Diagnosis of delivery vulnerability in a logistics system for logistics risk management

• Pages: 43-58
• DOI: https://doi.org/10.1108/IJLM-02-2019-0069
• Date: 16 December 2019
• Published date: 16 December 2019
• Author: Pei-Ju Wu,Pattra Chaipiyaphan
• Subject Matter: Logistics,Management science & operations

Diagnosis of delivery

vulnerability in a logistics system

for logistics risk management

Pei-Ju Wu

Department of Transportation and Logistics,

Feng Chia University, Taichung, Taiwan and

Innovation Center for Intelligent Transportation and Logistics,

Feng Chia University, Taichung, Taiwan, and

Pattra Chaipiyaphan

Feng Chia University, Taichung, Taiwan

Abstract

Purpose –Delivery vulnerability is a critically important theme in logistics risk management. However,

while logistics service providers often collect and retain massive amounts of logistics data, they seldom utilize

such information to diagnose recurrent day-to-day logistics risks. Hence, the purpose of this paper is to

investigate delivery vulnerabilities in a logistics system using its own accumulated data.

Design/methodology/approach –This study utilizes pragmatic business analytics to derive insights on

logistics risk management from operations data in a logistics system. Additionally, normal accident theory

informs the discussion of its management implications.

Findings –This study’s analytical results reveal that a tightly coupled logistics system can align with

normal accident theory. Specifically, the vulnerabilities of such a system comprise not only multi-components

but also interactive ones.

Research limitations/implications –The tailored business analytics comprise a research foundation for

logistics risk management. Additionally, the important research implications of this study’s analytical results

arrived at via such results’integration with normal accident theory demonstrate the value of that theory to

logistics risk management.

Practical implications –The trade-offs between logistics risk and logistics-system efficiency should be

carefully evaluated. Moreover, improvements to such systems’internal resilience can help to alleviate

potential logistics vulnerabilities.

Originality/value –This pioneering analytical study scrutinizes the critical vulnerability issues of a

logistics service provider and therefore represents a valuable contribution to the field of logistics risk

management. Moreover, it provides a guide to retrieving valuable insights from existing stockpiles of

delivery-vulnerability data.

Keywords Supply chain risk, Asia, Logistics services, Mixed method, Logistics strategy

Paper type Research paper

1. Introduction

Supply-chain vulnerability is the extent to which a supply chain is likely to be influenced by a

risk incident (Heckmann et al., 2015). Interconnected and complicated supply chains, such as

those characterized by delivery complexity (Brandon-Jones et al., 2014), are more prone to

vulnerabilities (Fiksel et al., 2015; Zhao et al., 2019). Logistics accidents, which are commonplace

in logistics operations, are collectively termed delivery vulnerability in this study. Flow

complexity tends to be the biggest influence on a supply network’s robustness and

post-disruption service level (Adenso-Diaz et al., 2018). Unsurprisingly, therefore, the increasing

complexity of internal and external risk events is causing considerable difficulties for

logistics providers, especially because their heterogeneous accident data cannot be explained

straightforwardly by reference to a small number of discrete factors (Aqlan and Lam, 2015).

The prevention of accidents is of vital concern in the field of logistics risk management,

as they threaten the safety of logistics providers, destroy goods (Miller and Saldanha, 2016)

Received 26 February 2019

Revised 30 July 2019

7 October 2019

Accepted 30 October 2019

The current issue and full text archive of this journal is available on Emerald Insight at:

www.emeraldinsight.com/0957-4093.htm

Diagnosis of

delivery

vulnerability in a

logistics system

TheInternationalJournalof

LogisticsManagement

Vol.31 No. 1, 2020

pp.43-58

©EmeraldPublishingLimited

0957-4093

DOI10.1108/IJLM-02-2019-0069

43

and impede smooth logistics operations. As such, effective risk management can not only

protect the lives and livelihoods of a broad range of stakeholders such as truck drivers

(Miller, 2017) but boost companies’competitive advantage (Gualandris and Kalchschmidt,

2015). Although logistics service providers’insurance policies can compensate them when

accidents occur (albeit only monetarily), the costs of such policies are escalating along with

the frequency and expense of logistics accidents (Wu et al., 2017).

Alleviating vulnerability can be achieved through the improvement of supply-chain

resilience (Datta, 2017), which refers to a company’s ability to scan the environment, prevent

unwanted disruptions and strategically allocate resources to manage risks (Gligor et al.,

2019). Nonetheless, the methods companies use to diagnose supply-chain disturbances

remain under-researched (Shukla and Naim, 2017). Based on their recent systematic

literature review, Fan and Stevenson (2018) recommended that future studies develop

methods for identifying and monitoring supply-chain risks and their interrelationships, and

utilize such tools to generate risk-management strategies for practitioners, because –despite

the considerable energy devoted to the management of logistics risk –the frequency and

severity of logistics accidents around the world remains high.

Big-data analytics has received substantial attention in logistics management (Hopkins

and Hawking, 2018). Questionnaire surveys are typically used for data collection in this

field, but they are rarely as effective as data-driven methods that extract essential

information directly from existing big-data stockpiles (Singh et al., 2018). The biggest

challenge to applying such data-driven methods in logistics is that the relevant companies

treat their data as proprietary and high-value, and thus are reluctant to provide it to

outsiders. Thus, primary data on logistics accidents have not been extensively used as the

basis of logistics risk-management actions, and the present study addresses such an

absence directly.

In recent years, normal accident theory has increasingly been applied to illuminate

aspects of tightly coupled operations (Scheibe and Blackhurst, 2018; Wiengarten et al., 2017).

However, relatively little research has utilized this theory in the sphere of logistics

operations, and with regard to delivery vulnerability in particular, it does not appear to have

been applied until now. Accordingly, the main purpose of this study is to diagnose the

delivery vulnerabilities in a particular logistics system, utilizing business analytics

grounded in normal accident theory and a real accident database obtained from a

logistics-services provider. Its subsidiary research objective is to test the interpretive value

of applying normal accident theory both to business-analytics results and to problems in the

field of logistics risk management.

Diagnosis of delivery vulnerability in a logistics system is crucial to effectively

managing that system’s logistics risk. Yet, while many logistics companies operate logistics

systems to satisfy their customers’requirements, few have thus far been willing or able to

make use of data derived from such systems in the assessment of delivery vulnerability.

Hence, this study utilizes a company’s own logistics data to diagnose its delivery

vulnerability, as the basis for a company-specific logistics-risk alleviation strategy. It is

guided by the following research questions:

RQ1. How can delivery vulnerability in a company’s logistics system best be evaluated?

RQ2. Based on data from such a system, what are the key factors a company must

consider if it is to reduce its delivery vulnerability?

RQ3. Based on data from such a system, what circumstances are most likely to induce

delivery vulnerability?

RQ4. Based on data from such a system and normal accident theory, what strategies can

the target company and similar companies adopt to mitigate logistics risk?

IJLM

44

31,1