Improved inventory management achieved by a leading oil and gas company through predictive modeling techniques
Business Challenge: Optimizing the inventory management process.
The client wanted to optimize its inventory management through improvement in accuracy of demand forecasting.
Situation: Constant issues with inventory management resulting in reduced customer satisfaction levels, and profitability.
The client was facing constant issues like stock outs, large inventory levels, inaccurate lead times, and inaccurate demand forecasting. These issues were affecting the customer satisfaction levels and profitability.
Solution/Approach: Regression model to predict future demand based on historic data.
We deployed a regression model to conduct analysis on historic information on products, retailers and inventory levels. In addition, we also conducted analysis on customer data to predict purchase patterns, trends and buying behavior. On the basis of this analysis, we were able to accurately forecast demand, uncover irregularities, and determine optimal inventory levels.
Impact: Improved accuracy of demand forecasting and improved inventory management.
The client was able to improve the accuracy of demand forecasting 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.