From Qualitative to Quantitative Data Valuation in Manufacturing Companies

Stein, Hannah; Holst, Lennard; Stich, Volker; Maaß, Wolfgang

doi:10.1007/978-3-030-85902-2_19

Cited by 8 publications

(15 citation statements)

References 20 publications

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“…Individual criteria are additionally categorised into quantitative and qualitative groups. Whereas multiple distinctions are possible to group criteria, such as non-monetary and monetary, qualitative and quantitative differentiation is another way to analyse data and assess models [47]. Quantitative criteria are based on numbers, and are measurable.…”

Section: Resultsmentioning

confidence: 99%

Additive vs. Conventional Manufacturing of Metal Components: Selection of the Manufacturing Process Using the AHP Method

et al. 2022

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Additive manufacturing (AM) processes have experienced significant technological developments over the past decade. Today, 3D-printed metal parts can almost achieve the mechanical properties of conventionally manufactured components; process times have been shortened, and the range of available materials has been widely expanded. The decision between conventional manufacturing and AM is therefore becoming more complex, considering technical and economic criteria along the entire product life cycle. To reflect the vision of the manufacturer, each decision needs to be based on individual preferences and strategies. The present research introduces a standardised and systemised multi-criteria decision-making process to choose between additive and conventional production. Multi-criteria decision models from within the literature are analysed and a holistic decision matrix is developed based on the analytic hierarchy process (AHP). The key novelty of the present research is the consideration of technical and economic categories along the whole product life cycle for decision making. The matrix allows an individual weighting of individual criteria along the product life cycle, starting with the conceptualisation of the product, and ending with marketing and after-sales. The approach is evaluated using two scenarios, including a control unit housing and a flat metal gasket, with different scopes of application. In conclusion, the developed multi-criteria decision matrix provides sufficient and repeatable results. The systematic decision process allows users to clearly identify the best production method for their individual use case.

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Section: Resultsmentioning

confidence: 99%

Additive vs. Conventional Manufacturing of Metal Components: Selection of the Manufacturing Process Using the AHP Method

et al. 2022

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“…As a third characteristic in the data valuation tooling dimension, a combination of interpersonal elaboration as well as models and applications is introduced, since particularly practical research approaches suggest these combined data valuation approaches [34,62].…”

Section: Data Valuation Toolingmentioning

confidence: 99%

“…This section assesses the DVBC taxonomy's utility and exempli es its intended usage, as suggested by [19]. In order to test our DVBC taxonomy, we have selected two contemporary data value determination approaches that follow a more technology-supported approach by [35] on the one hand and a more ecosystem-supported multilayered approach by [34] on the other hand. Figure 5 illustrates the ndings of the application, which are further described in Sections 5.1 and 5.2.…”

Section: Application the Dvdc Taxonomymentioning

confidence: 99%

“…Figure 1 Systematic literature review process based on [39,40] Figure 2 Taxonomy development method for information systems by [19]applied to this paper Application of the DVBC taxonomy to data valuation approaches by [34,35] Supplementary Files This is a list of supplementary les associated with this preprint. Click to download.…”

Section: Figuresmentioning

confidence: 99%

“…This data value can be of social-ecological, economical [25,26], functional and/or symbolic nature [10,26] with the purpose of adding a measurable business value [13]. Thereby, data value is determined by a multitude of value drivers [27,28], underlying theories [29][30][31][32][33], as well as frameworks [34,35].…”

Section: Data Valuation Business Capabilitymentioning

confidence: 99%

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Towards a taxonomy for business capabilities determining data value

Hafner¹,

Silva²

2023

Preprint

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Data and its valuation become increasingly crucial for enterprises and academia, which coincides a multitude of data valuation approaches including numerous affected focus areas, dimensions, and characteristics. Therefore, this paper analyzes different approaches to determine data value from a business capability perspective according to the TOGAF standard. Specifically, this paper deals with (a) the development of a taxonomy for data valuation business capabilities (DVBC) as well as (b) the taxonomy validation by the use of existing data valuation approaches. The applied methodologies are taxonomy development techniques for information systems, which are based on a previously executed systematic literature review with a sample size of 67 articles. Further, the data valuation business capability taxonomy is validated through applying two recent data valuation approaches from academia. As a result, the taxonomy developed consists of four business capability layers, nine dimensions, and 36 characteristics. The characteristics are of exclusive or non-exclusive nature, depending on their meaningfulness, and are validated by the successful application of two data valuation approaches. Compiled findings meet both objective and subjective quality standards. With the developed DVBC taxonomy, scholars and professionals are equipped with a tool to classify and structure their data valuation endeavors. In addition, the DVBC taxonomy bridges the domains of information systems, enterprise architecture management, and data management to serve as a foundation for interdisciplinary value generation with and through data.

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Bewertung von Unternehmensdatenbeständen: Wege zur Wertermittlung des wertvollsten immateriellen Vermögensgegenstandes

Stein¹,

Woud²,

Holuch³

et al. 2022

Datenwirtschaft Und Datentechnologie

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ZusammenfassungDieser Beitrag stellt dar, welche Chancen und Herausforderungen mit der Bewertung von Daten sowie der Abbildung monetärer Datenwerte verbunden sind und geht auf mögliche Lösungsansätze zur Bewertung von Unternehmensdatenbeständen, insbesondere im Kontext der industriellen Produktion, ein. Zunächst werden Grundlagen zur Charakterisierung, Nutzung und Verwertung von Daten sowie bestehende Methoden zur Bewertung von immateriellen Vermögensgegenständen dargestellt. Darauf aufbauend werden Chancen und Herausforderungen spezifiziert, potenzielle Lösungsansätze zur Datenbewertung abgeleitet und anschließend Anforderungen für die Datenbewertung beschrieben sowie die nutzenorientierte Datenbewertung skizziert.

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From Qualitative to Quantitative Data Valuation in Manufacturing Companies

Cited by 8 publications

References 20 publications

Additive vs. Conventional Manufacturing of Metal Components: Selection of the Manufacturing Process Using the AHP Method

Additive vs. Conventional Manufacturing of Metal Components: Selection of the Manufacturing Process Using the AHP Method

Towards a taxonomy for business capabilities determining data value

Bewertung von Unternehmensdatenbeständen: Wege zur Wertermittlung des wertvollsten immateriellen Vermögensgegenstandes

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