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Demand Forecasting



COVID 19 and the disruption that ensued has demonstrated the limit of conventional demand forecasting specially when limited data is available. The intent of this project was therefore to develop a statistically robust predictive analytics solution with R and R Shiny to support demand forecasting activities in manufacturing. This novel approach takes into consideration monthly factory outputs as well as beginning and end of month inventory levels as main factors in the forecasting models developed. 

Critical Tool Features

  1. Use of historical data to generate multiple forecast models and suggest the best one to the user

  2. Address low data volume by using hypothesis testing to aggregate months with similar sales patterns


Due to the confidential nature of the data used to complete the project the tool will not be shared