Risk Management in Supply Chain Management - Collaboration and Behavior

A special issue of Logistics (ISSN 2305-6290).

Deadline for manuscript submissions: 31 May 2024 | Viewed by 8530

Special Issue Editor

Department of Marketing and Supply Chain Management, East Carolina University, Greenville, NC, USA
Interests: quality management; supply chain; risk management; forecasting; quality control

Special Issue Information

Dear Colleagues,

Behavioral supply chain risk management (SCRM) and collaborative risk management have been identified as trending and emerging topics of interest for continued research in supply chain risk management (Pournader, Kach and Talluri, 2020; Friday, Ryan, Sridharan and Collins, 2018). These two areas share synergies, as supply chain collaboration and related behaviors form a critical nexus that can impact agility, resilience, and robustness. In truth, lacking attention to behavioral risk management and effective collaboration, supply chain members will have a very difficult time successfully navigating increasingly complex global supply chains.

The current global crisis brought on by COVID-19 has resulted in unprecedented challenges for supply chain managers, as they have had to quickly change strategies and engage with an increasing number of stakeholders. In some cases, this has resulted in a form of cooperative competition among supply chains as well as working closely with local governments, national governments, and international organizations. This increased environmental complexity is likely to persist in the long term and invites fresh perspectives and practices related to collaboration and behavior within and across supply chains.

According to Pournader et al. (2020), behavioral SCRM touches on personality, power, and social psychology. Examples include trust, power in buyer–supplier relationships, and how various types of power affect supplier behavior and performance. This area also involves managerial cognition and how managers adjust decisions based on risk knowledge. For collaborative risk management, Friday et al. (2018) define it as “…an interactive process based on mutual commitment between firms with a common objective to join and mitigate supply chain risks and related disruptions through co-development of strategic relational capabilities and sharing resources.”

Given recent global supply chain challenges, this Special Issue seeks to expand the body of knowledge on behavioral supply chain risk management, especially in relation to collaborative risk management. Authors are cordially invited to submit original research papers, empirical studies, and case studies for the Special Issue of Logistics until November 30, 2021. Manuscripts may be submitted that relate to the following topics:

  • Resource dependence risk;
  • Cognitive risks in supply decisions;
  • Intuition, expertise, and judgment models;
  • Impact on collaborative risk management for differentiated risk types (e.g., firm level, supply chain level, across supply chains);
  • Supply chain risk collaboration in widespread crisis situations (e.g., global pandemic);
  • Challenges to realizing effective and efficient collaborative risk management;
  • Application of theories in the exploration of supply chain collaboration and associated behaviors (e.g., relational view theory, prospect theory, etc.);
  • Studies involving collaborative risk management capabilities (ref. Friday et al., 2018).

This list is by no means exhaustive but is simply a sampling of potential topics. Other articles in the domain of behavioral SCRM and/or collaborative risk management are highly welcome. I look forward to your submissions.

Dr. Scott Dellana
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Logistics is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • supply chain
  • behavior
  • relationship
  • collaboration
  • integration
  • risk management

Published Papers (1 paper)

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Review

17 pages, 809 KiB  
Review
A Systematic Investigation of the Integration of Machine Learning into Supply Chain Risk Management
by Meike Schroeder and Sebastian Lodemann
Logistics 2021, 5(3), 62; https://doi.org/10.3390/logistics5030062 - 08 Sep 2021
Cited by 24 | Viewed by 6992
Abstract
The main objective of the paper is to analyze and synthesize existing scientific literature related to supply chain areas where machine learning (ML) has already been implemented within the supply chain risk management (SCRM) field, both in theory and in practice. Furthermore, we [...] Read more.
The main objective of the paper is to analyze and synthesize existing scientific literature related to supply chain areas where machine learning (ML) has already been implemented within the supply chain risk management (SCRM) field, both in theory and in practice. Furthermore, we analyzed which risks were addressed in the use cases as well as how ML might shape SCRM. For this purpose, we conducted a systematic literature review. The results showed that the applied examples relate primarily to the early identification of production, transport, and supply risks in order to counteract potential supply chain problems quickly. Through the analyzed case studies, we were able to identify the added value that ML integration can bring to the SCRM (e.g., the integration of new data sources such as social media or weather data). From the systematic literature analysis results, we developed four propositions, which can be used as motivation for further research. Full article
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