How AI can help companies manage the semiconductor supply chain

How AI can help companies manage the semiconductor supply chain

Chinese Yuichiro | moment | Getty Images

Businesses and consumers have been grappling with supply chain issues for months, resulting in alarming shortages of all kinds of products, including critical semiconductor chips.

And during CHIPS and Science Act, Signed into law in Augustto boost semiconductor manufacturing in the United States, there is no indication what effect the legislation will have on supply, or even when.

“The semiconductor supply chain remains constrained,” said Brandon Kollek, semiconductor industry leader and principal at consulting firm Deloitte. “Leads on average have decreased slightly, given the downturn in the consumer electronics sector [laptops and smartphones], the demand for memory decreased. But demand for high-performance data center, defense and automotive chips remains historically high, with some semiconductor companies seeing growth in the range of 40% or more.”

One potential near-term solution for companies that rely on semiconductors: advanced data analytics and artificial intelligence tools to help manage supply issues.

“The Covid-19 pandemic has clearly demonstrated the impact that unexpected events can have on global supply chains,” said Rohit Tandon, Managing Director and Head of Global Artificial Intelligence and Analytics Services at Deloitte. “However, AI can help the world avoid similar disruptions in the future.”

Anticipate display problems

By analyzing the vast amounts of data produced by today’s supply chains, AI can predict a range of unexpected events, such as weather behaviors, transportation bottlenecks and labor strikes, helping to anticipate problems and reroute shipments around them, Tandon said.

“AI can also enable significant improvements in other key supply chain areas, including demand forecasting, risk planning, supplier management, customer management, logistics, and warehousing,” Tandon said.

This can lead to improved operating efficiency and working capital management, increased transparency and accountability, and more accurate delivery estimates; There are fewer disruptions to supplies, Tandon said. “In addition, manufacturers using AI to emerge in their smart factory operations can better respond to potential disruptions to avoid delays and pivot if needed, enabling them to be more flexible while still meeting customer demands,” he said.

“Organizations can leverage data analytics tools to gain deeper insights across the supply chain,” Tandon said. “These tools are designed to improve demand forecasting and support data sharing with customers and partners.” Additionally, organizations can use AI to predict or predict events related to the supply chain such as logistical challenges, geopolitical issues, and supply disruptions.

They can either implement actions independently or recommend actions to be taken by stakeholders, Tandon said, “ultimately helping companies build resilience in their supply chains.”

When deploying these supply chain management tools, Tandon said, it is a good idea to start with a small and narrow scope and develop the depth and breadth of models and algorithms where the results demonstrate their accuracy and value.

High quality data is also important. “Basic data is key, because bad data means bad analytics,” Tandon said. “The lack of transparency across the supply chain is often the result of inconsistent and incomplete data across the product, supplier and customer. [fixing] Data issues provide the foundation for data quality that builds confidence in the outputs of the analytics and AI process. “

Rand Technology, an independent semiconductor distributor, uses data analytics to solve customer supply challenges.

said Jennifer Straw, vice president of solutions and sourcing for the Americas and EMEA at Rand. “This way, OEMs and contract manufacturers can support their stock mix of components.”

Additionally, data and analysis are especially important during the manufacturer’s new product introduction phase in the material selection list, Straw said. “It is critical, during this phase, to identify where you can build flexibility into the design so that there are multiple sources of semiconductors on the list of approved materials,” she said.

In this way, manufacturers are not dependent on a single semiconductor provider, which may affect business in the current environment. “We are leveraging advanced analytics to help determine the availability of these semiconductors and identify trends and patterns, such as gaps, price hikes or product change notices, before products are in production,” Strawon said. Rand also uses technology to make decisions about future scenarios and to determine how much buffer the company might want to secure, she said.

Rand also uses advanced data analytics to identify trends and patterns that enable him to strategically guide clients through risky market conditions. “By modeling and real-time insight into availability, market shifts and conditions globally, we are able to help reduce risks and map out strategies in advance that can be used when we notice certain changes and disruptions in the industry,” Straw said.



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