Demand Forecasting with Data Science

Are you looking to increase profits?

Are you looking to reduce your operational costs?

Have you wondered about the term demand forecasting and whether you should be implementing it?

If you have answered yes, to any of the above questions or if you are a voracious reader looking to learn about demand forecasting, Welcome! Bsquare will help you understand demand forecasting and why every industrial business should embrace it.

Our demand forecasting series starts with a gentle introduction into demand forecasting, especially in Industrial IoT (IIoT). Be sure to check out parts 2, 3, and 4 in the series as well.

What is Demand Forecasting in IIoT?

Demand Fluctuates Over TimeDemand forecasting in industrial IoT focuses on predicting demand based on past historical data and relevant sensors. Demand forecasting could be predicting consumer demand for energy or it could be predicting the production output of a factory site. Demand forecasting not only helps the businesses plan well, but it also helps in optimizing raw material procurement, influencing supplier choice and planning transportation logistics. Demand forecasting is especially useful for industrial IoT customers who can reduce considerable cost by optimizing their operations. As more companies move towards using IoT and data for tracking and monitoring different aspects of their operations, the same data could be leveraged to improve demand forecasting. Demand forecasting can be further improved by customizing the model to accommodate domain-specific requirements and to suit the unique business needs of the customer.

Why Forecast Demand?

Apart from reducing operational costs, a predictive demand forecasting model would have the following advantages:

  • Better Tracking – Traditionally demand forecasting has been done by experienced supervisors working on the field for decades. Even then, demand forecasting in the past has usually been based on gut-check and did not usually have empirical foundation. Due to this reason, it is often difficult to track how well the forecasts match the actual demand. A predictive demand forecasting model, in addition to tracking how the forecasts match up to actual demand, would also be able to use that data to further improve the model.
  • Employee Turnover – As experienced talent retires or moves away, their extensive knowledge on business processes and demand forecasting is also lost. Predictive demand forecasting model helps capture the knowledge and makes even an entry-level supervisor equipped to plan for the fluctuations in demand.
  • Better customer service – With appropriate demand forecasting model, the businesses will be able to meet the demands of their customers, thereby providing superior service and increasing customer satisfaction.

Stay tuned for our next blog in this series.

Are you an industrial IoT customer looking to save money by having an efficient demand forecasting model, then talk to us. Our 20 years of experience in IoT combined with our expertise in data science, will help you navigate the complex world of demand and customer behavior.  For questions about how Bsquare can help reduce your operational cost, talk to us.