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Data literacy in the supply chain — groundwork for big data potentials in cross-company value chains

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Supply Management Research

Part of the book series: Advanced Studies in Supply Management ((ASSM))

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Zusammenfassung

In supply chains, not only goods are transported, at the same time, an enormous amount of cross‐company data is generated. Whoever draws the information that really matters for decision‐making from all the data is able to optimize processes, to increase customer satisfaction, to open up new digital business models and to expand in the end the competitive position on the market. However, the decisive factor for this is to build up a reasonable database. An unstructured approach often leads to the creation of inefficient data centers. This is the starting point of the article. The information supply chain illustrates the need for proactive data management in the supply chain. After this, a maturity model for data management competency is developed based on a large‐scale empirical study.

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Literatur

  • Batista, G.; Monard, M. (2003): An analysis of four missing data treatment methods for supervised learning, in: Applied Artificial Intelligence, Vol. 17, Nr. 5/6, pp. 519– 533.

    Google Scholar 

  • Beattie, V.; Pratt, K. (2003): Issues concerning web‐based business reporting: an analysis of the views of interested parties, in: The British Accounting Review, Vol. 35, Nr. 2, pp. 155–187.

    Article  Google Scholar 

  • Bethea, T.J.; Krishna, V.; Zhu, G. (2007): Extracting relevant named entities for automated expense reimbursement, in: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, pp. 1004–1012.

    Google Scholar 

  • Bierwirth, B.; Schocke, K.‐O. (2017): Lead‐time optimization potential of digitization in Air Cargo, in: Kersten, W.; Blecker, T.; Ringle, C.M. (eds.): Digitalization in supply chain management and logistics. Smart and digital solutions for an industry 4.0 environment, Berlin, pp. 75–98.

    Google Scholar 

  • Burgin, M. (2010): Theory of information: fundamentality, diversity and unification, Los Angeles.

    Google Scholar 

  • Chaudhuri, S.; Dayal, U. (1997): An overview of data warehousing and OLAP technology, in: ACM Sigmod Record, Vol. 26, Nr. 1, pp. 65–74.

    Article  Google Scholar 

  • Chu, X.; Ilyas, I.F.; Papotti, P. (2013): Holistic data cleaning: putting violations into context, in: 29th IEEE International Conference on Data Engineering Proceedings, pp. 458–469.

    Google Scholar 

  • Conn, S. (2005): OLTP and OLAP data integration: a review of feasible implementation methods and architectures for real time data analysis, in: Proceedings. IEEE SoutheastCon 2005, pp. 515–520.

    Google Scholar 

  • Cooper, D.R.; Schindler, P.S. (2011): Business research methods, 11th ed., New York.

    Google Scholar 

  • Dennert, A.; Gossling, A.; Krause, J.; Wollschlaeger, M.; Montoya, A. (2012): Vertical data integration in automation based on IEC 61499, in: 9th IEEE International Workshop on Factory Communication Systems, pp. 99–102.

    Google Scholar 

  • Doan, A.; Halevy, A.; Ives, Z. (2012): Principals of data integration, Waltham.

    Google Scholar 

  • Edmunds, A.; Morris, A. (2000): The problem of information overload in business organisations: a review of the literature, in: International Journal of Information Management, Vol. 20, Nr. 1, pp.17–28.

    Article  Google Scholar 

  • Field, A. (2013): Discovering statistics using IBM SPSS statistics, 4th ed., Los Angeles.

    Google Scholar 

  • Gallay, O.; Korpela, K.; Tapio, N.; Nurminen, J.K. (2017): A peer‐to‐peer platform for decentralized logistics, in: Kersten, W.; Blecker, T.; Ringle, C.M. (eds.): Digitalization in supply chain management and logistics. Smart and digital solutions for an industry 4.0 environment, Berlin, pp. 405–426.

    Google Scholar 

  • Gregoire, T.G.; Driver, B.L. (1987): Analysis of ordinal data to detect population differences, in: Psychological Bulletin, Vol. 101, Nr. 1, pp. 159–165.

    Article  Google Scholar 

  • Han, J.; Kamber, M.; Pei, J. (2012): Data mining concepts and techniques, 3rd ed., Amsterdam.

    Google Scholar 

  • Hazen, B.T.; Boone, C.A.; Ezell, J.D.; Jones‐Farmer, L.A. (2014): 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, in: International Journal of Production Economics, Vol. 154, Nr. 8, pp. 72–80.

    Article  Google Scholar 

  • Jaccard, J.; Wan, C.K. (1996): LISREL approaches to interaction effects in multiple regressions, Thousand Oaks.

    Google Scholar 

  • Kell, D.; Oliver, S. (2003): Here is the evidence, now what is the hypothesis? The complementary roles of inductive and hypothesis‐driven science in the postgenomic era, in: Bioessays, Vol. 26, Nr. 1, pp. 99–105.

    Article  Google Scholar 

  • Madnick, S.E. (1995): Integrating information from global systems: dealing with the “On‐ and Off‐Ramps” of the information superhighway, in: Journal of Organizational Computing and Electronic Commerce, Vol. 5, Nr. 2, pp. 69‐82.

    Article  Google Scholar 

  • Msg Systems AG (2018): Daten‐Power für intelligente Logistik. Available at: https://www.msg.group/images/msggroup/branchen/travel‐logistics/MSG_Flyer_Big‐Data‐Logistik_DE.pdf (accessed: September 27, 2018).

  • Naisbitt, J. (1982): Megatrends: ten new directions transforming our lives, New York.

    Google Scholar 

  • Nakatani, K.; Chuang, T.T.; Zhou, D. (2006): Data synchronization technology: standards, business values and implications, in: Communications of AIS, Vol. 17, Nr. 1, pp. 962–994.

    Google Scholar 

  • Otto, B.; Hüner, K.M.; Österle, H. (2012): Toward a functional reference model for master data quality management, in: Information Systems and e‐Business Management, Vol. 10, Nr. 3, pp. 395–425.

    Article  Google Scholar 

  • Price, R.; Neiger, D.; Shanks, G. (2008): Developing a measurement instrument for subjective aspects of information quality, in: Communications of AIS, Vol. 22, Nr. 3, pp. 49–74.

    Google Scholar 

  • Rai, A.; Patnayakuni, R.; Seth, N. (2006): Firm performance impacts of digitally enabled supply chain integration capabilities, in: MIS Quarterly, Vol. 30, Nr. 2, pp. 225– 246.

    Article  Google Scholar 

  • Roggen, D.; Calatroni, A.; Rossi, M.; Holleczek, T.; Förster, K.; Tröster, G.; Lukowicz, P.; Bannach, D.; Pirkl, G.; Ferscha, A.; Doppler, J.; Holzmann, C.; Kurz, M.; Holl, G.; Chavarriaga, R.; Sagha, H.; Bayati, H.; Creatura, M.; Millan, J.R. (2010): Collecting complex activity datasets in highly rich networked sensor environments, in: 2010 Seventh International Conference on Networked Sensing Systems (INSS), pp. 233–240.

    Google Scholar 

  • Sarawagi, S.; Agrawal, R.; Megiddo, N. (1998): Discovery‐driven exploration of OLAP data cubes, in: Schek, H.J.; Alonso, G.; Saltor, F.; Ramos, I. (eds): Advances in Database Technology – EDBT’98, Berlin, Heidelberg, pp. 168–182.

    Google Scholar 

  • Schauer, S.; Stamer, M.; Bosse, C.; Pavlidis, M.; Mouratidis, H.; König, S.; Papastergiou, S. (2017): An adaptive supply chain cyber risk management methodology, in: Kersten, W.; Blecker, T.; Ringle, C.M. (eds.): Digitalization in supply chain management and logistics. Smart and digital solutions for an industry 4.0 environment, Berlin, pp. 255–273.

    Google Scholar 

  • Sekaran, U.; Bougie, R. (2013): Research methods for business. A skill‐building approach, West Sussex.

    Google Scholar 

  • Shankaranarayanan, G.; Cai, Y. (2006): Supporting data quality management in decision‐ making, in: Decision Support Systems, Vol. 42, Nr. 1, pp. 302–317.

    Article  Google Scholar 

  • Sheskin, D.J. (2011): Handbook of parametric and nonparametric statistical procedures, London.

    Google Scholar 

  • Smalheiser, N. (2002): Informatics and hypothesis‐driven research, in: Embo reports, Vol. 3, Nr. 8, pp. 701–803.

    Google Scholar 

  • Strong, D.; Lee, Y.; Wang, R. (1997): Data quality in context, in: Communications of the ACM, Vol. 40, Nr. 5, pp. 103–110.

    Article  Google Scholar 

  • Taub, E.A. (2018): Car navigation systems plot a course forward against phone apps, in: New York Times, February 1st, 2018.

    Google Scholar 

  • Umanath, N.S.; Scamell, R.W. (2014): Data modeling and database design, 2nd ed., New York.

    Google Scholar 

  • Venkatasubramanian, V. (2009): Drowning in data. Informatics and modeling challenges in a data‐rich networked world, in: AIChE Journal, Vol. 55, Nr. 1, pp. 2–8.

    Article  Google Scholar 

  • Wagner, J.; Kontny, H. (2017): Use‐case of self‐organizing adaptive supply chain, in: Kersten, W.; Blecker, T.; Ringle, C.M. (eds.): Digitalization in supply chain management and logistics. Smart and digital solutions for an industry 4.0 environment, Berlin, pp. 255–273.

    Google Scholar 

  • Waller, M.A.; Fawcett, S.E. (2013): Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management, in: Journal of Business Logistics, Vol. 34, Nr. 2, pp. 77–84.

    Article  Google Scholar 

  • Wang, R.Y.; Strong, D.M. (1996): Beyond accuracy: what data quality means to data consumers, in: Journal of Management Information Systems, Vol. 12, Nr. 4, pp. 5–33.

    Article  Google Scholar 

  • Wellbrock, W.; Hein, C. (2018): Cross‐company data management as a strategic advantage – an exploratory study, in: International Journal of Economics and Law, Vol. 8, Nr. 1, pp. 113–124.

    Google Scholar 

  • Xue, L.; Zhang, C.; Ling, H.; Zhao, X. (2014): Risk mitigation in supply chain digitization: system modularity and information technology governance, in: Journal of Management Information System, Vol. 30, Nr. 1, pp. 325–352.

    Article  Google Scholar 

  • Zhao, H. (2007): Semantic matching across heterogeneous data sources, in: Communications of the ACM, Vol. 50, Nr. 1, pp. 44–50.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Campus Schwäbisch Hall, Hochschule Heilbronn, Schwäbisch Hall, Deutschland

    Wanja Wellbrock, Christoph Hein & Daniela Ludin

Authors
  1. Wanja Wellbrock
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  2. Christoph Hein
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  3. Daniela Ludin
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Editor information

Editors and Affiliations

  1. Universität Mannheim, Mannheim, Germany

    Christoph Bode

  2. Universität Würzburg, Würzburg, Germany

    Ronald Bogaschewsky

  3. Universität der Bundeswehr München, Neubiberg, Germany

    Michael Eßig

  4. Technische Universität Dresden , Dresden, Germany

    Rainer Lasch

  5. Universität St. Gallen, St. Gallen, Switzerland

    Wolfgang Stölzle

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© 2019 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature

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Wellbrock, W., Hein, C., Ludin, D. (2019). Data literacy in the supply chain — groundwork for big data potentials in cross-company value chains. In: Bode, C., Bogaschewsky, R., Eßig, M., Lasch, R., Stölzle, W. (eds) Supply Management Research. Advanced Studies in Supply Management. Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-26954-8_7

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  • DOI: https://doi.org/10.1007/978-3-658-26954-8_7

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  • Publisher Name: Springer Gabler, Wiesbaden

  • Print ISBN: 978-3-658-26953-1

  • Online ISBN: 978-3-658-26954-8

  • eBook Packages: Business and Economics (German Language)

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