K
Kathleen Martin
Guest
The past two years have brought a torrent of challenges for the automotive supply chain. Even beyond Covid-19, there have been blocked canals, congested ports, driver shortages and multiple natural disasters that have surprised OEMs and suppliers around the world. The pandemic exacerbated these issues and created new ones, most notably a critically restricted supply of semiconductor chips.
A key culprit is making these challenges worse: outdated software.
“If you’re reacting to a supply chain crisis with old and inflexible software written in the 1990s, it's a bit like playing a game of chess where you can only see some of the pieces,” says Liam Mawe, Global Head of Automotive and Mobility at Palantir Technologies. “Data and analytics in the supply chain should light up the entire chess board and guide decision-makers in the supply chain to make moves with the full available context towards the strategic goals of the company.”
For many business leaders today, it’s becoming increasingly clear that data technology needs to bridge the gap between analytics and operations. Companies like Palantir lead the way by integrating data in common operating platforms designed for operational decision-making. This creates creating environments where supply-chain crises can be managed intelligently with contextual detail or prevented altogether.
This approach is part of a broader push to revolutionize the automotive industry supply chain by introducing new tools harnessing the power of data and AI technology. Two of the key avenues towards making this happen: leveraging enterprise-wide data and the power of artificial intelligence (AI).
A unified semantic layer
According to Mawe, data tends towardentropy. There are potentially billions of nodes to map across a supply chain and many of them can be concealed in siloed departments or dated pieces of software, both of which hinder transparency.
“The antidote to this challenge is an enterprise-wide semantic layer that effectively translates raw data into a recognizable language for end-users,” says Mawe.
Palantir calls this common language an "ontology,”and it’s the first step towards building a digital twin of an enterprise, a simulation that allows decision-makers to understand complex systems within their organizations. By integrating siloed data systems, Palantir creates a common operating picture from which users can run AI models, test scenarios and prevent problems before they happen.
According to a study by McKinsey & Co., a global consulting firm, “Successfully implementing AI-enabled supply-chain management has enabled early adopters to improve logistics costs by 15 percent, inventory levels by 35 percent and service levels by 65 percent.”
Continue reading: https://www.autonews.com/sponsored/harnessing-data-analytics-and-ai-powered-decision-making-supply-chain-resiliency
A key culprit is making these challenges worse: outdated software.
“If you’re reacting to a supply chain crisis with old and inflexible software written in the 1990s, it's a bit like playing a game of chess where you can only see some of the pieces,” says Liam Mawe, Global Head of Automotive and Mobility at Palantir Technologies. “Data and analytics in the supply chain should light up the entire chess board and guide decision-makers in the supply chain to make moves with the full available context towards the strategic goals of the company.”
For many business leaders today, it’s becoming increasingly clear that data technology needs to bridge the gap between analytics and operations. Companies like Palantir lead the way by integrating data in common operating platforms designed for operational decision-making. This creates creating environments where supply-chain crises can be managed intelligently with contextual detail or prevented altogether.
This approach is part of a broader push to revolutionize the automotive industry supply chain by introducing new tools harnessing the power of data and AI technology. Two of the key avenues towards making this happen: leveraging enterprise-wide data and the power of artificial intelligence (AI).
A unified semantic layer
According to Mawe, data tends towardentropy. There are potentially billions of nodes to map across a supply chain and many of them can be concealed in siloed departments or dated pieces of software, both of which hinder transparency.
“The antidote to this challenge is an enterprise-wide semantic layer that effectively translates raw data into a recognizable language for end-users,” says Mawe.
Palantir calls this common language an "ontology,”and it’s the first step towards building a digital twin of an enterprise, a simulation that allows decision-makers to understand complex systems within their organizations. By integrating siloed data systems, Palantir creates a common operating picture from which users can run AI models, test scenarios and prevent problems before they happen.
According to a study by McKinsey & Co., a global consulting firm, “Successfully implementing AI-enabled supply-chain management has enabled early adopters to improve logistics costs by 15 percent, inventory levels by 35 percent and service levels by 65 percent.”
Continue reading: https://www.autonews.com/sponsored/harnessing-data-analytics-and-ai-powered-decision-making-supply-chain-resiliency
