Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
ATZ-Fachkongress

Chassis.tech plus 2024


4 Kongresse in einer Veranstaltung

Treffen Sie in München die Fachleute von Chassis, Lenkung, Bremsen sowie Reifen/Räder.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
An Adaptive Scheduling Method Based on Cloud Technology: A Structural Steelwork Industry Case Study Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

Big Data Analysis as a Digital Service: Evidence Form Manufacturing Firms

verfasst von : Bojan Lalic, Ugljesa Marjanovic, Slavko Rakic, Marko Pavlovic, Tanja Todorovic, Nenad Medic

Erschienen in: Proceedings of 5th International Conference on the Industry 4.0 Model for Advanced Manufacturing

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Digital disruption is propelling manufacturers to move on towards digital transformation and deliver digital services based on predictive analytics. The literature agrees that digital technologies (i.e. big data) facilitate the service innovation of manufacturers by creating digital servitization. However, little research has specifically focused on the empirical data that analyze use of digital technologies in manufacturing firms in terms of technological intensity. The present study investigates the interaction between big data analysis, as a digital service, and firm characteristics (i.e. firm size and technological intensity). Our analysis used the Serbian dataset of 240 manufacturing firms from the European Manufacturing Survey conducted in 2018. The empirical results show that, in manufacturing firms, digital service based on predictive analytics is highly utilized in medium size firms. Furthermore, results indicate that high technology manufacturing firms in Serbia are not yet utilizing digital technologies to facilitate the service innovation in comparison to other innovation intensity characteristics.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Strategic Approach for Robotics Development in SME by Value Linkage Concept
Nächstes Kapitel Abrasive Flow Machining of 3D Printed Metal Parts – A Scientific Review with Extension on Industrial Needs
Literatur
1.
Marjanovic, U., Rakic, S., Lalic, B.: Digital servitization: the next ‘big thing’ in manufacturing industries. In: Advances in Production Management Systems. Production Management for the Factory of the Future, pp. 510–517 (2019)
2.
Lalic, B., Todorovic, T., Cvetkovic, N., Tasic, N., Marjanovic, U.: Strategic outsourcing of SMEs in the context of industry 4.0: evidence from Serbia. In: 3rd International Conference on the Industry 4.0 Model for Advanced Manufacturing, pp. 139–145 (2018)
3.
Grosso, C., Forza, C.: Users’ social-interaction needs while shopping via online sales configurators. Int. J. Ind. Eng. Manag. 10(2), 139–154 (2019)CrossRef
4.
Ardolino, M., Rapaccini, M., Saccani, N., Gaiardelli, P., Crespi, G., Ruggeri, C.: The role of digital technologies for the service transformation of industrial companies. Int. J. Prod. Res. 56(6), 2116–2132 (2018)CrossRef
5.
Kohtamäki, M., Parida, V., Patel, P.C., Gebauer, H.: The relationship between digitalization and servitization: the role of servitization in capturing the financial potential of digitalization. Technol. Forecast. Soc. Chang. 151(July), 2020 (2019)
6.
Hoffmann-Walbeck, T.: Smart factory: JDF and XJDF. J. Graph. Eng. Des. 9(1), 5–9 (2018)CrossRef
7.
Hoy, M.B.: Big data: an introduction for librarians. Med. Ref. Serv. Q. (2014)
8.
Belcastro, L., Marozzo, F., Talia, D.: Programming models and systems for big data analysis. Int. J. Parallel Emergent Distrib. Syst. (2019)
9.
Viktor Mayer-Schönberger, K.C.: A summary of ‘big data: a revolution that will transform how we live, work, and think’ by Viktor Mayer-Schonberger and Kenneth Cukier [blog post]. New Books Br. (2013)
10.
Bendre, M.R., Thool, V.R.: Analytics, challenges and applications in big data environment: a survey. J. Manag. Analytics (2016)
11.
Niebel, T., Rasel, F., Viete, S.: BIG data–BIG gains? Understanding the link between big data analytics and innovation. Econ. Innov. New Technol. (2019)
12.
Jun, C., Lee, J.Y., Kim, B.H.: Cloud-based big data analytics platform using algorithm templates for the manufacturing industry. Int. J. Comput. Integr. Manuf. 32, 723–738 (2019)CrossRef
13.
Liu, C., Li, H., Tang, Y., Lin, D., Liu, J.: Next generation integrated smart manufacturing based on big data analytics, reinforced learning, and optimal routes planning methods. Int. J. Comput. Integr. Manuf. 32, 820–831 (2019)CrossRef
14.
Tan, K.H., Ji, G., Lim, C.P., Tseng, M.L.: Using big data to make better decisions in the digital economy. Int. J. Prod. Res. 55, 4998–5000 (2017)CrossRef
15.
Zawislak, P.A., Fracasso, E.M., Tello-Gamarra, J.: Technological intensity and innovation capability in industrial firms. Innov. Manag. Rev. 15(2), 189–207 (2018)CrossRef
16.
Eurostat: Eurostat indicators on High-tech industry and Knowledge – intensive services (2018)
17.
Ivanisević, A., Lošonc, A., Morača, S., Vrgović, P.: Exploring the business planning practices in SMEs in a developing country. Int. J. Ind. Eng. Manag. 10(1), 105–114 (2019)CrossRef
18.
Iazzolino, G., Migliano, G., Dattilo, M.I.: The impact of intellectual capital on firms’ characteristics: an empirical analysis on european listed manufacturing companies. Int. J. Ind. Eng. Manag. 10(3), 219–237 (2019)CrossRef
19.
Lalic, B., Rakic, S., Marjanovic, U.: Use of industry 4.0 and organisational innovation concepts in the Serbian textile and apparel industry. Fibres Text. East. Eur. 27(3), 10–18 (2019)CrossRef
20.
Segarra-Ciprés, M., Bou-Llusar, J.C., Roca-Puig, V.: Exploring and exploiting external knowledge: the effect of sector and firm technological intensity. Innov. Manag. Policy Pract. 14(2), 203–217 (2012)CrossRef
21.
Berić, D., Stefanović, D., Lalić, B., Ćosić, I.: The implementation of ERP and MES Systems as a support to industrial management systems. Int. J. Ind. Eng. Manag. 9(2), 77–86 (2018)
22.
Lalić, B., Medić, N., Delić, M., Tasić, N., Marjanović, U.: Open innovation in developing regions: an empirical analysis across manufacturing companies. Int. J. Ind. Eng. Manag. 8(3), 111–120 (2017)
23.
Medic, N., Marjanovic, U., Zivlak, N., Anisic, Z., Lalic, B.: Hybrid fuzzy MCDM method for selection of organizational innovations in manufacturing companies. In: International Symposium on Innovation and Entrepreneurship (ISIE), pp. 109–116 (2018)
24.
Marjanovic, U., Lalic, B., Majstorovic, V., Medic, N., Prester, J., Iztok, P.: How to increase share of product-related services in revenue? Strategy towards servitization. In: Advances in Production Management Systems. Smart Manufacturing for Industry 4.0, vol. 536, pp. 57–64 (2018)
25.
Dachs, B., Biege, S., Borowiecki, M., Lay, G., Jäger, A., Schartinger, D.: Servitisation of European manufacturing: empirical evidence from a large-scale database. Serv. Ind. J. 34(1), 5–23 (2014)CrossRef
Metadaten
Titel
Big Data Analysis as a Digital Service: Evidence Form Manufacturing Firms
verfasst von
Bojan Lalic
Ugljesa Marjanovic
Slavko Rakic
Marko Pavlovic
Tanja Todorovic
Nenad Medic
Copyright-Jahr
2020
Buch
Proceedings of 5th International Conference on the Industry 4.0 Model for Advanced Manufacturing

Print ISBN: 978-3-030-46211-6

Electronic ISBN: 978-3-030-46212-3

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-46212-3_19

Premium Partner

    Marktübersichten

    Die im Laufe eines Jahres in der „adhäsion“ veröffentlichten Marktübersichten helfen Anwendern verschiedenster Branchen, sich einen gezielten Überblick über Lieferantenangebote zu verschaffen. 

    Zur Marktübersicht
    Bildnachweise