TY - GEN
T1 - Reuse optimization and tipping-point resilience in supply chains
AU - Edwards, Christine M.
AU - Nilchiani, Roshanak R.
AU - Wade, Jon
AU - Strickland, Karl
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/4
Y1 - 2019/4
N2 - As systems are optimized, they can become more brittle and less resilient. A novel resilience index introduced in 2016 measures how close a system is to tipping points [1] and was shown to work on supply chain models in 2018 [2]. This paper expands on that research through assessing how well those methods work on real supply-chain systems through case studies in reuse optimization. The results show an interesting conflict between supply-chain reuse optimization and resilience. As a system is increasingly optimized through more efficient use and re-use of materials, its resilience decreases. This can be measured by the proximity of its resilience index to the expected tipping-point of the system. Past the tipping point, the available resources and production rates cannot meet demand. Discussion of the results explore how this resilience index can be used as an important factor in system optimizations, and the implications of these findings in the management of supply chains and other systems.
AB - As systems are optimized, they can become more brittle and less resilient. A novel resilience index introduced in 2016 measures how close a system is to tipping points [1] and was shown to work on supply chain models in 2018 [2]. This paper expands on that research through assessing how well those methods work on real supply-chain systems through case studies in reuse optimization. The results show an interesting conflict between supply-chain reuse optimization and resilience. As a system is increasingly optimized through more efficient use and re-use of materials, its resilience decreases. This can be measured by the proximity of its resilience index to the expected tipping-point of the system. Past the tipping point, the available resources and production rates cannot meet demand. Discussion of the results explore how this resilience index can be used as an important factor in system optimizations, and the implications of these findings in the management of supply chains and other systems.
KW - Complex systems
KW - Data analytics
KW - Network theory
KW - Resilience
KW - Supply chain
UR - http://www.scopus.com/inward/record.url?scp=85073167062&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85073167062&partnerID=8YFLogxK
U2 - 10.1109/SYSCON.2019.8836752
DO - 10.1109/SYSCON.2019.8836752
M3 - Conference contribution
AN - SCOPUS:85073167062
T3 - SysCon 2019 - 13th Annual IEEE International Systems Conference, Proceedings
BT - SysCon 2019 - 13th Annual IEEE International Systems Conference, Proceedings
T2 - 13th Annual IEEE International Systems Conference, SysCon 2019
Y2 - 8 April 2019 through 11 April 2019
ER -