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Exploring the processing of product returns from a complex adaptive system perspective

Exploring the processing of product returns from a complex adaptive system perspective The purpose of this paper is to explore the processing of product returns at five case companies using a complex adaptive systems (CAS) logic to identify agent interactions, organization, schema, learning and the emergence of adaptations in the reverse supply chain.Design/methodology/approachUsing a multiple-case study design, this research applies abductive reasoning to examine data from in-depth, semi-structured interviews and direct researcher observations collected during site visits at case companies.FindingsCostly or high-risk returns may require agents to specialize the depth of their mental schema. Processing agents need freedom to interact, self-organize and learn from other agents to generate emergent ideas and adapt.Practical implicationsLimiting the depth of individual agent schema allows managers to better allocate labor to processing product returns during peak volume. To boost adaptability, managers need to craft a dynamic environment that encourages agents with diverse schema to interact, anticipate, and self-organize to brainstorm new ideas. Managers need to resist the urge to “control” the dynamic environment that ensues.Originality/valueThis paper builds on existing research that studies the key decision points in the analysis of product returns by exploring how processing-agent behaviors can create adaptability in the reverse supply chain. Additionally, this research follows in the tradition of Choi et al. (2001) and Surana et al. (2005) and proposes the application of CAS to a specific part of the supply chain – the processing of product returns. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The International Journal of Logistics Management Emerald Publishing

Exploring the processing of product returns from a complex adaptive system perspective

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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
0957-4093
DOI
10.1108/ijlm-08-2018-0216
Publisher site
See Article on Publisher Site

Abstract

The purpose of this paper is to explore the processing of product returns at five case companies using a complex adaptive systems (CAS) logic to identify agent interactions, organization, schema, learning and the emergence of adaptations in the reverse supply chain.Design/methodology/approachUsing a multiple-case study design, this research applies abductive reasoning to examine data from in-depth, semi-structured interviews and direct researcher observations collected during site visits at case companies.FindingsCostly or high-risk returns may require agents to specialize the depth of their mental schema. Processing agents need freedom to interact, self-organize and learn from other agents to generate emergent ideas and adapt.Practical implicationsLimiting the depth of individual agent schema allows managers to better allocate labor to processing product returns during peak volume. To boost adaptability, managers need to craft a dynamic environment that encourages agents with diverse schema to interact, anticipate, and self-organize to brainstorm new ideas. Managers need to resist the urge to “control” the dynamic environment that ensues.Originality/valueThis paper builds on existing research that studies the key decision points in the analysis of product returns by exploring how processing-agent behaviors can create adaptability in the reverse supply chain. Additionally, this research follows in the tradition of Choi et al. (2001) and Surana et al. (2005) and proposes the application of CAS to a specific part of the supply chain – the processing of product returns.

Journal

The International Journal of Logistics ManagementEmerald Publishing

Published: Sep 10, 2019

Keywords: North America; Case study; Reverse logistics; Supply chain processes

References