Harnessing data analytics and AI-powered decision-making for supply chain resiliency – Automotive News

Traditionally, the most difficult part of mapping a supply chain is identifying all the different silos and sidings where pertinent information may be stored.

With automotive, you could spend your entire life trying to model the entire supply chain and all the global implications, says Jafaar Beydoun, sales director at software firm o9 Solutions. For something like EV batteries, you could get all the way to lithium mining. Its possible to model all those relationships in time, he says, but its better for companies to focus first on their most vital partners and suppliers.

One of our clients was using our software for supply-and-demand predictions, but they realized that without getting the suppliers directly involved, their information was always outdated, Beydoun says. To get real-time information in one place, they figured out the most critical and highest-spend suppliers and first integrated the ones they worked best with first, then adopted suppliers farther down the list later on.

Mapping this way is iterative and after perfecting the onboarding and data exchange processes with the first set of 15 suppliers, its easier to extend it to the next group of 10 or 15, all the way down to the smallest suppliers. As an example, Beydoun cites a small supplier in Bangladesh that did not have a reliable internet connection, but which could upload a single weekly data sheet to inform forecasting models.

This OEM reached almost total supply-chain visibility in two years with this phased approach, about half the time it would have taken if theyd tried to implement all vendors at once, he says.Both o9 Solutions and Palantir use Amazon Web Services (AWS) as their cloud-computing platform, which allows them to spin up computing power as needed. But AWSs expertise comes into play in other ways, too.

Many organizations are reconfiguring their IT needs around the platforms that Amazon Web Services provides, says Manish Govil, the companys supply chain global segment leader. We have the ability to gather and organize data from disparate systems, such as point-of-sale, ERP and Internet of Things (IoT) devices. AWS data ingestion, transmission and storage pipeline provides the capability to stitch together data from those disparate systems for end-to-end visibility.

The company also has plenty of expertise managing its own supply chain, with many partners who have highly specialized capabilities in different areas of the supply chain. We understand demand-shaping, sensing and planning, transportation management, real-time transportation visibility and warehouse management, Govil says. There are a lot of organizations that can provide one or two of these areas of expertise, but we have a very extensive ecosystem that brings them all together.

Along with Apple, Proctor & Gamble, McDonalds, and Unilever, Amazon is one of five companies deemed a supply-chain master by consulting firm Gartner, best known for its annual list of the top 25 supply-chain management companies. That experience informs how AWS helps other companies build similar types of systems, Govil says. There are already business networks that have built the connective tissue for supply-chain visibility through AWS.

While the data is owned by the individual companies, this connectivity allows very specific data to be shared from many disparate players far faster than any conventional data-gathering process and speed is of the essence.

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Harnessing data analytics and AI-powered decision-making for supply chain resiliency - Automotive News