Digital Transformation Program Manager IBM Bromont, QC, Canada
Across the industry, we at IBM see a common thread. Only 30% of semiconductor companies tap into digital transformation at scale, leaving $90 Billion annual value potential largely untouched. OSATs especially face a series of challenges spurred on by their current technologies and data dependencies. They rely heavily on manual operations, with limited visibility into critical performance metrics, lack real-time access to information, while running analytics on rudimentary tools like Excel. The current technology infrastructure, characterized by scattered systems and data overload, prevents effective data utilization and response times. The absence of advanced analytics and AI/ML integration limits the potential for strategic insights and data-driven decision-making.
These challenges make quality inspections and yield management untenable with business expansion. The need for smart manufacturing with AI/ML capabilities is critical to improve inspection processes and enhance overall operational visibility. By leveraging AI/ML models, manufacturers can analyze a combination of structured and unstructured data, to identify critical parameters impacting quality and yield. These models enable the detection of anomalies in real-time, facilitating proactive quality control measures. Additionally, AI-driven insights can streamline decision-making processes, reducing the reliance on manual inspections and enabling a more efficient allocation of resources. By establishing a data foundation and integrating these advanced capabilities, manufacturers can achieve a higher level of precision and accuracy in their quality assurance practices, ultimately leading to improved product quality and operational efficiency.
Target State: The modernization of existing infrastructure and developing a smart factory through the deployment of a data fabric to consolidate data for enhanced operational insights. Advanced AI/ML-driven inspection capabilities, like anomaly detection, will then be implemented. Establishing a centralized control tower for real-time, comprehensive automation for production oversight will lead to optimization of production, material handling, and quality assurance increase efficiencies and minimize waste across the entire production process. Last, operators will be enabled with real-time data assistance and AI-driven decision tools, making decisions on the production floor.
Case Study: IBM Bromont
What: IBM Assembly and Test is a North American leader in semiconductor packaging technology, products and services. Located in Bromont, Canada, with an 850,000 sq. ft manufacturing facility next to a 161,000 sq. ft development facility, it is the largest OSAT site in North America and offers advanced flip chip assembly and test.
IBM Bromont had identified opportunities within key focus areas: 1. Data Fabric as a foundation for transformation 2. Enhanced operator experience 3. End to end production visibility.
In response to these opportunities, IBM Bromont has set out to transform their operations through implementation of Smart Manufacturing strategies and technologies. In our presentation, we will detail the specific motivations faced by Bromont, our roadmap, the actions taken, and anticipated value to the business for undergoing this transformation.
