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dc.contributor.authorMeierhofer, Jürg-
dc.contributor.authorBenedech, Rodolfo Andres-
dc.contributor.authorSchweiger, Lukas-
dc.date.accessioned2023-07-21T07:15:24Z-
dc.date.available2023-07-21T07:15:24Z-
dc.date.issued2023-05-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/28333-
dc.description.abstractIntroduction / purpose: Smart services in an industrial context have the purpose of creating mutual value for the diverse actors of a business ecosystem (Baines & Lightfoot, 2013; Rapaccini & Adrodegari, 2022). To enable sustained value creation in dynamically changing contexts, managers must make decisions at different levels about allocating or reconfiguring service-oriented resources (Meierhofer et al., 2021). On an operational level, event-driven decisions, such as specific maintenance actions on equipment, are required (Schweiger et al., 2022). On a strategic level, there are decisions about resource reconfigurations, e.g., for adjusting the structure or capabilities of the service resources to a changing context. The decisions to allocate or reconfigure resources may have significant implications on operational costs or customer performance, thus customer satisfaction and loyalty (Schweiger & Meierhofer, 2023). Therefore, correcting decisions after their implementation generates high costs. With the data available in digitally enabled service systems, i.e., smart service systems, the decision makers can be supported by data-based models to a large extent and thus take better informed decisions, which reduces the risks for operational costs and customer performance. However, utilizing data-driven decisions represents a high hurdle to overcome, in particular for SMEs (Kugler, 2020). The purpose of this paper is therefore to develop a decision support framework that enables SMEs to overcome the hurdles for utilizing data-driven decision making in the design and operations of industrial smart services. Theoretical background: The management decision process creates value through the following steps: (a) elaborating possible decision options, (b) assessing these options qualitatively or quantitatively, and (c) choosing the option with the best assessment. Qualitative management judgment can supplement quantitative assessment(Holsapple, 2008). Service-Dominant Logic (S-D L) can be used to conceptualize management decision support as a service (Vargo & Lusch, 2008). Management is supported by smart services in completing these decision jobs. With data-driven decision support services, quantifying the value provided can be traced back to the question of the value of applying data for deciding on service configurations (Meierhofer et al., 2022). Research methodology: A quantitative online study was conducted based on a previous study on assessing the value of data and a series of interviews (Meierhofer et al., 2022). To support the need for decision support in smart service context, the results were used to develop a decision support procedure to help companies make better-informed decisions in the right context. The new procedure is being applied in specific cases to validate it. Findings (actual / expected): SMEs recognize the importance of data for informing their decisions, but lack methodologies for quantitative decision support. To support this decision procedure and keep the costs of decision making manageable for SMEs, we developed a multi-step decision support framework. It starts with a qualitative analysis of the decision problem, followed by quantitative steps. In the first quantitative step, a rough calculation of the mutual value creation for different decision scenarios is derived based on system models of the service processes. On the strategic level, for example, this comprises different service configurations and their contribution to mutual value creation in the ecosystem. If this rough quantification does not provide sufficient actionable insights or detailed operational decision parameters need to be found, one moves to the second quantitative step of the procedure. Here, a more refined simulation model comprising the specific service processes is developed, allowing for the comparison of different service scenarios in detail. In a final step, one selects the optimum decisions. This step is semi-quantitative, as it combines the quantified outcomes of the models with human experience taking into account factors that cannot reflect technical data (Rowley, 2007). Theoretical and practical contributions: The new decision support framework for smart services integrates service value creation concepts with quantitative modeling of business processes into a procedural framework. It thus advances the methodology for designing and operating smart service systems. Moreover, the framework has been developed and validated with industrial partners, making it designed to be applicable by practitioners in their business environments.de_CH
dc.language.isoende_CH
dc.publisherAston Universityde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectSmart servicesde_CH
dc.subjectDecision supportde_CH
dc.subjectQuantitative modellingde_CH
dc.subjectSimulationde_CH
dc.subject.ddc658.403: Entscheidungsfindung, Informationsmanagementde_CH
dc.titleData driven decision support framework for industrial smart services design and operationsde_CH
dc.typeKonferenz: Posterde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
zhaw.publisher.placeBirminghamde_CH
zhaw.conference.detailsSpring Servitization Conference (SSC), Helsinki, Finland, 8-9 May 2023de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end221de_CH
zhaw.pages.start220de_CH
zhaw.parentwork.editorBigdeli, Ali Z.-
zhaw.parentwork.editorKohtamäki, Marko-
zhaw.parentwork.editorRabetino, Rodrigo-
zhaw.parentwork.editorBaines, Tim-
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewPeer review (Abstract)de_CH
zhaw.title.proceedingsThe Role of Servitization in Grand Challengesde_CH
zhaw.funding.zhawData Sharing Frameworkde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Meierhofer, J., Benedech, R. A., & Schweiger, L. (2023). Data driven decision support framework for industrial smart services design and operations [Conference poster]. In A. Z. Bigdeli, M. Kohtamäki, R. Rabetino, & T. Baines (Eds.), The Role of Servitization in Grand Challenges (pp. 220–221). Aston University.
Meierhofer, J., Benedech, R.A. and Schweiger, L. (2023) ‘Data driven decision support framework for industrial smart services design and operations’, in A.Z. Bigdeli et al. (eds) The Role of Servitization in Grand Challenges. Birmingham: Aston University, pp. 220–221.
J. Meierhofer, R. A. Benedech, and L. Schweiger, “Data driven decision support framework for industrial smart services design and operations,” in The Role of Servitization in Grand Challenges, May 2023, pp. 220–221.
MEIERHOFER, Jürg, Rodolfo Andres BENEDECH und Lukas SCHWEIGER, 2023. Data driven decision support framework for industrial smart services design and operations. In: Ali Z. BIGDELI, Marko KOHTAMÄKI, Rodrigo RABETINO und Tim BAINES (Hrsg.), The Role of Servitization in Grand Challenges. Conference poster. Birmingham: Aston University. Mai 2023. S. 220–221
Meierhofer, Jürg, Rodolfo Andres Benedech, and Lukas Schweiger. 2023. “Data Driven Decision Support Framework for Industrial Smart Services Design and Operations.” Conference poster. In The Role of Servitization in Grand Challenges, edited by Ali Z. Bigdeli, Marko Kohtamäki, Rodrigo Rabetino, and Tim Baines, 220–21. Birmingham: Aston University.
Meierhofer, Jürg, et al. “Data Driven Decision Support Framework for Industrial Smart Services Design and Operations.” The Role of Servitization in Grand Challenges, edited by Ali Z. Bigdeli et al., Aston University, 2023, pp. 220–21.


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