3 min readA New Way to Manage the Supply Chain As supply chains are increasingly [...]
As supply chains are increasingly powered by advanced technologies and interconnected systems, organizations are fundamentally changing the way they manage their value chain. Data-driven supply chain solutions provide organizations with the ability to process and take action on insights in real time and at a granular level. A range of AI techniques are involved in this process; from data science to machine learning.
AI/ML effectively takes into account factors that traditional supply chain solutions cannot track or quantify over time. Supply chains generate an enormous amount of data, which add value in a variety of use cases.
Here are three ways AI & the IoT are effectively transforming the supply chain:
Combining machine learning with advanced analytics, IoT sensors and real-time monitoring provide end-to-end visibility across the supply chain. Traditionally, asset monitoring was limited to tracking numbers and barcodes. But with IoT-enabled devices and AI/ML technologies, organizations can track products from ‘floor to home/plant/field’ and beyond.
Organizations are equipped with a more complete understanding of their customers – their usage behavior and buying habits – allowing them to form tighter connections and a more personalized experience. Digitized supply chains empower organizations with a holistic view of its operations and evolving end-to-end business ecosystem.
There are a number of methods to forecast demand for next-generation products. From pragmatic approaches, directly asking channel partners and sales teams for their forecasts, to using more advanced statistical methods. But one of the most challenging aspects of managing the supply chain is accurately predicting when assets will require maintenance and the production demands that will be influenced as a result.
AI/ML algorithms are capable of analyzing large, diverse data sets, enabling industry players to quickly identify parts that require immediate replacement or assets at risk of failure. This improves demand forecasting accuracy, taking into account internal and external factors that influence demand and were not known before, for example leveraging asset failure rates to determine spare parts requirements.
Combining AI/ML with related technologies across the supply chain empowers organizations with greater contextual intelligence across supply chain operations, translating into lower inventory and operations costs and a quicker customer response time.
Companies rely heavily on external suppliers for parts and components, distribution and aftermarket services. With a visual on critical sensor readings and asset states over time, organizations can empower dealer networks with condition-based monitoring and automated reports. Improving supplier quality management by finding patterns in quality levels.
Companies are extending the life of connected assets by finding new patterns in the usage data collected. By analyzing machine data, organizations are able to determine which factors (internal and external) influence the machines performance. AL/ML technologies allow organizations to determine which equipment has the most faults and alerts, what the trends are over time, how this varies across the value chain.. and so on.
By ensuring the operational effectiveness of equipment, organizations have a more accurate measure of overall asset health, a key metric many manufacturers and supply chain operators rely on.
Today’s supply chains require an entirely new operating platform or architecture modeled around real-time asset data and enriched with insights not visible with traditional analytics tools. AI/ML technologies will play an essential role in transforming the supply chain.
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