Process Control in Agile Supply Chain Networks

Pearson, Michael; Masson, Ron; Swain, Anthony

doi:10.1057/9781137541253_7

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…While the signs of impending natural disasters can sometimes be detected at an early stage and used for warnings, detecting their precursors is diffi cult and routine predictions remain elusive. The location, magnitude, and time of occurrence cannot be forecasted (L. Pearson 2012 ). Moreover, these natural disaster events are more likely to destroy the infrastructure (e.g., roads, bridges, and power lines) and superstructure (e.g., buildings) of the affected area, creating long-term disruptions for supply chains ( Altay and Ramirez 2010 ).…”

Section: The Multiple Shades Of Natural Force-induced Risksmentioning

confidence: 99%

“…The fundamental difference between conventional preparedness and intelligent preparedness lies in the way in which weather big data are leveraged (or lack thereof ) in managing weather-related risks. Facilitated by advance in wireless transmission network, large amounts of weather data are collected from weather satellites, remote sensing and GPS systems, and crowdsourced data ( Greengard 2014 ;O'Reilly 2014 ;M. Pearson 2012 ;Rosenbaum 2011 ).…”

Section: Toward Intelligent Preparednessmentioning

confidence: 99%

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Weatherproofing Supply Chains: Enable Intelligent Preparedness with Data Analytics

Coyle¹,

Ruamsook²,

Symon³

2016

Transportation Journal

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Catastrophic events, occurrences of severe weather, and year-over-year changes in the weather pose various degrees of risks for companies and their supply chains. These risks range from severe, prolonged supply chain disruptions, to critical stockouts, and to escalating costs due to last-minute implementation of the emergency procedures. When these risks come to fruition, the financial impacts can easily reach millions of dollars or more. This study introduces a new concept of intelligent preparedness—preparedness based on the ability to sense, capture, and analyze weather data and turn it into actionable insights. The fundamental difference between conventional preparedness and intelligent preparedness lies in the way in which weather big data are leveraged (or lack thereof) in managing weather-related risks. A case example of an early implementer is presented to illustrate the important role that data analytics play in enabling managers to take advantage of the weather big data in effectuating intelligent preparedness.

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Section: The Multiple Shades Of Natural Force-induced Risksmentioning

confidence: 99%

Section: Toward Intelligent Preparednessmentioning

confidence: 99%