Challenges Building a Data Value Chain to Enable Data-Driven Decisions: A Predictive Maintenance Case in 5G-Enabled Manufacturing
Paper i proceeding, 2018

Improvements in data storage and processing technologies have led many managers to change how they make decisions, relying less on intuition and more on data. This trend is especially notable for the manufacturing industry where Big Data applications, i.e. data analytics, are mentioned as an important enabler of value creation with the event of the fourth industrial revolution. Designing and building the entire data value chain that enables Big Data applications in manufacturing requires new knowledge about digital technologies combined with already established knowledge about the specific manufacturing processes. This paper focuses on the convergence of these different knowledge spaces applied to a specific case of implementing a Big Data application for predictive maintenance. Every step of building the data value chain from data acquisition to system feedback is presented and discussed in terms of the major challenges that were observed during the project. Results show that, just as the literature suggests, the knowledge gaps between different domains is a key component to manage for succeeding when building Big Data applications in the context of future manufacturing and maintenance.

Cyber-Physical Production Systems

Industry 4.0

Big Data

Data-Driven Decision making

5G

Predictive Maintenance

Författare

Magnus Åkerman

Chalmers, Industri- och materialvetenskap, Produktionssystem

Maja Bärring

Chalmers, Industri- och materialvetenskap, Produktionssystem

Camilla Lundgren

Chalmers, Industri- och materialvetenskap, Produktionssystem

Johan Stahre

Chalmers, Industri- och materialvetenskap, Produktionssystem

Mats Folkesson

Ericsson AB

Victor Berggren

Ericsson AB

Ulrika Engström

Ericsson AB

Martin Friis

SKF

28th International Conference in Flexible Automation and Intelligent Manufacturing (FAIM)
Columbus, Ohio, USA,

Ämneskategorier

Produktionsteknik, arbetsvetenskap och ergonomi

Annan data- och informationsvetenskap

Tillförlitlighets- och kvalitetsteknik

Styrkeområden

Produktion

Mer information

Senast uppdaterat

2018-09-06