The client is a worldwide networking and telecommunications equipment leader, founded and headquartered in San Jose, California. The enterprise develops, manufactures, and sells networking hardware, telecommunications equipment and other high-technology services and products. The company’s revenue is over 50 billion yearly. The leadership understood the complexity and volatility of their product demand and customers purchasing patterns. Looking to improve their demand forecast accuracy, the client reached out to Pluto7 and Google Cloud for a solution that would significantly impact their revenue and profits.
The client looked to Pluto7 because of their proven industry and domain experience of improving demand forecasting at the enterprise level for hi-technology companies. Pluto7 not only brought domain expertise but also a deep understanding of AI, ML and Google Cloud. The client wanted to partner with a company that would drive innovation for business transformation. As a Google Cloud premier partner, Pluto7 offers demand planning and supply chain planning solutions that are industry proven and highly accurate.
The client asked Pluto7 to build a solution that predicts demand and forecasts sales revenue for a year in the future. The products sold worldwide have demand that is complex with configurable product offerings and varying levels of volatility in purchasing patterns. They were looking for significant accuracy in the forecasting which would have a significant impact on revenue and profit.
Pluto7 leveraged Google Cloud to build a machine learning solution with high accuracy. The solution allows the leadership to see which products contributed to revenue and have a forecast for all product demand, even in complex situations.
The proof of concept was successful. The solution built on Google Cloud is highly accurate, scalable, and flexible. Pluto7’s solution not only provided the client with higher forecast accuracy for the most successful products, but for all their products. The solution enables the generation of usable forecasts for new product introductions and products with intermittent demand. As a result, the leadership is able to make better decisions and manage their demand and supply planning more effectively. The insights from the solution can be leveraged to improve not only operations, but also marketing. The identification of similar product demand patterns for offerings that are not obviously similar or complementary allows the marketing team to make better decisions as well.
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