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Using AI Analytics to Avoid Supply Chain Disruption

Is there any way for manufacturers to predict machine failures before they happen to avoid supply chain disruptions, delay and customer dissatisfaction? An emerging solution is the use of Artificial Intelligence (AI)data analytics in the supply chain, which has the most potential to minimize supply chain disruption as well as to dramatically reduce costs of supply chain.

Predictive Analytics

According to Wollenhaupt (2016), downtime in auto manufacturing can cost $1.3 million per hour, according to published reports. The diagram below depicts the prediction of a motor failure.

Predictive Analytics
Picture taken from https://d1.awsstatic.com/Marketplace/scenarios/bi/Q42017/BIA12-predictive-maintenance-scenario-brief.pdf

Manufacturers now have the tools and resources to apply “predictive maintenance” on their machines and factories. With predictive maintenance, significant reductions in unplanned downtime can save millions of dollars and keep customers happy.

The Data Analytics Eco-System to Prevent Downtime

Manufacturers can now use AI technology, Machine Learning, Deep Learning, IoT and Big Data to integrate smart sensors with their machinery and to develop smarter supply chains and manufacturing processes. Doing so will increase visibility into their supply chains and a greater ability to reduce supply chain disruptions through proactive and predictive maintenance.

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Alok Sharma, ADPSM
Alok Sharma has substantive years of experience in the professional field of procurement and supply management, and specifically in the area of technology adoption. He is a member of Singapore Institute of Purchasing and Materials Management (SIPMM). Alok holds a Bachelor degree in Engineering and he completed the Advanced Diploma in Procurement and Supply Management (ADPSM) course on January 2019 at SIPMM Institute.

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