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Many global multinationals accelerated their investments in digitizing data during the pandemic. According to Colin Masson, a director of research at ARC Advisory Group, the opportunity to mine these vast quantities of data to achieve business value is “NOW.” Mr. Masson leads ARC’s research on industrial AI and data fabrics.
And the foundation that holds all of this together is your master data. Even if you invest in sophisticated inventory management systems, if your master data isn’t accurate, you’ll fail. For example, in some instances simply adjusting delivery windows can save more than you can through rate negotiations.
This year, a recurring theme that I saw was about using supply chain data to improve the customer experience across the entire value chain. Here are the ones that stood out to me, especially as it relates to supply chain data. The single data cloud runs on Snowflake, one of Blue Yonder’s partners.
SCCN solutions allow trading partners to collaborate across defined trading partner processes based on a common data model. For example, a buyer might say, “You only shipped me 800 of the 1000 products I ordered.” SAP’s Business Network is a supply chain collaboration network. And the supplier might reply, “I only agreed to ship 800.”
I might tell Alexa, for example, “Play the station Smooth Jazz!” The manager would not be required to drill down through web page after web page and look at dense tabular data to get the answer. Most of the new in-context GenAI solutions have been pre-trained on 200,000 pages of SAP’s training and technical documents.
That would enable the printing of manifests and proof-of-delivery documents by shipment, to be issued to the delivery crews the following day, and the picking documents for the warehouse. If youre choosing route planning software that integrates with vehicle tracking, you shouldnt let the valuable data go to waste.
What are some examples of Supply Chain Automation? Predictive Analytics and Demand Forecasting – Modern supply chain systems analyse historical data, market trends and even weather patterns to predict future demand. You’ll see your documentation processing transform from a time-consuming bottleneck into a smooth operation.
Instead start with the foundation of your AI strategy, which should be an understanding of your company’s supply chain and your data. Consider a planner in Brazil working with the previous lead time prediction example, who has forgotten how to update the parameters. Because it doesn’t understand, we need humans at the helm.
The digital twin, for example, can be subjected to numerous stress tests that mimic real-world conditions and observe how different variables interact and impact the entire network. For example, the analysis from stress testing can reveal a particular supplier or production resource is a frequent point of failure under high-demand scenarios.
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