“Large semiconductor manufacturer leveraged tailored Machine Learning models to automate and improve demand forecasting.”
-Sr. Director of Applications, Semiconductor Company
SR. DIRECTOR OF APPLICATIONS, SEMICONDUCTOR COMPANY
The client is a semiconductor manufacturer with over five billion devices used in consumer electronics such as smartphones, Google Home and Alexa. This client ignited the human interface revolution with their touch, display and biometrics products – all built on the company’s storied research and development, extensive intellectual property and global partnerships. With solutions designed to optimize the user experiences in the mobile, PC and automotive industries, the client combines ease of use, functionality and aesthetics to enable products that help make users’ digital lives more productive, secure and enjoyable.
The client has experienced rapid growth, with its products being used by many OEMs of mobile devices and related products. With various end users and a large variety of mobile products, the company has not been able to develop its own reliable demand forecast for each unique product. This has resulted in unreliable forecasts causing downstream supply chain issues like excess and obsolete inventory levels exceeding 30% of build plan which is very costly to the client.
Hence this client looked to Google Cloud and Pluto7 to help them proactively reduce excess and obsolete inventory through better demand forecasting. The thought process was that by using accurate forecast to drive the production schedules, they will produce products that were in line demand which results in reducing the amount of excess inventory.
The product demand forecast needs to be of sufficiently high accuracy in order to help drive optimized downstream supply chain planning and execution. To achieve a highly accurate forecast, multiple sets of data need to be used. Internal data such as regional aspects, sales orders, order shipments, product catalog, inventory levels and related information needs to be analyzed for trends and correlations.
Achieving this requires the ability to pull all the data together and process it at business real-time processing speeds, while leveraging advanced predictive capabilities like those provided by Artificial Intelligence and Machine Learning. The key element here is that each product and customer has unique demand patterns which can mainly be identified and monitored efficiently through ML based forecasting models that learn continuously. This is a perfect problem for Google Cloud Platform with its data storage, analytics at scale and associated Machine Learning to solve.
With these objectives in mind, the client partnered with Pluto7, leveraging Google Cloud Platform to transform their data. Utilizing analytics, Machine Learning and Artificial intelligence, Pluto7 and GCP helped them create product demand forecasts with over 90%+ accuracy, and one Machine Learning forecast that beat the current forecast 8 out of 10 product lines.