Semiconductor Enterprise leveraged tailored ML models to improve demand forecasting

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“Large semiconductor manufacturer leveraged tailored Machine Learning models to automate and improve demand forecasting.”

-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.

Why We Chose Pluto7

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.

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Industry High-tech

Solutions Demand-ml


  • Currently, business leads are hesitant to use automated forecasts for unique products since accuracy is low and unreliable.
  • The client wanted more control over their supply management, especially around usage, multi-echelon inventory, and inventory postponement techniques by leveraging their forecast.
  • The client is operating at excess and obsolete levels at 30% over build plan inventory which is costly.


  • ML and AI models were able to develop a demand forecast with over 90%+ accuracy that was utilized by product forecast team as an innovative solution
  • With this high level of accuracy, business leads are now realizing the paradigm shift and going through the change management driving adoption of ML generated forecasts, reducing the number of manual overrides
  • Over 4 weeks of parallel testing, the ML model outperformed existing forecast 8 out of 10 product lines.

Products Used

  • Google Cloud Platform
  • Google Cloud Dataprep
  • Google BigQuery
  • Google Cloud Storage
  • Google Tensorflow
  • Google Machine Learning

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