Accenture Academy Blog
The H.J. Heinz Company, one the world’s leading consumer packaged goods (CPG) companies, relies on retail scanner data, point of sale (POS) data, to assess the health of its business and provide insights into future sales. Heinz is not an exception in the CPG industry. Most CPG companies rely on this type of downstream data, provided by retailers or obtained from third-party sources, to offer insights into sales patterns and forecast future sales.

However, simply collecting POS data on an ongoing basis does not in itself provide the benefits these companies require. To extract the needed information, companies have to apply their analytics capabilities to turn information into business intelligence that can be used for better decision making.

One important aspect of a company’s analytics capability is the ability to use forecasting methods to effectively forecast future sales. There are many different ways to forecast sales. Some of the most popular in the CPG industry are time series forecasting methods, where algorithms are applied to data to create forecasts.

One of the most complex time methods is the Box-Jenkins approach. In fact, the Box-Jenkins approach is often called a “black box,” implying that the forecaster cannot really understand how the approach works. However, this is simply a myth. Forecasters can discover how the approach works and use it effectively by following a step-by-step process.

Have you ever wondered if the Box-Jenkins approach could improve your company’s forecast accuracy but do not know how the approach really works? In the Accenture Academy course Improving Forecast Accuracy with Box-Jenkins, you will discover how the approach uses a four-step process to create forecasts and how to follow that process in an iterative fashion. The course can help you utilize the Box-Jenkins forecasting approach to improve your company’s forecast accuracy, resulting in improved business decision making and performance.

Institute of Business Forecasting and Planning. “How Point of Sales Data Are Used in Demand Forecasting at Heinz in North America.” April 9, 2009.http://www.demand-planning.com/2009/04/09/test/.
 

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