In this exclusive op-ed for The Manufacturer, Michael Miller, CEO of SourceDay, looks at how AI and machine learning (ML)-powered procurement tools will help CHIPS Act projects yield success.
Since its enactment into law two years ago, The CHIPS and Science Act has played an integral role in advancing the United States’ position as the leader in semiconductor manufacturing against Taiwan, China, and South Korea. The Semiconductor Industry Association (SIA) reported that the Biden Administration has already given over $33bn in grants and $28bn in loans to 20 U.S.-based manufacturers. And there’s more on the way. However, the potential repeal of the CHIPS Act could have significant implications for the U.S. semiconductor industry, affecting domestic manufacturing incentives and global supply chain dynamics.
In fact, SIA anticipates the total investment in CHIPS Act projects will amount to $380bn over two decades, though a majority of which will be awarded by 2030. As a result, we’ll undoubtedly see a flurry of U.S. manufacturers expanding their domestic production of semiconductors to meet the growing demand for next-generation electronics and artificial intelligence (AI). Yet this will be no small feat as they navigate common supply chain risks and work to achieve the full vision of the legislation.
It’s why manufacturers must leverage cutting-edge, AI and machine learning (ML)-powered procurement tools that enhance visibility, predictability, and management of their direct materials spending. Yes, you read that correctly—-the technology powered by semiconductor chips actually helps manufacturers in their journey to reclaiming the top spot in semiconductor fabrication, specifically with:
- Strengthening supplier reliability
- Improving on-time delivery rates of elements needed to create semiconductor chips
- Mitigating risk across the supply chain
Here are three reasons why employing these tools will help CHIPS Act projects yield success and ensure the U.S. can rise to the top of the semiconductor manufacturing leaderboard.
Supplier reliability improvement
Over 70% of supply chain pitfalls happen within the first mile—a critical period of the product journey that analyst firm Spend Matters defines as issuing a purchase order, delivering parts to a manufacturing or distribution warehouse, and all of the steps in between. Supplier reliability plays a critical role in avoiding first-mile issues. However, manual processes often hinder manufacturers from cultivating and sustaining a strong supplier network.
As more manufacturers accelerate the rate of semiconductor production in the U.S., they need to be equipped with AI and ML-powered platforms that facilitate seamless communication with suppliers. These tools provide instant updates to manufacturers on order status, inventory levels, and delivery schedules helping them identify areas of improvement and facilitate collaboration on both ends of the supply chain, ultimately strengthening the partnership.
In addition, AI and ML can be used proactively to uncover cost-saving opportunities within the supply chain. These insights arm manufacturers with the intelligence they need to negotiate better terms with their suppliers, ultimately building deep trust and enhancing their long-term relationships. As CHIPS Act projects take place over the next 20 years, manufacturers will achieve their mission if they have an army of suppliers in it for the long haul. Utilizing AI and ML ensures manufacturers can maintain a network of suppliers that work in partnership with them at every step along the way.
Increased on-time delivery rates
The CHIPS Act sets a high bar for U.S.-based manufacturers to boost domestic manufacturing. To achieve this vision, suppliers must guarantee the consistent, timely shipment and delivery of materials needed to create semiconductor chips. Utilizing AI and ML-powered platforms eliminates shipment delays by providing manufacturers with real-time updates, which reduces the risk of disrupting operations and late customer deliveries.
When organizations adopt AI for chip manufacturing, they can see a 30 to 50 percent improvement in on-time deliveries. This has a positive impact on production volume and revenue size, and ultimately demand flow throughout the supply chain. By ensuring that materials arrive on schedule and that finished products are delivered promptly, companies enhance their competitive edge and contribute to the overall success of the semiconductor industry.
Reduce risks throughout the semiconductor supply chain
Supply chains are risk magnets—especially when it comes to semiconductors. Within the next 10 years, global semiconductor demand will prompt a 56 percent increase in manufacturing. As U.S. manufacturers ramp up their domestic production of semiconductor chips and work to meet market demands, they must effectively avoid roadblocks. This can be accomplished by leveraging AI and ML tools for better visibility into the purchase order (PO) lifecycle.
Archaic and manual processes invite human error to drive up costs and increase risk. But you can’t fix what you can’t see. AI and ML detect potential disruptions within the supply chain by managing inventory and monitoring demand. In addition, utilizing AI and ML platforms enable manufacturers to achieve greater visibility within the supply chain as they can track movements in real time, predict potential risks, and identify proactive measures to mitigate them.
These tools also analyze historical data to forecast demand fluctuations, helping to optimize resources and reduce waste. With AI and ML integrated into their supply operations, they can uncover risk blind spots that would have been undetectable. Using these tools to reduce risks within the supply chain before they occur creates a proactive approach instead of a reactive one, carrying forth the vision of the CHIPS Act.
Looking ahead to the future, the success of this landmark legislation will depend on the tools and strategies that U.S. manufacturers utilize as they boost domestic production of semiconductors. Leveraging AI and ML will ultimately lead to the development and refinement of AI innovation in the U.S. for decades to come.
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