Risk perception and decision making in the supply chain: theory and practice
| Date | 13 August 2018 |
| Pages | 1322-1326 |
| DOI | https://doi.org/10.1108/IMDS-08-2018-605 |
| Published date | 13 August 2018 |
| Author | Ying Kei Tse,S.H. Chung,Kulwant S. Pawar |
| Subject Matter | Information & knowledge management,Information systems,Data management systems,Knowledge management,Knowledge sharing,Management science & operations,Supply chain management,Supply chain information systems,Logistics,Quality management/systems |
Guest editorial
1. Risk perception and decision making in the supply chain: theory and
practice
1.1 Introduction
All courses of action are risky, so prudence is notavoiding danger (it’s impossible), but calculating
risk and actingdecisively. Make mistakesof ambition and not mistakesof sloth. Develop the strength
to do bold things, not the strength to suffer. Niccolò Machiavelli (1469–1527), The Prince (1532)
For over 60 years, academics and practitioners from different backgrounds, including
psychology, sociology and management, have studied the perception of risk and how
different decision making affects daily life and business activities. Although it is almost 600
years since Machiavelli stressed the importance of calculation of risk and effective response
to it, approaches to risk measurement and assessment, and to decision making in risky
situations, continue to develop and evolve. In the business world, managers strive to find
ways to understand how different internal and external factors influence risk, how to judge
and interpret the available evidence on the possibility of loss, and how to take individual
actions to manage the risk (Slovic, 2000). In this decade, a number of risk management
frameworks (e.g. IS0 31000) have been proposed and employed in different areas. These
frameworks provide foundations and building blocks for managers to collect available data
to analyse risk. Most importantly, such frameworks allow managers to gather knowledge
intellectually, to properly judge their experience and to assess the current situation, so as to
enter into the most appropriate decision.
Against a background of massive change in many different fields, innovations such as new
supply chain structure (e.g. Global Supply Chain, Belt and Road opportunities, Wang, 2016),
policy change (e.g. Paris Agreement, Jacobs, 2016, new tariffs in trade war) and the development
of new technology (e.g. Internet of things (IoT),Ben-Daya et al., 2017; AI, Gunasekaran and Ngai,
2014; block chain, Rahmadika and Rhee, 2018) are increasing the number and complexity of
risk-bearing activities in the upstream supply network. These supply chain risks are diverse,
and include, for example, supply interruption, product recall/withdrawal, terrorism and
environmental and ethical issues. They are both complicated and very difficult to deal with,
since they involve different entities in the supply chain (Tse et al., 2018). As such, they present
particular challenges in supply chain risk management (SCRM), and cannot be evaluated using
a solely qualitative or quantitative approach. Moreover, it is important to bear in mind that the
response actions should not strengthen the tolerances or the risk impact on the supply chain, but
should focus on building a more resilient supply chain to improve the company compe titiveness.
Driven by the awareness of and serious concerns regarding risk and decision making in
supply chain research, this special issue aims to highlight the contemporary research that is
using various methodologies, including mathematics modelling, survey-based research,
case study-based research, panel data research, data analytics, integrated decision-making
model and review studies. It includes a total of 12 research studies, which can be categorised
into the following dimensions.
Industrial Management & Data
Systems
Vol. 118 No. 7, 2018
pp. 1322-1326
© Emerald PublishingLimited
0263-5577
DOI 10.1108/IMDS-08-2018-605
The authors wish to thank the contributors of this special issue, and the editor of Industrial
Management & Data Systems –Professor Hing Kai Chan for encouragement and support. In addition,
the authors would like to thank the anonymous reviewers who provided excellent review comments
and helped the authors’contributors improve their research works.
1322
IMDS
118,7
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