Predictive Maintenance Reduces Downtime
Every hour of downtime can cost a manufacturer millions of dollars. Artificial intelligence is making it possible to predict maintenance issues faster, before they become expensive problems.
With predictive maintenance, manufacturers deploy a network of sensors to continuously monitor and gather real-time data about their equipment’s health. AI models can draw from historical equipment data and compare that against new information it receives to determine if a machine is functioning normally or not.
The BMW Group plant in Regensburg, Germany, uses an in-house cloud platform to monitor mobile load carriers that bring vehicles through the assembly hall. An AI algorithm monitors power consumption and any abnormal conveyor movement. If the data is abnormal, an alarm sounds and the whole line stops.
At Toyota’s Indiana assembly plant, maintenance workers use a cloud-based asset management system from IBM to assess the health of equipment and components, and proactively address potential issues. The result has been a 50% reduction of downtime, 70% fewer breakdowns and 25% lower maintenance costs.
DISCOVER: How IoT data can transform your manufacturing business.
Supply Chain Optimization Minimizes Disruption
A successful manufacturing operation relies on a healthy supply chain but with many complex data sources, disruptions are hard to predict. However, AI is helping manufacturers scale their outputs more accurately. IT leaders can scale hybrid and multiclouds during peak hours while leaving sensitive supply chain data on local environments for processing.
“By harnessing the potential of machine learning, automation and advanced analytics in a hybrid cloud environment, organizations can gain a sixth sense, anticipating everything from demand fluctuations to sourcing delays. With this foresight, they can reinvent their supply chain strategies, shifting from a reactive to a proactive stance,” according to an IBM report.
“Mapping the supply chain is a crucial step towards enhancing its resilience, and AI tools can provide substantial assistance in this regard,” write supply chain professors Maxime C. Cohen and Christopher S. Tang in a blog for the Georgetown Journal of International Affairs.
“These tools can gather records like product orders, customs declarations, and freight bookings, which are often represented in various formats and languages,” the authors explain. “AI algorithms can extract relevant data from both structured and unstructured documents with high precision. AI tools can compile and synthesize this raw data, enabling a firm to map out its different supply chain tiers.”
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