Closing the Gap between Smart Manufacturing Applications and Data Management

Smart manufacturing refers to the intensified collaboration of machines, products, and people throughout the manufacturing and the supply chain. This facilitates innovative products, services, business models, and processes. Smart manufacturing is premised on emerging technologies such as cloud computing, mobile computing, the Internet of Things, data analytics, and artificial intelligence. A plethora of companies struggles with the implementation of corresponding applications. In research and practice, we see general data management approaches with primary attention on building architectures that are not tailored to fit a particular domain/ application scenario. However, a robust data management concept is vital, as smart manufacturing decisively depends on data. To address this substantial deficit, we conduct a comprehensive literature review, an expert workshop, and semistructured expert interviews with one of the leading German automotive manufacturers. The result is a catalog of requirements and a framework for data management that fosters the implementation of smart manufacturing applications.

[1]  Lihui Wang,et al.  Big data analytics based fault prediction for shop floor scheduling , 2017 .

[2]  Dazhong Wu,et al.  Deep learning for smart manufacturing: Methods and applications , 2018, Journal of Manufacturing Systems.

[3]  Christian Mathis,et al.  Data Lakes , 2017, Datenbank-Spektrum.

[4]  Sepideh Ebrahimi,et al.  Data analytics competency for improving firm decision making performance , 2018, J. Strateg. Inf. Syst..

[5]  Nrusimham Ammu,et al.  Big Data Challenges , 2013 .

[6]  Rajkumar Buyya,et al.  Big Data computing and clouds: Trends and future directions , 2013, J. Parallel Distributed Comput..

[7]  Amit P. Sheth,et al.  Changing Focus on Interoperability in Information Systems:From System, Syntax, Structure to Semantics , 1999 .

[8]  David Romero,et al.  Smart manufacturing: Characteristics, technologies and enabling factors , 2019 .

[9]  C. Humby,et al.  Process Mining: Data science in Action , 2014 .

[10]  Dilpreet Singh,et al.  A survey on platforms for big data analytics , 2014, Journal of Big Data.

[11]  Richard T. Watson,et al.  Analyzing the Past to Prepare for the Future: Writing a Literature Review , 2002, MIS Q..

[12]  Huang Fang Managing data lakes in big data era: What's a data lake and why has it became popular in data management ecosystem , 2015, 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER).

[13]  Nectaria Tryfona,et al.  starER: a conceptual model for data warehouse design , 1999, DOLAP '99.

[14]  Alan R. Hevner,et al.  Design Science in Information Systems Research , 2004, MIS Q..

[15]  Benjamin T. Hazen,et al.  Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications , 2014 .

[16]  Birgit Vogel-Heuser,et al.  Industrie 4.0 in Produktion, Automatisierung und Logistik. Anwendung · Technologien · Migration , 2014 .

[17]  Andrew Kusiak,et al.  Data-driven smart manufacturing , 2018, Journal of Manufacturing Systems.

[18]  Sherali Zeadally,et al.  Handling big data: research challenges and future directions , 2016, The Journal of Supercomputing.

[19]  Dursun Delen,et al.  Data, information and analytics as services , 2013, Decis. Support Syst..

[20]  Izak Benbasat,et al.  The Case Research Strategy in Studies of Information Systems , 1987, MIS Q..

[21]  Natalia Miloslavskaya,et al.  Big Data, Fast Data and Data Lake Concepts , 2016, BICA.

[22]  I. Song,et al.  Analytics over large-scale multidimensional data: the big data revolution! , 2011, DOLAP '11.

[23]  Fei Tao,et al.  Big Data in product lifecycle management , 2015, The International Journal of Advanced Manufacturing Technology.

[24]  J. Ross,et al.  Digital Business Transformation and the Changing Role of the IT Function , 2017 .

[25]  Ranjit Bose,et al.  Advanced analytics: opportunities and challenges , 2009, Ind. Manag. Data Syst..

[26]  Sankalp Sah,et al.  High Performance Computing and Big Data , 2018 .

[27]  Christian Paul Wirths,et al.  Data Innovation @ AXA Germany: Journey Towards a Data-Driven Insurer , 2019, Digitalization Cases.

[28]  Catherine Cleophas,et al.  Excavating the Treasure of IoT Data: An Architecture to Empower Rapid Data Analytics for Predictive Maintenance of Connected Vehicles , 2017, ICIS.

[29]  Walter Brenner,et al.  The Impact of Digitalization on the IT Department , 2018, Business & Information Systems Engineering.

[30]  Sascha Stowasser,et al.  Das Produktivitätsmanagement des Industrial Engineering , 2012 .

[31]  Jasmin Pielorz,et al.  Semantic Interoperability as Key to IoT Platform Federation , 2016, InterOSS@IoT.

[32]  Tom Fawcett,et al.  Data Science and its Relationship to Big Data and Data-Driven Decision Making , 2013, Big Data.

[33]  Krish Krishnan,et al.  Data Warehousing in the Age of Big Data , 2013 .

[34]  Ralph Kimball,et al.  The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling , 1996 .

[35]  Barbara Wixom,et al.  An Empirical Investigation of the Factors Affecting Data Warehousing Success , 2001, MIS Q..

[36]  Ralph Kimball,et al.  The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling , 2013 .

[37]  Susan Greener,et al.  Business Research Methods , 2008 .

[38]  Hugh J. Watson,et al.  Tutorial: Big Data Analytics: Concepts, Technologies, and Applications , 2014, Commun. Assoc. Inf. Syst..

[39]  J. Manyika Big data: The next frontier for innovation, competition, and productivity , 2011 .

[40]  Bala M. Balachandran,et al.  Challenges and Benefits of Deploying Big Data Analytics in the Cloud for Business Intelligence , 2017, KES.

[41]  Melnned M. Kantardzic Big Data Analytics , 2013, Lecture Notes in Computer Science.

[42]  Bart Baesens,et al.  Call for Papers MISQ Special Issue on Transformational Issues of Big Data and Analytics in Networked Business , 2014 .

[43]  Jennifer Widom,et al.  Research problems in data warehousing , 1995, CIKM '95.

[44]  Anne Laurent,et al.  The next information architecture evolution: the data lake wave , 2016, MEDES.

[45]  Surajit Chaudhuri,et al.  An overview of data warehousing and OLAP technology , 1997, SGMD.

[46]  Salvatore T. March,et al.  Design and natural science research on information technology , 1995, Decis. Support Syst..

[47]  Man Ho Au,et al.  Security and privacy in big data , 2016, Concurr. Comput. Pract. Exp..

[48]  Thomas Y. Choi,et al.  Renaissance of case research as a scientific method , 2014 .

[49]  Inderpal Singh Mumick,et al.  Selection of views to materialize in a data warehouse , 1997, IEEE Transactions on Knowledge and Data Engineering.

[50]  Eberhard Abele,et al.  Wandlungsfähige Produktionssysteme: Heute die Industrie von morgen gestalten , 2008 .

[51]  Yonggang Wen,et al.  Toward Scalable Systems for Big Data Analytics: A Technology Tutorial , 2014, IEEE Access.

[52]  Johannes Diemer Sichere Industrie-4.0-Plattformen auf Basis von Community-Clouds , 2017, Handbuch Industrie 4.0.

[53]  Frank Klawonn,et al.  Guide to Intelligent Data Analysis - How to Intelligently Make Sense of Real Data , 2010, Texts in Computer Science.

[54]  Carol V. Brown,et al.  Designing data governance , 2010, CACM.

[55]  K. Eisenhardt Building theories from case study research , 1989, STUDI ORGANIZZATIVI.

[56]  Guo Chao Peng,et al.  Barriers of embedding big data solutions in smart factories: insights from SAP consultants , 2019, Ind. Manag. Data Syst..