CASE STUDY

90% Accuracy in Demand Forecasting Achieved by a Consumer Goods Manufacturer through Predictive Modeling Techniques

Sep 22, 2016

Business Challenge

Eliminating issues with the inventory management process.

The client wanted to optimize its inventory management through improvement in the accuracy of its demand forecasting process.

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Situation

Constant issues with inventory management resulting in reduced customer satisfaction levels and profitability.

The client was facing constant issues of stock-outs, large inventory levels, inaccurate lead times, and inaccurate demand forecasting. These problems were affecting customer satisfaction levels and creating a negative impact on profitability.

Solution/Approach

Regression model to predict future demand based on historic data.

We deployed a regression model to conduct an analysis of historic information on products, retailers, and inventory levels. In addition, we also conducted an analysis of customer data to predict purchase patterns, trends, and buying behavior. Based on this analysis, we were able to accurately forecast demand, uncover irregularities, and determine optimal inventory levels.

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Impact

Improved accuracy of demand forecasting and improved inventory management.

The client was able to achieve improved accuracy of demand forecasting by up to 90%. They were also able to streamline the order management process and achieve better inventory management. This resulted in improved customer satisfaction and profitability.

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