How analytics and artificial intelligence can help companies manage the semiconductor supply chain
Businesses and customers have been dealing with problems in the supply chain for months, which has led to shortages of a number of products, including important semiconductor chips.
"The semiconductor supply chain is still constrained," said Brandon Kulik, a Deloitte Consulting principal and semiconductor industry leader. "Due to softening in the consumer electronics segment [laptops and smartphones], average lead times have decreased slightly, and demand for memory has decreased." However, demand for higher-performance data center chips, defense chips, and automotive chips remains historically high, with some semiconductor companies experiencing 40% or greater growth. "
Companies that depend on semiconductors may be able to solve their supply problems soon by using advanced data analytics and artificial intelligence tools.
"The COVID-19 pandemic vividly demonstrated the impact that unexpected events can have on global supply chains," said Rohit Tandon, managing director and Deloitte's global AI & analytics services leader. However, artificial intelligence can help the world avoid similar disruptions in the future.
Anticipating supply issues
Tandon says that AI can predict a wide range of unexpected events, such as weather patterns, transportation bottlenecks, and labor strikes, by analyzing the huge amounts of data that today's supply chains generate. This lets companies plan for problems and reroute shipments to avoid them.
"AI can also make big changes in other important parts of the supply chain, like demand forecasting, risk planning, supplier management, customer management, logistics, and storage," Tandon said.
According to Tandon, this can lead to improved operational efficiency and working capital management, greater transparency and accountability, more accurate delivery estimates, and fewer supply disruptions. "Also, manufacturers who use AI for visibility in their smart factory operations can better respond to potential disruptions to avoid delays and pivot if needed. This makes them more resilient while still being able to meet customer demands," he said.
Tandon explained that "organizations can leverage data analytics tools for deeper insights across the supply chain." "These tools are intended to improve demand prediction while also facilitating data sharing with customers and partners." AI can also be used to predict or forecast events in the supply chain, like logistics problems, geopolitical problems, and supply disruptions.
They can take actions on their own or suggest actions to stakeholders, which, according to Tandon, "helps companies build resilience into their supply chains."
Tandon suggests starting with a small and narrow scope when using these tools for supply chain management. As the results show how accurate and useful they are, the depth and breadth of the models and algorithms can be slowly increased.
High-quality data is also essential. "Underlying data is critical, because bad data equals bad analytics," Tandon explained. "Inconsistent and incomplete data across products, suppliers, and customers often results in a lack of transparency across the supply chain." " Establishing data governance processes that adhere to common definitions and [resolving] data issues lays the groundwork for data quality, which fosters trust in the outcomes of the analytics and AI processes."
Rand Technology, an independent distributor of semiconductors, uses data analytics to solve customer problems that have to do with supply.
Rand's vice president of solutions and sourcing for the Americas and EMEA, said that "For example, if a customer has an inventory surplus, we use data and analytics to identify other users of these products and create an opportunity to rehome them." "In this way, OEMs and contract manufacturers can shore up their component inventory mix."
Furthermore, data and analytics are especially important during the bill of materials selection phase of a manufacturer's new product introduction, according to Strawn. "It's critical to identify where you can build flexibility into the design during this phase so that there are multiple sources for semiconductors on the approved list of materials," she says.
As a result, manufacturers are not reliant on a single semiconductor provider, which could have an impact on business in the current environment. "We use advanced analytics to help determine the availability of these semiconductors and to identify trends and patterns, such as gaps, price increases, or product change notices, before products go into production," Strawn explained. Rand also uses the technology to make decisions about future scenarios and how much buffer stock a company should keep on hand, according to her.
Rand also employs advanced data analytics to identify trends and patterns that allow it to strategically guide customers through perilous market conditions. "With modeling and real-time visibility into availability, market shifts, and conditions globally," Strawn explained, "we are able to help reduce risks and map out strategies that can be used when we notice certain changes and disruptions in the industry."