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Sales volumes forecasting & what-if tool

A main player operating in commercial distribution needed to forecast sales volumes for the medium and short term with the capability to adjust the prediction on the market sensitivity basis, particularly for month, store, and product cluster. Softlab approached the problem by breaking it down into three main phases: clustering of sales, forecast modeling, and dashboarding and what-if tool. The Softlab team performed a descriptive analysis to cluster sales, designed and evaluated end-to-end Machine Learning solutions for forecasting and developed a dashboard to perform what-if analysis directly on forecasted sales volumes with the capability to create different scenarios in real time mode.

 

Category:
Data Analytics

For clustering sales, Softlab performed a descriptive analysis of the products' sales volumes to identify groups of products with similar trends and behavior. For forecast modeling, the team designed, evaluated and built an end-to-end Machine Learning solution, including Arima, Sarima, Sarimax, and Exponential smoothing. Finally, Softlab developed a dashboard with user-settable parameters, allowing what-if analysis to adjust the forecasted sales volumes manually and in real-time mode. The dashboard created different scenarios to assess the adjustment effect on all other measures and KPIs.

 

 

Softlab's solution resulted in efficient sales volume forecasting with the capability to adjust the prediction based on market sensitivity. The dashboard allowed what-if analysis directly on forecasted sales for real-time scenario testing. Additionally, the solution provided cluster analysis of sales to identify product groups, enabling accurate sales forecasting.

 

 

 

Softlab employed Machine Learning solutions in the forecast modeling phase, including Arima, Sarima, Sarimax, and Exponential smoothing. The team developed a dashboard with user-settable parameters to enable what-if analysis directly on forecasted sales volumes, allowing users to manually adjust and create different scenarios to assess the adjustment effect on all other measures and KPIs.

 

 

 

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