Bibliographic Details
| Title: |
Mitigating manifest supply chain disruptions. |
| Authors: |
Riley, Jason M.1 (AUTHOR) jmr099@shsu.edu, Klein, Richard2 (AUTHOR) rklein@fiu.edu, Miller, Janis3 (AUTHOR) Janism@clemson.edu, Sridharan, V.3 (AUTHOR) suhas@clemson.edu |
| Source: |
TQM Journal. 2020, Vol. 32 Issue 5, p939-957. 19p. |
| Subject Terms: |
Supply chain disruptions, Structural equation modeling, Value at risk, Supply chain management, Supply chains |
| Abstract: |
Purpose: The purpose of this paper is to understand if organizations can leverage recovery/continuous improvement (RCI) capabilities and two competencies to mitigate manifest supply chain (SC) disruptions. Specifically, the authors examine how learning from previous experience and SC disruption-orientation affects organizations' capability to recover/continuously improve once a SC disruption has manifested. In addition, knowing that organizational inertia likely exists during disruptions, the authors examine the mediating effects of routine rigidity on proposed relationships. Design/methodology/approach: To determine how these antecedents impact an organization's RCI capabilities, the authors collected survey data from 219 procurement managers and analyzed these records using structural equation modeling. Findings: The results indicate that by fostering SC disruption-orientation and developing competencies to learn from previous experience, firms can enhance their RCI capabilities, which in turn improves operational performance. Furthermore, the authors demonstrate how routine rigidity mediates the positive effects these antecedents have on the RCI capabilities construct. Originality/value: By developing these risk management (RM) tactics and managing routine rigidity, organizations broaden their continuous improvement capability, which enables practitioners to respond to and recover from manifest disruptions. When used in conjunction with other RM tactics, such as inventory and/or redundant capacity, organizations can address an array of disruption scenarios. [ABSTRACT FROM AUTHOR] |
|
Copyright of TQM Journal is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Education Research Complete |
