Supply chains robustness: Challenges and opportunities

Monostori, Judit

doi:10.1016/j.procir.2017.12.185

Cited by 54 publications

(39 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…While an agile supply chain refers to speed and quickness of recovering from the disruption in the supply chain [20,26,69]. On the other hand, a robust supply chain is one that stays functional during uncertainties and can withstand disruptions and maintain the original state [32,70]. Although the previous literature has shown the importance of SC resilience, SC agility, and SC robustness, it is astonishing that existing studies have treated them separately.…”

Section: Managing Supply Chain Risksmentioning

confidence: 99%

“…Monsotori distinguished SC robustness according to its types, such as SC robustness on a small and large scale, SC robustness on the global and local scale, SC robustness as active and passive, and SC robustness as operational and structural scales. Whatever the scale it is, SC robustness ability copes with internal and external SC disruptions and enhances SC performance [32].…”

Section: Supply Chain Robustnessmentioning

confidence: 99%

“…SC agility refers to reaction speed and quickness to changing situations, while SC resilience deals with the situation in mature and steady manners [11]. Alternatively, SC robustness is a proactive approach that deals with risks through a reduction in complex organization structures, complex processes, maintaining excess resources, and developing more stable processes that resist SCRs [32][33][34]. Among SCR mitigation strategies based on SC resilience, SC agility, and SC robustness, previous studies have frequently cited an increase in capacity, increase in responsiveness, increase in flexibility, aggregate demand, increase in capability, redundant supplier, increase in inventory, and postponement [17,25,35].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Neutrosophic AHP and TOPSIS Framework for Supply Chain Risk Assessment in Automotive Industry of Pakistan

et al. 2019

View full text Add to dashboard Cite

Risk is inherent in all parts of life and brings consequences, but when it specifically emerges in supply chains, it is susceptible. Therefore, this study aims at identifying and assessing supply chain risks and developing criteria for managing these risks. Supply chain (SC) risks consist of complex, uncertain, and vague information, but risk assessment techniques in the literature have been unable to handle complexity, uncertainty, and vagueness. Therefore, this study presents a holistic approach to supply chain risk management. In this paper, neutrosophic (N) theory is merged with the analytic hierarchy process (AHP) and technique for order of preference by similarity to ideal solution (TOPSIS) to deal with complexity, uncertainty, and vagueness. Then the proposed methodology is practically implemented through a case study on the automotive industry. SC resilience, SC agility, and SC robustness were selected as criteria for managing supply chain risks and analyzed using N-AHP. Furthermore, seventeen risks were identified and assessed by using N-TOPSIS. Results suggest supply chain resilience is the most important criterion for managing supply chain risks. Moreover, supplier delivery delays, supplier quality problems, supplier communication failures, and forecasting errors are the most vulnerable risks that occur in supply chains of the automotive industry in Pakistan.

show abstract

Section: Managing Supply Chain Risksmentioning

confidence: 99%

Section: Supply Chain Robustnessmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Neutrosophic AHP and TOPSIS Framework for Supply Chain Risk Assessment in Automotive Industry of Pakistan

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The literature analysis shows that complex networks become more vulnerable to severe disruptions which change the SC structures and are involved with SC structural dynamics (Ivanov, Sokolov, and Kaeschel 2010;Monostori 2018;Ivanov 2018a). Moreover, the ripple effect in the SC depends on structural network composition and complexity (Han and Shin 2016;Sokolov et al 2016;Levner and Ptuskin 2017;Ivanov 2017aIvanov , 2018bPavlov et al 2018).…”

Section: Semantic Level: Structural Properties Complexity Role and mentioning

confidence: 99%

Low-Certainty-Need (LCN) supply chains: a new perspective in managing disruption risks and resilience

Ivanov

Dolgui

2018

International Journal of Production Research

248

View full text Add to dashboard Cite

This study suggests a new approach to supply chain (SC) disruption risk management where SC behaviour is less dependent on the certainty of our knowledge about the environment and its changes. The unpredictability of the occurrence of disruption and its magnitude suggests that designing SCs with a low need for 'certainty' may be as important, if not more so, than predetermined disruption control strategies. In this setting, this study calls for the development of a new perspective in SC disruption management, i.e. low-certainty-need (LCN) SCs. A number of principles and concepts is derived in recent, relevant literature to structure the characteristics of the LCN framework and its management. Structural variety, process flexibility, and parametrical redundancy are identified as key LCN SC characteristics that ensure efficient disruption resistance as well as recovery resource allocation. Two efficiency capabilities of the LCN SC are shown, i.e. low need for uncertainty consideration in planning decisions and low need for recovery coordination efforts based on a combination of lean and resilient elements. The results allow the identification of an LCN SC framework, concepts and technologies for its implementation as well as missing themes and new research questions which contribute to a better understanding of SC disruption risks. Special focus is directed on the digital technology usage in the LCN framework implementation.

show abstract

“…A supply chain is a system of organizations, people, activities, information and resources involved in transforming resources, materials and components into a finished product that is delivered to the end customer. Please refer to the book by Hugos (2018) for an introduction to supply chain management and the recent works by Moktadir et al (2018a, b) on sustainable supply chain management and robustness of supply chains by Monostori (2018). The systemic, strategic coordination of the traditional business functions and the tactics across these business functions within a particular company and across businesses within the supply chain for improving the long-term performance is the essence of supply chain management (Mentzer et al 2001).…”

Section: Introductionmentioning

confidence: 99%

Dynamic configuration and collaborative scheduling in supply chains based on scalable multi-agent architecture

Hsieh

2018

J Ind Eng Int

View full text Add to dashboard Cite

Due to diversified and frequently changing demands from customers, technological advances and global competition, manufacturers rely on collaboration with their business partners to share costs, risks and expertise. How to take advantage of advancement of technologies to effectively support operations and create competitive advantage is critical for manufacturers to survive. To respond to these challenges, development of a dynamic scheme to better manage collaborative workflows is urgent. In this paper, we will study how to develop a flexible and scalable framework to dynamically and coherently configure workflows that can meet order requirements based on multi-agent systems (MAS). Configuring and scheduling collaborative workflows is a challenging problem due to the computational complexity involved, distributed architecture and dependency among different partners' workflows. To achieve flexibility and reduce the cost and time involved in configuration of a supply chain network, we propose an approach that combines MAS, contract net protocol, workflow models and automated transformation of the workflow models to dynamically formulate the scheduling problem. To attain scalability, we develop a solution algorithm to solve the optimization problem by a collaborative and distributed computation scheme. We implement a software system based on industrial standards, including FIPA and the Petri net workflow specification model. In addition, we also illustrate effectiveness and analyze scalability of our approach by examples. Our approach facilitates collaboration between partners and provides a scalable solution for the increasing size of supply chain networks.

show abstract

Supply chains robustness: Challenges and opportunities

Cited by 54 publications

References 24 publications

A Neutrosophic AHP and TOPSIS Framework for Supply Chain Risk Assessment in Automotive Industry of Pakistan

A Neutrosophic AHP and TOPSIS Framework for Supply Chain Risk Assessment in Automotive Industry of Pakistan

Low-Certainty-Need (LCN) supply chains: a new perspective in managing disruption risks and resilience

Dynamic configuration and collaborative scheduling in supply chains based on scalable multi-agent architecture

Contact Info

Product

Resources

About