Continuous Improvement by Statistical Forecasting

Jason Dols, Senior Manager Demand Planning at PepsiCo, believes that organizations can improve the performance of statistical forecasting by developing a process to drive continuous improvement.

Organizations that use statistical forecasting expect their processes to provide a highly accurate forecast. “At PepsiCo Chicago, we have made great progress to improve the performance of our statistical forecasts by developing a continuous improvement process”, says Dols.

“Before you begin this process, it is critical to have clear organizational accountability and a strong team in place that possesses strong analytical and technical capabilities coupled with the business knowledge to know what drives sales”.

Next, organizations should develop a consistent, repeatable process to evaluate their statistical forecasting performance and identify the key drivers of forecast error. “By identifying the key drivers of error, you can then identify and prioritize where there are opportunities for continuous improvement and how best to pursue them further.”

Furthermore, organizations will typically expect the performance of the statistical forecasts to improve over time in turn raising supply chain performance and customer satisfaction.

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