Abstract
In a networked world, companies depend on fast and smart decisions, especially when it comes to reacting to external change. With the wealth of data available today, smart decisions can increasingly be based on data analysis and be supported by IT systems that leverage AI. A global pandemic brings external change to an unprecedented level of unpredictability and severity of impact. Resilience therefore becomes an essential factor in most decisions when aiming at making and keeping them smart. In this chapter, we study the characteristics of resilient systems and test them with four use cases in a wide-ranging set of application areas. In all use cases, we highlight how AI can be used for data analysis to make smart decisions and contribute to the resilience of systems.
Chapter PDF
Similar content being viewed by others
References
Akerkar, R. 2019. Artificial Intelligence for Business: Springer Briefs in Business. Springer.
Araujo, Theo, et al. 2020. “In AI we trust? Perceptions about automated decision-making by artificial intelligence”. AI & SOCIETY ISSN: 1435-5655. https://doi.org/10.1007/s00146-019-00931-w.
Bader, Verena, and Stephan Kaiser. 2019. “Algorithmic decision-making? The user interface and its role for human involvement in decisions supported by artificial intelligence”. Organization 26 (5): 655–672. ISSN: 1350-5084. https://doi.org/10.1177/1350508419855714.
Bansal, Gagan, et al. 2019. “Updates in human-AI teams: Understanding and addressing the performance/compatibility tradeoff”. In Proceedings of the AAAI Conference on Artificial Intelligence, 33:2429–2437.
Blau, B., et al. 2009. “Service Value Networks”. In 2009 IEEE Conference on Commerce and Enterprise Computing, 194–201.
Blei, David M, Andrew Y Ng, and Michael I Jordan. 2003. “Latent Dirichlet allocation”. Journal of machine Learning research 3 (Jan): 993–1022.
Burton, Jason W., Mari-Klara Stein, and Tina Blegind Jensen. 2020. “A systematic review of algorithm aversion in augmented decision making”. Journal of Behavioral Decision Making 33 (2): 220–239. ISSN: 0894-3257. https://doi.org/10.1002/bdm.2155.
Cheema-Fox, Alexander, et al. 2020. “Corporate Resilience and Response During COVID-19”. Harvard Business School Accounting and Management Unit Working Paper No. 20-108.
Croston, J. D. 1972. “Forecasting and Stock Control for Intermittent Demands”. Operational Research Quarterly (1970-1977) 23 (3): 289. ISSN: 00303623. https://doi.org/10.2307/3007885.
Diener, Michael, Leopold Blessing, and Rappel Nina. 2016. “Tackling the Cloud Adoption Dilemma - A User Centric Concept to Control Cloud Migration Processes by Using Machine Learning Technologies”. In International Conference on Availability, Reliability and Security (ARES).
Edwards, J. S., Y. Duan, and P.C. Robins. 2000. “An analysis of expert systems for business decision making at different levels and in different roles”. European Journal of Information Systems 9 (1): 36–46.
Fan, Zhi-Ping, Yu-Jie Che, and Zhen-Yu Chen. 2017. “Product sales forecasting using online reviews and historical sales data: A method combining the Bass model and sentiment analysis”. Journal of Business Research 74:90–100. ISSN: 0148-2963. https://doi.org/10.1016/j.jbusres.2017.01.010. http://www.sciencedirect.com/science/article/pii/S0148296317300231.
Feng, Qi, and J George Shanthikumar. 2018. “How research in production and operations management may evolve in the era of big data”. Production and Operations Management27 (9): 1670–1684.
Flath, Christoph M., and Nikolai Stein. 2017. “Towards a Data Science Toolbox for Industrial Analytics Applications”. Computers in Industry 94:16–25.
Folke, Carl, et al. 2002. “Resilience and Sustainable Development: Building Adaptive Capacity in a World of Transformations”. AMBIO: A Journal of the Human Environment 31 (5): 437–440. https://doi.org/10.1579/0044-7447-31.5.437.
Foxall, Gordon R. 2017. Advanced introduction to consumer behavior analysis. Elgar advanced introductions. Cheltenham, UK: Edward Elgar. ISBN: 1784716928.
Frank Stein and Arnold Greenland. 2014. “Producing Insights from Information through Analytics”. In Business Analytics, ed. by Jay Liebowitz, 29–54. CRC Press Taylor and Francis Group.
Friedman, Ted. 2009. “Risks and Challenges in Data Migrations and Conversions”. Retrieved from Gartner Research Portal.
Gholami, Mahdi Fahmideh, et al. 2017. “Challenges in migrating legacy software systems to the cloud — an empirical study”. Information Systems 67:100–113. ISSN: 0306-4379. https://doi.org/10.1016/j.is.2017.03.008. http://www.sciencedirect.com/science/article/pii/S0306437917301564.
Gilbert, CHE, and Erric Hutto. 2014. “Vader: A parsimonious rule-based model for sentiment analysis of social media text”. In Eighth International Conference on Weblogs and Social Media (ICWSM-14). Available at (20/04/16) http://comp.social.gatech.edu/papers/icwsm14.vader.hutto.pdf , 81:82.
Golan, Maureen S., Laura H. Jernegan, and Igor Linkov. 2020. “Trends and applications of resilience analytics in supply chain modeling: systematic literature review in the context of the COVID-19 pandemic”. Environment systems & decisions: 1–22. https://doi.org/10.1007/s10669-020-09777-w.
Gunderson, Lance H., and C. S. Holling. 2002. Panarchy: Understanding transformations in human and natural systems. Washington, DC: Island Press. ISBN: 9781559638579.
Hartmann, Nathaniel N., and Bruno Lussier. 2020. “Managing the sales force through the unexpected exogenous COVID-19 crisis”. Industrial Marketing Management 88:101–111. ISSN: 0019-8501. https://doi.org/10.1016/j.indmarman.2020.05.005 http://www.sciencedirect.com/science/article/pii/S0019850120302972.
Holling, C. S. 2001. “Understanding the complexity of economic, ecological, and social systems”. Ecosystems 4:390–405. https://doi.org/10.1007/s10021-00-0101-5.
Ivanov, Dmitry. 2020. “Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case”. Transportation research. Part E, Logistics and transportation review 136:101922. https://doi.org/10.1016/j.tre.2020.101922.
Jackson, Scott, and Timothy L. J. Ferris. 2013. “Resilience principles for engineered systems”. Systems Engineering16 (2): 152–164. doi: 10.1002/sys21228. eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/sys.21228
Kamar, Ece, Severin Hacker, and Eric Horvitz. 2012. “Combining human and machine intelligence in large-scale crowdsourcing.” In AAMAS. 12:467–474.
Kasparov, Garry. 2017. Deep thinking: where machine intelligence ends and human creativity begins. Hachette UK.
Kiefer, Daniel, and Clemens van Dinther. 2020. “Demand Forecasting Intermittent and Lumpy Time Series: Deep Learning a magic bullet?”
Krenzer, Adrian, et al. 2019. “Augmented Intelligence for Quality Control of Manual Assembly Processes using Industrial Wearable Systems”. In Proceedings of the 40th International Conference on Information Systems (ICIS).
Kusiak, A., et al. 2000. “Autonomous decision-making: a data mining approach”. IEEE transactions on information technology in biomedicine: a publication of the IEEE Engineering in Medicine and Biology Society 4 (4): 274–284. ISSN: 1089-7771. https://doi.org/10.1109/4233.897059.
Lakhmi C. Jain. 2009. “Advances in Decision Making”. In Recent Advances in Decision Making, ed. by Janusz Kacprzyk et al., 1–6. Berlin, Heidelberg: Springer Berlin Heidelberg. ISBN: 978-3-642-02186-2.
Lepenioti, Katerina, et al. 2020. “Prescriptive analytics: Literature review and research challenges”. International Journal of Information Management 50:57–70. ISSN: 02684012. https://doi.org/10.1016/j.ijinfomgt.2019.04.003.
Loureiro, A.L.D., V. L. Miguéis, and Lucas F.M. da Silva. 2018. “Exploring the use of deep neural networks for sales forecasting in fashion retail”. Decision Support Systems114:81–93. ISSN: 01679236. https://doi.org/10.1016/j.dss.2018.08.010
Manekar, S, and Pradeepini Gera. 2017. “Opportunity and Challenges for Migrating Big Data Analytics in Cloud”. IOP Conference Series: Materials Science and Engineering225 (): 012148. https://doi.org/10.1088/1757-899X/225/1/012148.
McKone, Kathleen E, and Elliott N Weiss. 2002. “Guidelines for implementing predictive maintenance”. Production and Operations Management11 (2): 109–124.
Oberdorf, Felix, et al. 2020. “ADR for Big-Data IT Artifact Development: An Escalation Management Example”. In Proceedings of the 41st International Conference on Information Systems (ICIS).
Paul, H Yi, Ferdinand K Hui, and Daniel SW Ting. 2018. “Artificial intelligence and radiology: collaboration is key”. Journal of the American College of Radiology15 (5): 781–783.
Peter R. Winters. 1960. “Forecasting Sales by Exponentially Weighted Moving Averages”. Management Science6 (3): 324–342.
Phillips-Wren, G., and L. Jain. 2006. “Knowledge-based intelligent Information and Engineering Systems”. Chap. Artificial Intelligence for Decision Making, ed. by Bogdan Gabrys, Robert J. Howlett, and Lakhmi Jain, 531–536. Springer.
Phillips-Wren, G. (2012).Phillips-Wren, Gloria. 2012. “AI tools in Decision Making Support Systems: a review”. International Journal on Artificial Intelligence Tools21 (02): 1240005. ISSN: 0218-2130. https://doi.org/10.1142/S0218213012400052.
Pierre Haren and David Simchi-Levi. 2020. “How Coronavirus Could Impact the Global Supply Chain by Mid-March”. Harvard Business Review2020 (03).
Ricardo, David. 1817. The Principles of Political Economy and Taxation. Reprint from 1926. London and Toronto: J.M. Dent/Sons.
Sharma, Amalesh, Anirban Adhikary, and Sourav Bikash Borah. 2020. “Covid-19′s impact on supply chain decisions: Strategic insights from NASDAQ 100 firms using Twitter data”. Journal of Business Research117:443–449. ISSN: 0148-2963. https://doi.org/10.1016/j.jbusres.2020.05.035. http://www.sciencedirect.com/science/article/pii/S0148296320303210.
Shrestha, Yash Raj, Shiko M. Ben-Menahem, and Georg von Krogh. 2019. “Organizational Decision-Making Structures in the Age of Artificial Intelligence”. California Management Review61 (4): 66–83. ISSN: 0008-1256. https://doi.org/10.1177/0008125619862257.
Stein, Nikolai, Jan Meller, and Christoph M Flath. 2018. “Big data on the shop-floor: sensor-based decision-support for manual processes”. Journal of Business Economics88 (5): 593–616.
Strobl, Stefan, Mario Bernhart, and Thomas Grechenig. 2020. “Towards a Topology for Legacy System Migration”. In Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops, 586–594. IC-SEW’20. Seoul, Republic of Korea: Association for Computing Machinery. ISBN: 9781450379632. https://doi.org/10.1145/3387940.3391476.
Stubbs, Evan. 2014. “Business Analytics: An Introduction”. Chap. The Value of Business Analytics, ed. by Jay Liebowitz, 1–28. CRC Press, Taylor / Francis Group.
Wang, Dakuo, et al. 2019. “Human-AI Collaboration in Data Science: Exploring Data Scientists’ Perceptions of Automated AI”. Proceedings of the ACM on Human-Computer Interaction3 (CSCW): 1–24.
Wuest, Thorsten, Christopher Irgens, and Klaus-Dieter Thoben. 2014. “An approach to monitoring quality in manufacturing using supervised machine learning on product state data”. Journal of Intelligent Manufacturing25 (5): 1167–1180.
Wuest, Thorsten, et al. 2016. “Machine learning in manufacturing: advantages, challenges, and applications”. Production & Manufacturing Research4 (1): 23–45.
Yin, Jianhua, and Jianyong Wang. 2014. “A Dirichlet multinomial mixture model-based approach for short text clustering”. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, 233–242.
van Dinther, Clemens. 2007. Adaptive Bidding in Single-Sided Auctions Under Uncertainty: An Agent-based Approach in Market Engineering. Whitestein Series in Software Agent Technologies and Autonomic Computing. Basel: Birkhaäuser Verlag. ISBN: 978-3764380946.
– 2008. “Agent-based Simulation for Research in Economics”. In Handbook on Information Technology in Finance, ed. by Detlef Seese, Christof Weinhardt, and Frank Schlottmann, 421–442. Berlin, Heidelberg: Springer Berlin Heidelberg. ISBN: 978-3-540-49487-4. https://doi.org/10.1007/978-3-540-49487-4_18.
van Dinther, Clemens, and Svenja Mauch. 2019. “Chancen der künstlichen Intelligenz zur Prognose im Mittelstand”. Decision Growth, no. 3: 21–27. https://decision-growth.de/Magazin//catalogs/Growth_Magazin_III/growth-Magazin-Ausgabe-3/pdf/complete.pdf.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Copyright information
© 2021 The Author(s)
About this chapter
Cite this chapter
Blau, B., Dinther, C.v., Flath, C.M., Knapper, R., Rolli, D. (2021). Data Analytics for Smart Decision-Making and Resilient Systems. In: Gimpel, H., et al. Market Engineering . Springer, Cham. https://doi.org/10.1007/978-3-030-66661-3_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-66661-3_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-66660-6
Online ISBN: 978-3-030-66661-3
eBook Packages: Computer ScienceComputer Science (R0)