The global semiconductor industry is anticipated to grow to US$1 trillion in revenues by 2030, doubling in this decade. This growth is expected to require investment in high-end advanced wafer manufacturing materials, equipment, and services—but also in the back-end AT solutions and services.
Currently, more than 90% of the AT base is in Asia, either in foundries or OSAT providers. With around US$100 billion pledged to support localization of semiconductor capacity, several companies in the industry are adding new AT capacity in Europe, North America, and Southeast Asia. And that could exacerbate the existing challenge of disparate and siloed supply chain tech and systems, rendering it even more difficult to predict potential supply chain disruptions. This is where integrated data platforms, next-generation ERP, planning, and supplier collaboration systems, along with AI and cognitive technologies, are expected to make OSAT processes more efficient and help sense and preemptively plan for future supply chain shocks.
Beyond OSATs, data analytics platforms that are integrated into ERP, planning, and procurement systems can help semiconductor companies predict events that could disrupt the supply chain: unexpected weather events, transport bottlenecks, logistics challenges that require rerouting shipments, and labor-related issues. Sharing real-time data and intelligence across the ecosystem—encompassing the equipment and tool suppliers, chip design companies, wafer fab facilities, AT facilities, distributors, OEM and ODM customers, and more—is likely essential to build a digitally connected supply chain.
To realize maximum benefit from AI tech and data-driven solutions, data quality matters. So in 2023, semiconductor companies need to modernize their ERP systems and integrate diverse data sources, such as customer data, manufacturing data, and financial and operational data. And they should consider investing in data management and data analytics modernization solutions, establishing the required data governance and fixing data quality issues.
Strategic questions to consider:
- Given the current economic and industry trends (rising interest rates, falling demand in some important chip sectors, high inventories), how can companies best revamp and accelerate digital transformation in the face of the adverse trends?
- When deploying technology and AI tools to digitize supply chain management, what entities and variables should be considered as part of the advanced and predictive analytics data-driven decision model?
- As localization accelerates through the next years, how much of a negative impact will there be on the supply chain and data modernization efforts? How do companies effectively interconnect new and existing entities (e.g., a fab or site) into an increasingly complex global network?
- How can companies deploy resources to modernize and integrate their IT systems when budgets are getting tighter? And how can they balance near-term IT implementations with strategic long-term investments in next-generation digital technologies?