovation

Nächstes Kapitel From Data Science to Value Creation

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Titel: More Observations, More Variables or More Quality? - Data Acquisition Strategies to Enhance Uncertainty Analytics for Industrial Service Contracting
verfasst von: Björn Schmitz
Gerhard Satzger
Ralf Gitzel
Verlag: Springer International Publishing
Buch: Exploring Services Science
Print ISBN: 978-3-319-56924-6

Electronic ISBN: 978-3-319-56925-3

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-56925-3_13

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