By: Ravi Krishnaiah and Lewis Tang

Wafer fabrication in the semiconductor industry is investment-intensive and agonizingly complex. The process involves dozens of organizations, from design to commercialization, across half a dozen nations. Adding to the challenge is the rise in global economic uncertainty, the turnover of human capital, and geo-political wobbles. Together, this could easily add up to one of the most severe tests the industry is being put through. There is an urgent need to address the growing vulnerabilities affecting the industry—and the first line of defense is to create a resilient supply chain.

Semiconductor supply chains are especially vulnerable. There are over 50 points in the chain where a single region holds over 65%[i] of the global market share[ii]. About 75% of semiconductor manufacturing capacity and many suppliers of key materials—such as silicon wafers, photoresists, and other specialty chemicals—are located in China and East Asia, a region exposed to significant seismic activity and geo-political tensions[iii]. The world’s most advanced semiconductor manufacturing capacity—in nodes below ten nanometers—is in South Korea (8%) and Taiwan (92%)[iv]. These regions are prone to natural disasters and international conflicts that could interrupt the supply of chips. If solutions are not found, the expected global investment of about US$3 trillion in R&D and capital expenditure[v] over the next decade to drive scale can be jeopardized. Traditional solutions to address the challenges are likely to deliver limited improvement in supply chain resiliency.

Fortunately, better alternatives are emerging with the rise of generative AI. The technology is poised to transform supply chain management. Businesses can optimize their operations, lower costs, and sharpen their competitive edge by harnessing the power of predictive analytics, process automation, supplier transparency, and risk mitigation.

Four ways that frame the promise of generative AI

Generative AI drives transformation by creating new content (images, text, audio, video) based on the data it is trained on. Recent advances make it simpler to use it and realize value. Here, we analyze the top four ways generative AI can improve resilience:

1. Enhancing resilience via optimizing supplier networks: In recent years, the industry’s just-in-time (JIT) manufacturing model has injected efficiency and cost savings into operations. But the benefits have come at the cost of supply chain resiliency. Generative AI can be leveraged to restore the balance between cost and resiliency by making it easier to optimize supplier networks and manage tactical activities such as creating multi-sourcing options or allowing sourcing from different suppliers based on demand, availability, cost, and proximity to the area of demand (thus reducing trade concerns and cost). These tasks traditionally require supply chain professionals to catalog and reference a vast number of documents. Generative AI can act as a quick and efficient associate in the process.Some organizations are getting ahead with this approach. They are building generative AI-based bots designed to negotiate costs. One study showed that 65% of vendors preferred negotiating with bots over humans[vi].Beyond optimizing supply chain networks, generative AI can be combined with Natural Language Processing (NLP) to extract insights from large and cumbersome contracts, uncategorized supplier communications, and supplier performance metrics. Supply chain professionals can be better equipped to handle contract renewal strategies and drive informed supplier-related decisions by using generative AI to summarize and contextualize large volumes of data and identify trends.

3. Enhancing resilience via intelligent demand forecasting: Among the most effective approaches to enhancing resilience is to improve the ability to forecast demand accurately. This requires supply chain professionals to pull data from multiple sources and leverage Machine Learning (ML), advanced analytics, and scenario planning. Now, generative AI can make that task considerably more manageable. With models trained on historical data and market trends, generative AI can contextualize everything using real-time information for factors such as seasonality, weather patterns, the impact of promotions, and changing economic conditions. Generative AI can also produce intelligent risk assessments, simulations, and mitigation strategies that help planners proactively manage and mitigate risks.A study shows that 39% of mid-sized companies had planned to increase inventories in the third quarter of 2023, and 56% said they plan to increase inventories in the next two quarters[vii]. Meeting such forecasts accurately is the key to creating production and distribution plans, managing optimal inventory levels, improving customer satisfaction, and boosting business sustainability.
4. Enhancing resilience via optimizing risk management: Adopting modern technologies, such as cloud supply chain management that integrates with an ERP, brings the entire supply chain into view by connecting partners and enabling mobility, accountability, and proactive response to potential problems. Adding generative AI to the technology mix, organizations can optimize their risk management capabilities by monitoring and leveraging data from suppliers, transport providers, and the shop floor —which provides granular control over inventory and supplier relationships. Today’s generative AI tools can suggest several courses of action when supplier relationships deteriorate and cannot be relied on. Risk management could easily be the area where supply chain professionals find generative AI handy – leveraging it to analyze and modify plans and resource allocations based on real-time data.
5. Enhancing resilience via improving the robustness of the wafer fabrication process: A blank, non-conducive silicon wafer goes through five major steps before it is ready with hundreds of identical integrated circuits and is prime for assembly, testing, and packaging. The five stages—oxidation and coating, lithography, etching, doping, metal deposition, and etching—produce a complete wafer but can also inject anomalies into the chip. Generative AI, combined with imaging techniques, can be used to detect these anomalies without the need for labeled data or using machine learning. With generative AI, the defect detection rates can be improved by several magnitudes compared to traditional methods.Supply chain teams can learn from the use of AI in the adjacent Logistics industry. Here, AI is used to optimize picking routes, slash delivery time, and lower costs. Adding generative AI brings in the possibility of making better routing decisions based on fuel pricing, traffic, and weather conditions or lowering response time by considering new warehouse locations based on changing demand patterns.

Getting started – the right way, one step at a time

Generative AI can transform supply chains but cannot be leveraged if the organization is not ready to integrate the technology. To prepare, the organization should remember the strengths and weaknesses of the technology and focus on:

1. Securing data and putting stringent privacy measures into practice. Protecting sensitive data should be the #1 priority.
2. Identifying high-value use cases that can benefit from generative AI without disrupting legacy systems or traditional business models.
3. Training the generative AI model on reliable, unbiased data within well-defined and well-understood boundaries—so the generative AI engine does not spring hallucinatory (aka fabricated information) surprises.
4. Bringing the required teams into the loop—so teams, partners, and nodes in the supply chain affected by generative AI can be trained to use the technology.
5. Starting modestly with a use case that will not present a significant risk to the supply chain—then learning from the controlled implementation and understanding the risks before scaling adoption.
6. Consulting the right partners —and working with those with experience in deploying generative AI, domain expertise, and a collaborative approach to strategy, tools, road mapping, and rollouts. These consulting partners (LTIMindtree being one such leading partner) can provide guidance in adopting generative AI.

Using generative AI to build supply chain resilience is a matter of urgency. Organizations that begin now will have gained the experience to scale their efforts with confidence—and ensure customer satisfaction as well as maintain a competitive advantage.

[i] Strengthening the Global Semiconductor Supply Chain in an Uncertain Era, Boston Consulting Group (BCG), April 1, 2021:  https://www.bcg.com/publications/2021/strengthening-the-global-semiconductor-supply-chain

[ii] The level of risk associated with each of these varies

[iii] Strengthening the Global Semiconductor Supply Chain in an Uncertain Era, Boston Consulting Group (BCG), April 1, 2021:  https://www.bcg.com/publications/2021/strengthening-the-global-semiconductor-supply-chain

[iv] ibid

[v] ibid

[vi] ibid

[vii] Q3 2023 Middle Market Business Index, RSM US LLP, September 21, 2023: https://rsmus.com/middle-market/mmbi.html