TY - GEN
T1 - Identification of tipping points in supply chain dynamics using effective dimension and resilience index
AU - Edwards, Christine M.
AU - Muhle, Eric
AU - Wolma, Kyle
AU - Bishop, Amber
AU - Nilchiani, Roshanak R.
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/5/30
Y1 - 2018/5/30
N2 - When a supply chain model is translated into a relatively new and revolutionary mathematical dimension that reveals the location of tipping points, a critical resilience index can be extracted that measures whether supply chains are sustainable or close to collapse. This paper shows the translation of supply chain dynamics equations into this effective plane, the extraction of the critical resilience index, and simulation results that show how the resilience index correlates with stability of the supply chain. These results have great potential for improving the understanding of supply chain management, revealing how close a supply chain is to collapse and providing a metric to help make supply chains more sustainable. For example, one potential application is to use this model to ensure that agri-food supply chains are resilient to continue supporting growing populations.
AB - When a supply chain model is translated into a relatively new and revolutionary mathematical dimension that reveals the location of tipping points, a critical resilience index can be extracted that measures whether supply chains are sustainable or close to collapse. This paper shows the translation of supply chain dynamics equations into this effective plane, the extraction of the critical resilience index, and simulation results that show how the resilience index correlates with stability of the supply chain. These results have great potential for improving the understanding of supply chain management, revealing how close a supply chain is to collapse and providing a metric to help make supply chains more sustainable. For example, one potential application is to use this model to ensure that agri-food supply chains are resilient to continue supporting growing populations.
KW - Complex systems
KW - Data analytics
KW - Network theory
KW - Resilience
KW - Supply chain
UR - http://www.scopus.com/inward/record.url?scp=85048876758&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048876758&partnerID=8YFLogxK
U2 - 10.1109/SYSCON.2018.8369556
DO - 10.1109/SYSCON.2018.8369556
M3 - Conference contribution
AN - SCOPUS:85048876758
T3 - 12th Annual IEEE International Systems Conference, SysCon 2018 - Proceedings
SP - 1
EP - 4
BT - 12th Annual IEEE International Systems Conference, SysCon 2018 - Proceedings
T2 - 12th Annual IEEE International Systems Conference, SysCon 2018
Y2 - 24 April 2018 through 26 April 2018
ER -