o9 Solutions, Inc. > Case Studies > Revolutionizing Semiconductor Manufacturing with IoT: A Case Study

Revolutionizing Semiconductor Manufacturing with IoT: A Case Study

Technology Category

Functional Applications - Inventory Management Systems
Functional Applications - Warehouse Management Systems (WMS)

Applicable Industries

Electronics
Semiconductors

Applicable Functions

Procurement
Warehouse & Inventory Management

Use Cases

Digital Twin
Inventory Management

About The Customer

The customer is a global technology company that designs and manufactures semiconductors and software. They sell their products to electronic design engineers and manufacturers in markets worldwide. The company operates on an outsourced fabless model, which means they design and sell hardware devices and semiconductor chips but outsource the fabrication (or 'fab') of the devices and chips to a specialized manufacturer called a semiconductor foundry. This model requires extensive coordination and accurate planning to ensure efficiency and profitability.

The Challenge

The global technology company, a designer and manufacturer of semiconductors and software, was grappling with a highly manual and disconnected approach to managing their forecasts. The company relied heavily on spreadsheets, the Adexa system, and emails, which made the process inefficient and prone to errors. The supply side, which included inventory planning, order planning, and scheduling, was also entirely manual, necessitating extensive coordination due to the outsourced fabless model. The company's demand planning processes were not agile enough and lacked high accuracy levels. Furthermore, wafer inventory was challenging to manage and often too high due to the large dependency on manual planning. The order commit accuracy was poor, as the overall planning did not consider constraints and business rules.

The Solution

The company adopted the o9 platform to streamline their forecasting and planning processes. The platform enabled the company to create forecasts for all time horizons — weekly, monthly, quarterly, and annually — in both units and dollars. The o9 platform also improved the planning process for buffer planning and wafer planning on a fully integrated cloud-native platform, leading to more efficient and agile inventory management and reducing wafer inventory. The o9 Solver Capability was used to model constraints and business rules, such as customer-specific configurations, sourcing splits, inventory sweep, into the planning process. This made the overall planning more accurate and increased order promising performance. The o9’s Enterprise Knowledge Graph was used to optimize demand, buffer (MEIO), wafer, and supply planning. The company also replaced the Adexa system, homegrown solutions, and Excel with the o9 platform.

Operational Impact

The implementation of the o9 platform brought about significant operational improvements for the company. The platform's ability to create forecasts for all time horizons in both units and dollars streamlined the company's forecasting process, making it more efficient and accurate. The improved planning process for buffer planning and wafer planning led to more agile inventory management and a reduction in wafer inventory. The modeling of constraints and business rules into the planning process increased the accuracy of overall planning and improved order promising performance. The company also benefited from the o9’s Enterprise Knowledge Graph, which optimized demand, buffer (MEIO), wafer, and supply planning. The replacement of the Adexa system, homegrown solutions, and Excel with the o9 platform simplified the company's operations and reduced reliance on manual processes.

Quantitative Benefit