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Organizational Complexity as a Contributing Factor to Underperformance

Author

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  • Uwe Beyer

    (Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven, 53757 Sankt Augustin, Germany)

  • Oliver Ullrich

    (Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven, 53757 Sankt Augustin, Germany)

Abstract

A high organizational complexity above the cognitive limits of employees and management can contribute to a business performing sub-standardly in comparison to its competitors. As businesses can grow over time into a state of high complexity, an active approach to complexity management has to be taken. This paper presents the NES classification method, aimed at facilitating the modeling and assessment of organizational complexity. Here, any system is classified as belonging to one of three classes: class N natural, class E engineered, or class S slipped systems. Operationalizing NES by applying the Tree Attribute Matrix modeling method, this paper describes exemplary observations made as part of consulting projects that demonstrated typical underperformance situations resulting from their organizations “slipping” into structures of high complexity, and includes management measures to reduce complexity and thereby improve performance.

Suggested Citation

  • Uwe Beyer & Oliver Ullrich, 2022. "Organizational Complexity as a Contributing Factor to Underperformance," Businesses, MDPI, vol. 2(1), pages 1-15, March.
    • Handle: RePEc:gam:jbusin:v:2:y:2022:i:1:p:5-96:d:762807
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    References listed on IDEAS

    as
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    3. Eppinger, Steven D. & Browning, Tyson R., 2012. "Design Structure Matrix Methods and Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262017520, April.
    4. Luca Fraccascia & Ilaria Giannoccaro & Vito Albino, 2018. "Resilience of Complex Systems: State of the Art and Directions for Future Research," Complexity, Hindawi, vol. 2018, pages 1-44, August.
    5. Yash Daultani & Mohit Goswami & Omkarprasad S. Vaidya & Sushil Kumar, 2019. "Inclusive risk modeling for manufacturing firms: a Bayesian network approach," Journal of Intelligent Manufacturing, Springer, vol. 30(8), pages 2789-2803, December.
    6. Randall,Alan, 2011. "Risk and Precaution," Cambridge Books, Cambridge University Press, number 9780521766159, October.
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