Multi-level supply chain disruption and coordinated response

2021 ◽  
Author(s):  
Vivek Sagar Mahanta
Keyword(s):  
Supply Chain ◽  
Supply Chain Disruption ◽  
Multi Level

Analysis of the Supply Chain Disruption Risks in the Malaysian Automotive Remanufacturing Industry-a Case Study

2020 ◽  
Vol 12 (05) ◽  
Author(s):  
Nuramilawahida Mat Ropi ◽  
◽  
Hawa Hishamuddin ◽  
Dzuraidah Abd Wahab ◽  
◽  
...  
Keyword(s):  
Supply Chain ◽  
Supply Chain Disruption ◽  
Disruption Risks

scholarly journals Heeding Supply Chain Disruption Warnings: When And How Do Cross‐Functional Teams Ensure Firm Robustness?

2021 ◽  
Author(s):  
Thomas A. De Vries ◽  
Gerben S. Van Der Vegt ◽  
Kirstin Scholten ◽  
Dirk Pieter Van Donk
Keyword(s):  
Supply Chain ◽  
Supply Chain Disruption ◽  
Functional Teams

scholarly journals Mathematical model of the feedback between global supply chain disruption and COVID-19 dynamics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xingyu Li ◽  
Amin Ghadami ◽  
John M. Drake ◽  
Pejman Rohani ◽  
Bogdan I. Epureanu
Keyword(s):  
Mathematical Model ◽  
Supply Chain ◽  
Care Delivery ◽  
Global Supply Chain ◽  
Economic Losses ◽  
Chain Model ◽  
Supply Chain Disruption ◽  
Disease Networks ◽  
Resource Allocation Strategy ◽  
The Impact

AbstractThe pandemic of COVID-19 has become one of the greatest threats to human health, causing severe disruptions in the global supply chain, and compromising health care delivery worldwide. Although government authorities sought to contain the spread of SARS-CoV-2, by restricting travel and in-person activities, failure to deploy time-sensitive strategies in ramping-up of critical resource production exacerbated the outbreak. Here, we developed a mathematical model to analyze the effects of the interaction between supply chain disruption and infectious disease dynamics using coupled production and disease networks built on global data. Analysis of the supply chain model suggests that time-sensitive containment strategies could be created to balance objectives in pandemic control and economic losses, leading to a spatiotemporal separation of infection peaks that alleviates the societal impact of the disease. A lean resource allocation strategy can reduce the impact of supply chain shortages from 11.91 to 1.11% in North America. Our model highlights the importance of cross-sectoral coordination and region-wise collaboration to optimally contain a pandemic and provides a framework that could advance the containment and model-based decision making for future pandemics.

scholarly journals Cross-Docking Operations for Supply Chain Logistics in JIT Production and Distribution Systems

1970 ◽  
Vol 8 (1-2) ◽  
pp. 219-230
Author(s):  
Gyan Bahadur Thapa ◽  
Tanka Nath Dhamala ◽  
Shankar Raj Pant
Keyword(s):  
Supply Chain ◽  
Distribution Systems ◽  
Operation Time ◽  
Just In Time ◽  
Sequencing Problem ◽  
Supply Chain Logistics ◽  
Research Areas ◽  
Cross Docking ◽  
Production And Distribution ◽  
Multi Level

The multi-level production problem is one of the challenging research areas in supply chain management. We present brief literature review and mathematical models of multi-level just-in-time sequencing problem with a view of cross-docking approach for supply chain logistics. Describing cross-docking operations, we propose a mathematical model for the cross-docking supply chain logistics problem to minimize the operation time as truck sequencing problem. We establish a proposition as the synthesis of the production and logistics.Key Words: Just-in-time; Supply chain; Logistics; Cross-dock; Operation timeDOI: http://dx.doi.org/10.3126/jie.v8i1-2.5114Journal of the Institute of Engineering Vol. 8, No. 1&2, 2010/2011Page: 219-230Uploaded Date: 20 July, 2011

Minimizing supply chain disruption risk through enhanced flexibility

2009 ◽  
Vol 39 (5) ◽  
pp. 404-427 ◽  
Author(s):  
Joseph B. Skipper ◽  
Joe B. Hanna
Keyword(s):  
Supply Chain ◽  
Multiple Regression ◽  
Planning Process ◽  
Research Effort ◽  
Risk Exposure ◽  
Contingency Planning ◽  
Management Support ◽  
Supply Chain Disruption ◽  
Content Type ◽  
Disruption Risk

PurposeThe purpose of this paper is to examine the use of a strategic approach (contingency planning) to minimize risk exposure to a supply chain disruption. Specifically, the relationship between several attributes of a contingency planning process and flexibility are examined.Design/methodology/approachThis effort develops a model that will provide both researchers and practitioners a means of determining the attributes with the highest relationship to flexibility. The model is then tested using multiple regression techniques.FindingsBased on the sample used in this survey, top management support, resource alignment, information technology usage, and external collaboration provide the largest contributions to flexibility. Flexibility has been shown to enhance the ability to minimize risk exposure in the event of a supply chain disruption.Research limitations/implicationsIn this research effort, the multiple regression results produced an R2 of 0.45, indicating that additional variables of interest may need to be identified and investigated. Furthermore, a wider range of respondents could make the results more generalizable.Practical implicationsThis effort will help to allow managers at multiple levels to understand the primary planning attributes to use to increase flexibility.Originality/valueThe paper develops a model that can be used to identify the specific areas that can lead to improved flexibility. Based on the model, managers, and planners can develop appropriate strategies for minimizing risk exposure in the event of a supply chain disruption.

Supply chain disruption for crude oil due to the effect of Covid-19 pandemic: Evidence from Vietnam

2021 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Giang Hoang Huong ◽  
Son Ta Anh ◽  
Luan Thanh Le ◽  
Bui Duy Linh ◽  
Ngoc Vu Thi Minh
Keyword(s):  
Supply Chain ◽  
Crude Oil ◽  
Supply Chain Disruption

scholarly journals Reading on and between the lines: risk identification in collaborative and adversarial buyer–supplier relationships

2018 ◽  
Vol 23 (4) ◽  
pp. 351-376 ◽  
Author(s):  
Yiyi Fan ◽  
Mark Stevenson
Keyword(s):  
Social Capital ◽  
Supply Chain ◽  
Risk Identification ◽  
Social Capital Theory ◽  
Future Research ◽  
Supplier Relationships ◽  
Content Type ◽  
Signalling Theory ◽  
Multi Level ◽  
The Impact

Purpose This paper aims to investigate how supply chain risks can be identified in both collaborative and adversarial buyer–supplier relationships (BSRs). Design/methodology/approach This research includes a multiple-case study involving ten Chinese manufacturers with two informants per organisation. Data have been interpreted from a multi-level social capital perspective (i.e. from both an individual and organisational level), supplemented by signalling theory. Findings Buyers use different risk identification strategies or apply the same strategy in different ways according to the BSR type. The impact of organisational social capital on risk identification is contingent upon the degree to which individual social capital is deployed in a way that benefits an individual’s own agenda versus that of the organisation. Signalling theory generally complements social capital theory and helps further understand how buyers can identify risks, especially in adversarial BSRs, e.g. by using indirect signals from suppliers or other supply chain actors to “read between the lines” and anticipate risks. Research limitations/implications Data collection is focussed on China and is from the buyer side only. Future research could explore other contexts and include the supplier perspective. Practical implications The types of relationships that are developed by buyers with their supply chain partners at an organisational and an individual level have implications for risk exposure and how risks can be identified. The multi-level analysis highlights how strategies such as employee rotation and retention can be deployed to support risk identification. Originality/value Much of the extant literature on supply chain risk management is focussed on risk mitigation, whereas risk identification is under-represented. A unique case-based insight is provided into risk identification in different types of BSRs by using a multi-level social capital approach complemented by signalling theory.

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