How AI can help companies manage the semiconductor supply chain

How AI can help companies manage the semiconductor supply chain

Yuichiro Chino | time | Getty Images

Businesses and consumers have been grappling with supply chain issues for months, leading to annoying shortages of all kinds of products, including all-important semiconductor chips.

And while the CHIPS and Science Act, signed into law in August, is designed to boost U.S. semiconductor manufacturing, it’s unclear what effect the legislation will have on supply, or even when.

“The semiconductor supply chain is still constrained,” said Brandon Kulik, semiconductor industry leader and principal at Deloitte Consulting. “Delivery times have on average decreased slightly, given the easing of the consumer electronics segment [laptops and smartphones], and the memory demand decreased. But demand for higher-performance data center, defense and automotive chips remains historically high, with some semiconductor companies seeing growth in the order of 40% or more. »

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

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

Anticipate supply problems

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

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

This can lead to improved operational efficiency and working capital management, greater transparency and accountability, and more accurate delivery estimates; and fewer supply disruptions, Tandon said. “In addition, manufacturers who use AI for visibility into their smart factory operations can better respond to potential disruptions to avoid delays and pivot if necessary, allowing them to be more resilient while continuing to respond. customer requests,” he said.

“Organizations can leverage data analytics tools to gain deeper insights across the entire 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 forecast supply chain events such as logistical challenges, geopolitical issues, and supply disruptions.

They can either execute actions on their own or recommend actions that stakeholders should take, “ultimately helping companies build resilience in their supply chains,” Tandon said.

When deploying these tools for supply chain management, it’s a good idea to start with a small and narrow scope and evolve the depth and breadth of models and algorithms as the results grow. show their accuracy and value, Tandon said.

High quality data is also important. “Underlying data is critical because bad data means bad analytics,” Tandon said. “Lack of transparency in the supply chain is often the result of inconsistent and incomplete data on products, suppliers and customers. Put in place data governance processes that align with definitions and [fixing] data issues provide the foundation for data quality that builds confidence in the results of the analytics and AI process.”

Rand Technology, an independent semiconductor distributor, uses data analytics to solve customer sourcing issues.

“For example, if a customer needs to reduce their excess inventory, we use data and analytics to identify other users of those products and create an opportunity to repatriate them,” said Jennifer Strawn, vice president of solutions and sourcing for the Americas and EMEA at Rand. “In this way, OEMs and subcontractors are able to bolster their component inventory.”

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

This way, manufacturers are not dependent on a single semiconductor supplier, which in today’s environment could impact business. “We leverage advanced analytics to help determine the availability of these semiconductors and to spot trends and patterns, such as gaps, price increases or product change notices, before products go live. be in production,” Strawn said. Rand also uses the technology to make decisions about future scenarios and to determine how much buffer inventory a company might want to secure, she said.

Rand also uses advanced data analytics to identify trends and patterns that allow it to strategically guide clients through perilous market conditions. “With real-time modeling and visibility into global availability, market shifts and conditions,” Strawn said, “we are able to help reduce risk and map to advance the strategies that can be used when we see certain changes and disruptions in the industry”.

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