Information technology and risk management in supply chains

Leveraging supply chain disruption orientation for resilience: the roles of supply chain risk management practices and analytics capability

Purpose

Building on the information processing perspective, the authors propose that both internal and external supply chain risk management (SCRM) practices play essential roles in mediating supply chain disruption orientation (SCDO) to exercise an influence on resilience. The authors also put forward analytics capability as an important moderator in the above-mediated relationship.

Design/methodology/approach

The authors collected 170 match-paired questionnaires from Chinese firms to test our model. The authors further interviewed some managers to supplement key quantitative results.

Findings

First, SCDO positively affects resilience via internal and external SCRM practices. Second, the mediating effects of internal and external SCRM practices are stronger when analytics capability is higher. Third, analytics capability positively moderates the positive effect of SCDO on SCRM practices. Meanwhile, it does not moderate the positive effect of SCRM practices on resilience.

Research limitations/implications

Our study contributes to SCRM-related and IT-related literature by considering the content, mediating mechanisms (i.e. internal and external SCRM practices) and boundary conditions (i.e. data analytic capability) of SCDO in shaping resilience in the digital supply chain.

Practical implications

Our study helps remind managers that firms build disruption orientation, develop different SCRM practices and leverage analytics capability to improve resilience amid unexpected and unplanned disruptions.

Originality/value

Our study sheds light on the roles of both internal and external SCRM practices. Furthermore, this research helps explain how SCDO motivates resilience through SCRM practices, particularly for those firms that have higher analytics capability.

How to enhance supply chain resilience: a logistics approach

Purpose

Logistics capability is an important enabler of supply chain resilience (SCR). However, few studies have analyzed the underlying influence mechanism of logistics capability on SCR in extreme conditions, such as those of the COVID-19 pandemic. The purpose of this study is to increase understanding of the role of logistics capabilities in constituting a resilient supply chain.

Design/methodology/approach

Drawing upon the dynamic capability perspective and contingency theory, the proposed conceptual framework aims to demonstrate the relationship between a firm's logistics capabilities and SCR. Furthermore, the conceptual framework is illustrated by empirical evidence from a case study of a Chinese manufacturing company, which focuses on extracting practical lessons from the COVID-19 pandemic.

Findings

The findings suggest that digitalization, innovativeness, and modularization comprise potential mediating pathways for firm logistics capability to affect SCR and government policies, risk management culture, trust and cooperation moderate the effect positively. The potential associations are identified and elucidated by detecting the corresponding strategies and practices of a Chinese manufacturer that performed well amid the COVID-19 pandemic.

Practical implications

This study provides specific guidelines for logistics managers to enhance SCR during the COVID-19 pandemic. Seeing SCR as a dynamic capability, the framework is also instructive for manufacturers, supply chain members, and policymakers to achieve the sustained competitive advantage of supply chains.

Originality/value

The findings expand the understanding of enhancing SCR in a logistics approach. The empirical validation of propositions in the case study reveals a new vista for research on SCR.

AI technologies and their impact on supply chain resilience during COVID-19

Purpose

COVID-19 has pushed many supply chains to re-think and strengthen their resilience and how it can help organisations survive in difficult times. Considering the availability of data and the huge number of supply chains that had their weak links exposed during COVID-19, the objective of the study is to employ artificial intelligence to develop supply chain resilience to withstand extreme disruptions such as COVID-19.

Design/methodology/approach

We adopted a qualitative approach for interviewing respondents using a semi-structured interview schedule through the lens of organisational information processing theory. A total of 31 respondents from the supply chain and information systems field shared their views on employing artificial intelligence (AI) for supply chain resilience during COVID-19. We used a process of open, axial and selective coding to extract interrelated themes and proposals that resulted in the establishment of our framework.

Findings

An AI-facilitated supply chain helps systematically develop resilience in its structure and network. Resilient supply chains in dynamic settings and during extreme disruption scenarios are capable of recognising (sensing risks, degree of localisation, failure modes and data trends), analysing (what-if scenarios, realistic customer demand, stress test simulation and constraints), reconfiguring (automation, re-alignment of a network, tracking effort, physical security threats and control) and activating (establishing operating rules, contingency management, managing demand volatility and mitigating supply chain shock) operations quickly.

Research limitations/implications

As the present research was conducted through semi-structured qualitative interviews to understand the role of AI in supply chain resilience during COVID-19, the respondents may have an inclination towards a specific role of AI due to their limited exposure.

Practical implications

Supply chain managers can utilise data to embed the required degree of resilience in their supply chains by considering the proposed framework elements and phases.

Originality/value

The present research contributes a framework that presents a four-phased, structured and systematic platform considering the required information processing capabilities to recognise, analyse, reconfigure and activate phases to ensure supply chain resilience.

The role of digitalized information sharing for flexibility capability utilization: lessons from Germany and Japan

Purpose

Information-sharing and flexibility are considered to be major tools for risk mitigation and supply chain resiliency. However, less light has been shed on the role of information-sharing as an enabler to utilize flexibility capabilities before and after supply chain disruptions. The aim of this paper is to provide indications on how digitalized information-sharing (DIS) enhances flexibility capability utilization.

Design/methodology/approach

The methodology follows a qualitative approach, including 31 transcribed semi-structured interviews with supply chain experts in Germany and Japan.

Findings

The findings indicate that DIS supports flexibility capability utilization in pre- and post-disruptive supply chain management. First, the enhancement of estimated transport time accuracy supports rapid supplier and transport mode adjustment. Second, while the effects of DIS in manufacturing are limited without pre-existing flexibility capabilities, steady internal and external DIS utilizes exciting manufacturing flexibility to cope with disruptions beyond production. Third, track and trace technologies enhance the value of shared data and allow flexibility in the form of demand-oriented distribution, but companies unable to adopt technologies can still enhance flexibility capabilities with DIS using the existing infrastructure.

Originality/value

This paper highlights the essential role of digital information-sharing for flexibility utilization in supply chain risk management. While existing studies engaged with flexibility and information-sharing in supply chain risk management, this study contributes by emphasizing digital information-sharing as a key triggering enabler for flexibility in pre- and post-disruptive phases.