Our Demand Forecasting model helped improve the accuracy and hence the Customer Service level.

A leading PCB manufacturer was facing multiple issues due to inaccurate demand forecast. The underestimated forecasts led to unmet demands and bad customer experience, while the overestimated forecasts leading to high inventory holdings. The client now wants to improve the accuracy in demand forecast.

The data sources were disparate and data quality was poor. Hereby, in order to implement an efficient and accurate forecasting model, we cleaned the data to make it available for analytics. The Time Series Analysis and ARIMA were used to forecast the Demand and plan the strategies for optimizing the inventory costs.

The client acknowledged accuracy of the forecast model as it helped in reducing the inventory costs by 31%. Also, the Customer Service level increased by 12% by reducing the unmet demands. This helped in improving the revenue significantly.