By Tim Williams & Vijay Dayalan
Machine learning has taken various shapes and forms over the last few years. Industries like transportation, healthcare, manufacturing & finance have leveraged the potential of these technologies to improve their capabilities.
A few examples of machine learning capabilities:
Artificial intelligence (AI) can be used to give shape to massive unstructured data sets and recognize patterns, allowing companies to categorize and manage their spend in real-time through the cloud.
For example, cluster analysis uses intelligent algorithms to find patterns in invoices, purchase orders, and master data and combine the information automatically. Clean data can then be used to make strategic decisions on how to allocate future spend across the supply base.
Machine learning can provide a “guided workflow” where an end user doesn’t need to have a complex knowledge of buying channels or procurement policies. Instead the workflow, led by AI, pinpoints the right approval mechanism and buying channels.
Machine learning allows for an agile supply network that is easily adaptable to unexpected changes. With cloud-based protocols and external solutions, any new functionality or capability is easily adopted into an existing procurement operation.
ML & AI Across Supply Chain Functions
Above are some examples of how machine learning can have an impact on key supply chain functions. A machine learning algorithm is like a mind-map that connects various aspects of the business and helps real-time decision making. Companies like UPS, IBM, Honeywell, and Paypal are using machine learning to improve their capabilities across different functions of their supply chain.
For companies looking to implement machine learning, the critical first step is to assess the end -to-end maturity of its supply chain. CGN provides comprehensive supply chain solutions that connect end-to-end aspects of the network. To learn more please contact us.
For more analysis on artificial intelligence, check out our recent Crain's Chicago Business roundtable discussion here.