This post is co-written by Goktug Cinar, Michael Binder, and Adrian Horvath from Bosch Center for Artificial Intelligence (BCAI).
Revenue forecasting is a challenging yet crucial task for strategic business decisions and fiscal planning in most organizations. Often, revenue forecasting is manually performed by financial analysts and is both time consuming and subjective. Such manual efforts are especially challenging for large-scale, multinational business organizations that require revenue forecasts across a wide range of product groups and geographical areas at multiple levels of granularity. This requires not only accuracy but also hierarchical coherence of the forecasts.
Bosch is a multinational corporation with entities operating in multiple sectors, including automotive, industrial solutions, and consumer goods. Given the impact of accurate and coherent revenue forecasting on healthy business operations, the Bosch Center for Artificial Intelligence (BCAI) has been heavily investing in the use of machine learning (ML) to improve the efficiency and accuracy

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