IDEAS home Printed from https://ideas.repec.org/a/spr/minecn/v35y2022i3d10.1007_s13563-022-00337-z.html
   My bibliography  Save this article

Big data analyses for real-time tracking of risks in the mineral raw material markets: implications for improved supply chain risk management

Author

Listed:
  • Peter Buchholz

    (German Mineral Resources Agency (DERA) at the Federal Institute for Geosciences and Natural Resources (BGR))

  • Arne Schumacher

    (German Mineral Resources Agency (DERA) at the Federal Institute for Geosciences and Natural Resources (BGR))

  • Siyamend Barazi

    (German Mineral Resources Agency (DERA) at the Federal Institute for Geosciences and Natural Resources (BGR))

Abstract

Resilient supply chains have become a key issue for manufacturing companies to ensure a stable supply for their manufacturing processes and for governments to ensure the stable supply of essential goods to society. Building diversified supply chains and monitoring the performance of suppliers are major mitigation strategies to counteract disruptions at an early stage. Supply chain risk management and monitoring of supply chains using big data analytics are getting increasing attention. The growing number of data sources has huge implications on the reporting of incidents that may disrupt supply chains. The data sources may stem from a variety of internet sources like daily media reports provided on websites, social media or specialist media, or they may stem from specific databases. The sooner this information is disclosed to stakeholders and analysed the better the preventive strategies generally are. Timely information prolongs the reaction time and may help to reduce the severity of an incident. This paper highlights a science-based real-time tracking analysis of risks in the mineral raw material markets for the period 2019 to 2021 using big data analytics provided by a commercial system. A set of data for 12 selected mineral raw materials was provided by the authors and analysed using more than 100 risk indicators from 14 major risk categories as part of a commercial big data system. The extracted information can have imminent value to identify supply shortages, production halts or price peaks at an early stage. The main question was to find out whether such big data analytics are precise enough to detect potential, globally relevant, supply shortages in mineral raw material markets in due time. The results of this paper show that using big data analytics can be a very effective tool to extract relevant information about supply sources and to react timely in case of disruptions or social or environmental mismanagement on the supplier side. However, the nature of big data sources suggests that the parameters of the applied models need elaborate configuration. Each raw mineral market has its own peculiarities in terms of volume, mode of transport, market concentration or countries of origin. These factors influence the relevance of the reported incidents. Furthermore, some incidents have a spurious or only minor connection to the individual markets. For these reasons, we conclude that only supervised models reap the most benefits in the monitoring of mineral raw material markets.

Suggested Citation

  • Peter Buchholz & Arne Schumacher & Siyamend Barazi, 2022. "Big data analyses for real-time tracking of risks in the mineral raw material markets: implications for improved supply chain risk management," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 35(3), pages 701-744, December.
    • Handle: RePEc:spr:minecn:v:35:y:2022:i:3:d:10.1007_s13563-022-00337-z
    DOI: 10.1007/s13563-022-00337-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13563-022-00337-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13563-022-00337-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Peter Buchholz & Friedrich-W. Wellmer & Dennis Bastian & Maren Liedtke, 2020. "Leaning against the wind: low-price benchmarks for acting anticyclically in the metal markets," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 33(1), pages 81-100, July.
    2. Steven B. Young & Shannon Fernandes & Michael O. Wood, 2019. "Jumping the Chain: How Downstream Manufacturers Engage with Deep Suppliers of Conflict Minerals," Resources, MDPI, vol. 8(1), pages 1-24, January.
    3. Kersten, Wolfgang & Blecker, Thorsten & Ringle, Christian M. (ed.), 2017. "Digitalization in Supply Chain Management and Logistics: Smart and Digital Solutions for an Industry 4.0 Environment," Proceedings of the Hamburg International Conference of Logistics (HICL), Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management, volume 23, number 23.
    4. Stamer, Vincent, 2021. "Thinking outside the container: A machine learning approach to forecasting trade flows," Kiel Working Papers 2179, Kiel Institute for the World Economy (IfW Kiel).
    5. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    6. George Baryannis & Sahar Validi & Samir Dani & Grigoris Antoniou, 2019. "Supply chain risk management and artificial intelligence: state of the art and future research directions," International Journal of Production Research, Taylor & Francis Journals, vol. 57(7), pages 2179-2202, April.
    7. Kersten, Wolfgang & Blecker, Thorsten & Ringle, Christian M. (ed.), 2015. "Innovations and Strategies for Logistics and Supply Chains: Technologies, Business Models and Risk Management," Proceedings of the Hamburg International Conference of Logistics (HICL), Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management, volume 20, number 20.
    8. Rosenau-Tornow, Dirk & Buchholz, Peter & Riemann, Axel & Wagner, Markus, 2009. "Assessing the long-term supply risks for mineral raw materials--a combined evaluation of past and future trends," Resources Policy, Elsevier, vol. 34(4), pages 161-175, December.
    9. Wenzel, Hannah & Smit, Daniel & Sardesai, Saskia, 2019. "A literature review on machine learning in supply chain management," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Blecker, Thorsten & Ringle, Christian M. (ed.), Artificial Intelligence and Digital Transformation in Supply Chain Management: Innovative Approaches for Supply Chains. Proceedings of the Hamburg Int, volume 27, pages 413-441, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Valérie Mignon & Pauline Bucciarelli & Emmanuel Hache, 2024. "Evaluating criticality of strategic metals: Are the Herfindahl–Hirschman Index and usual concentration thresholds still relevant?," Working Papers hal-04452384, HAL.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Friedrich-W. Wellmer, 2022. "What we have learned from the past and how we should look forward," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 35(3), pages 765-795, December.
    2. Brylowski, Martin & Schröder, Meike & Lodemann, Sebastian & Kersten, Wolfgang, 2021. "Machine learning in supply chain management: A scoping review," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Ringle, Christian M. & Blecker, Thorsten (ed.), Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management. Proceedings of the Hamburg Internationa, volume 31, pages 377-406, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    3. Croce, M.M. & Nguyen, Thien T. & Raymond, S. & Schmid, L., 2019. "Government debt and the returns to innovation," Journal of Financial Economics, Elsevier, vol. 132(3), pages 205-225.
    4. Nikolay Hristov & Markus Roth, 2019. "Uncertainty Shocks and Financial Crisis Indicators," CESifo Working Paper Series 7839, CESifo.
    5. Deshuai Hou & Luhan Shi & Hong He & Jian Xiong, 2023. "Research on the Deviation of Corporate Green Behaviour under Economic Policy Uncertainty Based on the Perspective of Green Technology Innovation in Chinese Listed Companies," Sustainability, MDPI, vol. 15(9), pages 1-27, May.
    6. Manfred M. Fischer & Florian Huber & Michael Pfarrhofer, 2018. "The transmission of uncertainty shocks on income inequality: State-level evidence from the United States," Papers 1806.08278, arXiv.org.
    7. Idriss Fontaine, 2021. "Uncertainty and Labour Force Participation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(2), pages 437-471, April.
    8. Haddou, Samira, 2024. "Determinants of CDS in core and peripheral European countries: A comparative study during crisis and calm periods," The North American Journal of Economics and Finance, Elsevier, vol. 71(C).
    9. Kučerová, Zuzana & Pakši, Daniel & Koňařík, Vojtěch, 2024. "Macroeconomic fundamentals and attention: What drives european consumers’ inflation expectations?," Economic Systems, Elsevier, vol. 48(1).
    10. Müller, Karsten, 2020. "German forecasters' narratives: How informative are German business cycle forecast reports?," Working Papers 23, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
    11. Salzmann, Leonard, 2020. "The Impact of Uncertainty and Financial Shocks in Recessions and Booms," VfS Annual Conference 2020 (Virtual Conference): Gender Economics 224588, Verein für Socialpolitik / German Economic Association.
    12. Yingce Yang & Junjie Guo & Ruihong He, 2023. "The Asymmetric Impact of the Oil Price and Disaggregate Shocks on Economic Policy Uncertainty: Evidence From China," SAGE Open, , vol. 13(2), pages 21582440231, June.
    13. Chan, Yue-Cheong & Saffar, Walid & Wei, K.C. John, 2021. "How economic policy uncertainty affects the cost of raising equity capital: Evidence from seasoned equity offerings," Journal of Financial Stability, Elsevier, vol. 53(C).
    14. Wang, Yubao & Huang, Xiaozhou & Huang, Zhendong, 2024. "Energy-related uncertainty and Chinese stock market returns," Finance Research Letters, Elsevier, vol. 62(PB).
    15. Yoshito Funashima, 2022. "Economic policy uncertainty and unconventional monetary policy," Manchester School, University of Manchester, vol. 90(3), pages 278-292, June.
    16. Ongena, Steven & Savaşer, Tanseli & Şişli Ciamarra, Elif, 2022. "CEO incentives and bank risk over the business cycle," Journal of Banking & Finance, Elsevier, vol. 138(C).
    17. Li, Xiao-Ming, 2017. "New evidence on economic policy uncertainty and equity premium," Pacific-Basin Finance Journal, Elsevier, vol. 46(PA), pages 41-56.
    18. Metiu, Norbert, 2021. "Anticipation effects of protectionist U.S. trade policies," Journal of International Economics, Elsevier, vol. 133(C).
    19. Marina Diakonova & Luis Molina & Hannes Mueller & Javier J. Pérez & Cristopher Rauh, 2022. "The information content of conflict, social unrest and policy uncertainty measures for macroeconomic forecasting," Working Papers 2232, Banco de España.
    20. Lee, Seung Jung & Liu, Lucy Qian & Stebunovs, Viktors, 2022. "Risk-taking spillovers of U.S. monetary policy in the global market for U.S. dollar corporate loans," Journal of Banking & Finance, Elsevier, vol. 138(C).

    More about this item

    Keywords

    Big data; Supply chain; Market;
    All these keywords.

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:minecn:v:35:y:2022:i:3:d:10.1007_s13563-022-00337-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.