KPMG helped a global food products manufacturer leverage predictive analytics for faster and more accurate financial forecasting.
Though it has been bringing iconic products to the global market for more than 125 years, our client was not content to rest on its laurels. The company realized that manually produced forecasts took too long and were often inaccurate and out of date. They were looking for more transparency and better visibility into the drivers of its business. In order to reach their goals, the food product manufacturer chose KPMG to help leverage advance analytics and predictive modeling, all while reducing time and effort.
KPMG’s intelligent forecasting models drove significant improvements in our client’s forecasting performance and insights, including:
Artificial intelligence (Al) and machine learning, combined with advanced modeling techniques, are expected to refine the company’s planning models and continue improving their ability to learn and predict over time.
The company turned to KPMG to help the company innovate its volume, margin and earnings forecasts with intelligent forecasting, incorporating a variety of Al and machine learning capabilities. We conducted two proofs of concept over four months—a bottom-up approach approach for a specific brand and a top-down approach for enterprise-wide revenue and earnings.
Our work proceeded in three phases:
Working with the KPMG Lighthouse team of data scientists, data engineers, and advanced analytics consultants, we were able to quickly explore and analyze the company’s data in multiple ways. The team used Al technologies in the KPMG Ignite platform to compare different types of modeling techniques and ultimately recommend specific customized models. We also leveraged a suite of proprietary technology accelerators, including the global KPMG Signals Repository, which contains more than 50,000 external data signals, continually refreshed to significantly improve the accuracy of predictions.
Ultimately, we were able to help company executives more completely understand the drivers of their business. We also gave them greater visibility into the internal and external data that should drive each model. By taking advantage of KPMG’s Al and machine learning capabilities, the company can rapidly produce an intelligent forecast for its brands and integrate it into the company’s overall financial planning process.
The result is an iterative process that automatically updates the forecast engine with new financial results, data, and signals and produces increasingly accurate forecasts over time.
Based on this proof of concept, our client is now expanding it’s modeling across brands and varying financial scenarios.
Intelligent forecasts are considerably faster and more accurate than those generated manually. By leveraging these benefits, managers are freed up to do what they do best: strategic thinking and creative execution.
By introducing data signals that haven’t historically been considered, the organization can gain greater insight into what drives the business and uncover new competitive threats as well as new markets.
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