Amazon Web Services recently launched AWS Supply Chain, a new cloud application that promises to improve supply chain visibility and provide actionable insights to mitigate supply chain risk, reduce costs, and improve service. ‘client experience.
We wanted to know more, so we contacted Diego Pantoja-Navajas, Vice President, AWS Supply Chain with Amazon Web Services. Looked…
Smart Industry: What role does machine learning play in building supply chain resilience?
Diego: Machine learning is a broadly transformative technology that is being used to improve the resilience of supply chains. We see this play out on many different levels.
Our customers have told us that they need ways to aggregate data from a myriad of disparate ERP and supply chain systems into a single canonical data model, so they have all their supply chain data in the same place in the same form. Machine learning and natural language processing have been instrumental in enabling customers to aggregate this data faster and easier, without the need to re-platform on a new system, automating much of the heavy lifting. involved in linking data from one system to another.
This is the first challenge that machine learning helps customers meet: bringing together all the necessary data from across the supply chain so that it can be used to generate useful insights, insights and actions. . However, once you have all the data you need in one place, you still need to make sense of it. Our supply chain customers often deal with data from millions or even billions of transactions, far too much for an unattended human brain process.
Fortunately, one of the hallmarks of machine learning is its ability to extract meaning from very large datasets. So, with the help of machine learning technology, once you have all your data in one place, you can use this technology to extract meaning from it to perform business tasks such as forecasting lead times. sourcing suppliers based on real-time conditions, identifying potential demand spikes, or asking machine learning models to suggest recommended actions to address supply chain risks.
And it’s still early. We expect machine learning to be used for an increasing number of supply chain applications as more and more customers migrate to the cloud.
Smart Industry: How is supply chain resilience evolving as we emerge from this period of supply chain crisis? Are we more resilient with the hard lessons learned?
Diego: The supply chain tensions encountered over the past two years are a symptom of an underlying shift from a supply-based model to a new demand-based model, exacerbated by exogenous shocks quite important. Supply chains that have been built to respond reactively to supply shocks and growing customer demands (rather than anticipate them) mean that when disruptions do occur, businesses are less able to mitigate these. disturbances.
A lesson learned is that we need a better modernized infrastructure and apply the latest advances in machine learning in a way that doesn’t cost too much and doesn’t take too long to deploy, and works. with existing investments in the supply chain.
We have the technology to make supply chains more resilient, and in a way that does not introduce high upfront capital or time costs. Based on the conversations we are currently having with our customers, we are very optimistic about how these advancements can make supply chains more resilient in the future.
Smart Industry: How do outdated ERP systems inhibit modern supply chains? What is the solution ?
Diego: A persistent challenge has been consolidating data from different ERP systems into one place, and in something approaching real-time, so that supply chain managers can make quick rebalancing decisions before inventory issues do affect their ability to deliver on customer promises. This data conversion process and subsequent coordination with suppliers has inhibited supply chain resilience.
As previously stated, customers need a way to extract the data they already have from their ERP and supply chain management systems without overhauling their IT or running costly and time-consuming custom integration projects. . Machine learning and the cloud present new ways to enable real-time visibility, and to do so in a way that doesn’t disrupt any existing supply chain investments.
Smart Industry: What will the manufacturing supply chain look like in five years?
Diego: We look forward to better and more predictive supply chains that can anticipate customer demand cycles, as well as anticipate likely supply chain risks and ways to overcome them. Advances in AI and machine learning are evolving rapidly, and we believe supply chains will benefit from these developments in very significant ways.
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